Selective gene expression profiling contributes to a better understanding of the molecular pathways underlying the histological changes observed after RHMVL

Background

In previous studies, five vasoactive drugs were investigated for their effect on the recovery process after extended liver resection without observing relevant improvements. We hypothesized that an analysis of gene expression could help to identify potentially druggable pathways and could support the selection of promising drug candidates.

Methods

Liver samples obtained from rats after combined 70% partial hepatectomy and right median hepatic vein ligation (n = 6/group) sacrificed at 0 h, 24 h, 48 h, and 7days were selected for this study. Liver samples were collected from differentially perfused regions of the median lobe (obstruction-zone, border-zone, normal-zone). Gene expression profiling of marker genes regulating hepatic hemodynamics, vascular remodeling, and liver regeneration was performed with microfluidic chips. We used 3 technical replicates from each sample. Raw data were normalized using LEMming and differentially expressed genes were identified using LIMMA.

Results

The strongest differences were found in obstruction-zone at 24 h and 48 h postoperatively compared to all other groups. mRNA expression of marker genes from hepatic hemodynamics pathways (iNOS,Ptgs2,Edn1) was most upregulated.

Conclusion

These upregulated genes suggest a strong vasoconstrictive effect promoting arterial hypoperfusion in the obstruction-zone. Reducing iNOS expression using selective iNOS inhibitors seems to be a promising approach to promote vasodilation and liver regeneration.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01364-z.

Disorder-Promoted Splitting in Quasiparticle Interference at Nesting Vectors

Inelastic interactions of quantum systems with the environment usually wash coherent effects out. In the case of Friedel oscillations, the presence of disorder leads to a fast decay of the oscillation amplitude. Here we show both experimentally and theoretically that in three-dimensional topological insulator Bi₂Te₃ there is a nesting-induced splitting of coherent scattering vectors which follows a peculiar evolution in energy. The effect becomes experimentally observable when the lifetime of quasiparticles shortens due to disorder. The amplitude of the splitting allows an evaluation of the lifetime of the electrons. A similar phenomenon should be observed in any system with a well-defined scattering vector regardless of its topological properties.

Increased Expression of RUNX1 in Liver Correlates with NASH Activity Score in Patients with Non-Alcoholic Steatohepatitis (NASH)

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.

Module-detection approaches for the integration of multilevel omics data highlight the comprehensive response of Aspergillus fumigatus to caspofungin

Background

Omics data provide deep insights into overall biological processes of organisms. However, integration of data from different molecular levels such as transcriptomics and proteomics, still remains challenging. Analyzing lists of differentially abundant molecules from diverse molecular levels often results in a small overlap mainly due to different regulatory mechanisms, temporal scales, and/or inherent properties of measurement methods. Module-detecting algorithms identifying sets of closely related proteins from protein-protein interaction networks (PPINs) are promising approaches for a better data integration.

Results

Here, we made use of transcriptome, proteome and secretome data from the human pathogenic fungus Aspergillus fumigatus challenged with the antifungal drug caspofungin. Caspofungin targets the fungal cell wall which leads to a compensatory stress response. We analyzed the omics data using two different approaches: First, we applied a simple, classical approach by comparing lists of differentially expressed genes (DEGs), differentially synthesized proteins (DSyPs) and differentially secreted proteins (DSePs); second, we used a recently published module-detecting approach, ModuleDiscoverer, to identify regulatory modules from PPINs in conjunction with the experimental data. Our results demonstrate that regulatory modules show a notably higher overlap between the different molecular levels and time points than the classical approach. The additional structural information provided by regulatory modules allows for topological analyses. As a result, we detected a significant association of omics data with distinct biological processes such as regulation of kinase activity, transport mechanisms or amino acid metabolism. We also found a previously unreported increased production of the secondary metabolite fumagillin by A. fumigatus upon exposure to caspofungin. Furthermore, a topology-based analysis of potential key factors contributing to drug-caused side effects identified the highly conserved protein polyubiquitin as a central regulator. Interestingly, polyubiquitin UbiD neither belonged to the groups of DEGs, DSyPs nor DSePs but most likely strongly influenced their levels.

Conclusion

Module-detecting approaches support the effective integration of multilevel omics data and provide a deep insight into complex biological relationships connecting these levels. They facilitate the identification of potential key players in the organism’s stress response which cannot be detected by commonly used approaches comparing lists of differentially abundant molecules.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0620-8) contains supplementary material, which is available to authorized users.

The Diurnal Timing of Starvation Differently Impacts Murine Hepatic Gene Expression and Lipid Metabolism - A Systems Biology Analysis Using Self-Organizing Maps

Organisms adapt their metabolism and draw on reserves as a consequence of food deprivation. The central role of the liver in starvation response is to coordinate a sufficient energy supply for the entire organism, which has frequently been investigated. However, knowledge of how circadian rhythms impact on and alter this response is scarce. Therefore, we investigated the influence of different timings of starvation on global hepatic gene expression. Mice (n = 3 each) were challenged with 24-h food deprivation started in the morning or evening, coupled with refeeding for different lengths and compared with ad libitum fed control groups. Alterations in hepatocyte gene expression were quantified using microarrays and confirmed or complemented with qPCR, especially for lowly detectable transcription factors. Analysis was performed using self-organizing maps (SOMs), which bases on clustering genes with similar expression profiles. This provides an intuitive overview of expression trends and allows easier global comparisons between complex conditions. Transcriptome analysis revealed a strong circadian-driven response to fasting based on the diurnal expression of transcription factors (e.g., Ppara, Pparg). Starvation initiated in the morning produced known metabolic adaptations in the liver; e.g., switching from glucose storage to consumption and gluconeogenesis. However, starvation initiated in the evening produced a different expression signature that was controlled by yet unknown regulatory mechanisms. For example, the expression of genes involved in gluconeogenesis decreased and fatty acid and cholesterol synthesis genes were induced. The differential regulation after morning and evening starvation were also reflected at the lipidome level. The accumulation of hepatocellular storage lipids (triacylglycerides, cholesteryl esters) was significantly higher after the initiation of starvation in the morning compared to the evening. Concerning refeeding, the gene expression pattern after a 12 h refeeding period largely resembled that of the corresponding starvation state but approached the ad libitum control state after refeeding for 21 h. Some components of these regulatory circuits are discussed. Collectively, these data illustrate a highly time-dependent starvation response in the liver and suggest that a circadian influence cannot be neglected when starvation is the focus of research or medicine, e.g., in the case of treating victims of sudden starvation events.

Molecular signatures of liver dysfunction are distinct in fungal and bacterial infections in mice

Rationale: The liver is a central organ not only for metabolism but also immune function. Life-threatening infections of both bacterial and fungal origin can affect liver function but it is yet unknown whether molecular changes differ depending on the pathogen. We aimed to determine whether the hepatic host response to bacterial and fungal infections differs in terms of hepatic metabolism and liver function. Methods: We compared murine models of infection, including bacterial peritoneal contamination and infection (PCI), intraperitoneal and systemic C. albicans infection, at 6 and 24 h post-infection, to sham controls. The molecular hepatic host response was investigated by the detection of regulatory modules based on large-scale protein-protein interaction networks and expression data. Topological analysis of these regulatory modules was used to reveal infection-specific biological processes and molecular mechanisms. Intravital microscopy and immunofluorescence microscopy were used to further analyze specific aspects of pathophysiology such as cholestasis. Results: Down-regulation of lipid catabolism and bile acid synthesis was observed after 6 h in all infection groups. Alterations in lipid catabolism were characterized by accumulation of long chain acylcarnitines and defective beta-oxidation, which affected metabolism by 6 h. While PCI led to an accumulation of unconjugated bile acids (BA), C. albicans infection caused accumulation of conjugated BA independent of the route of infection. Hepatic dye clearance and transporter expression revealed reduced hepatic uptake in fungal infections vs. defects in secretion following polybacterial infection. Conclusion: Molecular phenotypes of lipid accumulation and cholestasis allow differentiation between pathogens as well as routes of infection at early stages in mice. Targeted metabolomics could be a useful tool for the profiling of infected/septic patients and the type of pathogen, with subsequent customization and targeting of therapy.

Proteomics of Aspergillus fumigatus Conidia-containing Phagolysosomes Identifies Processes Governing Immune Evasion

Invasive infections by the human pathogenic fungus Aspergillus fumigatus start with the outgrowth of asexual, airborne spores (conidia) into the lung tissue of immunocompromised patients. The resident alveolar macrophages phagocytose conidia, which end up in phagolysosomes. However, A. fumigatus conidia resist phagocytic degradation to a certain degree. This is mainly attributable to the pigment 1,8-dihydroxynaphthalene (DHN) melanin located in the cell wall of conidia, which manipulates the phagolysosomal maturation and prevents their intracellular killing. To get insight in the underlying molecular mechanisms, we comparatively analyzed proteins of mouse macrophage phagolysosomes containing melanized wild-type (wt) or nonmelanized pksP mutant conidia. For this purpose, a protocol to isolate conidia-containing phagolysosomes was established and a reference protein map of phagolysosomes was generated. We identified 637 host and 22 A. fumigatus proteins that were differentially abundant in the phagolysosome. 472 of the host proteins were overrepresented in the pksP mutant and 165 in the wt conidia-containing phagolysosome. Eight of the fungal proteins were produced only in pksP mutant and 14 proteins in wt conidia-containing phagolysosomes. Bioinformatical analysis compiled a regulatory module, which indicates host processes affected by the fungus. These processes include vATPase-driven phagolysosomal acidification, Rab5 and Vamp8-dependent endocytic trafficking, signaling pathways, as well as recruitment of the Lamp1 phagolysosomal maturation marker and the lysosomal cysteine protease cathepsin Z. Western blotting and immunofluorescence analyses confirmed the proteome data and moreover showed differential abundance of the major metabolic regulator mTOR. Taken together, with the help of a protocol optimized to isolate A. fumigatus conidia-containing phagolysosomes and a potent bioinformatics algorithm, we were able to confirm A. fumigatus conidia-dependent modification of phagolysosomal processes that have been described before and beyond that, identify pathways that have not been implicated in A. fumigatus evasion strategy, yet.Mass spectrometry proteomics data are available via ProteomeXchange with identifiers PXD005724 and PXD006134.

ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks

The identification of disease-associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. However, their identification is hampered by the detection of protein communities within large-scale, whole-genome PPINs. A presented successful strategy detects a PPIN's community structure based on the maximal clique enumeration problem (MCE), which is a non-deterministic polynomial time-hard problem. This renders the approach computationally challenging for large PPINs implying the need for new strategies. We present ModuleDiscoverer, a novel approach for the identification of regulatory modules from PPINs and gene expression data. Following the MCE-based approach, ModuleDiscoverer uses a randomization heuristic-based approximation of the community structure. Given a PPIN of Rattus norvegicus and public gene expression data, we identify the regulatory module underlying a rodent model of non-alcoholic steatohepatitis (NASH), a severe form of non-alcoholic fatty liver disease (NAFLD). The module is validated using single-nucleotide polymorphism (SNP) data from independent genome-wide association studies and gene enrichment tests. Based on gene enrichment tests, we find that ModuleDiscoverer performs comparably to three existing module-detecting algorithms. However, only our NASH-module is significantly enriched with genes linked to NAFLD-associated SNPs. ModuleDiscoverer is available at http://www.hki-jena.de/index.php/0/2/490 (Others/ModuleDiscoverer).

Computational Modeling in Liver Surgery

The need for extended liver resection is increasing due to the growing incidence of liver tumors in aging societies. Individualized surgical planning is the key for identifying the optimal resection strategy and to minimize the risk of postoperative liver failure and tumor recurrence. Current computational tools provide virtual planning of liver resection by taking into account the spatial relationship between the tumor and the hepatic vascular trees, as well as the size of the future liver remnant. However, size and function of the liver are not necessarily equivalent. Hence, determining the future liver volume might misestimate the future liver function, especially in cases of hepatic comorbidities such as hepatic steatosis. A systems medicine approach could be applied, including biological, medical, and surgical aspects, by integrating all available anatomical and functional information of the individual patient. Such an approach holds promise for better prediction of postoperative liver function and hence improved risk assessment. This review provides an overview of mathematical models related to the liver and its function and explores their potential relevance for computational liver surgery. We first summarize key facts of hepatic anatomy, physiology, and pathology relevant for hepatic surgery, followed by a description of the computational tools currently used in liver surgical planning. Then we present selected state-of-the-art computational liver models potentially useful to support liver surgery. Finally, we discuss the main challenges that will need to be addressed when developing advanced computational planning tools in the context of liver surgery.

Pathophysiological changes of inflammatory syndrome in multiple sclerosis after instituting therapeutic plasmapheresis

In autoimmune conditions affecting the central and peripheral CNS as well as in multiple sclerosis (MS), the inflammatory syndrome is present with the onset of this disease. The present paper aimed to highlight the inflammatory syndrome based on the leukergia test, the total blood viscosity test, blood filterability test as well as on other tests. The early instituting of the therapeutic plasmapheresis beneficially modified the clinical status, the biological and pathophysiological behavior of the patient's illness. Objective of the paper: The aim was to highlight the importance, advantages, and pathophysiological changes after therapeutic plasmapheresis in five cases, in patients hospitalized with the diagnosis of multiple sclerosis. Material and method: In order to emphasize the inflammatory syndrome, the determination of leukergia assay, the total blood viscosity test and the blood filterability test were added to regular examinations, conducted on the batch of patients included in the study. Results and discussions: As a result of using therapeutic plasmapheresis, the inflammatory parameters in patients with multiple sclerosis improved beneficially as it was proven by the values of inflammatory tests before and after plasmapheresis. Conclusions: In the treatment of multiple sclerosis, plasmapheresis proved to be a medical method that significantly reduced autoimmune inflammatory "installed" syndrome.

Dual-species transcriptional profiling during systemic candidiasis reveals organ-specific host-pathogen interactions

Candida albicans is a common cause of life-threatening fungal bloodstream infections. In the murine model of systemic candidiasis, the kidney is the primary target organ while the fungal load declines over time in liver and spleen. To better understand these organ-specific differences in host-pathogen interaction, we performed gene expression profiling of murine kidney, liver and spleen and determined the fungal transcriptome in liver and kidney. We observed a delayed transcriptional immune response accompanied by late induction of fungal stress response genes in the kidneys. In contrast, early upregulation of the proinflammatory response in the liver was associated with a fungal transcriptome resembling response to phagocytosis, suggesting that phagocytes contribute significantly to fungal control in the liver. Notably, C. albicans hypha-associated genes were upregulated in the absence of visible filamentation in the liver, indicating an uncoupling of gene expression and morphology and a morphology-independent effect by hypha-associated genes in this organ. Consistently, integration of host and pathogen transcriptional data in an inter-species gene regulatory network indicated connections of C. albicans cell wall remodelling and metabolism to the organ-specific immune responses.

Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens

In the emerging field of systems biology of fungal infection, one of the central roles belongs to the modeling of gene regulatory networks (GRNs). Utilizing omics-data, GRNs can be predicted by mathematical modeling. Here, we review current advances of data-based reconstruction of both small-scale and large-scale GRNs for human pathogenic fungi. The advantage of large-scale genome-wide modeling is the possibility to predict central (hub) genes and thereby indicate potential biomarkers and drug targets. In contrast, small-scale GRN models provide hypotheses on the mode of gene regulatory interactions, which have to be validated experimentally. Due to the lack of sufficient quantity and quality of both experimental data and prior knowledge about regulator–target gene relations, the genome-wide modeling still remains problematic for fungal pathogens. While a first genome-wide GRN model has already been published for Candida albicans, the feasibility of such modeling for Aspergillus fumigatus is evaluated in the present article. Based on this evaluation, opinions are drawn on future directions of GRN modeling of fungal pathogens. The crucial point of genome-wide GRN modeling is the experimental evidence, both used for inferring the networks (omics ‘first-hand’ data as well as literature data used as prior knowledge) and for validation and evaluation of the inferred network models.

Novel application of multi-stimuli network inference to synovial fibroblasts of rheumatoid arthritis patients

Background

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.

Methods

A GRN was therefore inferred from RA-SFBs treated with 4 different stimuli (IL-1 β, TNF- α, TGF- β, and PDGF-D). Data from time series microarray experiments (0, 1, 2, 4, 12 h; Affymetrix HG-U133 Plus 2.0) were batch-corrected applying ‘ComBat’, analyzed for differentially expressed genes over time with ‘Limma’, and used for the inference of a robust GRN with NetGenerator V2.0, a heuristic ordinary differential equation-based method with soft integration of prior knowledge.

Results

Using all genes differentially expressed over time in RA-SFBs for any stimulus, and selecting the genes belonging to the most significant gene ontology (GO) term, i.e., ‘cartilage development’, a dynamic, robust, moderately complex multi-stimuli GRN was generated with 24 genes and 57 edges in total, 31 of which were gene-to-gene edges. Prior literature-based knowledge derived from Pathway Studio or manual searches was reflected in the final network by 25/57 confirmed edges (44%). The model contained known network motifs crucial for dynamic cellular behavior, e.g., cross-talk among pathways, positive feed-back loops, and positive feed-forward motifs (including suppression of the transcriptional repressor OSR2 by all 4 stimuli.

Conclusion

A multi-stimuli GRN highly concordant with literature data was successfully generated by network inference from the gene expression of stimulated RA-SFBs. The GRN showed high reliability, since 10 predicted edges were independently validated by literature findings post network inference. The selected GO term ‘cartilage development’ contained a number of differentiation markers, growth factors, and transcription factors with potential relevance for RA. Finally, the model provided new insight into the response of RA-SFBs to multiple stimuli implicated in the pathogenesis of RA, in particular to the ‘novel’ potent growth factor PDGF-D.

GRN2SBML: automated encoding and annotation of inferred gene regulatory networks complying with SBML

GRN2SBML automatically encodes gene regulatory networks derived from several inference tools in systems biology markup language. Providing a graphical user interface, the networks can be annotated via the simple object access protocol (SOAP)-based application programming interface of BioMart Central Portal and minimum information required in the annotation of models registry. Additionally, we provide an R-package, which processes the output of supported inference algorithms and automatically passes all required parameters to GRN2SBML. Therefore, GRN2SBML closes a gap in the processing pipeline between the inference of gene regulatory networks and their subsequent analysis, visualization and storage.

AVAILABILITY

GRN2SBML is freely available under the GNU Public License version 3 and can be downloaded from http://www.hki-jena.de/index.php/0/2/490.

SUPPLEMENTARY INFORMATION

General information on GRN2SBML, examples and tutorials are available at the tool's web page.

Atomic-scale engineering of magnetic anisotropy of nanostructures through interfaces and interlines

The central goals of nanoscale magnetic materials science are the self-assembly of the smallest structure exhibiting ferromagnetic hysteresis at room temperature, and the assembly of these structures into the highest density patterns. The focus has been on chemically ordered alloys combining magnetic 3d elements with polarizable 5d elements having high spin–orbit coupling and thus yielding the desired large magneto-crystalline anisotropy. The chemical synthesis of nanoparticles of these alloys yields disordered phases requiring annealing to transform them to the high-anisotropy L1₀ structure. Despite considerable efforts, so far only part of the nanoparticles can be transformed without coalescence. Here we present an alternative approach to homogeneous alloys, namely the creation of nanostructures with atomically sharp bimetallic interfaces and interlines. They exhibit unexpectedly high magnetization reversal energy with values and directions of the easy magnetization axes strongly depending on chemistry and texture. We find significant deviations from the expected behaviour for commonly used element combinations. Ab-initio calculations reproduce these results and unravel their origin.

The design and assembly of nanostructures exhibiting ferromagnetic hysteresis at room temperature are recognized goals for high-density data storage. Here, the authors engineer nanostructures with atomically sharp bimetallic interfaces and interlines, which exhibit large magnetic anisotropy and high temperature hysteresis.

Low temperature ferromagnetism in chemically ordered FeRh nanocrystals

In sharp contrast to previous studies on FeRh bulk, thin films, and nanoparticles, we report the persistence of ferromagnetic order down to 3 K for size-selected 3.3 nm diameter nanocrystals embedded into an amorphous carbon matrix. The annealed nanoparticles have a B2 structure with alternating atomic Fe and Rh layers. X-ray magnetic dichroism and superconducting quantum interference device measurements demonstrate ferromagnetic alignment of the Fe and Rh magnetic moments of 3 and 1μ(B), respectively. The ferromagnetic order is ascribed to the finite-size induced structural relaxation observed in extended x-ray absorption spectroscopy.

Inference of dynamical gene-regulatory networks based on time-resolved multi-stimuli multi-experiment data applying NetGenerator V2.0

BACKGROUND

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.

RESULTS

The NetGenerator V2.0 algorithm, a heuristic for network inference, is proposed and described. It automatically generates a system of differential equations modelling structure and dynamics of the network based on time-resolved gene expression data. In contrast to a previous version, the inference considers multi-stimuli multi-experiment data and contains different methods for integrating prior knowledge. The resulting significant changes in the algorithmic procedures are explained in detail. NetGenerator is applied to relevant benchmark examples evaluating the inference for data from experiments with different stimuli. Also, the underlying GRN of chondrogenic differentiation, a real-world multi-stimulus problem, is inferred and analysed.

CONCLUSIONS

NetGenerator is able to determine the structure and parameters of GRNs and their dynamics. The new features of the algorithm extend the range of possible experimental set-ups, results and biological interpretations. Based upon benchmarks, the algorithm provides good results in terms of specificity, sensitivity, efficiency and model fit.

The extended TILAR approach: a novel tool for dynamic modeling of the transcription factor network regulating the adaption to in vitro cultivation of murine hepatocytes

BACKGROUND

Network inference is an important tool to reveal the underlying interactions of biological systems. In the liver, a complex system of transcription factors is active to distribute signals and induce the cellular response following extracellular stimuli. Plenty of information is available about single transcription factors important for the different functions of the liver, but little is known about their causal relations to each other.

RESULTS

Given a DNA microarray time series dataset of collagen monolayers cultured murine hepatocytes, we identified 22 differentially expressed genes for which the corresponding protein is known to exhibit transcription factor activity. We developed the Extended TILAR (ExTILAR) network inference algorithm based on the modeling concept of the previously published TILAR algorithm. Using ExTILAR, we inferred a transcription factor network based on gene expression data which puts these important genes into a functional context. This way, we identified a previously unknown relationship between Tgif1 and Atf3 which we validated experimentally. Beside its known role in metabolic processes, this extends the knowledge about Tgif1 in hepatocytes towards a possible influence of processes such as proliferation and cell cycle. Moreover, two positive (i.e. double negative) regulatory loops were predicted that could give rise to bistable behavior. We further evaluated the performance of ExTILAR by systematic inference of an in silico network.

CONCLUSIONS

We present the ExTILAR algorithm, which combines the advantages of the regression based inference algorithm TILAR, like large network sizes processable and low computational costs, with the advantages of dynamic network models based on ordinary differential equation (i.e. in silico knock-down simulations). Like TILAR, ExTILAR makes use of various prior-knowledge types such as transcription factor binding site information and gene interaction knowledge to infer biologically meaningful gene regulatory networks. Therefore, ExTILAR is especially useful when a large number of genes is modeled using a small number of experimental data points.

Highly anisotropic Dirac cones in epitaxial graphene modulated by an island superlattice

We present a new method to engineer the charge carrier mobility and its directional asymmetry in epitaxial graphene by using metal cluster superlattices self-assembled onto the moiré pattern formed by graphene on Ir(111). Angle-resolved photoemission spectroscopy reveals threefold symmetry in the band structure associated with strong renormalization of the electron group velocity close to the Dirac point giving rise to highly anisotropic Dirac cones. We further find that the cluster superlattice also affects the spectral-weight distribution of the carbon bands as well as the electronic gaps between graphene states.