Refine
Year of publication
Document Type
- Article (12)
- Doctoral Thesis (5)
- Postprint (3)
Is part of the Bibliography
- yes (20) (remove)
Keywords
- gene expression (20) (remove)
Polynucleobacter asymbioticus strain QLW-P1DMWA-1T represents a group of highly successful heterotrophic ultramicrobacteria that is frequently very abundant (up to 70% of total bacterioplankton) in freshwater habitats across all seven continents. This strain was originally isolated from a shallow Alpine pond characterized by rapid changes in water temperature and elevated UV radiation due to its location at an altitude of 1300 m. To elucidate the strain’s adjustment to fluctuating environmental conditions, we recorded changes occurring in its transcriptomic and proteomic profiles under contrasting experimental conditions by simulating thermal conditions in winter and summer as well as high UV irradiation. To analyze the potential connection between gene expression and regulation via methyl group modification of the genome, we also analyzed its methylome. The methylation pattern differed between the three treatments, pointing to its potential role in differential gene expression. An adaptive process due to evolutionary pressure in the genus was deduced by calculating the ratios of non-synonymous to synonymous substitution rates for 20 Polynucleobacter spp. genomes obtained from geographically diverse isolates. The results indicate purifying selection.
Polynucleobacter asymbioticus strain QLW-P1DMWA-1T represents a group of highly successful heterotrophic ultramicrobacteria that is frequently very abundant (up to 70% of total bacterioplankton) in freshwater habitats across all seven continents. This strain was originally isolated from a shallow Alpine pond characterized by rapid changes in water temperature and elevated UV radiation due to its location at an altitude of 1300 m. To elucidate the strain’s adjustment to fluctuating environmental conditions, we recorded changes occurring in its transcriptomic and proteomic profiles under contrasting experimental conditions by simulating thermal conditions in winter and summer as well as high UV irradiation. To analyze the potential connection between gene expression and regulation via methyl group modification of the genome, we also analyzed its methylome. The methylation pattern differed between the three treatments, pointing to its potential role in differential gene expression. An adaptive process due to evolutionary pressure in the genus was deduced by calculating the ratios of non-synonymous to synonymous substitution rates for 20 Polynucleobacter spp. genomes obtained from geographically diverse isolates. The results indicate purifying selection.
SLocX predicting subcellular localization of Arabidopsis proteins leveraging gene expression data
(2011)
Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mito-chondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins.
Honey bees are important model organisms for neurobiology, because they display a large array of behaviors. To link behavior with individual gene function, quantitative polymerase chain reaction is frequently used. Comparing gene expression of different individuals requires data normalization using adequate reference genes. These should ideally be expressed stably throughout lifetime. Unfortunately, this is frequently not the case. We studied how well three commonly used reference genes are suited for this purpose and measured gene expression in the brains of honey bees differing in age and social role. Although rpl32 is used most frequently, it only remains stable in expression between newly emerged bees, nurse-aged bees, and pollen foragers but shows a peak at the age of 12 days. The genes gapdh and ef1 alpha-f1, in contrast, are expressed stably in the brain throughout all age groups except newly emerged bees. According to stability software, gapdh was expressed most stably, followed by rpl32 and ef1 alpha-f1.
Gene expression data is analyzed to identify biomarkers, e.g. relevant genes, which serve for diagnostic, predictive, or prognostic use. Traditional approaches for biomarker detection select distinctive features from the data based exclusively on the signals therein, facing multiple shortcomings in regards to overfitting, biomarker robustness, and actual biological relevance. Prior knowledge approaches are expected to address these issues by incorporating prior biological knowledge, e.g. on gene-disease associations, into the actual analysis. However, prior knowledge approaches are currently not widely applied in practice because they are often use-case specific and seldom applicable in a different scope. This leads to a lack of comparability of prior knowledge approaches, which in turn makes it currently impossible to assess their effectiveness in a broader context.
Our work addresses the aforementioned issues with three contributions. Our first contribution provides formal definitions for both prior knowledge and the flexible integration thereof into the feature selection process. Central to these concepts is the automatic retrieval of prior knowledge from online knowledge bases, which allows for streamlining the retrieval process and agreeing on a uniform definition for prior knowledge. We subsequently describe novel and generalized prior knowledge approaches that are flexible regarding the used prior knowledge and applicable to varying use case domains. Our second contribution is the benchmarking platform Comprior. Comprior applies the aforementioned concepts in practice and allows for flexibly setting up comprehensive benchmarking studies for examining the performance of existing and novel prior knowledge approaches. It streamlines the retrieval of prior knowledge and allows for combining it with prior knowledge approaches. Comprior demonstrates the practical applicability of our concepts and further fosters the overall development and comparability of prior knowledge approaches. Our third contribution is a comprehensive case study on the effectiveness of prior knowledge approaches. For that, we used Comprior and tested a broad range of both traditional and prior knowledge approaches in combination with multiple knowledge bases on data sets from multiple disease domains. Ultimately, our case study constitutes a thorough assessment of a) the suitability of selected knowledge bases for integration, b) the impact of prior knowledge being applied at different integration levels, and c) the improvements in terms of classification performance, biological relevance, and overall robustness.
In summary, our contributions demonstrate that generalized concepts for prior knowledge and a streamlined retrieval process improve the applicability of prior knowledge approaches. Results from our case study show that the integration of prior knowledge positively affects biomarker results, particularly regarding their robustness. Our findings provide the first in-depth insights on the effectiveness of prior knowledge approaches and build a valuable foundation for future research.
The control of gene expression by transcriptional regulators and other types of functionally relevant DNA transactions such as chromatin remodeling and replication underlie a vast spectrum of biological processes in all organisms. DNA transactions require the controlled interaction of proteins with DNA sequence motifs which are often located in nucleosome-depleted regions (NDRs) of the chromatin. Formaldehyde-assisted isolation of regulatory elements (FAIRE) has been established as an easy-to-implement method for the isolation of NDRs from a number of eukaryotic organisms, and it has been successfully employed for the discovery of new regulatory segments in genomic DNA from, for example, yeast, Drosophila, and humans. Until today, however, FAIRE has only rarely been employed in plant research and currently no detailed FAIRE protocol for plants has been published. Here, we provide a step-by-step FAIRE protocol for NDR discovery in Arabidopsis thaliana. We demonstrate that NDRs isolated from plant chromatin are readily amenable to quantitative polymerase chain reaction and next-generation sequencing. Only minor modification of the FAIRE protocol will be needed to adapt it to other plants, thus facilitating the global inventory of regulatory regions across species.
Orthogonal systems for heterologous protein expression as well as for the engineering of synthetic gene regulatory circuits in hosts like Saccharomyces cerevisiae depend on synthetic transcription factors (synTFs) and corresponding cis-regulatory binding sites. We have constructed and characterized a set of synTFs based on either transcription activator-like effectors or CRISPR/Cas9, and corresponding small synthetic promoters (synPs) with minimal sequence identity to the host’s endogenous promoters. The resulting collection of functional synTF/synP pairs confers very low background expression under uninduced conditions, while expression output upon induction of the various synTFs covers a wide range and reaches induction factors of up to 400. The broad spectrum of expression strengths that is achieved will be useful for various experimental setups, e.g., the transcriptional balancing of expression levels within heterologous pathways or the construction of artificial regulatory networks. Furthermore, our analyses reveal simple rules that enable the tuning of synTF expression output, thereby allowing easy modification of a given synTF/synP pair. This will make it easier for researchers to construct tailored transcriptional control systems.
Orthogonal systems for heterologous protein expression as well as for the engineering of synthetic gene regulatory circuits in hosts like Saccharomyces cerevisiae depend on synthetic transcription factors (synTFs) and corresponding cis-regulatory binding sites. We have constructed and characterized a set of synTFs based on either transcription activator-like effectors or CRISPR/Cas9, and corresponding small synthetic promoters (synPs) with minimal sequence identity to the host’s endogenous promoters. The resulting collection of functional synTF/synP pairs confers very low background expression under uninduced conditions, while expression output upon induction of the various synTFs covers a wide range and reaches induction factors of up to 400. The broad spectrum of expression strengths that is achieved will be useful for various experimental setups, e.g., the transcriptional balancing of expression levels within heterologous pathways or the construction of artificial regulatory networks. Furthermore, our analyses reveal simple rules that enable the tuning of synTF expression output, thereby allowing easy modification of a given synTF/synP pair. This will make it easier for researchers to construct tailored transcriptional control systems.
Recent advances in high-throughput omics techniques render it possible to decode the function of genes by using the "guilt-by-association" principle on biologically meaningful clusters of gene expression data. However, the existing frameworks for biological evaluation of gene clusters are hindered by two bottleneck issues: (1) the choice for the number of clusters, and (2) the external measures which do not take in consideration the structure of the analyzed data and the ontology of the existing biological knowledge. Here, we address the identified bottlenecks by developing a novel framework that allows not only for biological evaluation of gene expression clusters based on existing structured knowledge, but also for prediction of putative gene functions. The proposed framework facilitates propagation of statistical significance at each of the following steps: (1) estimating the number of clusters, (2) evaluating the clusters in terms of novel external structural measures, (3) selecting an optimal clustering algorithm, and (4) predicting gene functions. The framework also includes a method for evaluation of gene clusters based on the structure of the employed ontology. Moreover, our method for obtaining a probabilistic range for the number of clusters is demonstrated valid on synthetic data and available gene expression profiles from Saccharomyces cerevisiae. Finally, we propose a network-based approach for gene function prediction which relies on the clustering of optimal score and the employed ontology. Our approach effectively predicts gene function on the Saccharomyces cerevisiae data set and is also employed to obtain putative gene functions for an Arabidopsis thaliana data set.
Plasma secretion of acid sphingomyelinase is a hallmark of cellular stress response resulting in the formation of membrane embedded ceramide-enriched lipid rafts and the reorganization of receptor complexes. Consistently, decompartmentalization of ceramide formation from inert sphingomyelin has been associated with signaling events and regulation of the cellular phenotype. Herein, we addressed the question of whether the secretion of acid sphingomyelinase is involved in host response during sepsis. We found an exaggerated clinical course in mice genetically deficient in acid sphingomyelinase characterized by an increased bacterial burden, an increased phagocytotic activity, and a more pronounced cytokine storm. Moreover, on a functional level, leukocyte-endothelial interaction was found diminished in sphingomyelinase-deficient animals corresponding to a distinct leukocytes' phenotype with respect to rolling and sticking as well as expression of cellular surface proteins.(jlr) We conclude that hydrolysis of membrane-embedded sphingomyelin, triggered by circulating sphingomyelinase, plays a pivotal role in the first line of defense against invading microorganisms. This function might be essential during the early phase of infection leading to an adaptive response of remote cells and tissues.-Jbeily, N., I. Suckert, F. A. Gonnert, B. Acht, C. L. Bockmeyer, S. D. Grossmann, M. F. Blaess, A. Lueth, H.-P. Deigner, M. Bauer, and R. A. Claus. Hyperresponsiveness of mice deficient in plasma-secreted sphingomyelinase reveals its pivotal role in early phase of host response. J. Lipid Res. 2013. 54: 410-424.