Network-based analysis of gene expression data

The methods of molecular biology for the quantitative measurement of gene expression have undergone a rapid development in the past two decades. High-throughput assays with the microarray and RNA-seq technology now enable whole-genome studies in which several thousands of genes can be measured at a time. However, this has also imposed serious challenges on data storage and analysis, which are subject of the young, but rapidly developing field of computational biology. To explain observations made on such a large scale requires suitable and accordingly scaled models of gene regulation. Detailed models, as available for single genes, need to be extended and assembled in larger networks of regulatory interactions between genes and gene products. Incorporation of such networks into methods for data analysis is crucial to identify molecular mechanisms that are drivers of the observed expression. As methods for this purpose emerge in parallel to each other and without knowing the standard of truth, results need to be critically checked in a competitive setup and in the context of the available rich literature corpus. This work is centered on and contributes to the following subjects, each of which represents important and distinct research topics in the field of computational biology: (i) construction of realistic gene regulatory network models; (ii) detection of subnetworks that are significantly altered in the data under investigation; and (iii) systematic biological interpretation of detected subnetworks. For the construction of regulatory networks, I review existing methods with a focus on curation and inference approaches. I first describe how literature curation can be used to construct a regulatory network for a specific process, using the well-studied diauxic shift in yeast as an example. In particular, I address the question how a detailed understanding, as available for the regulation of single genes, can be scaled-up to the level of larger systems. I subsequently inspect methods for large-scale network inference showing that they are significantly skewed towards master regulators. A recalibration strategy is introduced and applied, yielding an improved genome-wide regulatory network for yeast. To detect significantly altered subnetworks, I introduce GGEA as a method for network-based enrichment analysis. The key idea is to score regulatory interactions within functional gene sets for consistency with the observed expression. Compared to other recently published methods, GGEA yields results that consistently and coherently align expression changes with known regulation types and that are thus easier to explain. I also suggest and discuss several significant enhancements to the original method that are improving its applicability, outcome and runtime. For the systematic detection and interpretation of subnetworks, I have developed the EnrichmentBrowser software package. It implements several state-of-the-art methods besides GGEA, and allows to combine and explore results across methods. As part of the Bioconductor repository, the package provides a unified access to the different methods and, thus, greatly simplifies the usage for biologists. Extensions to this framework, that support automating of biological interpretation routines, are also presented. In conclusion, this work contributes substantially to the research field of network-based analysis of gene expression data with respect to regulatory network construction, subnetwork detection, and their biological interpretation. This also includes recent developments as well as areas of ongoing research, which are discussed in the context of current and future questions arising from the new generation of genomic data

Bioconductor's EnrichmentBrowser: seamless navigation through combined results of set- & network-based enrichment analysis

Background: Enrichment analysis of gene expression data is essential to find functional groups of genes whose interplay can explain experimental observations. Numerous methods have been published that either ignore (set-based) or incorporate (network-based) known interactions between genes. However, the often subtle benefits and disadvantages of the individual methods are confusing for most biological end users and there is currently no convenient way to combine methods for an enhanced result interpretation. Results: We present the EnrichmentBrowser package as an easily applicable software that enables (1) the application of the most frequently used set-based and network-based enrichment methods, (2) their straightforward combination, and (3) a detailed and interactive visualization and exploration of the results. The package is available from the Bioconductor repository and implements additional support for standardized expression data preprocessing, differential expression analysis, and definition of suitable input gene sets and networks. Conclusion: The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. It combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways

The therapeutic potential of truffle fungi: a patent survey

The purpose of this article is to research and retrieve patent information regarding the therapeutic use of truffles. Truffles have a unique value as a foodstuff and impact positively on human health and well-being. They are applied in such industries as the pharmaceutical industry and the cosmetic industry. Patent documentation available in the Espacenet network and the Patentscope service were analyzed by key word and patent specifications were examined to describe state of the art and to identify scientific research trends in therapeutic applications of truffles. Medicinal properties of truffles such as the anticancer or cardiovascular effect, a reduction in blood lipids, immunological resistance and increased energy were identified. Other therapeutic benefits include sedative action, prevention of hormonal imbalances in women, pre-menopause symptom relief, senile urethritis and prostate disorders, sleep disorders and increased absorption of calcium from milk. Truffles can also be used to alleviate symptoms of milk intolerance such as diarrhoea or bloating, to ease rheumatic pains and to treat and prevent further development or recurrence of senile cataract

Network-based analysis of gene expression data

The methods of molecular biology for the quantitative measurement of gene expression have undergone a rapid development in the past two decades. High-throughput assays with the microarray and RNA-seq technology now enable whole-genome studies in which several thousands of genes can be measured at a time. However, this has also imposed serious challenges on data storage and analysis, which are subject of the young, but rapidly developing field of computational biology. To explain observations made on such a large scale requires suitable and accordingly scaled models of gene regulation. Detailed models, as available for single genes, need to be extended and assembled in larger networks of regulatory interactions between genes and gene products. Incorporation of such networks into methods for data analysis is crucial to identify molecular mechanisms that are drivers of the observed expression. As methods for this purpose emerge in parallel to each other and without knowing the standard of truth, results need to be critically checked in a competitive setup and in the context of the available rich literature corpus. This work is centered on and contributes to the following subjects, each of which represents important and distinct research topics in the field of computational biology: (i) construction of realistic gene regulatory network models; (ii) detection of subnetworks that are significantly altered in the data under investigation; and (iii) systematic biological interpretation of detected subnetworks. For the construction of regulatory networks, I review existing methods with a focus on curation and inference approaches. I first describe how literature curation can be used to construct a regulatory network for a specific process, using the well-studied diauxic shift in yeast as an example. In particular, I address the question how a detailed understanding, as available for the regulation of single genes, can be scaled-up to the level of larger systems. I subsequently inspect methods for large-scale network inference showing that they are significantly skewed towards master regulators. A recalibration strategy is introduced and applied, yielding an improved genome-wide regulatory network for yeast. To detect significantly altered subnetworks, I introduce GGEA as a method for network-based enrichment analysis. The key idea is to score regulatory interactions within functional gene sets for consistency with the observed expression. Compared to other recently published methods, GGEA yields results that consistently and coherently align expression changes with known regulation types and that are thus easier to explain. I also suggest and discuss several significant enhancements to the original method that are improving its applicability, outcome and runtime. For the systematic detection and interpretation of subnetworks, I have developed the EnrichmentBrowser software package. It implements several state-of-the-art methods besides GGEA, and allows to combine and explore results across methods. As part of the Bioconductor repository, the package provides a unified access to the different methods and, thus, greatly simplifies the usage for biologists. Extensions to this framework, that support automating of biological interpretation routines, are also presented. In conclusion, this work contributes substantially to the research field of network-based analysis of gene expression data with respect to regulatory network construction, subnetwork detection, and their biological interpretation. This also includes recent developments as well as areas of ongoing research, which are discussed in the context of current and future questions arising from the new generation of genomic data

Der Protonentransport über den humanen Monocarboxylattransporter 1 wird durch das anionische Substrat vermittelt

Es ist bekannt, dass Laktat einen universellen Nährstoff für Zellen darstellt. Der Austausch des Anions über Zellmembranen hinweg wird im Menschen zu einem großen Anteil von Vertretern der Monocarboxylattransporter- Familie (MCT-Familie) vermittelt. Daher gelten MCT als Arzneistoff-Target. Ein umfassendes Verständnis der mechanistischen Zusammenhänge innerhalb des Transporters könnte die Entwicklung hoch wirksamer Inhibitoren erleichtern und so den Fortschritt in der Behandlung von Krebserkrankungen beschleunigen. In dieser Arbeit wurden MCT1 und verschiedene Punktmutanten des Transporters heterolog in Saccharomyces cerevisiae exprimiert. In verschiedenen Funktionsassays wurden zum einen Substrate und Nicht-Substrate des Transporters identifiziert, zum anderen wurden Substrataffinität, die pH-Abhängigkeit des Transportes und dessen kinetische Parameter bestimmt. Das gewählte Expressionssystem zeichnet sich durch eine hohe Stabilität gegenüber extrazellulären pH-Werten aus und lässt bekanntermaßen die Expression von MCT1 auch ohne das Begleitprotein Basigin zu. Dies erlaubte in der vorliegenden Arbeit die Charakterisierung des Einflusses von Basigin auf den Transport und die funktionelle Charakterisierung des Transporters ohne diese Interferenzen. Die in dieser Arbeit präsentierten Messwerte resultieren in einem postulierten Transportmechanismus. Dabei wird das Proton vom Lysin 38 über das gebundene Substrat hinweg bis zum Aspartat 309 transportiert. Der präsentierte Mechanismus ist mit dem aktuellen Wissensstand zu MCT1 vereinbar und könnte auch für die Zwitterionen-Transporter der Familie gelten. Damit legt diese Arbeit die Grundlage, um die Familie der MCT vollständig zu charakterisieren und zu verstehen

Kinetics of N2O production and reduction in a nitrate-contaminated aquifer inferred from laboratory incubation experiments

Knowledge of the kinetics of N2O production and reduction in groundwater is essential for the assessment of potential indirect emissions of the greenhouse gas. In the present study, we investigated this kinetics using a laboratory approach. The results were compared to field measurements in order to examine their transferability to the in situ conditions. The study site was the unconfined, predominantly sandy Fuhrberger Feld aquifer in northern Germany. A special characteristic of the aquifer is the occurrence of the vertically separated process zones of heterotrophic denitrification in the near-surface groundwater and of autotrophic denitrification in depths beyond 2-3 m below the groundwater table, respectively. The kinetics of N2O production and reduction in both process zones was studied during long-term anaerobic laboratory incubations of aquifer slurries using the 15N tracer technique. We measured N2O, N2, NO3-, NO2-, and SO42- concentrations as well as parameters of the aquifer material that were related to the relevant electron donors, i.e. organic carbon and pyrite. The laboratory incubations showed a low denitrification activity of heterotrophic denitrification with initial rates between 0.2 and 13 μg N kg-1 d-1. The process was carbon limited due to the poor availability of its electron donor. In the autotrophic denitrification zone, initial denitrification rates were considerably higher, ranging between 30 and 148 μg N kg-1 d-1, and NO3- as well as N2O were completely removed within 60 to 198 days. N2O accumulated during heterotrophic and autotrophic denitrification, but maximum concentrations were substantially higher during the autotrophic process. The results revealed a satisfactory transferability of the laboratory incubations to the field scale for autotrophic denitrification, whereas the heterotrophic process less reflected the field conditions due to considerably lower N2O accumulation during laboratory incubation. Finally, we applied a conventional model using first-order-kinetics to determine the reaction rate constants k1 for N2O production and k2 for N2O reduction, respectively. The goodness of fit to the experimental data was partly limited, indicating that a more sophisticated approach is essential to describe the investigated reaction kinetics satisfactorily.DF

Towards the first linkage map of the Didymella rabiei genome.

A genetic map was developed for the ascomycete Didymella rabiei (Kovachevski) v. Arx (anamorph: Ascochyta rabiei Pass. Labr.), the causal agent of Ascochyta blight in chickpea (Cicer arietinum L.). The map was generated with 77 F1 progeny derived from crossing an isolate from the U.S.A. and an isolate from Syria. A total of 232 DAF (DNA AmplificationFingerprinting) primers and 37 STMS (Sequence-Tagged Microsatellite Site) primer pairs were tested for polymorphism between the parental isolates; 50 markers were mapped, 36 DAFs and 14 STMSs. These markers cover 261.4cM in ten linkage groups. Nineteen markers remained unlinked. Significant deviation from the expected 1:1 segregation ratios was observed for only two markers (Prob. of x2 <0.05). The implications of our results on ploidy level of the asexual spores are discussed

The Interplay Between Pore‐Scale Heterogeneity, Surface Roughness, and Wettability Controls Trapping in Two‐Phase Fluid Displacement in Porous Media

Predicting the compactness of the invasion front and the amount of trapped fluid left behind is of crucial importance to applications ranging from microfluidics and fuel cells to subsurface storage of carbon and hydrogen. We examine the interplay of wettability, macro‐ and pore scale heterogeneity (pore angularity and pore wall roughness), and its influence on flow patterns formation and trapping efficiency in porous media by a combination of 3D micro‐CT imaging with 2D direct visualization of micromodels. We observe various phase transitions between the following capillary flow regimes (phases): (a) compact advance, (b) wetting and drainage Invasion percolation, (c) Ordinary percolation

The therapeutic potential of truffle fungi: a patent survey

The purpose of this article is to research and retrieve patent information regarding the therapeutic use of truffles. Truffles have a unique value as a foodstuff and impact positively on human health and well-being. They are applied in such industries as the pharmaceutical industry and the cosmetic industry. Patent documentation available in the Espacenet network and the Patentscope service were analyzed by key word and patent specifications were examined to describe state of the art and to identify scientific research trends in therapeutic applications of truffles. Medicinal properties of truffles such as the anticancer or cardiovascular effect, a reduction in blood lipids, immunological resistance and increased energy were identified. Other therapeutic benefits include sedative action, prevention of hormonal imbalances in women, pre-menopause symptom relief, senile urethritis and prostate disorders, sleep disorders and increased absorption of calcium from milk. Truffles can also be used to alleviate symptoms of milk intolerance such as diarrhoea or bloating, to ease rheumatic pains and to treat and prevent further development or recurrence of senile cataract