In Vivo Ligands of MDA5 and RIG-I in Measles Virus-Infected Cells

RIG-I-like receptors (RLRs: RIG-I, MDA5 and LGP2) play a major role in the innate immune response against viral infections and detect patterns on viral RNA molecules that are typically absent from host RNA. Upon RNA binding, RLRs trigger a complex downstream signaling cascade resulting in the expression of type I interferons and proinflammatory cytokines. In the past decade extensive efforts were made to elucidate the nature of putative RLR ligands. In vitro and transfection studies identified 5'-triphosphate containing blunt-ended double-strand RNAs as potent RIG-I inducers and these findings were confirmed by next-generation sequencing of RIG-I associated RNAs from virus-infected cells. The nature of RNA ligands of MDA5 is less clear. Several studies suggest that double-stranded RNAs are the preferred agonists for the protein. However, the exact nature of physiological MDA5 ligands from virus-infected cells needs to be elucidated. In this work, we combine a crosslinking technique with next-generation sequencing in order to shed light on MDA5-associated RNAs from human cells infected with measles virus. Our findings suggest that RIG-I and MDA5 associate with AU-rich RNA species originating from the mRNA of the measles virus L gene. Corresponding sequences are poorer activators of ATP-hydrolysis by MDA5 in vitro, suggesting that they result in more stable MDA5 filaments. These data provide a possible model of how AU-rich sequences could activate type I interferon signaling

RNA sequencing data : hitchhiker's guide to expression analysis

Gene expression is the fundamental level at which the results of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq data sets, as well as the performance of the myriad of methods developed. In this review, we give an overview of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on the quantification of gene expression and statistical approaches for differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies

LocTree3 prediction of localization

The prediction of protein sub-cellular localization is an important step toward elucidating protein function. For each query protein sequence, LocTree2 applies machine learning (profile kernel SVM) to predict the native sub-cellular localization in 18 classes for eukaryotes, in six for bacteria and in three for archaea. The method outputs a score that reflects the reliability of each prediction. LocTree2 has performed on par with or better than any other state-of-the-art method. Here, we report the availability of LocTree3 as a public web server. The server includes the machine learning-based LocTree2 and improves over it through the addition of homology-based inference. Assessed on sequence-unique data, LocTree3 reached an 18-state accuracy Q18 = 80 ± 3% for eukaryotes and a six-state accuracy Q6 = 89 ± 4% for bacteria. The server accepts submissions ranging from single protein sequences to entire proteomes. Response time of the unloaded server is about 90 s for a 300-residue eukaryotic protein and a few hours for an entire eukaryotic proteome not considering the generation of the alignments. For over 1000 entirely sequenced organisms, the predictions are directly available as downloads. The web server is available at http://www.rostlab.org/services/loctree3

Collective action and individual adaptation in natural resource management under the threat of ecosystem change: Insights from economic experiments

Scientific evidence shows that climate change increases the frequency of climate extremes across the globe. These climate extremes exogenously pressure local resource users by causing destruction of natural resources, often affecting ecosystems that already have deteriorated due to overexploitation in the past. The future state of natural resources and entire ecosystems is thereby determined by both exogenous (climate) and endogenous (management by resource users) dynamics. The combination of both the uncertain changing environmental conditions and manmade overexploitation will make the sustainable management of natural resources by local resource users more challenging in the future. Depending on the underlying ecosystem dynamics, the combination of overexploitation and climate extremes may cause sudden abrupt shifts in natural resources if a resource is driven to its critical threshold (tipping point). These shifts are termed regime shifts. In its most drastic form, a regime shift results in the collapse of the resource with severe economic consequences. Ecological and meteorological warning and forecast systems could potentially warn of approaching regime shifts and climate extremes, thereby motivating the resource users for more sustainable resource management and investments in protective adaptation. Self-governance of natural resources highly depends on collective action. Resource users need to cooperate and coordinate their resource extraction strategies to keep a resource at a sustainable level of regrowth and to prevent it reaching a critical threshold. Policy makers and ecologists must decide when and how to inform local resource users about the potential threat of crossing critical thresholds. However, critical thresholds are often unknown and ecological early warning signals only provide uncertain threshold knowledge. Knowing if the communication of early imprecise threshold information bears a risk to hamper collective action is thus critical. In addition, in some cases, individual adaptation behaviour determines how far an individual experiences economic losses due to climate extremes. In these cases, the issue is not about collective action, but rather about individuals’ responsiveness to early warnings. To further understand human behaviour in the light of the aforementioned ecological dynamics, three economic experiments were designed and implemented. The results of these experiments are presented in the three academic papers of this thesis (Chapter 2 to 4): The first paper, titled “The interaction of shock experience and threshold knowledge in natural resource management”, was co-authored by myself with Aneeque Javaid and Stefanie Engel (Chapter 2). This paper addresses the lack of evidence in the literature on the impact of the interaction of exogenously and endogenously driven change in ecosystems on collective action. To analyse this interaction and find the main driver of change in groups’ resource extraction strategies, a novel, (quasi-) continuous-time common-pool resource (CPR) experiment was designed and implemented in the laboratory. The CPR experiment incorporates both dynamics: an unexpected exogenous shock that causes resource scarcity and a critical threshold, at which the resource collapses. The impact of initial resource scarcity on groups’ extraction behaviour is compared to the impact of shock driven scarcity. Furthermore, the effect of shock experience on extraction strategies in the future is assessed. The results indicate that while group members cooperate less when experiencing an exogenous shock to their resource, the knowledge of a critical threshold still motivates successful coordination. However, cooperation amongst group members and efficiency of resource extraction is more sensitive to the resource scarcity itself, than the experience of an exogenous shock. There is no significant effect of shock experience on group’s future extraction strategies. The second paper, “Are imprecise early warnings a potential benefit or threat to sustainable resource management?”, was co-authored by myself with Tobias Vorlaufer and Stefanie Engel (Chapter 3). This paper asks whether an imprecise early threshold warning alters cooperation amongst resource users and analyses if there is a danger of deteriorating individuals’ responses to the certain threshold knowledge by giving an imprecise early warning. On the one hand, imprecise early warnings could raise awareness about the resource’s dynamics and thus, encourage collective action. On the other hand, imprecise early warnings could be taken as a sign of an inevitable upcoming loss of the resource. Thus, resource users increase their individual extraction efforts and collective action fails. To assess the effect of imprecise early warnings on collective action, two additional treatments for the CPR experiment were designed and implemented. The two treatments differ in the degree of uncertainty about the threshold level in the beginning. While groups in both treatments know of the critical threshold, only one treatment receives an imprecise early threshold warning in form of a known threshold range. The experimental results show no effect of such an imprecise early warning on cooperation and coordination amongst group members in comparison to groups who only know of the mere threshold existence. The third paper, “False and missed alarms in seasonal forecasts affect individual adaptation choices”, was again co-authored by myself with Tobias Vorlaufer and Stefanie Engel (Chapter 4). It analyses the effect of varying forecast accuracy on individuals’ responsiveness to climate forecast systems. Climate extremes can result in economic losses if individuals are not adequately prepared. The effect of climate forecast systems however, likely depends on their accuracy and individuals’ responsiveness to inaccurate climate warnings. An online experiment was designed and implemented to assess individuals’ responsiveness to climate forecasts that issue potentially inaccurate warnings about approaching climate extremes leading to the experience of false and/or missed alarms. The results of this experiment indicate that experiencing false alarms more frequently leads to a decrease in individuals’ adaptation investments in response to future warnings (so called “cry-wolf-effect”), but has no impact on individuals’ responsiveness to the forecast if no warning is issued. In contrast, experiencing missed alarms more frequently leads to an increase in individuals’ responsiveness and investment in adaptation regardless whether or not a warning is issued by future forecasts. Individuals who experienced missed alarms more frequently react more sensitive on warnings per se than individuals without this experience. If they receive a warning their adaptation behaviour is less affected by the forecasted probability of the extreme climate event. This thesis extends the understanding of human behaviour in light of changing ecosystem dynamics and provides more information regarding how natural resource management can be improved by using forecast and ecological prediction systems. The improved understanding of interactions between human behaviour and ecosystem change contributes to the exchange between policy makers, social scientists, ecologists and resource users

ARMOR: An Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data

The extensive generation of RNA sequencing (RNA-seq) data in the last decade has resulted in a myriad of specialized software for its analysis. Each software module typically targets a specific step within the analysis pipeline, making it necessary to join several of them to get a single cohesive workflow. Multiple software programs automating this procedure have been proposed, but often lack modularity, transparency or flexibility. We present ARMOR, which performs an end-to-end RNA-seq data analysis, from raw read files, via quality checks, alignment and quantification, to differential expression testing, geneset analysis and browser-based exploration of the data. ARMOR is implemented using the Snakemake workflow management system and leverages conda environments; Bioconductor objects are generated to facilitate downstream analysis, ensuring seamless integration with many R packages. The workflow is easily implemented by cloning the GitHub repository, replacing the supplied input and reference files and editing a configuration file. Although we have selected the tools currently included in ARMOR, the setup is modular and alternative tools can be easily integrated

ARMOR: An Automated Reproducible MOdular Workflow for Preprocessing and Differential Analysis of RNA-seq Data

The extensive generation of RNA sequencing (RNA-seq) data in the last decade has resulted in a myriad of specialized software for its analysis. Each software module typically targets a specific step within the analysis pipeline, making it necessary to join several of them to get a single cohesive workflow. Multiple software programs automating this procedure have been proposed, but often lack modularity, transparency or flexibility. We present ARMOR, which performs an end-to-end RNA-seq data analysis, from raw read files, via quality checks, alignment and quantification, to differential expression testing, geneset analysis and browser-based exploration of the data. ARMOR is implemented using the Snakemake workflow management system and leverages conda environments; Bioconductor objects are generated to facilitate downstream analysis, ensuring seamless integration with many R packages. The workflow is easily implemented by cloning the GitHub repository, replacing the supplied input and reference files and editing a configuration file. Although we have selected the tools currently included in ARMOR, the setup is modular and alternative tools can be easily integrated

Synaptic accumulation of FUS triggers age-dependent misregulation of inhibitory synapses in ALS-FUS mice

FUS is a primarily nuclear RNA-binding protein with important roles in RNA processing and transport. FUS mutations disrupting its nuclear localization characterize a subset of amyotrophic lateral sclerosis (ALS-FUS) patients, through an unidentified pathological mechanism. FUS regulates nuclear RNAs, but its role at the synapse is poorly understood. Here, we used super-resolution imaging to determine the physiological localization of extranuclear, neuronal FUS and found it predominantly near the vesicle reserve pool of presynaptic sites. Using CLIP-seq on synaptoneurosome preparations, we identified synaptic RNA targets of FUS that are associated with synapse organization and plasticity. Synaptic FUS was significantly increased in a knock-in mouse model of ALS-FUS, at presymptomatic stages, accompanied by alterations in density and size of GABAergic synapses. RNA-seq of synaptoneurosomes highlighted age-dependent dysregulation of glutamatergic and GABAergic synapses. Our study indicates that FUS accumulation at the synapse in early stages of ALS-FUS results in synaptic impairment, potentially representing an initial trigger of neurodegeneration

Synaptic FUS accumulation triggers early misregulation of synaptic RNAs in a mouse model of ALS

Mutations disrupting the nuclear localization of the RNA-binding protein FUS characterize a subset of amyotrophic lateral sclerosis patients (ALS-FUS). FUS regulates nuclear RNAs, but its role at the synapse is poorly understood. Using super-resolution imaging we determined that the localization of FUS within synapses occurs predominantly near the vesicle reserve pool of presynaptic sites. Using CLIP-seq on synaptoneurosomes, we identified synaptic FUS RNA targets, encoding proteins associated with synapse organization and plasticity. Significant increase of synaptic FUS during early disease in a mouse model of ALS was accompanied by alterations in density and size of GABAergic synapses. mRNAs abnormally accumulated at the synapses of 6-month-old ALS-FUS mice were enriched for FUS targets and correlated with those depicting increased short-term mRNA stability via binding primarily on multiple exonic sites. Our study indicates that synaptic FUS accumulation in early disease leads to synaptic impairment, potentially representing an initial trigger of neurodegeneration

Synaptic FUS accumulation triggers early misregulation of synaptic RNAs in a mouse model of ALS

International audienceMutations disrupting the nuclear localization of the RNA-binding protein FUS characterize a subset of amyotrophic lateral sclerosis patients (ALS-FUS). FUS regulates nuclear RNAs, but its role at the synapse is poorly understood. Using super-resolution imaging we determined that the localization of FUS within synapses occurs predominantly near the vesicle reserve pool of presynaptic sites. Using CLIP-seq on synaptoneurosomes, we identified synaptic FUS RNA targets, encoding proteins associated with synapse organization and plasticity. Significant increase of synaptic FUS during early disease in a mouse model of ALS was accompanied by alterations in density and size of GABAergic synapses. mRNAs abnormally accumulated at the synapses of 6-month-old ALS-FUS mice were enriched for FUS targets and correlated with those depicting increased short-term mRNA stability via binding primarily on multiple exonic sites. Our study indicates that synaptic FUS accumulation in early disease leads to synaptic impairment, potentially representing an initial trigger of neurodegeneration