8,760 research outputs found

    R-Pyocin Regulation, Release, and Susceptibility in Pseudomonas aeruginosa

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    Pseudomonas aeruginosa is a Gram-negative opportunistic pathogen and a major determinant of declining lung function in individuals with cystic fibrosis (CF). P. aeruginosa possesses many intrinsic antibiotic resistance mechanisms and isolates from chronic CF lung infections develop increasing resistance to multiple antibiotics over time. Chronic infection with P. aeruginosa remains one of the main causes of mortality and morbidity in CF patients, thus new therapeutic interventions are necessary. R-type pyocins are narrow spectrum, phage tail-like bacteriocins, specifically produced by P. aeruginosa to kill other strains of P. aeruginosa. Due to their specific anti-pseudomonal activity and similarity to bacteriophage, R-pyocins have potential as additional therapeutics for P. aeruginosa, either in isolation, in combination with antibiotics, or as an alternative to phage therapy. There are five subtypes of R-pyocin (types R1-R5), and it is thought that each P. aeruginosa strain uniquely produces only one of these, suggesting a degree of strain-specificity. P. aeruginosa from CF lung infections develop increasing resistance to antibiotics, making new treatment approaches essential. It is known P. aeruginosa populations in CF chronic lung infection become phenotypically and genotypically diverse over time, however, little is known of the efficacy of R-pyocins against heterogeneous populations. Even less is known regarding the timing and regulation of R-pyocins in CF lung infections, or if P. aeruginosa utilizes R-pyocin production during infection for competition or otherwise – which may influence pressure towards R-pyocin resistance. In this work, I evaluated R-pyocin type and susceptibility among P. aeruginosa isolates sourced from CF infections and found that (i) R1-pyocins are the most prevalent R-type among respiratory infection and CF strains; (ii) a large proportion of P. aeruginosa strains lack R-pyocin genes entirely; (iii) isolates from P. aeruginosa populations collected from the same patient at a single time point have the same R-pyocin type; (iv) there is heterogeneity in susceptibility to R-pyocins within P. aeruginosa populations and (v) susceptibility is likely driven by diversity of LPS phenotypes within clinical populations. These findings suggest that there is likely heterogeneity in response to other types of LPS-binding antimicrobials, including phage, which is important for consideration of antimicrobials as therapeutics. To investigate the prevalence of R2-pyocin susceptible strains in CF, I then utilized 110 isolates of P. aeruginosa collected from five individuals with CF to test for R2-pyocin susceptibility and identify LPS phenotypes. From our collection we i) estimated that approximately 83% of sputum samples contain heterogenous P. aeruginosa populations without R2-pyocin resistant isolates and all sputum samples contained susceptible isolates; ii) we found that there is no correlation between R2-pyocin susceptibility and LPS phenotypes, and iii) we estimate that approximately 76% of isolates sampled from sputum lack O-specific antigen, 42% lack common antigen, and 27% exhibit altered LPS cores. This finding highlights that perhaps LPS packing density may play a more influential role in mediating R-pyocin susceptibility in infection. Finding the majority of our sampled P. aeruginosa populations to be R2-pyocin susceptible further supports the potential of these narrow-spectrum antimicrobials despite facing heterogenous susceptibility among diverse populations. In order to evaluate how R-pyocins may influence strain competition and growth in CF lung infection, I assessed R-pyocin activity in an infection-relevant environment (Synthetic Cystic Fibrosis Sputum Medium; SCFM2) and found that (i) R-pyocins genes are transcribed more in the CF nutrient environment than in rich laboratory medium and (ii) in a structured, CF-like environment, R-pyocin induction is costly to producing strains in competition rather than beneficial. Our work suggests that R-pyocins may not be essential in CF lung infection and can be costly to producing cells in the presence of stress response-inducing stimuli, such as those commonly found in infection. In this thesis I have studied R-pyocin susceptibility, regulation and release utilizing a biobank of whole populations of P. aeruginosa collected from 11 individuals with CF, as well as the CF infection model (SCFM) to understand the mechanisms of R-pyocin activity in an infection-relevant context and the role R-pyocins play in shaping P. aeruginosa populations during infection. The findings of this work have illuminated the impact of P. aeruginosa heterogeneity on R-pyocin susceptibility, furthered our understanding of R-pyocins as potential therapeutics, and built upon our knowledge of bacteriocin-mediated interactions.Ph.D

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Effects of municipal smoke-free ordinances on secondhand smoke exposure in the Republic of Korea

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    ObjectiveTo reduce premature deaths due to secondhand smoke (SHS) exposure among non-smokers, the Republic of Korea (ROK) adopted changes to the National Health Promotion Act, which allowed local governments to enact municipal ordinances to strengthen their authority to designate smoke-free areas and levy penalty fines. In this study, we examined national trends in SHS exposure after the introduction of these municipal ordinances at the city level in 2010.MethodsWe used interrupted time series analysis to assess whether the trends of SHS exposure in the workplace and at home, and the primary cigarette smoking rate changed following the policy adjustment in the national legislation in ROK. Population-standardized data for selected variables were retrieved from a nationally representative survey dataset and used to study the policy action’s effectiveness.ResultsFollowing the change in the legislation, SHS exposure in the workplace reversed course from an increasing (18% per year) trend prior to the introduction of these smoke-free ordinances to a decreasing (−10% per year) trend after adoption and enforcement of these laws (β2 = 0.18, p-value = 0.07; β3 = −0.10, p-value = 0.02). SHS exposure at home (β2 = 0.10, p-value = 0.09; β3 = −0.03, p-value = 0.14) and the primary cigarette smoking rate (β2 = 0.03, p-value = 0.10; β3 = 0.008, p-value = 0.15) showed no significant changes in the sampled period. Although analyses stratified by sex showed that the allowance of municipal ordinances resulted in reduced SHS exposure in the workplace for both males and females, they did not affect the primary cigarette smoking rate as much, especially among females.ConclusionStrengthening the role of local governments by giving them the authority to enact and enforce penalties on SHS exposure violation helped ROK to reduce SHS exposure in the workplace. However, smoking behaviors and related activities seemed to shift to less restrictive areas such as on the streets and in apartment hallways, negating some of the effects due to these ordinances. Future studies should investigate how smoke-free policies beyond public places can further reduce the SHS exposure in ROK

    Using machine learning to predict pathogenicity of genomic variants throughout the human genome

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    Geschätzt mehr als 6.000 Erkrankungen werden durch Veränderungen im Genom verursacht. Ursachen gibt es viele: Eine genomische Variante kann die Translation eines Proteins stoppen, die Genregulation stören oder das Spleißen der mRNA in eine andere Isoform begünstigen. All diese Prozesse müssen überprüft werden, um die zum beschriebenen Phänotyp passende Variante zu ermitteln. Eine Automatisierung dieses Prozesses sind Varianteneffektmodelle. Mittels maschinellem Lernen und Annotationen aus verschiedenen Quellen bewerten diese Modelle genomische Varianten hinsichtlich ihrer Pathogenität. Die Entwicklung eines Varianteneffektmodells erfordert eine Reihe von Schritten: Annotation der Trainingsdaten, Auswahl von Features, Training verschiedener Modelle und Selektion eines Modells. Hier präsentiere ich ein allgemeines Workflow dieses Prozesses. Dieses ermöglicht es den Prozess zu konfigurieren, Modellmerkmale zu bearbeiten, und verschiedene Annotationen zu testen. Der Workflow umfasst außerdem die Optimierung von Hyperparametern, Validierung und letztlich die Anwendung des Modells durch genomweites Berechnen von Varianten-Scores. Der Workflow wird in der Entwicklung von Combined Annotation Dependent Depletion (CADD), einem Varianteneffektmodell zur genomweiten Bewertung von SNVs und InDels, verwendet. Durch Etablierung des ersten Varianteneffektmodells für das humane Referenzgenome GRCh38 demonstriere ich die gewonnenen Möglichkeiten Annotationen aufzugreifen und neue Modelle zu trainieren. Außerdem zeige ich, wie Deep-Learning-Scores als Feature in einem CADD-Modell die Vorhersage von RNA-Spleißing verbessern. Außerdem werden Varianteneffektmodelle aufgrund eines neuen, auf Allelhäufigkeit basierten, Trainingsdatensatz entwickelt. Diese Ergebnisse zeigen, dass der entwickelte Workflow eine skalierbare und flexible Möglichkeit ist, um Varianteneffektmodelle zu entwickeln. Alle entstandenen Scores sind unter cadd.gs.washington.edu und cadd.bihealth.org frei verfügbar.More than 6,000 diseases are estimated to be caused by genomic variants. This can happen in many possible ways: a variant may stop the translation of a protein, interfere with gene regulation, or alter splicing of the transcribed mRNA into an unwanted isoform. It is necessary to investigate all of these processes in order to evaluate which variant may be causal for the deleterious phenotype. A great help in this regard are variant effect scores. Implemented as machine learning classifiers, they integrate annotations from different resources to rank genomic variants in terms of pathogenicity. Developing a variant effect score requires multiple steps: annotation of the training data, feature selection, model training, benchmarking, and finally deployment for the model's application. Here, I present a generalized workflow of this process. It makes it simple to configure how information is converted into model features, enabling the rapid exploration of different annotations. The workflow further implements hyperparameter optimization, model validation and ultimately deployment of a selected model via genome-wide scoring of genomic variants. The workflow is applied to train Combined Annotation Dependent Depletion (CADD), a variant effect model that is scoring SNVs and InDels genome-wide. I show that the workflow can be quickly adapted to novel annotations by porting CADD to the genome reference GRCh38. Further, I demonstrate the integration of deep-neural network scores as features into a new CADD model, improving the annotation of RNA splicing events. Finally, I apply the workflow to train multiple variant effect models from training data that is based on variants selected by allele frequency. In conclusion, the developed workflow presents a flexible and scalable method to train variant effect scores. All software and developed scores are freely available from cadd.gs.washington.edu and cadd.bihealth.org

    Unrecognized diversity and distribution of soil algae from Maritime Antarctica (Fildes Peninsula, King George Island)

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    IntroductionEukaryotic algae in the top few centimeters of fellfield soils of ice-free Maritime Antarctica have many important effects on their habitat, such as being significant drivers of organic matter input into the soils and reducing the impact of wind erosion by soil aggregate formation. To better understand the diversity and distribution of Antarctic terrestrial algae, we performed a pilot study on the surface soils of Meseta, an ice-free plateau mountain crest of Fildes Peninsula, King George Island, being hardly influenced by the marine realm and anthropogenic disturbances. It is openly exposed to microbial colonization from outside Antarctica and connected to the much harsher and dryer ice-free zones of the continental Antarctic. A temperate reference site under mild land use, SchF, was included to further test for the Meseta algae distribution in a contrasting environment.MethodsWe employed a paired-end metabarcoding analysis based on amplicons of the highly variable nuclear-encoded ITS2 rDNA region, complemented by a clone library approach. It targeted the four algal classes, Chlorophyceae, Trebouxiophyceae, Ulvophyceae, and Xanthophyceae, representing key groups of cold-adapted soil algae.ResultsA surprisingly high diversity of 830 algal OTUs was revealed, assigned to 58 genera in the four targeted algal classes. Members of the green algal class Trebouxiophyceae predominated in the soil algae communities. The major part of the algal biodiversity, 86.1% of all algal OTUs, could not be identified at the species level due to insufficient representation in reference sequence databases. The classes Ulvophyceae and Xanthophyceae exhibited the most unknown species diversity. About 9% of the Meseta algae species diversity was shared with that of the temperate reference site in Germany.DiscussionIn the small portion of algal OTUs for which their distribution could be assessed, the entire ITS2 sequence identity with references shows that the soil algae likely have a wide distribution beyond the Polar regions. They probably originated from soil algae propagule banks in far southern regions, transported by aeolian transport over long distances. The dynamics and severity of environmental conditions at the soil surface, determined by high wind currents, and the soil algae’s high adaptability to harsh environmental conditions may account for the high similarity of soil algal communities between the northern and southern parts of the Meseta

    Data Ethics and the Dilemma Created by Turing\u27s Learning Machines

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    The main purpose of this research is to shed light on the good and bad that has come about from the interaction of Big Data and Artificial Intelligence in society. Transparency with the public is paramount for the future of Artificial Intelligence. Without awareness, the public is blind to the parts of the Data Revolution that could help them or hinder them. The key question is what AI advancements are being made and what ethical problems do they pose to the general population? To help answer this question, it is best to examine the founding of Artificial Intelligence and the views the founders had on the matter of ethics

    Science and Innovations for Food Systems Transformation

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    This Open Access book compiles the findings of the Scientific Group of the United Nations Food Systems Summit 2021 and its research partners. The Scientific Group was an independent group of 28 food systems scientists from all over the world with a mandate from the Deputy Secretary-General of the United Nations. The chapters provide science- and research-based, state-of-the-art, solution-oriented knowledge and evidence to inform the transformation of contemporary food systems in order to achieve more sustainable, equitable and resilient systems

    Development of computational tools for variant calling in single-cell RNAseq

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    Single-cell sequencing technologies have unsurprisingly become a favourable choice for studying key biological questions about cell heterogeneity, rare cell types or lineages. It is only cell-level resolution that allows for an accurate analysis of internal cell processes such as mutagenesis. Eventually, single-cell RNAseq could provide an explanation of mechanisms that lead to the ultimate transformation of healthy tissues into cancerous lesions. One of the main interests of my lab is Barrett’s oesophagus. It is a highly clonal disease and a likely cancer precursor. We decided to take advantage of the single-cell RNAseq technology in order to attempt to identify the tissue of origin of the disease which, despite years of research, still remains unknown. However, the range of methods for identification of mutations in single cells is very limited. In order to address that, we developed our own single-cell RNAseq variant caller. We validated it on a publicly available breast cancer dataset by achieving a reasonable intersection of our results with the output of commonly used bulk tools. Furthermore, we showed that our caller was capable of identifying expected data characteristics such as known breast cancer signatures and mutations in breast cancer genes. We then applied our method to the Barrett’s dataset to investigate connections of Barrett’s with surrounding tissues. Contrary to the previous transcriptomic analysis conducted on the same dataset and indicating a Barrett’s-oesophagus connection, our results revealed a more likely link of Barrett’s with the stomach

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop
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