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Among the defense strategies developed in microbes over millions of years, the innate adaptive CRISPR-Cas immune systems have spread across most of bacteria and archaea. The flexibility, simplicity, and specificity of CRISPR-Cas systems have laid the foundation for CRISPR-based genetic tools. Yet, the efficient administration of CRISPR-based tools demands rational designs to maximize the on-target efficiency and off-target specificity. Specifically, the selection of guide RNAs (gRNAs), which play a crucial role in the target recognition of CRISPR-Cas systems, is non-trivial. Despite the fact that the emerging machine learning techniques provide a solution to aid in gRNA design with prediction algorithms, design rules for many CRISPR-Cas systems are ill-defined, hindering their broader applications.
CRISPR interference (CRISPRi), an alternative gene silencing technique using a catalytically dead Cas protein to interfere with transcription, is a leading technique in bacteria for functional interrogation, pathway manipulation, and genome-wide screens. Although the application is promising, it also is hindered by under-investigated design rules. Therefore, in this work, I develop a state-of-art predictive machine learning model for guide silencing efficiency in bacteria leveraging the advantages of feature engineering, data integration, interpretable AI, and automated machine learning. I first systematically investigate the influential factors that attribute to the extent of depletion in multiple CRISPRi genome-wide essentiality screens in Escherichia coli and demonstrate the surprising dominant contribution of gene-specific effects, such as gene expression level. These observations allowed me to segregate the confounding gene-specific effects using a mixed-effect random forest (MERF) model to provide a better estimate of guide efficiency, together with the improvement led by integrating multiple screens. The MERF model outperformed existing tools in an independent high-throughput saturating screen. I next interpret the predictive model to extract the design rules for robust gene silencing, such as the preference for cytosine and disfavoring for guanine and thymine within and around the protospacer adjacent motif (PAM) sequence. I further incorporated the MERF model in a web-based tool that is freely accessible at www.ciao.helmholtz-hiri.de.
When comparing the MERF model with existing tools, the performance of the alternative gRNA design tool optimized for CRISPRi in eukaryotes when applied to bacteria was far from satisfying, questioning the robustness of prediction algorithms across organisms. In addition, the CRISPR-Cas systems exhibit diverse mechanisms albeit with some similarities. The captured predictive patterns from one dataset thereby are at risk of poor generalization when applied across organisms and CRISPR-Cas techniques. To fill the gap, the machine learning approach I present here for CRISPRi could serve as a blueprint for the effective development of prediction algorithms for specific organisms or CRISPR-Cas systems of interest. The explicit workflow includes three principle steps: 1) accommodating the feature set for the CRISPR-Cas system or technique; 2) optimizing a machine learning model using automated machine learning; 3) explaining the model using interpretable AI. To illustrate the applicability of the workflow and diversity of results when applied across different bacteria and CRISPR-Cas systems, I have applied this workflow to analyze three distinct CRISPR-Cas genome-wide screens. From the CRISPR base editor essentiality screen in E. coli, I have determined the PAM preference and sequence context in the editing window for efficient editing, such as A at the 2nd position of PAM, A/TT/TG downstream of PAM, and TC at the 4th to 5th position of gRNAs. From the CRISPR-Cas13a screen in E. coli, in addition to the strong correlation with the guide depletion, the target expression level is the strongest predictor in the model, supporting it as a main determinant of the activation of Cas13-induced immunity and better characterizing the CRISPR-Cas13 system. From the CRISPR-Cas12a screen in Klebsiella pneumoniae, I have extracted the design rules for robust antimicrobial activity across K. pneumoniae strains and provided a predictive algorithm for gRNA design, facilitating CRISPR-Cas12a as an alternative technique to tackle antibiotic resistance.
Overall, this thesis presents an accurate prediction algorithm for CRISPRi guide efficiency in bacteria, providing insights into the determinants of efficient silencing and guide designs. The systematic exploration has led to a robust machine learning approach for effective model development in other bacteria and CRISPR-Cas systems. Applying the approach in the analysis of independent CRISPR-Cas screens not only sheds light on the design rules but also the mechanisms of the CRISPR-Cas systems. Together, I demonstrate that applied machine learning paves the way to a deeper understanding and a broader application of CRISPR-Cas systems.
Cognition refers to the ability to of animals to acquire, process, store and use vital information from the environment. Cognitive processes are necessary to predict the future and reduce the uncertainty of the ever-changing environment. Classically, research on animal cognition focuses on decisive cognitive tests to determine the capacity of a species by the testing the ability of a few individuals. This approach views variability between these tested key individuals as unwanted noise and is thus often neglected. However, inter-individual variability provides important insights to behavioral plasticity, cognitive specialization and brain modularity. Honey bees Apis mellifera are a robust and traditional model for the study of learning, memory and cognition due to their impressive capabilities and rich behavioral repertoire. In this thesis I have applied a novel view on the learning abilities of honey bees by looking explicitly at individual differences in a variety of learning tasks. Are some individual bees consistently smarter than some of her sisters? If so, will a smart individual always perform good independent of the time, the context and the cognitive requirements or do bees show distinct isolated ‘cognitive modules’?
My thesis presents the first comprehensive investigation of consistent individual differences in the cognitive abilities of honey bees. To speak of an individual as behaving consistently, a crucial step is to test the individual multiple times to examine the repeatability of a behavior. I show that free-flying bees remain consistent in a visual discrimination task for three consecutive days. Successively, I explored individual consistency in cognitive proficiency across tasks involving different sensory modalities, contexts and cognitive requirements. I found that free-flying bees show a cognitive specialization between visual and olfactory learning but remained consistent across a simple discrimination task and a complex concept learning task. I wished to further explore individual consistency with respect to tasks of different cognitive complexity, a question that has never been tackled before in an insect. I thus performed a series of four experiments using either visual or olfactory stimuli and a different training context (free-flying and restrained) and tested bees in a discrimination task, reversal learning and negative patterning. Intriguingly, across all these experiments I evidenced the same results: The bees’ performances were consistent across the discrimination task and reversal learning and negative patterning respectively. No association was evidenced between reversal learning and negative patterning. After establishing the existence of consistent individual differences in the cognitive proficiency of honey bees I wished to determine factors which could underlie these differences. Since genetic components are known to underlie inter-individual variability in learning abilities, I studied the effects of genetics on consistency in cognitive proficiency by contrasting bees originating from either from a hive with a single patriline (low genetic diversity) or with multiple patrilines (high genetic diversity). These two groups of bees showed differences in the patterns of individually correlated performances, indicating a genetic component accounts for consistent cognitive individuality. Another major factor underlying variability in learning performances is the individual responsiveness to sucrose solution and to visual stimuli, as evidenced by many studies on restrained bees showing a positive correlation between responsiveness to task relevant stimuli and learning performances. I thus tested whether these relationships between sucrose/visual responsiveness and learning performances are applicable for free-flying bees. Free-flying bees were again subjected to reversal learning and negative patterning and subsequently tested in the laboratory for their responsiveness to sucrose and to light. There was no evidence of a positive relationship between sucrose/visual responsiveness and neither performances of free-flying bees in an elemental discrimination, reversal learning and negative patterning. These findings indicate that relationships established between responsiveness to task relevant stimuli and learning proficiency established in the laboratory with restrained bees might not hold true for a completely different behavioral context i.e. for free-flying bees in their natural environment.
These results show that the honey bee is an excellent insect model to study consistency in cognitive proficiency and to identify the underlying factors. I mainly discuss the results with respect to the question of brain modularity in insects and the adaptive significance of individuality in cognitive abilities for honey bee colonies. I also provide a proposition of research questions which tie in this theme of consistent cognitive proficiency and could provide fruitful areas for future research.
There is a great need for valuable ex vivo models that allow for assessment of cartilage repair strategies to reduce the high number of animal experiments. In this paper we present three studies with our novel ex vivo osteochondral culture platform. It consists of two separated media compartments for cartilage and bone, which better represents the in vivo situation and enables supply of factors pecific to the different needs of bone and cartilage. We investigated whether separation of the cartilage and bone compartments and/or culture media results in the maintenance of viability, structural and functional properties of cartilage tissue. Next, we valuated for how long we can preserve cartilage matrix stability of osteochondral explants during long-term culture over 84 days. Finally, we determined the optimal defect size that does not show spontaneous self-healing in this culture system. It was demonstrated that separated compartments for cartilage and bone in combination with tissue-specific medium allow for long-term culture of osteochondral explants while maintaining cartilage viability, atrix tissue content, structure and mechanical properties for at least 56 days. Furthermore, we could create critical size cartilage defects of different sizes in the model. The osteochondral model represents a valuable preclinical ex vivo tool for studying clinically relevant cartilage therapies, such as cartilage biomaterials, for their regenerative potential, for evaluation of drug and cell therapies, or to study mechanisms of cartilage regeneration. It will undoubtedly reduce the number of animals needed for in vivotesting.
SMART (Simple Modular Architecture Research Tool) is a web resource (https://smart.embl.de) for the identification and annotation of protein domains and the analysis of protein domain architectures. SMART version 9 contains manually curatedmodels formore than 1300 protein domains, with a topical set of 68 new models added since our last update article (1). All the new models are for diverse recombinase families and subfamilies and as a set they provide a comprehensive overview of mobile element recombinases namely transposase, integrase, relaxase, resolvase, cas1 casposase and Xer like cellular recombinase. Further updates include the synchronization of the underlying protein databases with UniProt (2), Ensembl (3) and STRING (4), greatly increasing the total number of annotated domains and other protein features available in architecture analysis mode. Furthermore, SMART's vector-based protein display engine has been extended and updated to use the latest web technologies and the domain architecture analysis components have been optimized to handle the increased number of protein features available.
Interpreting gaze behavior is essential in evaluating interaction partners, yet the ‘semantics of gaze’ in dynamic interactions are still poorly understood. We aimed to comprehensively investigate effects of gaze behavior patterns in different conversation contexts, using a two-step, qualitative-quantitative procedure. Participants watched video clips of single persons listening to autobiographic narrations by another (invisible) person. The listener’s gaze behavior was manipulated in terms of gaze direction, frequency and direction of gaze shifts, and blink frequency; emotional context was manipulated through the valence of the narration (neutral/negative). In Experiment 1 (qualitative-exploratory), participants freely described which states and traits they attributed to the listener in each condition, allowing us to identify relevant aspects of person perception and to construct distinct rating scales that were implemented in Experiment 2 (quantitative-confirmatory). Results revealed systematic and differential meanings ascribed to the listener’s gaze behavior. For example, rapid blinking and fast gaze shifts were rated more negatively (e.g., restless and unnatural) than slower gaze behavior; downward gaze was evaluated more favorably (e.g., empathetic) than other gaze aversion types, especially in the emotionally negative context. Overall, our study contributes to a more systematic understanding of flexible gaze semantics in social interaction.
Background
Deregulated expression of MYC is a driver of colorectal carcinogenesis, suggesting that decreasing MYC expression may have significant therapeutic value. CIP2A is an oncogenic factor that regulates MYC expression. CIP2A is overexpressed in colorectal cancer (CRC), and its expression levels are an independent marker for long-term outcome of CRC. Previous studies suggested that CIP2A controls MYC protein expression on a post-transcriptional level.
Methods
To determine the mechanism by which CIP2A regulates MYC in CRC, we dissected MYC translation and stability dependent on CIP2A in CRC cell lines.
Results
Knockdown of CIP2A reduced MYC protein levels without influencing MYC stability in CRC cell lines. Interfering with proteasomal degradation of MYC by usage of FBXW7-deficient cells or treatment with the proteasome inhibitor MG132 did not rescue the effect of CIP2A depletion on MYC protein levels. Whereas CIP2A knockdown had marginal influence on global protein synthesis, we could demonstrate that, by using different reporter constructs and cells expressing MYC mRNA with or without flanking UTR, CIP2A regulates MYC translation. This interaction is mainly conducted by the MYC 5′UTR.
Conclusions
Thus, instead of targeting MYC protein stability as reported for other tissue types before, CIP2A specifically regulates MYC mRNA translation in CRC but has only slight effects on global mRNA translation. In conclusion, we propose as novel mechanism that CIP2A regulates MYC on a translational level rather than affecting MYC protein stability in CRC.
Formation and treatment of biofilms present a great challenge for health care and industry. About 80% of human infections are associated with biofilms including biomaterial centered infections, like infections of prosthetic heart valves, central venous catheters, or urinary catheters. Additionally, biofilms can cause food and drinking water contamination. Biofilm research focusses on application of experimental biofilm models to study initial adherence processes, to optimize physico-chemical properties of medical materials for reducing interactions between materials and bacteria, and to investigate biofilm treatment under controlled conditions. Exploring new antimicrobial strategies plays a key role in a variety of scientific disciplines, like medical material research, anti-infectious research, plant engineering, or wastewater treatment. Although a variety of biofilm models exist, there is a lack of standardization for experimental protocols, and designing experimental setups remains a challenge. In this study, a number of experimental parameters critical for material research have been tested that influence formation and stability of an experimental biofilm using the non-pathogenic model strain of Pseudomonas fluorescens. These parameters include experimental time frame, nutrient supply, inoculum concentration, static and dynamic cultivation conditions, material properties, and sample treatment during staining for visualization of the biofilm. It was shown, that all tested parameters critically influence the experimental biofilm formation process. The results obtained in this study shall support material researchers in designing experimental biofilm setups.
In this work, we present a multimodal approach to three-dimensionally quantify and visualize fiber orientation and resin-rich areas in carbon-fiber-reinforced polymers manufactured by vacuum infusion. Three complementary image modalities were acquired by Talbot–Lau grating interferometer (TLGI) X-ray microcomputed tomography (XCT). Compared to absorption contrast (AC), TLGI-XCT provides enhanced contrast between polymer matrix and carbon fibers at lower spatial resolutions in the form of differential phase contrast (DPC) and dark-field contrast (DFC). Consequently, relatively thin layers of resin, effectively indiscernible from image noise in AC data, are distinguishable. In addition to the assessment of fiber orientation, the combination of DPC and DFC facilitates the quantification of resin-rich areas, e.g., in gaps between fiber layers or at binder yarn collimation sites. We found that resin-rich areas between fiber layers are predominantly developed in regions characterized by a pronounced curvature. In contrast, in-layer resin-rich areas are mainly caused by the collimation of fibers by binder yarn. Furthermore, void volume around two adjacent 90°-oriented fiber layers is increased by roughly 20% compared to a random distribution over the whole specimen.
The article deals with the pedagogical content knowledge of mathematical modelling as part of the professional competence of pre-service teachers. With the help of a test developed for this purpose from a conceptual model, we examine whether this pedagogical content knowledge can be promoted in its different facets—especially knowledge about modelling tasks and about interventions—by suitable university seminars. For this purpose, the test was administered to three groups in a seminar for the teaching of mathematical modelling: (1) to those respondents who created their own modelling tasks for use with students, (2) to those trained to intervene in mathematical modelling processes, and (3) participating students who are not required to address mathematical modelling. The findings of the study—based on variance analysis—indicate that certain facets (knowledge of modelling tasks, modelling processes, and interventions) have increased significantly in both experimental groups but to varying degrees. By contrast, pre-service teachers in the control group demonstrated no significant change to their level of pedagogical content knowledge.