Multi-omic landscape of rheumatoid arthritis: re-evaluation of drug adverse effects

Overview of methods for characterization and visualization of a protein–protein interaction network in a multi-omics integration context

At the heart of the cellular machinery through the regulation of cellular functions, protein–protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.

The greater inflammatory pathway-high clinical potential by innovative predictive, preventive, and personalized medical approach

BACKGROUND AND LIMITATIONS

Impaired wound healing (WH) and chronic inflammation are hallmarks of non-communicable diseases (NCDs). However, despite WH being a recognized player in NCDs, mainstream therapies focus on (un)targeted damping of the inflammatory response, leaving WH largely unaddressed, owing to three main factors. The first is the complexity of the pathway that links inflammation and wound healing; the second is the dual nature, local and systemic, of WH; and the third is the limited acknowledgement of genetic and contingent causes that disrupt physiologic progression of WH.

PROPOSED APPROACH

Here, in the frame of Predictive, Preventive, and Personalized Medicine (PPPM), we integrate and revisit current literature to offer a novel systemic view on the cues that can impact on the fate (acute or chronic inflammation) of WH, beyond the compartmentalization of medical disciplines and with the support of advanced computational biology.

CONCLUSIONS

This shall open to a broader understanding of the causes for WH going awry, offering new operational criteria for patients' stratification (prediction and personalization). While this may also offer improved options for targeted prevention, we will envisage new therapeutic strategies to reboot and/or boost WH, to enable its progression across its physiological phases, the first of which is a transient acute inflammatory response versus the chronic low-grade inflammation characteristic of NCDs.

Host-Microbiome Synergistic Control on Sphingolipid Metabolism by Mechanotransduction in Model Arthritis

Chronic inflammatory autoimmune disorders are systemic diseases with increasing incidence and still lack a cure. More recently, attention has been placed in understanding gastrointestinal (GI) dysbiosis and, although important progress has been made in this area, it is currently unclear to what extent microbiome manipulation can be used in the treatment of autoimmune disorders. Via the use of appropriate models, rheumatoid arthritis (RA), a well-known exemplar of such pathologies, can be exploited to shed light on the currently overlooked effects of existing therapies on the GI microbiome. In this direction, we here explore the crosstalk between the GI microbiome and the host immunity in model arthritis (collagen induced arthritis, CIA). By exploiting omics from samples of limited invasiveness (blood and stools), we assess the host-microbiome responses to standard therapy (methotrexate, MTX) combined with mechanical subcutaneous stimulation (MS) and to mechanical stimulation alone. When MS is involved, results reveal the sphingolipid metabolism as the trait d’union among known hallmarks of (model) RA, namely: Imbalance in the S1P-S1PR1 axis, expansion of Prevotella sp., and invariant Natural Killer T (iNKT)-penia, thus offering the base of a rationale to mechanically modulate this pathway as a therapeutic target in RA.

Talk to your gut: the oral-gut microbiome axis and its immunomodulatory role in the etiology of rheumatoid arthritis

Microbial communities inhabiting the human body, collectively called the microbiome, are critical modulators of immunity. This notion is underpinned by associations between changes in the microbiome and particular autoimmune disorders. Specifically, in rheumatoid arthritis, one of the most frequently occurring autoimmune disorders worldwide, changes in the oral and gut microbiomes have been implicated in the loss of tolerance against self-antigens and in increased inflammatory events promoting the damage of joints. In the present review, we highlight recently gained insights in the roles of microbes in the etiology of rheumatoid arthritis. In addition, we address important immunomodulatory processes, including biofilm formation and neutrophil function, which have been implicated in host-microbe interactions relevant for rheumatoid arthritis. Lastly, we present recent advances in the development and evaluation of emerging microbiome-based therapeutic approaches. Altogether, we conclude that the key to uncovering the etiopathogenesis of rheumatoid arthritis will lie in the immunomodulatory functions of the oral and gut microbiomes.

A cytokine protein-protein interaction network for identifying key molecules in rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic inflammatory disease of the synovial joints. Though the current RA therapeutics such as disease-modifying antirheumatic drugs (DMARDs), nonsteroidal anti-inflammatory drugs (NSAIDs) and biologics can halt the progression of the disease, none of these would either dramatically reduce or cure RA. So, the identification of potential therapeutic targets and new therapies for RA are active areas of research. Several studies have discovered the involvement of cytokines in the pathogenesis of this disease. These cytokines induce signal transduction pathways in RA synovial fibroblasts (RASF). These pathways share many signal transducers and their interacting proteins, resulting in the formation of a signaling network. In order to understand the involvement of this network in RA pathogenesis, it is essential to identify the key transducers and their interacting proteins that are part of this network. In this study, based on a detailed literature survey, we have identified a list of 12 cytokines that induce signal transduction pathways in RASF. For these cytokines, we have built a signaling network using the protein-protein interaction (PPI) data that was obtained from public repositories such as HPRD, BioGRID, MINT, IntAct and STRING. By combining the network centrality measures with the gene expression data from the RA related microarrays that are available in the open source Gene Expression Omnibus (GEO) database, we have identified 24 key proteins of this signaling network. Two of these 24 are already drug targets for RA, and of the remaining, 12 have direct PPI links to some of the current drug targets of RA. Therefore, these key proteins seem to be crucial in the pathogenesis of RA and hence might be treated as potential drug targets.

A method for automated pathogenic content estimation with application to rheumatoid arthritis

Background

Sequencing technologies applied to mammals’ microbiomes have revolutionized our understanding of health and disease. Hence, to assess diseases’ progression as well as therapies longterm effects, the impact of maladies and drugs on the gut-intestinal (GI) microbiome has to be evaluated. Typical metagenomic analyses are run to associate to a condition (disease, therapy, diet) a pool of bacteria, whose eubiotic/dysbiotic potential is assessed either by α-diversity, a measure of the varieties populating the microbiome, or by Firmicutes to Bacteroides ratio, associated to systemic inflammation, and finally by manual and direct inspection of bacteria’s biological functions, when known. These approaches lead to results sometimes difficult to interpret in terms of the evolution towards a specific microbial composition, harmed by large areas of unknown.

Results

We propose to additionally evaluate a microbiome based on its global composition, by automatic annotation of pathogenic genera and statistical assessment of the net varied frequency of harmless versus harmful organisms. This application is intuitive, quantitative and computationally efficient and designed to cope with the currently incomplete species’ functional knowledge. Our results, applied to human GI-microbiome data exemplify how this layer of information provides additional insights into treatments’ impact on the GI microbiome, allowing to characterize a more physiologic effects of Prednisone versus Methotrexate, two treatments for rheumatoid arthritis (RA) a complex autoimmune systemic disease.

Conclusions

Our quantitative analysis integrates with previous approaches offering an additional systemic level of interpretation here applied, for its potential to translate into clinically relevant information, to the therapies for RA.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0344-6) contains supplementary material, which is available to authorized users.

Anti-arthritic Effects of Total Flavonoids from Juniperus sabina on Complete Freund's Adjuvant Induced Arthritis in Rats

Context:

Twigs and leaves of Juniperus sabina L. have been traditionally used as the medicinal herb in China for the treatment of many ailments including rheumatoid arthritis (RA).

Aims:

To confirm the therapeutic effect of total flavonoids from J. sabina (JSTF) on RA-induced by Complete Freund's Adjuvant (CFA) in rats.

Settings and Design:

Wistar rats (200 ± 20 g) were immunized by intradermal injection of 0.1 mL of CFA into the right hind metatarsal footpad. JSTF was administered orally at the dose of 125,250 and 500 mg/kg on 14 days after the induction of adjuvant arthritis. Tripterygium glycoside (20 mg/kg) was used as a positive control. Paw swelling, arthritic score, body weight loss, serum cytokines, inflammatory mediators, and histological change were measured.

Results:

We found that JSTF could ameliorate paw swelling of CFA rats, and significantly inhibit arthritic score (P < 0.05). The overproduction of tumor necrosis factor alpha and interleukin 1beta were remarkably suppressed in the serum of JSTF (125,500 mg/kg) treated rats (P < 0.05). Histopathological studies also showed a marked decrease of synovial inflammatory infiltration and synovial lining hyperplasia in the joints of JSTF-treated animals. Six flavonoids were isolated and from JSTF by various chromatographic methods and identified as follows: Catechin, quercitrin, isoquercitrin, isoscutellarein 7-O-β-D-xylopyranoside, isoscutellarein 7-O-β-D-xylopyranose-(1 → 3)-α-L-rhamnoside, and rutin.

Conclusions:

These results suggest the potential therapeutically effect of JSTF as an anti-arthritis agent toward CFA-induced arthritis in rats, and verified therapeutic applications of J. sabina on RA in folk medicine.

SUMMARY

Twigs and leaves of Juniperus sabina L. have been traditionally used as the medicinal herb in China for the treatment of rheumatoid arthritis

JSTF could ameliorate paw swelling of CFA rats, and significantly inhibit arthritic score

Histopathological studies showed a marked decrease of synovial inflammatory infiltration and synovial lining hyperplasia in the joints of JSTF-treated animals

Six flavonoids were isolated and from JSTF including: Catechin, quercitrin, isoquercitrin, isoscutellarein 7-O-β-D-xylopyranoside, isoscutellarein 7-O-β-D-xylopyranose-(1 → 3)-α-L- rhamnoside, and rutin.

Abbreviations used: JSTF: Total flavonoids from Juniperus sabina; CFA: Complete Freund's Adjuvant; TG: Tripterygium glycoside; TNF-α: Tumor necrosis factor alpha; IL-1β: Interleukin 1beta; IL-6: Interleukin 6; H and E: Hematoxylin and eosin.

Regulation of Transforming Growth Factor β-Activated Kinase Activation by Epigallocatechin-3-Gallate in Rheumatoid Arthritis Synovial Fibroblasts: Suppression of K(63) -Linked Autoubiquitination of Tumor Necrosis Factor Receptor-Associated Factor 6

OBJECTIVE

Transforming growth factor β-activated kinase 1 (TAK1) is a key MAPKKK family protein in interleukin-1β (IL-1β), tumor necrosis factor (TNF), and Toll-like receptor signaling. This study was undertaken to examine the posttranslational modification of TAK1 and its therapeutic regulation in rheumatoid arthritis (RA).

METHODS

The effect of TAK1, IL-1 receptor-associated kinase 1 (IRAK-1), and TNF receptor-associated factor 6 (TRAF6) inhibition was evaluated in IL-1β-stimulated human RA synovial fibroblasts (RASFs). Western blotting, immunoprecipitation, and 20S proteasome assay were used to study the ubiquitination process in RASFs. The efficacy of epigallocatechin-3-gallate (EGCG), a potent antiinflammatory molecule, in regulating these processes in RASFs was evaluated. Molecular docking was performed to examine the interaction of EGCG with human TAK1, IRAK-1, and TRAF6. These findings were confirmed using a rat model of adjuvant-induced arthritis (AIA).

RESULTS

Inhibition of TAK1, but not IRAK-1 or TRAF6, completely abrogated IL-1β-induced IL-6 and IL-8 synthesis in RASFs. EGCG inhibited TAK1 phosphorylation at Thr(184/187) and occupied the C(174) position, an ATP-binding site, to inhibit its kinase activity. EGCG pretreatment also inhibited K(63) -linked autoubiquitination of TRAF6, a posttranslational modification essential for TAK1 autophosphorylation, by forming a stable H bond at the K(124) position on TRAF6. Furthermore, EGCG enhanced proteasome-associated deubiquitinase expression to rescue proteins from proteasomal degradation. Western blot analyses of joint homogenates from rats with AIA showed a significant increase in K(48) -linked polyubiquitination, TAK1 phosphorylation, and TRAF6 expression when compared to naive rats. Administration of EGCG (50 mg/kg/day) for 10 days ameliorated AIA in rats by reducing TAK1 phosphorylation and K(48) -linked polyubiquitination.

CONCLUSION

Our findings provide a rationale for targeting TAK1 for the treatment of RA with EGCG.

An analysis of human microbe-disease associations

The microbiota living in the human body has critical impacts on our health and disease, but a systems understanding of its relationships with disease remains limited. Here, we use a large-scale text mining-based manually curated microbe-disease association data set to construct a microbe-based human disease network and investigate the relationships between microbes and disease genes, symptoms, chemical fragments and drugs. We reveal that microbe-based disease loops are significantly coherent. Microbe-based disease connections have strong overlaps with those constructed by disease genes, symptoms, chemical fragments and drugs. Moreover, we confirm that the microbe-based disease analysis is able to predict novel connections and mechanisms for disease, microbes, genes and drugs. The presented network, methods and findings can be a resource helpful for addressing some issues in medicine, for example, the discovery of bench knowledge and bedside clinical solutions for disease mechanism understanding, diagnosis and therapy.

Implementation of a Pan-Genomic Approach to Investigate Holobiont-Infecting Microbe Interaction: A Case Report of a Leukemic Patient with Invasive Mucormycosis

Disease can be conceptualized as the result of interactions between infecting microbe and holobiont, the combination of a host and its microbial communities. It is likely that genomic variation in the host, infecting microbe, and commensal microbiota are key determinants of infectious disease clinical outcomes. However, until recently, simultaneous, multiomic investigation of infecting microbe and holobiont components has rarely been explored. Herein, we characterized the infecting microbe, host, micro- and mycobiomes leading up to infection onset in a leukemia patient that developed invasive mucormycosis. We discovered that the patient was infected with a strain of the recently described Mucor velutinosus species which we determined was hypervirulent in a Drosophila challenge model and has a predisposition for skin dissemination. After completing the infecting M. velutinosus genome and genomes from four other Mucor species, comparative pathogenomics was performed and assisted in identifying 66 M. velutinosus-specific putatively secreted proteins, including multiple novel secreted aspartyl proteinases which may contribute to the unique clinical presentation of skin dissemination. Whole exome sequencing of the patient revealed multiple non-synonymous polymorphisms in genes critical to control of fungal proliferation, such as TLR6 and PTX3. Moreover, the patient had a non-synonymous polymorphism in the NOD2 gene and a missense mutation in FUT2, which have been linked to microbial dysbiosis and microbiome diversity maintenance during physiologic stress, respectively. In concert with host genetic polymorphism data, the micro- and mycobiome analyses revealed that the infection developed amid a dysbiotic microbiome with low α-diversity, dominated by staphylococci. Additionally, longitudinal mycobiome data showed that M. velutinosus DNA was detectable in oral samples preceding disease onset. Our genome-level study of the host-infecting microbe-commensal triad extends the concept of personalized genomic medicine to the holobiont-infecting microbe interface thereby offering novel opportunities for using synergistic genetic methods to increase understanding of infectious diseases pathogenesis and clinical outcomes.

Does the buck stop with the bugs?: an overview of microbial dysbiosis in rheumatoid arthritis

The human body is an environmental niche which is home to diverse co-habiting microbes collectively referred as the human microbiome. Recent years have seen the in-depth characterization of the human microbiome and associations with diseases. Linking of the composition or number of the human microbiota with diseases and traits date back to the original work of Elie Metchnikoff. Recent advances in genomic technologies have opened up finer details and dynamics of this new science with higher precision. Microbe-rheumatoid arthritis connection, largely related to the gut and oral microbiomes, has showed up as a result - apart from several other earlier, well-studied candidate autoimmune diseases. Although evidence favouring roles of specific microbial species, including Porphyromonas, Prevotella and Leptotricha, has become clearer, mechanistic insights still continue to be enigmatic. Manipulating the microbes by traditional dietary modifications, probiotics, and antibiotics and by currently employed disease-modifying agents seems to modulate the disease process and its progression. In the present review, we appraise the existing information as well as the gaps in knowledge in this challenging field. We also discuss the future directions for potential clinical applications, including prevention and management of rheumatoid arthritis using microbial modifications.

Systematic synergy modeling: understanding drug synergy from a systems biology perspective

Owing to drug synergy effects, drug combinations have become a new trend in combating complex diseases like cancer, HIV and cardiovascular diseases. However, conventional synergy quantification methods often depend on experimental dose–response data which are quite resource-demanding. In addition, these methods are unable to interpret the explicit synergy mechanism. In this review, we give representative examples of how systems biology modeling offers strategies toward better understanding of drug synergy, including the protein-protein interaction (PPI) network-based methods, pathway dynamic simulations, synergy network motif recognitions, integrative drug feature calculations, and “omic”-supported analyses. Although partially successful in drug synergy exploration and interpretation, more efforts should be put on a holistic understanding of drug-disease interactions, considering integrative pharmacology and toxicology factors. With a comprehensive and deep insight into the mechanism of drug synergy, systems biology opens a novel avenue for rational design of effective drug combinations.

Integrative omics analysis of rheumatoid arthritis identifies non-obvious therapeutic targets

Identifying novel therapeutic targets for the treatment of disease is challenging. To this end, we developed a genome-wide approach of candidate gene prioritization. We independently collocated sets of genes that were implicated in rheumatoid arthritis (RA) pathogenicity through three genome-wide assays: (i) genome-wide association studies (GWAS), (ii) differentially expression in RA fibroblast-like synoviocytes (FLS), and (iii) differentially methylation in RA FLS. Integrated analysis of these complementary data sets identified a significant enrichment of multi-evidence genes (MEGs) within pathways relating to RA pathogenicity. One MEG is Engulfment and Cell Motility Protein-1 (ELMO1), a gene not previously considered as a therapeutic target in RA FLS. We demonstrated in RA FLS that ELMO1 is: (i) expressed, (ii) promotes cell migration and invasion, and (iii) regulates Rac1 activity. Thus, we created links between ELMO1 and RA pathogenicity, which in turn validates ELMO1 as a potential RA therapeutic target. This study illustrated the power of MEG-based approaches for therapeutic target identification.