Development of bioinformatics tools for the rapid and sensitive detection of known and unknown pathogens from next generation sequencing data

Infectious diseases still remain one of the main causes of death across the globe. Despite huge advances in clinical diagnostics, establishing a clear etiology remains impossible in a proportion of cases. Since the emergence of next generation sequencing (NGS), a multitude of new research fields based on this technology have evolved. Especially its application in metagenomics – denoting the research on genomic material taken directly from its environment – has led to a rapid development of new applications. Metagenomic NGS has proven to be a promising tool in the field of pathogen related research and diagnostics. In this thesis, I present different approaches for the detection of known and the discovery of unknown pathogens from NGS data. These contributions subdivide into three newly developed methods and one publication on a real-world use case of methodology we developed and data analysis based on it. First, I present LiveKraken, a real-time read classification tool based on the core algorithm of Kraken. LiveKraken uses streams of raw data from Illumina sequencers to classify reads taxonomically. This way, we are able to produce results identical to those of Kraken the moment the sequencer finishes. We are furthermore able to provide comparable results in early stages of a sequencing run, allowing saving up to a week of sequencing time. While the number of classified reads grows over time, false classifications appear in negligible numbers and proportions of identified taxa are only affected to a minor extent. In the second project, we designed and implemented PathoLive, a real-time diagnostics pipeline which allows the detection of pathogens from clinical samples before the sequencing procedure is finished. We adapted the core algorithm of HiLive, a real-time read mapper, and enhanced its accuracy for our use case. Furthermore, probably irrelevant sequences automatically marked. The results are visualized in an interactive taxonomic tree that provides an intuitive overview and detailed metrics regarding the relevance of each identified pathogen. Testing PathoLive on the sequencing of a real plasma sample spiked with viruses, we could prove that we ranked the results more accurately throughout the complete sequencing run than any other tested tool did at the end of the sequencing run. With PathoLive, we shift the focus of NGS-based diagnostics from read quantification towards a more meaningful assessment of results in unprecedented turnaround time. The third project aims at the detection of novel pathogens from NGS data. We developed RAMBO-K, a tool which allows rapid and sensitive removal of unwanted host sequences from NGS datasets. RAMBO-K is faster than any tool we tested, while showing a consistently high sensitivity and specificity across different datasets. RAMBO-K rapidly and reliably separates reads from different species. It is suitable as a straightforward standard solution for workflows dealing with mixed datasets. In the fourth project, we used RAMBO-K as well as several other data analyses to discover Berlin squirrelpox virus, a deviant new poxvirus establishing a new genus of poxviridae. Near Berlin, Germany, several juvenile red squirrels (Sciurus vulgaris) were found with moist, crusty skin lesions. Histology, electron microscopy, and cell culture isolation revealed an orthopoxvirus-like infection. After standard workflows yielded no significant results, poxviral reads were assigned using RAMBO-K, enabling the assembly of the genome of the novel virus. With these projects, we established three new application-related methods each of which closes different research gaps. Taken together, we enhance the available repertoire of NGS-based pathogen related research tools and alleviate and fasten a variety of research projects

The Economics of Fuel Management: Wildfire, Invasive Plants, and the Dynamics of Sagebrush Rangelands in the Western United States

In this article we develop a simulation model to evaluate the economic efficiency of fuel treatments and apply it to two sagebrush ecosystems in the Great Basin of the western United States: the Wyoming Sagebrush Steppe and Mountain Big Sagebrush ecosystems. These ecosystems face the two most prominent concerns in sagebrush ecosystems relative to wildfire: annual grass invasion and native conifer expansion. Our model simulates long-run wildfire suppression costs with and without fuel treatments explicitly incorporating ecological dynamics, stochastic wildfire, uncertain fuel treatment success, and ecological thresholds. Our results indicate that, on the basis of wildfire suppression costs savings, fuel treatment is economically efficient only when the two ecosystems are in relatively good ecological health. We also investigate how shorter wildfire-return intervals, improved treatment success rates, and uncertainty about the location of thresholds between ecological states influence the economic efficiency of fuel treatments

How risk perception shapes collective action intentions in repressive contexts : a study of Egyptian activists during the 2013 post-coup uprising

This research was conducted while Arin Hovhannes Ayanian was a PhD candidate at the University of St Andrews, on a full scholarship from the School of Psychology and Neuroscience, University of St Andrews, Scotland.Social psychological research has overlooked collective action in repressive contexts, where activists face substantial personal risks. This paper examines the social psychological processes motivating activists to engage in collective action in risky contexts. We investigate the idea that perceived risks due to government sanctions can galvanize action through fuelling anger, shaping efficacy beliefs, and increasing identification with the movement. We also argue that anger, efficacy and identification motivate action intentions directly and indirectly through reducing the personal importance activists attach to these risks. We tested our hypotheses within a sample of Egyptian activists (N = 146) from two protest movements who protested against Morsi’s government and the military interventions, respectively, during the 2013 anti-Coup uprising. In line with our hypotheses, the perceived likelihood of risks was positively associated with anger and identity consolidation efficacy, and positively predicted action intentions indirectly through these variables. Risk was also associated with increased political efficacy, but only among anti-military protesters. Anger and political efficacy predicted action intentions directly and indirectly through reduced risk importance. Results also highlighted differential significance of emotional and instrumental motives for the two protest movements. We discuss directions for future research on the motivators of collective action in repressive contexts.PostprintPeer reviewe

Resistance in repressive contexts:A comprehensive test of psychological predictors

Empirical research on the social psychological antecedents of collective action has been conducted almost exclusively in democratic societies, where activism is relatively safe. The present research examines the psychological predictors of collective action intentions in contexts where resistance is met with significant repression by the authorities. Combining recent advancements in the collective action literature, our model examines the unique predictive roles of emotion (anger and fear), political identity consolidation and participative efficacies, politicized identification, and moral obligation, over and above past participation. It further investigates how these variables are shaped by perceptions of risks attributable to repression. Four survey studies test this model among protesters in Russia (N = 305), Ukraine (N = 136), Hong Kong (N = 115), and Turkey (N = 296). Meta-analytic integration of the findings highlights that, unlike in most current accounts of collective action, protesters in these contexts are not primarily driven by political efficacy. Rather, their involvement is contingent upon beliefs in the ability of protest to build a movement (identity consolidation and participative efficacies) and motivated by outrage at state repression, identification with the social movement, and a sense of moral obligation to act on their behalf. Results also confirm that risks attributable to state repression spur rather than quell resistance by increasing outrage, politicized identification, identity consolidation and participative efficacies, and moral obligation. The implications of these findings for models of collective action and our understanding of the motives underlying engagement in repressive contexts are discussed.PostprintPeer reviewe

Outcome of Different Sequencing and Assembly Approaches on the Detection of Plasmids and Localization of Antimicrobial Resistance Genes in Commensal Escherichia coli

Antimicrobial resistance (AMR) is a major threat to public health worldwide. Currently, AMR typing changes from phenotypic testing to whole-genome sequence (WGS)-based detection of resistance determinants for a better understanding of the isolate diversity and elements involved in gene transmission (e.g., plasmids, bacteriophages, transposons). However, the use of WGS data in monitoring purposes requires suitable techniques, standardized parameters and approved guidelines for reliable AMR gene detection and prediction of their association with mobile genetic elements (plasmids). In this study, different sequencing and assembly strategies were tested for their suitability in AMR monitoring in Escherichia coli in the routines of the German National Reference Laboratory for Antimicrobial Resistances. To assess the outcomes of the different approaches, results from in silico predictions were compared with conventional phenotypic- and genotypic-typing data. With the focus on (fluoro)quinolone-resistant E.coli, five qnrS-positive isolates with multiple extrachromosomal elements were subjected to WGS with NextSeq (Illumina), PacBio (Pacific BioSciences) and ONT (Oxford Nanopore) for in depth characterization of the qnrS1-carrying plasmids. Raw reads from short- and long-read sequencing were assembled individually by Unicycler or Flye or a combination of both (hybrid assembly). The generated contigs were subjected to bioinformatics analysis. Based on the generated data, assembly of long-read sequences are error prone and can yield in a loss of small plasmid genomes. In contrast, short-read sequencing was shown to be insufficient for the prediction of a linkage of AMR genes (e.g., qnrS1) to specific plasmid sequences. Furthermore, short-read sequencing failed to detect certain duplications and was unsuitable for genome finishing. Overall, the hybrid assembly led to the most comprehensive typing results, especially in predicting associations of AMR genes and mobile genetic elements. Thus, the use of different sequencing technologies and hybrid assemblies currently represents the best approach for reliable AMR typing and risk assessment

Collective Nostalgia Is Associated with Stronger Outgroup-Directed Anger and Participation in Ingroup-Favoring Collective Action

Collective nostalgia refers to longing for the way society used to be. We tested whether collective nostalgia is associated with ingroup-favoring collective action and whether this association is mediated by outgroup-directed anger and outgroup-directed contempt. We conducted an online study of Hong Kong residents (N = 111) during a large-scale democratic social movement, the Umbrella Movement, that took place in Hong Kong in 2014 in response to proposed electoral reforms by the Chinese government in Mainland China. Reported collective nostalgia for Hong Kong’s past was high in our sample and collective nostalgia predicted stronger involvement in ingroup-favoring collective action, and it did so indirectly via higher intensity of outgroup-directed anger (but not through outgroup-directed contempt). We argue that collective nostalgia has implications for strengthening ingroup-serving collective action, and we highlight the importance of arousal of group-based emotions in this process.Publisher PDFPeer reviewe

Dynamics of Western Juniper Woodland Expansion into Sagebrush Communities in Central Oregon

Western juniper (Juniperus occidentalis) woodlands in Oregon have expanded four-fold from 600,000 ha in 1930 to \u3e 2.6 million ha, often resulting in the reduction and fragmentation of sagebrush (Artemisia spp.) communities. We documented dynamics of western juniper across the John Day Ecological Province in central Oregon by recording size class and growth form at 178 sites. We used stratified random sampling, with strata based on vegetation association (sagebrush, juniper, other) and distance from juniper stands. Only 26 percent of sites contained pre-settlement trees (in other words, \u3e 140 years old), and \u3c 5 percent of the 2,254 junipers tallied were pre-settlement trees. Mean densities of pre-settlement trees by stratum ranged from 0 to 18 trees/ha, suggesting that historically, juniper was widely scattered across the landscape. Current densities of post-settlement trees ranged from 75 to 211 trees/ha in non-woodland strata to 457 trees/ha in the juniper stratum. Juniper in non-woodland strata was most abundant in sites adjacent to juniper stands and in sagebrush communities. Mean densities of post-settlement trees were greatest in the \u3e 2.0-m tall size class (82 trees/ha), followed by the 0.3 to 1-m tall size class (52 trees/ha). These densities pose substantial risk to sagebrush communities in central Oregon. Questions remain about the extent of western juniper woodlands across the species’ range that have replaced or are expanding into sagebrush communities versus sites that historically supported woodlands. However, our findings suggest that within sagebrush communities of the John Day province, intensive management through removal of western juniper may be prudent, while retaining pre-settlement trees