Aspergillus fumigatus is a thermotolerant human-pathogenic mold and the most common cause of invasive aspergillosis (IA) in immunocompromised patients. Its predominance is based on several factors most of which are still unknown. The thermotolerance of A. fumigatus is one of the traits which have been assigned to pathogenicity. It allows the fungus to grow at temperatures up to and above that of a fevered human host. To elucidate the mechanisms of heat resistance, we analyzed the change of the A. fumigatus proteome during a temperature shift from 30 degrees C to 48 degrees C by 2D-fluorescence difference gel electrophoresis (DIGE). To improve 2D gel image analysis results, protein spot quantitation was optimized by missing value imputation and normalization. Differentially regulated proteins were compared to previously published transcriptome data of A. fumigatus. The study was augmented by bioinformatical analysis of transcription factor binding sites (TFBSs) in the promoter region of genes whose corresponding proteins were differentially regulated upon heat shock.

While previous studies have shed light on the link between the structure of metabolism and its transcriptional regulation, the extent to which transcriptional regulation controls metabolism has not yet been fully explored. In this work, we address this problem by integrating a large number of experimental data sets with a model of the metabolism of Escherichia coli. Using a combination of computational tools including the concept of elementary flux patterns, methods from network inference and dynamic optimization, we find that transcriptional regulation of pathways reflects the protein investment into these pathways. While pathways that are associated to a high protein cost are controlled by fine-tuned transcriptional programs, pathways that only require a small protein cost are transcriptionally controlled in a few key reactions. As a reason for the occurrence of these different regulatory strategies, we identify an evolutionary trade-off between the conflicting requirements to reduce protein investment and the requirement to be able to respond rapidly to changes in environmental conditions.

Inference of inter-species gene regulatory networks based on gene expression data is an important computational method to predict pathogen-host interactions (PHIs). Both the experimental setup and the nature of PHIs exhibit certain characteristics. First, besides an environmental change, the battle between pathogen and host leads to a constantly changing environment and thus complex gene expression patterns. Second, there might be a delay until one of the organisms reacts. Third, toward later time points only one organism may survive leading to missing gene expression data of the other organism. Here, we account for PHI characteristics by extending NetGenerator, a network inference tool that predicts gene regulatory networks from gene expression time series data. We tested multiple modeling scenarios regarding the stimuli functions of the interaction network based on a benchmark example. We show that modeling perturbation of a PHI network by multiple stimuli better represents the underlying biological phenomena. Furthermore, we utilized the benchmark example to test the influence of missing data points on the inference performance. Our results suggest that PHI network inference with missing data is possible, but we recommend to provide complete time series data. Finally, we extended the NetGenerator tool to incorporate gene- and time point specific variances, because complex PHIs may lead to high variance in expression data. Sample variances are directly considered in the objective function of NetGenerator and indirectly by testing the robustness of interactions based on variance dependent disturbance of gene expression values. We evaluated the method of variance incorporation on dual RNA sequencing (RNA-Seq) data of Mus musculus dendritic cells incubated with Candida albicans and proofed our method by predicting previously verified PHIs as robust interactions.

Pathogens manipulate the cellular mechanisms of host organisms via pathogen-host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein-protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature.

Systematically extracting biological meaning from omics data is a major challenge in systems biology. Enrichment analysis is often used to identify characteristic patterns in candidate lists. FungiFun is a user-friendly Web tool for functional enrichment analysis of fungal genes and proteins. The novel tool FungiFun2 uses a completely revised data management system and thus allows enrichment analysis for 298 currently available fungal strains published in standard databases. FungiFun2 offers a modern Web interface and creates interactive tables, charts and figures, which users can directly manipulate to their needs.

Dietary restriction (DR) extends lifespan and promotes metabolic health in evolutionary distinct species. DR is widely believed to promote longevity by causing an energy deficit leading to increased mitochondrial respiration. We here show that inhibitors of mitochondrial complex I promote physical activity, stress resistance as well as lifespan of Caenorhabditis elegans despite normal food uptake, i.e. in the absence of DR. However, complex I inhibition does not further extend lifespan in dietarily restricted nematodes, indicating that impaired complex I activity mimics DR. Promotion of longevity due to complex I inhibition occurs independently of known energy sensors, including DAF-16/FoxO, as well as AAK-2/AMPK and SIR-2.1/sirtuins, or both. Consistent with the concept of mitohormesis, complex I inhibition transiently increases mitochondrial formation of reactive oxygen species (ROS) that activate PMK-1/p38 MAP kinase and SKN-1/NRF-2. Interference with this retrograde redox signal as well as ablation of two redox-sensitive neurons in the head of the worm similarly prevents extension of lifespan. These findings unexpectedly indicate that DR extends organismal lifespan through transient neuronal ROS signaling rather than sensing of energy depletion, providing unexpected pharmacological options to promote exercise capacity and healthspan despite unaltered eating habits.

Glucose restriction mimicked by feeding the roundworm Caenorhabditis elegans with 2-deoxy-D-glucose (DOG) - a glucose molecule that lacks the ability to undergo glycolysis - has been found to increase the life span of the nematodes considerably. To facilitate understanding of the molecular mechanisms behind this life extension, we analyzed transcriptomes of DOG-treated and untreated roundworms obtained by RNA-seq at different ages. We found that, depending on age, DOG changes the magnitude of the expression values of about 2 to 24 percent of the genes significantly, although our results reveal that the gross changes introduced by DOG are small compared to the age-induced changes. We found that 27 genes are constantly either up- or down-regulated by DOG over the whole life span, among them several members of the cytochrome P450 family. The monotonic change with age of the temporal expression patterns of the genes was investigated, leading to the result that 21 genes reverse their monotonic behaviour under impaired glycolysis. Put simply, the DOG-treatment reduces the gross transcriptional activity but increases the interconnectedness of gene expression. However, a detailed analysis of network parameters discloses that the introduced changes differ remarkably between individual signalling pathways. We found a reorganization of the hubs of the mTOR pathway when standard diet is replaced by DOG feeding. By constructing correlation based difference networks, we identified those signalling pathways that are most vigorously changed by impaired glycolysis. Taken together, we have found a number of genes and pathways that are potentially involved in the DOG-driven extension of life span of C. elegans. Furthermore, our results demonstrate how the network structure of ageing-relevant signalling pathways is reorganised under impaired glycolysis.

Rotenone inhibits the electron transfer from complex I to ubiquinone, in this way interfering with the electron transport chain in mitochondria. This chain of events induces increased levels of intracellular reactive oxygen species, which in turn can contribute to acceleration of telomere shortening and induction of DNA damage, ultimately resulting in aging. In this study, we investigated the effect of rotenone treatment in human fibroblast strains.

The pyruvate dehydrogenase regulator protein (PdhR) of Escherichia coli acts as a transcriptional regulator in a pyruvate dependent manner to control central metabolic fluxes. However, the complete PdhR regulon has not yet been uncovered. To achieve an extended understanding of its gene regulatory network, we combined large-scale network inference and experimental verification of results obtained by a systems biology approach.

Disease epidemiology during ageing shows a transition from cancer to degenerative chronic disorders as dominant contributors to mortality in the old. Nevertheless, it has remained unclear to what extent molecular signatures of ageing reflect this phenomenon. Here we report on the identification of a conserved transcriptomic signature of ageing based on gene expression data from four vertebrate species across four tissues. We find that ageing-associated transcriptomic changes follow trajectories similar to the transcriptional alterations observed in degenerative ageing diseases but are in opposite direction to the transcriptomic alterations observed in cancer. We confirm the existence of a similar antagonism on the genomic level, where a majority of shared risk alleles which increase the risk of cancer decrease the risk of chronic degenerative disorders and vice versa. These results reveal a fundamental trade-off between cancer and degenerative ageing diseases that sheds light on the pronounced shift in their epidemiology during ageing.

Given the important role of angiogenesis in liver pathology, the current study investigated the role of Runt-related transcription factor 1 (RUNX1), a regulator of developmental angiogenesis, in the pathogenesis of non-alcoholic steatohepatitis (NASH). Quantitative RT-PCRs and a transcription factor analysis of angiogenesis-associated differentially expressed genes in liver tissues of healthy controls, patients with steatosis and NASH, indicated a potential role of RUNX1 in NASH. The gene expression of RUNX1 was correlated with histopathological attributes of patients. The protein expression of RUNX1 in liver was studied by immunohistochemistry. To explore the underlying mechanisms, in vitro studies using RUNX1 siRNA and overexpression plasmids were performed in endothelial cells (ECs). RUNX1 expression was significantly correlated with inflammation, fibrosis and NASH activity score in NASH patients. Its expression was conspicuous in liver non-parenchymal cells. In vitro, factors from steatotic hepatocytes and/or VEGF or TGF- significantly induced the expression of RUNX1 in ECs. RUNX1 regulated the expression of angiogenic and adhesion molecules in ECs, including CCL2, PECAM1 and VCAM1, which was shown by silencing or over-expression of RUNX1. Furthermore, RUNX1 increased the angiogenic activity of ECs. This study reports that steatosis-induced RUNX1 augmented the expression of adhesion and angiogenic molecules and properties in ECs and may be involved in enhancing inflammation and disease severity in NASH.

Network inference of gene expression data is an important challenge in systems biology. Novel algorithms may provide more detailed gene regulatory networks (GRN) for complex, chronic inflammatory diseases such as rheumatoid arthritis (RA), in which activated synovial fibroblasts (SFBs) play a major role. Since the detailed mechanisms underlying this activation are still unclear, simultaneous investigation of multi-stimuli activation of SFBs offers the possibility to elucidate the regulatory effects of multiple mediators and to gain new insights into disease pathogenesis.

Recent and rapidly evolving progress on high-throughput measurement techniques and computational performance has led to the emergence of new disciplines, such as systems medicine and translational systems biology. At the core of these disciplines lies the desire to produce multiscale models: mathematical models that integrate multiple scales of biological organization, ranging from molecular, cellular and tissue models to organ, whole-organism and population scale models. Using such models, hypotheses can systematically be tested. In this review, we present state-of-the-art multiscale modelling of bacterial and fungal infections, considering both the pathogen and host as well as their interaction. Multiscale modelling of the interactions of bacteria, especially Mycobacterium tuberculosis, with the human host is quite advanced. In contrast, models for fungal infections are still in their infancy, in particular regarding infections with the most important human pathogenic fungi, Candida albicans and Aspergillus fumigatus. We reflect on the current availability of computational approaches for multiscale modelling of host-pathogen interactions and point out current challenges. Finally, we provide an outlook for future requirements of multiscale modelling.

Batch effects due to sample preparation or array variation (type, charge, and/or platform) may influence the results of microarray experiments and thus mask and/or confound true biological differences. Of the published approaches for batch correction, the algorithm "Combating Batch Effects When Combining Batches of Gene Expression Microarray Data" (ComBat) appears to be most suitable for small sample sizes and multiple batches.

Network inference from gene expression data is a typical approach to reconstruct gene regulatory networks. During chondrogenic differentiation of human mesenchymal stem cells (hMSCs), a complex transcriptional network is active and regulates the temporal differentiation progress. As modulators of transcriptional regulation, microRNAs (miRNAs) play a critical role in stem cell differentiation. Integrated network inference aimes at determining interrelations between miRNAs and mRNAs on the basis of expression data as well as miRNA target predictions. We applied the NetGenerator tool in order to infer an integrated gene regulatory network.

Inference of gene-regulatory networks (GRNs) is important for understanding behaviour and potential treatment of biological systems. Knowledge about GRNs gained from transcriptome analysis can be increased by multiple experiments and/or multiple stimuli. Since GRNs are complex and dynamical, appropriate methods and algorithms are needed for constructing models describing these dynamics. Algorithms based on heuristic approaches reduce the effort in parameter identification and computation time.

Reverse engineering of gene regulatory networks can be used to predict regulatory interactions of an organism faced with environmental changes, but can prove problematic, especially when focusing on complicated multi-factorial processes. Candida albicans is a major human fungal pathogen. During the infection process, this fungus is able to adapt to conditions of very low iron availability. Such adaptation is an important virulence attribute of virtually all pathogenic microbes. Understanding the regulation of iron acquisition genes will extend our knowledge of the complex regulatory changes during the infection process and might identify new potential drug targets. Thus, there is a need for efficient modelling approaches predicting key regulatory events of iron acquisition genes during the infection process.

Millions of humans and animals suffer from superficial infections caused by a group of highly specialized filamentous fungi, the dermatophytes, which exclusively infect keratinized host structures. To provide broad insights into the molecular basis of the pathogenicity-associated traits, we report the first genome sequences of two closely phylogenetically related dermatophytes, Arthroderma benhamiae and Trichophyton verrucosum, both of which induce highly inflammatory infections in humans.

D-Glucosamine (GlcN) is a freely available and commonly used dietary supplement potentially promoting cartilage health in humans, which also acts as an inhibitor of glycolysis. Here we show that GlcN, independent of the hexosamine pathway, extends Caenorhabditis elegans life span by impairing glucose metabolism that activates AMP-activated protein kinase (AMPK/AAK-2) and increases mitochondrial biogenesis. Consistent with the concept of mitohormesis, GlcN promotes increased formation of mitochondrial reactive oxygen species (ROS) culminating in increased expression of the nematodal amino acid-transporter 1 (aat-1) gene. Ameliorating mitochondrial ROS formation or impairment of aat-1-expression abolishes GlcN-mediated life span extension in an NRF2/SKN-1-dependent fashion. Unlike other calorie restriction mimetics, such as 2-deoxyglucose, GlcN extends life span of ageing C57BL/6 mice, which show an induction of mitochondrial biogenesis, lowered blood glucose levels, enhanced expression of several murine amino-acid transporters, as well as increased amino-acid catabolism. Taken together, we provide evidence that GlcN extends life span in evolutionary distinct species by mimicking a low-carbohydrate diet.

Network inference utilizes experimental high-throughput data for the reconstruction of molecular interaction networks where new relationships between the network entities can be predicted. Despite the increasing amount of experimental data, the parameters of each modeling technique cannot be optimized based on the experimental data alone, but needs to be qualitatively assessed if the components of the resulting network describe the experimental setting. Candidate list prioritization and validation builds upon data integration and data visualization. The application of tools supporting this procedure is limited to the exploration of smaller information networks because the display and interpretation of large amounts of information is challenging regarding the computational effort and the users' experience. The Ondex software framework was extended with customizable context-sensitive menus which allow additional integration and data analysis options for a selected set of candidates during interactive data exploration. We provide new functionalities for on-the-fly data integration using InterProScan, PubMed Central literature search, and sequence-based homology search. We applied the Ondex system to the integration of publicly available data for Aspergillus nidulans and analyzed transcriptome data. We demonstrate the advantages of our approach by proposing new hypotheses for the functional annotation of specific genes of differentially expressed fungal gene clusters. Our extension of the Ondex framework makes it possible to overcome the separation between data integration and interactive analysis. More specifically, computationally demanding calculations can be performed on selected sub-networks without losing any information from the whole network. Furthermore, our extensions allow for direct access to online biological databases which helps to keep the integrated information up-to-date.