Papers by Reinhard Guthke
Method and reactor for the amplification of dna
Gene network activity in cultivated primary hepatocytes is highly similar to diseased mammalian liver tissue
Archives of toxicology, Jan 23, 2016
It is well known that isolation and cultivation of primary hepatocytes cause major gene expressio... more It is well known that isolation and cultivation of primary hepatocytes cause major gene expression alterations. In the present genome-wide, time-resolved study of cultivated human and mouse hepatocytes, we made the observation that expression changes in culture strongly resemble alterations in liver diseases. Hepatocytes of both species were cultivated in collagen sandwich and in monolayer conditions. Genome-wide data were also obtained from human NAFLD, cirrhosis, HCC and hepatitis B virus-infected tissue as well as mouse livers after partial hepatectomy, CCl4 intoxication, obesity, HCC and LPS. A strong similarity between cultivation and disease-induced expression alterations was observed. For example, expression changes in hepatocytes induced by 1-day cultivation and 1-day CCl4 exposure in vivo correlated with R = 0.615 (p < 0.001). Interspecies comparison identified predominantly similar responses in human and mouse hepatocytes but also a set of genes that responded different...
The overexpression of recombinant proteins in microorganisms may lead to a metabolic depression o... more The overexpression of recombinant proteins in microorganisms may lead to a metabolic depression or collapse of the cell factory. In order to understand this process and to optimize the cellular productivity the stress response was investigated. The expression of the recombinant human superoxide dismutase (SOD) was induced under steady state conditions and the expression of all 4289 protein coding genes of the microorganism Escherichia coli was monitored using microarrays. After normalization by the LOWESS method 102 differentially expressed genes were selected by a novel criterion that includes the measurement error. These differentially expressed genes were clustered using the EcoCyc database and the fuzzy-c-means clustering method. The results from clustering were interpreted in terms of dynamic models, which have been constructed either via Singular Value Decomposition (SVD) or a novel heuristic algorithm for dynamic model structure optimization.
Bio Systems, Apr 1, 2009
Systems biology aims to develop mathematical models of biological systems by integrating experime... more Systems biology aims to develop mathematical models of biological systems by integrating experimental and theoretical techniques. During the last decade, many systems biological approaches that base on genome-wide data have been developed to unravel the complexity of gene regulation. This review deals with the reconstruction of gene regulatory networks (GRNs) from experimental data through computational methods. Standard GRN inference methods primarily use gene expression data derived from microarrays. However, the incorporation of additional information from heterogeneous data sources, e.g. genome sequence and protein-DNA interaction data, clearly supports the network inference process. This review focuses on promising modelling approaches that use such diverse types of molecular biological information. In particular, approaches are discussed that enable the modelling of the dynamics of gene regulatory systems. The review provides an overview of common modelling schemes and learning algorithms and outlines current challenges in GRN modelling.
Running Title: Dynamic Network Reconstruction Dynamic Network Reconstruction from Gene Expression Data Applied to Immune Response during Bacterial Infection
... Immune Response during Bacterial Infection (2004). Download: http://bioinformatics. oxfordjou... more ... Immune Response during Bacterial Infection (2004). Download: http://bioinformatics. oxfordjournals.org/cgi/repri CACHED: Download as a PDF. by Reinhard Guthke , Ulrich Möller , Martin Hoffmann , Frank Thies , Susanne Töpfer. ...
Applied Microbiology and Biotechnology, Apr 1, 1999
High-cell-density cultivation (HCDC) is required to improve microbial biomass and product formati... more High-cell-density cultivation (HCDC) is required to improve microbial biomass and product formation substantially. An overview of HCDC is given for microorganisms including bacteria, archae and eukarya (yeasts). Problems encountered by HCDC and their possible solutions are discussed. Improvements of strains, dierent types of bioreactors and cultivation strategies for successful HCDC are described. Stirredtank reactors with and without cell retention, a dialysismembrane reactor, a gas-lift reactor and a membrane cyclone reactor used for HCDC are outlined. Recently modi®ed traditional feeding strategies and new ones are included, in particular those for unlimited growth to very dense cultures. Emphasis is placed on robust fermentation control because of the growing industrial interest in this ®eld. Therefore, developments in the application of multivariate statistical control, arti®cial neural networks, fuzzy control and knowledge-based supervision (expert systems) are summarized. Recent advances using Escherichia coli ± the pioneer organism for HCDC ± are outlined.
Normalisation of 2D DIGE Data – On the Way to a Standard Operating Procedure
BIOINFORMATICS DISCOVERY NOTE Systems biology
Biotechnol Lett, 1985
SUE~ARY. An adaptive algorithm for on-line k~a estimation was developed. The accuracy of on-line ... more SUE~ARY. An adaptive algorithm for on-line k~a estimation was developed. The accuracy of on-line kLa e~timation was evaluated by means of digital and analog simulation. Simple difference schema developed on the basis of algorithm may be easily applicable in microcomputer-coupled fermen-tations~ In the last decade our effort in estimation of fermenter aeration capacity (kLa) concentrated on the development of methods for off-line kLa estimation . In this two stage procedure data describing the dynamic response of the apparatus to the step change of aeration conditions are first collected. In the second stage a large computer is used for mathematical data treatment. A qualitatively new approach in this field has been allowed when direct coupling of hybrid computer systems or digital microcomputers to dissolved oxygen (DO) probe for on-line measurement was possible. Yoshida(1980) et al. published a paper in which a series of sophisticated methods for on-line estimation by an analog computer was described. In the same year Sobotka and Votruba (1980) published a numerical method for adaptive kLa estimation related to the methods referred to above. It was shown earlier that time delay of DO probe may distort the estimated kLa value. Therefore, the computer simulation of this effect was performed in a hybrid computer system HRA-7201 (VEB Robotron,GDR and ARIE~A,Czechoslovakia) and on digital computer IBM 370/145. The DO probe time delay was modelled as a first order delayed system ( Fuchs et a1.1971) with time constant T(sec.). When using Yoshida method (1980) 267
Proteomic Profiling of Serological Responses to Aspergillus fumigatus Antigens in Patients with Invasive Aspergillosis
Journal of proteome research, Jan 6, 2016
Aspergillus fumigatus is the species that most commonly causes the opportunistic infection invasi... more Aspergillus fumigatus is the species that most commonly causes the opportunistic infection invasive aspergillosis (IA) in patients being treated for hematological malignancies. Little is known about the A. fumigatus proteins that trigger the production of Aspergillus-specific IgG antibodies during the course of IA. To characterize the serological response to A. fumigatus protein antigens, mycelial proteins were separated by 2-D gel electrophoresis. The gels were immunoblotted with sera from patients with probable and proven IA and control patients without IA. We identified 49 different fungal proteins, which gave a positive IgG antibody signal. Most of these antigens play a role in primary metabolism and stress responses. Overall, our analysis identified 18 novel protein antigens from A. fumigatus. To determine whether these antigens can be used as diagnostic or prognostic markers or exhibit a protective activity, we employed supervised machine learning with decision trees. We ident...
Data- and knowledge-based modeling of gene regulatory networks: an update
EXCLI journal, 2015
Gene regulatory network inference is a systems biology approach which predicts interactions betwe... more Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions.
Draft Genome Sequences of Fungus Aspergillus calidoustus
Genome announcements, 2016
Here, we report the draft genome sequence of Aspergillus calidoustus (strain SF006504). The funct... more Here, we report the draft genome sequence of Aspergillus calidoustus (strain SF006504). The functional annotation of A. calidoustus predicts a relatively large number of secondary metabolite gene clusters. The presented genome sequence builds the basis for further genome mining.
Genome-Wide Expression Profiling Reveals S100B as Biomarker for Invasive Aspergillosis
Frontiers in microbiology, 2016
Invasive aspergillosis (IA) is a devastating opportunistic infection and its treatment constitute... more Invasive aspergillosis (IA) is a devastating opportunistic infection and its treatment constitutes a considerable burden for the health care system. Immunocompromised patients are at an increased risk for IA, which is mainly caused by the species Aspergillus fumigatus. An early and reliable diagnosis is required to initiate the appropriate antifungal therapy. However, diagnostic sensitivity and accuracy still needs to be improved, which can be achieved at least partly by the definition of new biomarkers. Besides the direct detection of the pathogen by the current diagnostic methods, the analysis of the host response is a promising strategy toward this aim. Following this approach, we sought to identify new biomarkers for IA. For this purpose, we analyzed gene expression profiles of hematological patients and compared profiles of patients suffering from IA with non-IA patients. Based on microarray data, we applied a comprehensive feature selection using a random forest classifier. We...
Use of systems biology to decipher host microbial interactions and predict pathological consequences
Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases, Jan 22, 2016
In systems biology, researchers aim to understand complex biological systems as a whole, which is... more In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions; (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets; (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilising high-throughput data (e.g. SNP detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as novel str...
How to Predict Molecular Interactions between Species?
Frontiers in Microbiology, 2016
Organisms constantly interact with other species through physical contact which leads to changes ... more Organisms constantly interact with other species through physical contact which leads to changes on the molecular level, for example the transcriptome. These changes can be monitored for all genes, with the help of high-throughput experiments such as RNA-seq or microarrays. The adaptation of the gene expression to environmental changes within cells is mediated through complex gene regulatory networks. Often, our knowledge of these networks is incomplete. Network inference predicts gene regulatory interactions based on transcriptome data. An emerging application of high-throughput transcriptome studies are dual transcriptomics experiments. Here, the transcriptome of two or more interacting species is measured simultaneously. Based on a dual RNA-seq data set of murine dendritic cells infected with the fungal pathogen Candida albicans, the software tool NetGenerator was applied to predict an inter-species gene regulatory network. To promote further investigations of molecular inter-species interactions, we recently discussed dual RNA-seq experiments for host-pathogen interactions and extended the applied tool NetGenerator (Schulze et al., 2015). The updated version of NetGenerator makes use of measurement variances in the algorithmic procedure and accepts gene expression time series data with missing values. Additionally, we tested multiple modeling scenarios regarding the stimuli functions of the gene regulatory network. Here, we summarize the work by Schulze et al. (2015) and put it into a broader context. We review various studies making use of the dual transcriptomics approach to investigate the molecular basis of interacting species. Besides the application to host-pathogen interactions, dual transcriptomics data are also utilized to study mutualistic and commensalistic interactions. Furthermore, we give a short introduction into additional approaches for the prediction of gene regulatory networks and discuss their application to dual transcriptomics data. We conclude that the application of network inference on dual-transcriptomics data is a promising approach to predict molecular inter-species interactions.
Longitudinal RNA-Seq Analysis of Vertebrate Aging Identifies Mitochondrial Complex I as a Small-Molecule-Sensitive Modifier of Lifespan
Cell Systems, 2016
Mutations and genetic variability affect gene expression and lifespan, but the impact of variatio... more Mutations and genetic variability affect gene expression and lifespan, but the impact of variations in gene expression within individuals on their aging-related mortality is poorly understood. We performed a longitudinal study in the short-lived killifish, Nothobranchius furzeri, and correlated quantitative variations in gene expression during early adult life with lifespan. Shorter- and longer-lived individuals differ in their gene expression before the onset of aging-related mortality; differences in gene expression are more pronounced early in life. We identified mitochondrial respiratory chain complex I as a hub in a module of genes whose expression is negatively correlated with lifespan. Accordingly, partial pharmacological inhibition of complex I by the small molecule rotenone reversed aging-related regulation of gene expression and extended lifespan in N. furzeri by 15%. These results support the use of N. furzeri as a vertebrate model for identifying the protein targets, pharmacological modulators, and individual-to-individual variability associated with aging.
Network Inference by Considering Multiple Objectives: Insights from In Vivo Transcriptomic Data Generated by a Synthetic Network
Biocomp, 2010
Candida albicans infection leads to barrier breakdown and a MAPK/NF-κB mediated stress response in the intestinal epithelial cell line C2BBe1
Cellular microbiology, Jan 10, 2016
Intestinal epithelial cells (IEC) form a tight barrier to the gut lumen. Paracellular permeabilit... more Intestinal epithelial cells (IEC) form a tight barrier to the gut lumen. Paracellular permeability of the intestinal barrier is regulated by tight junction proteins and can be modulated by microorganisms and other stimuli. The polymorphic fungus Candida albicans, a frequent commensal of the human mucosa has the capacity of traversing this barrier and establishing systemic disease within the host. Infection of polarized C2BBe1 IEC with wild-type C. albicans led to a transient increase of transepithelial electric resistance (TEER) before subsequent barrier disruption, accompanied by a strong decline of junctional protein levels and substantial, but considerably delayed cytotoxicity. Time-resolved microarray-based transcriptome analysis of C. albicans challenged IEC revealed a prominent role of NF-κB and MAPK signaling pathways in the response to infection. Hence, we inferred a gene regulatory network based on differentially expressed NF-κB and MAPK pathway components and their predict...
Methods for supervised and unsupervised clustering and machine learning were studied in order to ... more Methods for supervised and unsupervised clustering and machine learning were studied in order to automatically model relationships between gene expression data and gene functions of the microorganism Escherichia coli. From a pre-selected subset of 265 genes (belonging to 3 functional groups) the function has been predicted with an accuracy of 63-71 % by various data mining methods described in this paper. Whereas some of these methods, i.e. K-means clustering, Kohonen's self-organizing maps (SOM), Eisen's hierarchical clustering and Quinlan's C4.5 decision tree induction algorithm have been applied to gene expression data analysis in the literature already, the fuzzy approach for gene expression data analysis is introduced by the authors. The fuzzy-C-means algorithm (FCM) and the Gustafson-Kessel algorithm for unsupervised clustering as well as the Adaptive Neuro-Fuzzy Inference System (ANFIS) were successfully applied to the functional classification of E. coli genes.
Fungal Genetics and Biology, Nov 1, 2010
FungiFun assigns functional annotations to fungal genes or proteins and performs gene set enrichm... more FungiFun assigns functional annotations to fungal genes or proteins and performs gene set enrichment analysis. Based on three different classification methods (FunCat, GO and KEGG), FungiFun categorizes genes and proteins for several fungal species on different levels of annotation detail. It is web-based and accessible to users without any programming skills. FungiFun is the first tool offering gene set enrichment analysis including the FunCat categorization. Two biological datasets for Aspergillus fumigatus and Candida albicans were analyzed using FungiFun, providing an overview of the usage and functions of the tool. FungiFun is freely accessible at https://www.omnifung.hki-jena.de/FungiFun/.
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Papers by Reinhard Guthke