Adapted Boolean network models for extracellular matrix formation

Identification of New, Functionally Relevant Mutations in the Coding Regions of the Human Fos and Jun Proto-Oncogenes in Rheumatoid Arthritis Synovial Tissue

In rheumatoid arthritis (RA), the expression of many pro-destructive/pro-inflammatory proteins depends on the transcription factor AP-1. Therefore, our aim was to analyze the presence and functional relevance of mutations in the coding regions of the AP-1 subunits of the fos and jun family in peripheral blood (PB) and synovial membranes (SM) of RA and osteoarthritis patients (OA, disease control), as well as normal controls (NC). Using the non-isotopic RNAse cleavage assay, one known polymorphism (T252C: silent; rs1046117; present in RA, OA, and NC) and three novel germline mutations of the cfos gene were detected: (i) C361G/A367G: Gln121Glu/Ile123Val, denoted as “fos121/123”; present only in one OA sample; (ii) G374A: Arg125Lys, “fos125”; and (iii) C217A/G374A: Leu73Met/Arg125Lys, “fos73/125”, the latter two exclusively present in RA. In addition, three novel somatic cjun mutations (604–606ΔCAG: ΔGln202, “jun202”; C706T: Pro236Ser, “jun236”; G750A: silent) were found exclusively in the RA SM. Tansgenic expression of fos125 and fos73/125 mutants in NIH-3T3 cells induced an activation of reporter constructs containing either the MMP-1 (matrix metalloproteinase) promoter (3- and 4-fold, respectively) or a pentameric AP-1 site (approximately 5-fold). Combined expression of these two cfos mutants with cjun wildtype or mutants (jun202, jun236) further enhanced reporter expression of the pentameric AP-1 construct. Finally, genotyping for the novel functionally relevant germline mutations in 298 RA, 288 OA, and 484 NC samples revealed no association with RA. Thus, functional cfos/cjun mutants may contribute to local joint inflammation/destruction in selected patients with RA by altering the transactivation capacity of AP-1 complexes.

Synovial tissue transcriptomes of long-standing rheumatoid arthritis are dominated by activated macrophages that reflect microbial stimulation

Advances in microbiome research suggest involvement in chronic inflammatory diseases such as rheumatoid arthritis (RA). Searching for initial trigger(s) in RA, we compared transcriptome profiles of highly inflamed RA synovial tissue (RA-ST) and osteoarthritis (OA)-ST with 182 selected reference transcriptomes of defined cell types and their activation by exogenous (microbial) and endogenous inflammatory stimuli. Screening for dominant changes in RA-ST demonstrated activation of monocytes/macrophages with gene-patterns induced by bacterial and fungal triggers. Gene-patterns of activated B- or T-cells in RA-ST reflected a response to activated monocytes/macrophages rather than inducing their activation. In contrast, OA-ST was dominated by gene-patterns of non-activated macrophages and fibroblasts. The difference between RA and OA was more prominent in transcripts of secreted proteins and was confirmed by protein quantification in synovial fluid (SF) and serum. In total, 24 proteins of activated cells were confirmed in RA-SF compared to OA-SF and some like CXCL13, CCL18, S100A8/A9, sCD14, LBP reflected this increase even in RA serum. Consequently, pathogen-like response patterns in RA suggest that direct microbial influences exist. This challenges the current concept of autoimmunity and immunosuppressive treatment and advocates new diagnostic and therapeutic strategies that consider microbial persistence as important trigger(s) in the etiopathogenesis of RA.

Discrepancy between Jun/Fos Proto-Oncogene mRNA and Protein Expression in the Rheumatoid Arthritis Synovial Membrane

Rheumatoid arthritis (RA) is a chronic inflammatory and destructive joint disease characterized by overexpression of pro-inflammatory/pro-destructive mediators, whose regulation has been the focus of our previous studies. Since the expression of these proteins commonly depends on AP-1, the expression of the AP-1-forming subunits cJun, JunB, JunD, and cFos was assessed in synovial membrane (SM) samples of RA, osteoarthritis (OA), joint trauma (JT), and normal controls (NC) using ELISA and qRT-PCR. With respect to an observed discrepancy between mRNA and protein levels, the expression of the mRNA stability-modifying factors AU-rich element RNA-binding protein (AUF)-1, tristetraprolin (TTP), and human antigen R (HuR) was measured. JunB and JunD protein expression was significantly higher in RA-SM compared to OA and/or NC. By contrast, jun/fos mRNA expression was significantly (cjun) or numerically decreased (junB, junD, cfos) in RA and OA compared to JT and/or NC. Remarkably, TTP and HuR were also affected by discrepancies between their mRNA and protein levels, since they were significantly decreased at the mRNA level in RA versus NC, but significantly or numerically increased at the protein level when compared to JT and NC. Discrepancies between the mRNA and protein expression for Jun/Fos and TTP/HuR suggest broad alterations of post-transcriptional processes in the RA-SM. In this context, increased levels of mRNA-destabilizing TTP may contribute to the low levels of jun/fos and ttp/hur mRNA, whereas abundant mRNA-stabilizing HuR may augment translation of the remaining mRNA into protein with potential consequences for the composition of the resulting AP-1 complexes and the expression of AP-1-dependent genes in RA.

Cellular and Molecular Effects of High-Molecular-Weight Heparin on Matrix Metalloproteinase 9 Expression

Blood sampling with different anticoagulants alters matrix metalloproteinase (MMP-) 9 expression, thus influencing its concentration and diagnostic validity. Here, we aimed to evaluate the effects of different anticoagulants on MMP-9 regulation. MMP-9 expression was assessed in response to ethylenediaminetetraacetic acid, citrate, and high-/low-molecular-weight heparin (HMWH, LMWH) in co-culture experiments using THP-1, Jurkat, and HT cells (representing monocytes, T, and B cells). Triple and double cell line co-culture experiments revealed that HMWH treatment of THP-1 and Jurkat led to a significant MMP-9 induction, whereas other anticoagulants and cell type combinations had no effect. Supernatant of HMWH-treated Jurkat cells also induced MMP-9 in THP-1 suggesting monocytes as MMP-9 producers. HMWH-induced cytokine/chemokine secretion was assessed in co-culture supernatant, and the influence of cytokines/chemokines on MMP-9 production was analyzed. These experiments revealed that Jurkat-derived IL-16 and soluble intercellular adhesion molecule (sICAM-) 1 are able to induce MMP-9 and IL-8 production by THP-1. As a consequence, the increased MMP-9 expression found in HMWH blood samples may be influenced by HMWH-dependent secretion of IL-16 and sICAM-1 by T cells resulting in an increased production of MMP-9 and IL-8 by monocytes. IL-8, in turn, may support MMP-9 and its own expression in a positive autocrine feedback loop.

A Boolean gene regulatory model of heterosis and speciation

BACKGROUND

Modelling genetic phenomena affecting biological traits is important for the development of agriculture as it allows breeders to predict the potential of breeding for certain traits. One such phenomenon is heterosis or hybrid vigor: crossing individuals from genetically distinct populations often results in improvements in quantitative traits, such as growth rate, biomass production and stress resistance. Heterosis has become a very useful tool in global agriculture, but its genetic basis remains controversial and its effects hard to predict. We have taken a computational approach to studying heterosis, developing a simulation of evolution, independent reassortment of alleles and hybridization of Gene Regulatory Networks (GRNs) in a Boolean framework. These artificial regulatory networks exhibit topological properties that reflect those observed in biology, and fitness is measured as the ability of a network to respond to external inputs in a pre-defined way.

RESULTS

Our model reproduced common experimental observations on heterosis using only biologically justified parameters, such as mutation rates. Hybrid vigor was observed and its extent was seen to increase as parental populations diverged, up until a point of sudden collapse of hybrid fitness. Thus, the model also describes a process akin to speciation due to genetic incompatibility of the separated populations. We also reproduce, for the first time in a model, the fact that hybrid vigor cannot easily be fixed by within a breeding line, currently an important limitation of the use of hybrid crops. The simulation allowed us to study the effects of three standard models for the genetic basis of heterosis: dominance, over-dominance, and epistasis.

CONCLUSION

This study describes the most detailed simulation of heterosis using gene regulatory networks to date and reproduces several phenomena associated with heterosis for the first time in a model. The level of detail in our model allows us to suggest possible warning signs of the impending collapse of hybrid vigor in breeding. In addition, the simulation provides a framework that can be extended to study other aspects of heterosis and alternative evolutionary scenarios.

Anti-inflammatory effects of reactive oxygen species - a multi-valued logical model validated by formal concept analysis

Background

Recent findings suggest that in pancreatic acinar cells stimulated with bile acid, a pro-apoptotic effect of reactive oxygen species (ROS) dominates their effect on necrosis and spreading of inflammation. The first effect presumably occurs via cytochrome C release from the inner mitochondrial membrane. A pro-necrotic effect – similar to the one of Ca²⁺ – can be strong opening of mitochondrial pores leading to breakdown of the membrane potential, ATP depletion, sustained Ca²⁺ increase and premature activation of digestive enzymes. To explain published data and to understand ROS effects during the onset of acute pancreatitis, a model using multi-valued logic is constructed. Formal concept analysis (FCA) is used to validate the model against data as well as to analyze and visualize rules that capture the dynamics.

Results

Simulations for two different levels of bile stimulation and for inhibition or addition of antioxidants reproduce the qualitative behaviour shown in the experiments. Based on reported differences of ROS production and of ROS induced pore opening, the model predicts a more uniform apoptosis/necrosis ratio for higher and lower bile stimulation in liver cells than in pancreatic acinar cells. FCA confirms that essential dynamical features of the data are captured by the model. For instance, high necrosis always occurs together with at least a medium level of apoptosis. At the same time, FCA helps to reveal subtle differences between data and simulations. The FCA visualization underlines the protective role of ROS against necrosis.

Conclusions

The analysis of the model demonstrates how ROS and decreased antioxidant levels contribute to apoptosis. Studying the induction of necrosis via a sustained Ca²⁺ increase, we implemented the commonly accepted hypothesis of ATP depletion after strong bile stimulation. Using an alternative model, we demonstrate that this process is not necessary to generate the dynamics of the measured variables. Opening of plasma membrane channels could also lead to a prolonged increase of Ca²⁺ and to necrosis. Finally, the analysis of the model suggests a direct experimental testing for the model-based hypothesis of a self-enhancing cycle of cytochrome C release and ROS production by interruption of the mitochondrial electron transport chain.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-014-0101-7) contains supplementary material, which is available to authorized users.

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.

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation

INTRODUCTION

Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory or degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers.

METHODS

Three multi-center genome-wide transcriptomic data sets (Affymetrix HG-U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule-based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl's statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio.

RESULTS

The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for 'RA'), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways.

CONCLUSION

First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10.

Formal concept analysis of disease similarity

Previous work shows that gene associations and network properties common between pairs of diseases can provide molecular evidence of comorbidity, but relationships among diseases may extend to larger groups. Formal concept analysis allows the study of multiple diseases based on a concept lattice whose structure indicates gene set commonality. We use the concept lattice for gene associations to evaluate the complexity of the relationships among diseases, and to identify concepts whose gene sets are candidates for further functional analysis. For this, we define a heuristic on the lattice structure that allows the identification of concepts whose gene sets indicate strong relationships among the included diseases, which are distinguished from other diseases in the family. Applying this approach to a family of renal diseases we demonstrate that this approach finds gene sets that may be promising for studying common (and differing) mechanism among a family of comorbid or phenotypically related diseases.