794 research outputs found
Functional genome annotation and transcriptome analysis of Pseudozyma hubeiensis BOT-O, an oleaginous yeast that utilizes glucose and xylose at equal rates
Pseudozyma hubeiensis is a basidiomycete yeast that has the highly desirable traits for lignocellulose valorisation of being equally efficient at utilization of glucose and xylose, and capable of their co-utilization. The species has previously mainly been studied for its capacity to produce secreted biosurfactants in the form of mannosylerythritol lipids, but it is also an oleaginous species capable of accumulating high levels of triacylglycerol storage lipids during nutrient starvation. In this study, we aimed to further characterize the oleaginous nature of P. hubeiensis by evaluating metabolism and gene expression responses during storage lipid formation conditions with glucose or xylose as a carbon source. The genome of the recently isolated P. hubeiensis BOT-O strain was sequenced using MinION long-read sequencing and resulted in the most contiguous P. hubeiensis assembly to date with 18.95 Mb in 31 contigs. Using transcriptome data as experimental support, we generated the first mRNA-supported P. hubeiensis genome annotation and identified 6540 genes. 80% of the predicted genes were assigned functional annotations based on protein homology to other yeasts. Based on the annotation, key metabolic pathways in BOT-O were reconstructed, including pathways for storage lipids, mannosylerythritol lipids and xylose assimilation. BOT-O was confirmed to consume glucose and xylose at equal rates, but during mixed glucose-xylose cultivation glucose was found to be taken up faster. Differential expression analysis revealed that only a total of 122 genes were significantly differentially expressed at a cut-off of |log2 fold change| ≥ 2 when comparing cultivation on xylose with glucose, during exponential growth and during nitrogen-starvation. Of these 122 genes, a core-set of 24 genes was identified that were differentially expressed at all time points. Nitrogen-starvation resulted in a larger transcriptional effect, with a total of 1179 genes with significant expression changes at the designated fold change cut-off compared with exponential growth on either glucose or xylose
Comparative genomics of recent adaptation in Candida pathogens
[eng] Fungal infections pose a serious health threat, affecting >1,000 million people and causing ~1.5 million deaths each year. The problem is growing due to insufficient diagnostic and therapeutic options, increased number of susceptible patients, expansion of pathogens partly linked to climate change and the rise of antifungal drug resistance. Among other fungal pathogens, Candida species are a major cause of severe hospital-acquired infections, with high mortality in immunocompromised patients. Various Candida pathogens constitute a public health issue, which require further efforts to develop new drugs, optimize currently available treatments and improve diagnostics. Given the high dynamism of Candida genomes, a promising strategy to improve current therapies and diagnostics is to understand the evolutionary mechanisms of adaptation to antifungal drugs and to the human host. Previous work using in vitro evolution, population genomics, selection inferences and Genome Wide Association Studies (GWAS) have partially clarified such recent adaptation, but various open questions remain. In the three research articles that conform this PhD thesis we addressed some of these gaps from the perspective of comparative genomics.
First, we addressed methodological issues regarding the analysis of Candida genomes. Studying recent adaptation in these pathogens requires adequate bioinformatic tools for variant calling, filtering and functional annotation. Among other reasons, current methods are suboptimal due to limited accuracy to identify structural variants from short read sequencing data. In addition, there is a need for easy-to-use, reproducible variant calling pipelines. To address these gaps we developed the “personalized Structural Variation detection” pipeline (perSVade), a framework to call, filter and annotate several variant types, including structural variants, directly from reads. PerSVade enables accurate identification of structural variants in any species of interest, such as Candida pathogens. In addition, our tool automatically predicts the structural variant calling accuracy on simulated genomes, which informs about the reliability of the calling process. Furthermore, perSVade can be used to analyze single nucleotide polymorphisms and copy number-variants, so that it facilitates multi-variant, reproducible genomic studies. This tool will likely boost variant analyses in Candida pathogens and beyond.
Second, we addressed open questions about recent adaptation in Candida, using perSVade for variant identification. On the one hand, we investigated the evolutionary mechanisms of drug resistance in Candida glabrata. For this, we used a large-scale in vitro evolution experiment to study adaptation to two commonly-used antifungals: fluconazole and anidulafungin. Our results show rapid adaptation to one or both drugs, with moderate fitness costs and through few mutations in a narrow set of genes. In addition, we characterize a novel role of ERG3 mutations in cross-resistance towards fluconazole in
anidulafungin-adapted strains. These findings illuminate the mutational paths leading to drug resistance and cross-resistance in Candida pathogens. On the other hand, we reanalyzed ~2,000 public genomes and phenotypes to understand the signs of recent selection and drug resistance in six major Candida species: C. auris, C. glabrata, C. albicans, C. tropicalis, C. parapsilosis and C. orthopsilosis. We found hundreds of genes under recent selection, suggesting that clinical adaptation is diverse and complex. These involve species-specific but also convergently affected processes, such as cell adhesion, which could underlie conserved adaptive mechanisms. In addition, using GWAS we predicted known drivers of antifungal resistance alongside potentially novel players. Furthermore, our analyses reveal an important role of generally-overlooked structural variants, and suggest an unexpected involvement of (para)sexual recombination in the spread of resistance. Taken together, our findings provide novel insights on how Candida pathogens adapt to human-related environments and suggest candidate genes that deserve future attention. In summary, the results of this thesis improve our knowledge about the mechanisms of recent adaptation in Candida pathogens, which may enable improved therapeutic and diagnostic applications.[cat] Les infeccions fúngiques representen una greu amenaça per a la salut, afectant a més de 1.000 milions de persones i causant aproximadament 1,5 milions de morts cada any. El problema està augmentant a causa d’unes opcions terapèutiques i diagnòstiques insuficients, l'increment del nombre de pacients susceptibles, l'expansió dels patògens parcialment vinculada al canvi climàtic i l'augment de la resistència als fàrmacs antifúngics. D’entre diversos fongs patògens, els llevats del gènere Candida són una causa important d'infeccions nosocomials, amb una alta mortalitat en pacients immunodeprimits. Diverses espècies de Candida constitueixen un problema de salut pública, cosa que requereix més esforços per a desenvolupar nous medicaments, optimitzar els tractaments disponibles i millorar els diagnòstics. Tenint en compte el dinamisme genòmic d’aquests patògens, una estratègia prometedora per millorar les teràpies i diagnòstics actuals és comprendre els mecanismes evolutius d'adaptació als fàrmacs antifúngics i a l’hoste humà. Treballs anteriors utilitzant l'evolució in vitro, la genòmica de poblacions, les inferències de selecció i els estudis d'associació de genoma complet (GWAS, per les sigles en anglès) han aclarit parcialment aquesta adaptació recent, però encara hi ha diverses preguntes obertes. En els tres articles que conformen aquesta tesi doctoral, hem abordat algunes d'aquestes preguntes des de la perspectiva de la genòmica comparativa.
En primer lloc, hem abordat qüestions metodològiques relatives a l'anàlisi dels genomes de les espècies Candida. L'estudi de l'adaptació recent en aquests patògens requereix eines bioinformàtiques adequades per a la detecció, filtratge i anotació funcional de variants genètiques. Entre altres raons, els mètodes actuals són subòptims a causa de la limitada precisió per identificar variants estructurals a partir de dades de seqüenciació amb lectures curtes. A més, hi ha una necessitat d’eines computacionals per a la detecció de variants que siguin senzilles d'utilitzar i reproduibles. Per abordar aquestes mancances, hem desenvolupat el mètode bioinformàtic "personalized Structural Variation detection" (perSVade), una eina que permet la detecció, filtratge i anotació de diversos tipus de variants, incloent-hi les variants estructurals, directament des de les lectures. PerSVade permet la identificació precisa de les variants estructurals en qualsevol espècie d'interès, com ara els patògens Candida. A més, la nostra eina prediu automàticament la precisió de la detecció d’aquestes variants en genomes simulats, la qual cosa informa sobre la fiabilitat del procés. Finalment, perSVade es pot utilitzar per analitzar altres tipus de variants, com els polimorfismes de nucleòtid únic o els canvis en el nombre de còpies, facilitant així estudis genòmics integrals i reproduibles. Aquesta eina probablement impulsarà les anàlisis genòmiques en els patògens Candida i també en altres espècies.
En segon lloc, hem abordat algunes de les preguntes obertes sobre l'adaptació recent en els llevats Candida, utilitzant perSVade per a la identificació de variants. D'una banda, hem investigat els mecanismes evolutius de resistència als fàrmacs antifúngics en Candida glabrata. Per a això, hem utilitzat un experiment
d'evolució in vitro a gran escala per estudiar l'adaptació a dos antifúngics comuns: el fluconazol i l’anidulafungina. Els nostres resultats mostren una adaptació ràpida a un o ambdós fàrmacs, amb un cost per al creixement moderat i a través de poques mutacions en un nombre reduït de gens. A més, hem caracteritzat un paper nou de les mutacions en ERG3 en la resistència creuada al fluconazol en soques adaptades a anidulafungina. Aquests descobriments aclareixen els processos mutacionals que condueixen a la resistència als fàrmacs i a la resistència creuada en els patògens Candida. D'altra banda, hem re-analitzat aproximadament 2.000 genomes i fenotips disponibles en repositoris públics per a comprendre els senyals genòmics de selecció recent i de resistència a fàrmacs antifúngics, en sis espècies rellevants de Candida: C. auris, C. glabrata, C. albicans, C. tropicalis, C. parapsilosis i C. orthopsilosis. Hem trobat centenars de gens sota selecció recent, suggerint que l'adaptació clínica és diversa i complexa. Aquests gens estan relacionats amb funcions específiques de cada espècie, però també trobem processos alterats de manera similar en diferents patògens, com per exemple l’adhesió cel·lular, cosa que indica fenòmens d’adaptació conservats. A part, utilitzant GWAS hem predit mecanismes esperats de resistència a antifúngics i també possibles nous factors. A més, les nostres anàlisis revelen un paper important de les variants estructurals, generalment poc estudiades, i suggereixen una implicació inesperada de la recombinació (para)sexual en la propagació de la resistència. En conjunt, els nostres descobriments proporcionen noves perspectives sobre com els patògens Candida s'adapten als entorns humans, i suggereixen gens candidats que mereixen investigacions futures. En resum, els resultats d’aquesta tesi milloren el nostre coneixement sobre els mecanismes d'adaptació recent en els patògens Candida, cosa que pot permetre el disseny de noves teràpies i diagnòstics
Automatic Generation of Personalized Recommendations in eCoaching
Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio
Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion
According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation & Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems
Metal Cations in Protein Force Fields: From Data Set Creation and Benchmarks to Polarizable Force Field Implementation and Adjustment
Metal cations are essential to life. About one-third of all proteins require metal cofactors to accurately fold or to function. Computer simulations using empirical parameters and classical molecular mechanics models (force fields) are the standard tool to investigate proteins’ structural dynamics and functions in silico. Despite many successes, the accuracy of force fields is limited when cations are involved. The focus of this thesis is the development of tools and strategies to create system-specific force field parameters to accurately describe cation-protein interactions. The accuracy of a force field mainly relies on (i) the parameters derived from increasingly large quantum chemistry or experimental data and (ii) the physics behind the energy formula.
The first part of this thesis presents a large and comprehensive quantum chemistry data set on a consistent computational footing that can be used for force field parameterization and benchmarking. The data set covers dipeptides of the 20 proteinogenic amino acids with different possible side chain protonation states, 3 divalent cations (Ca2+, Mg2+, and Ba2+), and a wide relative energy range. Crucial properties related to force field development, such as partial charges, interaction energies, etc., are also provided. To make the data available, the data set was uploaded to the NOMAD repository and its data structure was formalized in an ontology.
Besides a proper data basis for parameterization, the physics covered by the terms of the additive force field formulation model impacts its applicability. The second part of this thesis
benchmarks three popular non-polarizable force fields and the polarizable Drude model against a quantum chemistry data set. After some adjustments, the Drude model was found to reproduce the reference interaction energy substantially better than the non-polarizable force fields, which showed the importance of explicitly addressing polarization effects. Tweaking of the Drude model involved Boltzmann-weighted fitting to optimize Thole factors and Lennard-Jones parameters. The obtained parameters were validated by (i) their ability to reproduce reference interaction energies and (ii) molecular dynamics simulations of the N-lobe of calmodulin. This work facilitates the improvement of polarizable force fields for cation-protein interactions by quantum chemistry-driven parameterization combined with molecular dynamics simulations in the condensed phase.
While the Drude model exhibits its potential simulating cation-protein interactions, it lacks description of charge transfer effects, which are significant between cation and protein. The CTPOL model extends the classical force field formulation by charge transfer (CT) and polarization (POL). Since the CTPOL model is not readily available in any of the popular molecular-dynamics packages, it was implemented in OpenMM. Furthermore, an open-source parameterization tool, called FFAFFURR, was implemented that enables the (system specific) parameterization of OPLS-AA and CTPOL models. Following the method established in the previous part, the performance of FFAFFURR was evaluated by its ability to reproduce quantum chemistry energies and molecular dynamics simulations of the zinc finger protein.
In conclusion, this thesis steps towards the development of next-generation force fields to accurately describe cation-protein interactions by providing (i) reference data, (ii) a force field model that includes charge transfer and polarization, and (iii) a freely-available parameterization tool.Metallkationen sind für das Leben unerlässlich. Etwa ein Drittel aller Proteine benötigen Metall-Cofaktoren, um sich korrekt zu falten oder zu funktionieren. Computersimulationen unter Verwendung empirischer Parameter und klassischer Molekülmechanik-Modelle (Kraftfelder) sind ein Standardwerkzeug zur Untersuchung der strukturellen Dynamik und Funktionen von Proteinen in silico. Trotz vieler Erfolge ist die Genauigkeit der Kraftfelder begrenzt, wenn Kationen beteiligt sind. Der Schwerpunkt dieser Arbeit liegt auf der Entwicklung von Werkzeugen und Strategien zur Erstellung systemspezifischer Kraftfeldparameter zur genaueren Beschreibung von Kationen-Protein-Wechselwirkungen. Die Genauigkeit eines Kraftfelds hängt hauptsächlich von (i) den Parametern ab, die aus immer größeren quantenchemischen oder experimentellen Daten abgeleitet werden, und (ii) der Physik hinter der Kraftfeld-Formel.
Im ersten Teil dieser Arbeit wird ein großer und umfassender quantenchemischer Datensatz auf einer konsistenten rechnerischen Grundlage vorgestellt, der für die Parametrisierung und das Benchmarking von Kraftfeldern verwendet werden kann. Der Datensatz umfasst Dipeptide der 20 proteinogenen Aminosäuren mit verschiedenen möglichen Seitenketten-Protonierungszuständen, 3 zweiwertige Kationen (Ca2+, Mg2+ und Ba2+) und einen breiten relativen Energiebereich. Wichtige Eigenschaften für die Entwicklung von Kraftfeldern, wie Wechselwirkungsenergien, Partialladungen usw., werden ebenfalls bereitgestellt. Um die Daten verfügbar zu machen, wurde der Datensatz in das NOMAD-Repository hochgeladen und seine Datenstruktur wurde in einer Ontologie formalisiert.
Neben einer geeigneten Datenbasis für die Parametrisierung beeinflusst die Physik, die von den Termen des additiven Kraftfeld-Modells abgedeckt wird, dessen Anwendbarkeit. Der zweite Teil dieser Arbeit vergleicht drei populäre nichtpolarisierbare Kraftfelder und das polarisierbare Drude-Modell mit einem Datensatz aus der Quantenchemie. Nach einigen Anpassungen stellte sich heraus, dass das Drude-Modell die Referenzwechselwirkungsenergie wesentlich besser reproduziert als die nichtpolarisierbaren Kraftfelder, was zeigt, wie wichtig es ist, Polarisationseffekte explizit zu berücksichtigen. Die Anpassung des Drude-Modells umfasste eine Boltzmann-gewichtete Optimierung der Thole-Faktoren und Lennard-Jones-Parameter. Die erhaltenen Parameter wurden validiert durch (i) ihre Fähigkeit, Referenzwechselwirkungsenergien zu reproduzieren und (ii) Molekulardynamik-Simulationen des Calmodulin-N-Lobe. Diese Arbeit demonstriert die Verbesserung polarisierbarer Kraftfelder für Kationen-Protein-Wechselwirkungen durch quantenchemisch gesteuerte Parametrisierung in Kombination mit Molekulardynamiksimulationen in der kondensierten Phase.
Während das Drude-Modell sein Potenzial bei der Simulation von Kation - Protein - Wechselwirkungen zeigt, fehlt ihm die Beschreibung von Ladungstransfereffekten, die zwischen Kation und Protein von Bedeutung sind. Das CTPOL-Modell erweitert die klassische Kraftfeldformulierung um den Ladungstransfer (CT) und die Polarisation (POL). Da das CTPOL-Modell in keinem der gängigen Molekulardynamik-Pakete verfügbar ist, wurde es in OpenMM implementiert. Außerdem wurde ein Open-Source-Parametrisierungswerkzeug namens FFAFFURR implementiert, welches die (systemspezifische) Parametrisierung von OPLS-AA und CTPOL-Modellen ermöglicht. In Anlehnung an die im vorangegangenen Teil etablierte Methode wurde die Leistung von FFAFFURR anhand seiner Fähigkeit, quantenchemische Energien und Molekulardynamiksimulationen des Zinkfingerproteins zu reproduzieren, bewertet.
Zusammenfassend lässt sich sagen, dass diese Arbeit einen Schritt in Richtung der Entwicklung von Kraftfeldern der nächsten Generation zur genauen Beschreibung von Kationen-Protein-Wechselwirkungen darstellt, indem sie (i) Referenzdaten, (ii) ein Kraftfeldmodell, das Ladungstransfer und Polarisation einschließt, und (iii) ein frei verfügbares Parametrisierungswerkzeug bereitstellt
Genome analysis of Phytophthora cactorum strains associated with crown- and leather-rot in strawberry
Phytophthora cactorum has two distinct pathotypes that cause crown rot and leather rot in strawberry (Fragaria × ananassa). Strains of the crown rot pathotype can infect both the rhizome (crown) and fruit tissues, while strains of the leather rot pathotype can only infect the fruits of strawberry. The genome of a highly virulent crown rot strain, a low virulent crown rot strain, and three leather rot strains were sequenced using PacBio high fidelity (HiFi) long read sequencing. The reads were de novo assembled to 66.4–67.6 megabases genomes in 178–204 contigs, with N50 values ranging from 892 to 1,036 kilobases. The total number of predicted complete genes in the five P. cactorum genomes ranged from 17,286 to 17,398. Orthology analysis identified a core secretome of 8,238 genes. Comparative genomic analysis revealed differences in the composition of potential virulence effectors, such as putative RxLR and Crinklers, between the crown rot and the leather rot pathotypes. Insertions, deletions, and amino acid substitutions were detected in genes encoding putative elicitors such as beta elicitin and cellulose-binding domain proteins from the leather rot strains compared to the highly virulent crown rot strain, suggesting a potential mechanism for the crown rot strain to escape host recognition during compatible interaction with strawberry. The results presented here highlight several effectors that may facilitate the tissue-specific colonization of P. cactorum in strawberry.publishedVersio
Virkningen av lav doserate ioniserende stråling : studier av molekylære responser og transgenerasjonell genomisk ustabilitet gjort i mus
Mye av det vi vet om risiko for helseeffekter etter å ha blitt usatt for ioniserende stråling er kunnskap lært fra de som overlevde atombombene i Hiroshima og Nagasaki. Imidlertid er denne kunnskapen basert på effekter etter høye strålingsnivåer som skjedde over kort tid. Nyere forskningen viser i økende grad at lavere konsentrasjon av stråling (lav doserate), hvor eksponeringen skjer over lengre tid, påvirker cellene våre på andre måter enn stråling med høy konsentrasjon, slik som stråling fra en atombombe. Forandringene som skjer i genene, er antatt å være en viktig bidragsyter til responsene som oppstår ved lav doserate bestråling. Man antar at endringer i såkalte epigenetiske markører kan føre til langvarige forandringer av genuttrykket. De fleste som har studert dette har i all hovedsak undersøkt responsene kort tid etter bestrålingen. Det er derfor et behov for studier som undersøker om endringene er målbare også lenge etter bestrålingen. Dersom langvarige endringer i genuttrykket oppstår, er det videre viktig med kunnskap om hvilke mekanismer som har bidratt til dette.
Formålet med dette doktorgradsprosjektet har derfor vært å bidra med å tette kunnskapshullene knyttet til betydningen av bestrålingsfaktoren «doserate», som beskriver hvor mye man bestråles per tidsenhet. Dette er blitt gjort ved å studere effektene i musemodeller. To strålingsstudier ble gjennomført i FIGARO kilden som driftes av NMBU. Studiene hadde ulike doserater, (1,5 mGy/t til 1,5 Gy (Eksperiment A), og 2,5, 10 og 100 mGy/t til 3 Gy (Eksperiment B)) gitt med gamma stråling fra en kobolt-60 (60Co) kilde. Et viktig aspekt med prosjektet var å studere betydningen av lav doserate stråling som gis over lengre tid ved å undersøke responsene på tre ulike tidspunkter etter bestråling; kort (én dag), lang (>100 dager (3 måneder)), og over to generasjoner. I musene som ble direkte bestrålt (F0) fokuserte vi på å undersøke endringer i genprofilene. I tillegg ble det undersøkt om tilgjengeligheten til DNAet, hvor genene er kodet, som blant annet endres av epigenetisk markører (epigenomet), også endret seg etter bestrålingen. For å studere om effekter også var målbare to generasjoner etter mus-bestefar (F2) var blitt bestrålt, ble det brukt metoder som måler DNA-skader som kan føre til mutasjoner, og skader som oppstår i kromosomene når cellene deler seg (dannelse av mikrokjerner). Disse metodene ble benyttet for å finne ut om noe hadde endret seg i muse-barnebarna (F2) som kunne fører til ustabilitet i prosesser som omhandler genomet. For at prosjektet skulle kunne måle dette ble det etablert en meget sensitiv metode for å undersøke effekter på blodceller med mikrokjerner, i tillegg til å optimalisere metoden som måler DNA-skader i hvite blodceller («komet metoden») slik at tellingen av celler ble automatisert.
Resultatene én dag etter bestrålingen viste store endringer i både genuttrykket og i tilgjengeligheten av DNAet. Endringene var større jo høyere doseraten var, selv om alle hadde fått samme totale dose, altså så vi en doserate-spesifikk respons. Det viste seg også at genene var knyttet til biologiske signalveier koblet til kreft, fettmetabolisme og inflammasjon, for alle de målte doseratene. På de ulike tidspunktene var det stor forskjell på genekspresjon og DNA tilgjengelighet mellom lav og høy doserate. Vi brukte to ulike muselinjer i studien og det var store forskjeller mellom disse muselinjene, noe som viser at hvilken muselinje som benyttes har en stor effekt på de molekylære responsene man undersøker.
Selv tre måneder etter bestråling ble det funnet endret genuttrykk, men antall endrede gener var betraktelig færre sammenlignet med én dag etter bestråling. Det er vanskelig å vite den biologiske betydningen av at disse få genene var endret. Når det gjelder tilgjengeligheten til DNAet fant vi ingen endringer tre måneder etter lav doserate bestråling. Derimot, viste det seg at etter bestråling med akutt høy doserate ble det funnet reduserte tilgjengeligheten til gener, viktige for strålingsinduserte effekter, som blant annet har funksjoner i reparasjon av DNA-skader.
I muse-barnebarna av bestrålte muse-bestefedre undersøkte vi genomisk ustabilitet som er mål på nedarvede effekter. Det var ønskelig å studere effekter i muse-barnebarna da disse musene var de første som ble fertilisert av sædceller som ikke hadde vært påvirket av bestrålingen. I tillegg utsatte vi å avle neste generasjon av musene en periode slik at vi kunne være sikre på at sædcellene hadde oppstått fra en bestrålt stamcelle, og ikke blitt bestrålt selv. I blodprøver fra muse-barnebarna målte vi hvor mye DNA- og kromosom-skader som oppsto av seg selv, hvor mye skade som oppsto rett etter en akutt dose røntgenstråler (betydelig høyere dose enn de vi får ved medisinsk undersøkelse), og i tillegg hvor fort noen typer DNA-skader ble reparert. Resultatene viste at musene som hadde en bestrålt bestefar hadde høyere endogent nivå av DNA-skader (DNA-skade som oppstår «av seg selv»). Denne økningen var veldig lav, og det er vanskelig å konkludere om dette er et biologisk funn eller om det skyldes metodologiske aspekter. Det ble ikke funnet noen holdepunkter for endringer i genomisk ustabilitet etter en akutt dose med røntgenstråler, ei heller på evnen til å reparere DNA-skade.
Hovedfunnene fra dette doktorgradsprosjektet er at doserate har modulerende virkning på type og grad av effekt etter bestråling, og at doserate burde bli tatt med i betraktningen når man vurderer strålingseffekter. Når det gjelder overføring av effektene over generasjoner (transgenerasjonell arvelighet) er det vanskelig å konkludere, gitt de eksperimentelle forholdene, om resultatene skyldes reelle biologiske effekter eller metodologiske aspekter. Resultatene må derfor verifiseres i andre studier.Most of our epidemiological knowledge about radiation-induced health effects originates from atomic bomb survivors. However, the exposure from the atomic bombs was received over a short period and at high dose rates. An increasing number of studies suggest that low dose rate irradiation induces biological effects that differ from responses observed after acute high dose/high dose rate exposures. The modulation of gene expression constitutes an essential part of radiation-induced biological responses, while epigenetic changes have the ability to cause long-term changes in transcriptional programs. In most studies, responses are analysed shortly after exposure. Hence, there is a need to employ longer post-radiation timelines to investigate how (and if) perturbations in gene expression persist post-exposure.
The objectives of this PhD project have been to address research gaps concerning the impact of dose rate using mice models. The experiments were designed to gain information about the impact of low dose rate at three different post-irradiation timepoints; early- (one day), long-term (>100 days (three months)), and across generations. The design was strengthened by including high dose rate exposure groups to compare gene expression profiles and chromatin accessibility in directly exposed mice when all dose rate groups received the same total dose. Two mouse experiments were conducted using different dose rates of gamma radiation from a 60Co source. These spanned from low to high dose rates: 1.5 mGy/h to 1.5 Gy (Experiment A), and 2.5, 10 and 100 mGy/h to 3 Gy (Experiment B). The induced responses were evaluated by profiling the transcriptional activity and chromatin accessibility, in liver tissue using RNA-sequencing and the Assay for Transposase Accessible Chromatin (ATAC)-sequencing in directly exposed mice at the two post-radiation timepoints; early (one day) and later (three months). Effects manifested across generations were evaluated using DNA- and cytogenic damage to assess transgenerational inherited genomic instability through the paternal germline. The comet assay and a highly sensitive MN-assay were used for this purpose.
One day post-radiation, the expression of genes and the accessibility to the chromatin were perturbed in a dose rate-specific manner when irradiated to a total dose of 3 Gy. The enrichment of functional pathways indicated that the affected genes were linked to lipid metabolism and inflammation, in addition to cancer, for all dose rate exposures. The overall results suggest a dose-rate-specific response and that prolonged exposure to low dose rates could be assumed to introduce lower level of cellular stress per time inflicting other mechanisms than high dose rate exposures. Furthermore, as the design included the use of two strains of mice, the results displayed that the choice of mouse strain is highly relevant for the molecular outcomes.
Differentially expressed genes were present three months after both low and high dose rate, although to a lesser extent when compared to one day post-radiation. Concerning the long-term (three months after exposure) epigenomic profile, there was no evidence that low dose rate irradiation introduced epigenetic changes affecting the chromatin accessibility. This indicate that the differentially changed chromatin regions present one day after low dose rate irradiation were reverted to a profile comparable with controls. The impact of a high dose rate on the epigenome long-term was clearly different to that seen following low dose rate. Here, the accessibility of the chromatin was almost exclusively reduced, occurring in transcriptional start sites (promoter regions) adjacent to genes relevant for radiation-induced damage, like the repair of DNA double-strand breaks and activation of p53-related responses. In addition, accessibility was also reduced in promoter regions to genes linked to transcriptional regulation.
Paternal transgenerational (F2) genomic instability was used to investigate inheritance across generations, where F2 represents the first unexposed generation. The low dose rate irradiated (1,5 mGy/h) F0 generation was exposed for 45 days to a cumulative total dose of 1.5 Gy. Genomic instability was assessed in the blood samples from male F2 mice before and after a challenging dose of X-ray. Changes in the rate of repair of induced DNA lesions were also evaluated. The results did display statistically significant increased endogenously occurring DNA lesions. However, due to a low effect size, it is challenging to conclude whether the results represent a true biological finding or that it relates to methodological aspects. There was further no evidence that F0 low dose rate irradiation affected the level of DNA damage directly after X-ray, the rate of repair of the DNA lesions, or the formation of micronuclei in reticulocytes.
The overall findings of this PhD project suggest that low dose rate should be considered a significant dose-effect modulating irradiation factor after direct exposure. Concerning transgenerational inheritance, given the experimental conditions a conclusion is elusive. The significance of these results upon the risk for human health needs to be addressed in appropriate epidemiological studies
Epitranscriptomic regulation in breast cancer and PCB-induced liver disease.
Post-transcriptional RNA modifications including N6-methyladenosine (m6A) regulate mRNA stability, splicing, and translation. My research examined m6A in two disease models: breast cancer (BCa) and non-alcoholic fatty liver disease (NAFLD). Acquired resistance to endocrine therapies (ET) develops in approximately 20% of BCa patients with estrogen receptor α positive (ER+) tumors following treatment. The mechanisms by which tumor cells evade ET are not completely understood. Using a cell line model, we investigated the role of an m6A reader protein, HNRNPA2B1 (A2B1) that is upregulated in ET-resistant ER+ BCa cells. Stable overexpression of A2B1 in ET-sensitive MCF-7 cells (MCF-7-A2B1), results in ET resistance, whereas knockdown of A2B1 in ET-resistant cells restored ET-sensitivity. microRNAs (miRNAs) downregulated by transient overexpression of A2B1 were identified to target two key enzymes (PSAT1 and PHGDH) in the serine biosynthetic pathway (SSP) which is upregulated in ET-resistant BCa cells and in tumors from patients with ET-resistant disease. Using luciferase assays, PSAT1 and PHGDH were validated as bona fide targets of miRNAs downregulated by A2B1 (miR-145-5p and miR-424-5p targeting PSAT1, miR-34b-5p and miR-876-5p targeting PHGDH). Exogenous overexpression of the validated miRNAs decreased endogenous PSAT1 and PHGDH in ET-resistant BCa cells, resulting in increased sensitivity to ET in vitro. In the second model, alterations in the m6A epitranscriptome were identified in the livers of male C57Bl/6Jmice after a single, oral exposure to polychlorinated biphenyls (PCB), a class of persistent organic pollutants, in combination with 12 weeks on a high fat diet (HFD). Our results demonstrated that exposure to PCBs in combination with a HFD resulted in major changes to the mRNA and miRNA transcriptomes, and m6A epitranscriptome. Pathway analysis of the genes in which m6A peaks were altered identified pathways involved in the progression from steatosis to steatohepatitis in NAFLD. PCB exposures also resulted in changes to alternative splicing (AS) mechanisms and events, suggesting that PCB-induced m6A changes contribute to altered isoforms expression in NAFLD. Taken together, the results in this dissertation demonstrate the significant role of altered m6A in two common human diseases
Investigating the role of eIF4A2 in the spatial regulation of metabolic adaptation to hypoxia in colorectal cancer
Colorectal cancer is the third most common cancer worldwide and ranks second for cancer-related mortality. Hypoxia (< 1% pO2) is found in up to 50% of colorectal tumours and is associated with poor patient prognosis, increased metastatic potential and resistance to therapy. Hypoxia stabilises the hypoxia-inducible factors, HIF-1ɑ and HIF-2ɑ, to alter the transcriptome to drive molecular adaptation to hypoxic stress. Hypoxia also leads to changes in the translation machinery to alter protein synthesis. The changes introduced by these mechanisms contribute to the major hallmarks of cancer including metabolic reprogramming. However, how the oxygen gradient in tumours contributes to spatially defined metabolic adaptations and how this leads to therapeutic failure is unknown. Preliminary data hypothesised that the translation initiation factor eIF4A2 is a modulator of hypoxic adaptation and regulates colorectal cancer cell survival through the regulation of metabolic mRNA translation. Here, the eIF4A2 interaction landscape was investigated and revealed hypoxic interactions with other regulators of mRNA translation including eIF4G3, eIF4E1 and CNOT7. eIF4A2 knockout was shown to reduce spheroid growth and led to an increase in HIF-2ɑ expression. Furthermore, the expression of several predicted eIF4A2 target genes involved in amino acid biosynthesis and endocytosis were investigated. eIF4A2 knockout led to a reduction in the expression of the endocytosis regulator EHD1 and EHD1 knockdown reduced cancer cell survival. This work suggests eIF4A2 regulates the hypoxic translation of specific mRNAs, such as EHD1, through altered protein:protein interactions to regulate colorectal cancer cell survival. Moreover, a novel secondary ion mass spectrometry imaging technique for spatially resolving metabolite changes across the oxygen gradient within 3-D spheroid models and colorectal cancer xenografts is revealed. We pioneer high-pressure frozen orbiSIMS to simultaneously measure metabolites in situ across differentially oxygenated regions of tumours and colorectal cancer spheroid models. Correlation with RNA-sequencing helps predict the transcriptional changes behind this spatial metabolic adaptation and could be used to identify novel therapeutic targets important for tackling therapeutic resistance driven by hypoxia-induced metabolic reprogramming
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