Characterization of rheumatoid arthritis subtypes using symptom profiles, clinical chemistry and metabolomics measurements

Distinct oral DNA viral signatures in rheumatoid arthritis: a Pilot study

Background

Despite evidence linking viruses and oral microbiome to rheumatoid arthritis (RA), limited whole genome sequencing research has been conducted on the oral virome (a viral component of the microbiome) of untreated RA patients. This pilot research seeks to address this knowledge gap by comparing the oral virome of untreated rheumatoid arthritis patients (RAs) and healthy individuals (HCs).

Method

Whole genome DNA sequence of saliva samples from 45 participants including 21 RAs and 24 age and gender matched HCs was obtained from the BioProject: PRJEB6997. Metaphlan3 pipeline and LEfSe analysis were used for the viral signature detection. Wilcoxon pairwise test and ROC analysis were used to validate and predict signatures.

Results

RA exhibits higher alpha diversity compared to HCs. Callitrichine gammaherpesvirus 3, Human gammaherpesvirus 4 (EBV), Murid betaherpesvirus 8, and Suid alphaherpesvirus 1 were enriched in RAs, while Aotine betaherpesvirus 1 from the Cytomegalovirus genus was enriched in HCs. In addition, Saccharomyces cerevisiae killer virus M1 (ScV-M1) was found to be enriched in RAs, whereas bacteriophage Hk97virus (Siphoviridae) and Cd119virus (Myoviridae) were enriched in HCs.

Conclusion

This study identifies significant DNA oral viral signatures at species level as potential biomarkers for the early detection and diagnosis of rheumatoid arthritis.

Comparative Metabolomic Profiles of Vascular Involvement in Behçet's Disease

BACKGROUND

Behçet's disease is a systemic, inflammatory disease affecting multiple organs. Vascular involvement is the main cause of morbidity and mortality in Behçet's disease patients. Though clinically well-defined, there is limited information related to disease pathogenesis and vascular incidence in this patient group. The aim of this study is to investigate the unique metabolic signatures of Behçet's disease patients with vascular involvement.

METHODS

Metabolomic profiling was performed on serum samples of 48 Behçet's disease patients (18 with vascular involvement) and 40 healthy controls using gas chromatography-mass spectrometrybased untargeted metabolomics analysis. Multivariate and univariate statistical analyses were performed to find altered metabolites and pathways.

RESULTS

Untargeted metabolomics results showed that a total of 168 metabolites were identified. The comparison between the groups of Behçet's disease, vascular involvement in Behçet's disease, and the healthy control group showed that altered amino acid and oxidative stress pathways, especially with glutathione synthesis, could be an important stage for developing Behçet's disease.

CONCLUSION

In the present work, the untargeted metabolomics approach provided new molecular insights for a better understanding of Behçet's disease pathogenesis and also developing vascular involvement in Behçet's disease at the metabolite level. The results showed that vascular involvement in Behçet's disease could be highly linked with amino acid metabolism and also the antioxidant system, and these disease-related pathways could be evaluated with further experiments for diagnosis and prognosis of Behçet's disease and also for vascular involvement in Behçet's disease.

Mass spectrometry as a lens into molecular human nutrition and health

Mass spectrometry (MS) has developed over the last decades into the most informative and versatile analytical technology in molecular and structural biology (). The platform enables discovery, identification, and characterisation of non-volatile biomolecules, such as proteins, peptides, DNA, RNA, nutrients, metabolites, and lipids at both speed and scale and can elucidate their interactions and effects. The versatility, robustness, and throughput have rendered MS a major research and development platform in molecular human health and biomedical science. More recently, MS has also been established as the central tool for 'Molecular Nutrition', enabling comprehensive and rapid identification and characterisation of macro- and micronutrients, bioactives, and other food compounds. 'Molecular Nutrition' thereby helps understand bioaccessibility, bioavailability, and bioefficacy of macro- and micronutrients and related health effects. Hence, MS provides a lens through which the fate of nutrients can be monitored along digestion via absorption to metabolism. This in turn provides the bioanalytical foundation for 'Personalised Nutrition' or 'Precision Nutrition' in which design and development of diets and nutritional products is tailored towards consumer and patient groups sharing similar genetic and environmental predisposition, health/disease conditions and lifestyles, and/or objectives of performance and wellbeing. The next level of integrated nutrition science is now being built as 'Systems Nutrition' where public and personal health data are correlated with life condition and lifestyle factors, to establish directional relationships between nutrition, lifestyle, environment, and health, eventually translating into science-based public and personal heath recommendations and actions. This account provides a condensed summary of the contributions of MS to a precise, quantitative, and comprehensive nutrition and health science and sketches an outlook on its future role in this fascinating and relevant field.

The Holistic Philosophy of Traditional Chinese Medicine and Conflicts With Modern Medicine

Traditional Chinese medicine (TCM) has sparked the public's attention for its potential in new drug development and its holistic view toward health, which is totally different from the reductionistic science of modern medicine. Although many scholars try to connect TCM with precision medicine or apply new methods and technology to integrate TCM with modern medicine, the misunderstandings and gap between TCM and modern medicine limit the development of evidence-based TCM. Traditional Chinese medicine is actually a medical science encompassing not only medicine but also philosophy and art in direct contrast to molecular-based modern medicine. As more and more multidisciplinary studies are being published, finding ways to integrate TCM with modern or precision medicine through artificial intelligence, new study design and technology may become a critical issue. This article aims to briefly review the unique philosophy of TCM and its conflicts with modern medicine, with a focus on the potential integration of TCM and modern medicine. We also provide insight for the key attributes of TCM and the associated investigation with Western research approaches.

Rheumatoid arthritis characteristics and classification of heat and cold patterns-an observational study

Introduction

Traditional Chinese medicine (TCM) has been proven to be an effective complementary therapy in treating rheumatoid arthritis (RA). The cold pattern and the heat pattern were the two main TCM patterns for RA, which is crucial for TCM treatment. The cold pattern is characterized by fear of cold and wind, joint pain with a thin white tongue coating which can be relieved by hot herbs. In contrast, heat pattern patients suffer from severe joint pain with a yellow coating, with red swelling of the skin and high skin temperature which can be relieved by cooling herbs.

Objective

We aimed to classify the heat and cold patterns in RA patients with cluster analysis and factor analysis. Moreover, we aimed to explore the association of RA characteristics between these two patterns.

Methods

and Design: A cross-sectional observational research method was used, and data was collected on 300 RA patients in Hangzhou in China. Signs and symptoms associated with RA were clustered using SPSS 22.0 software. In addition, factor analysis was also used for the classification. After classification of heat and cold patterns, characteristics and treatment of the RA participants between the two patterns were explored.

Results

RA patients in the study were divided into two categories using cluster analysis. Twenty-two symptoms in the first category were included in the heat pattern of RA patients. After factor analysis, nine principal components were extracted to heat pattern. The component with the highest eigenvalue (2.530) were mainly contributed by shortness of breath, palpitation, heavy limbs, chest tightness and yellow greasy tongue with high factor loading values (0.765, 0.703, 0.504, 0.429 and 0.402, respectively). Ten symptoms in the second category were included in the cold pattern of RA patients. Four principal components were extracted to cold pattern. The component with the highest eigenvalue (2.089) were mainly contributed by joint distension and pain, joint stiffness, fatigue and upset with high factor loading values (0.597, 0.590, 0.491 and 0.481, respectively). Although there were no statistical differences between the levels of rheumatoid factor and anti-cyclic peptide containing citrulline (anti-CCP), the levels of C-reactive protein, platelet count and the disease activity score using 28 joint counts were significantly higher in the heat pattern RA patients compared to the ones in cold pattern. Moreover, heat pattern RA patients were more likely to be prescribed two more disease-modifying anti-rheumatic drugs (DMARDS) combined with Methotrexate (MTX) (70.59% versus 49.72%; P = 0.000).

Conclusions

In conclusion, heat and cold patterns in RA patients could be classified well using cluster analysis and factor analysis. Most of RA patients with heat pattern were active and likely to be prescribed two more DMARDs combined with MTX.

Big Data in Gastroenterology Research

Studying individual data types in isolation provides only limited and incomplete answers to complex biological questions and particularly falls short in revealing sufficient mechanistic and kinetic details. In contrast, multi-omics approaches to studying health and disease permit the generation and integration of multiple data types on a much larger scale, offering a comprehensive picture of biological and disease processes. Gastroenterology and hepatobiliary research are particularly well-suited to such analyses, given the unique position of the luminal gastrointestinal (GI) tract at the nexus between the gut (mucosa and luminal contents), brain, immune and endocrine systems, and GI microbiome. The generation of 'big data' from multi-omic, multi-site studies can enhance investigations into the connections between these organ systems and organisms and more broadly and accurately appraise the effects of dietary, pharmacological, and other therapeutic interventions. In this review, we describe a variety of useful omics approaches and how they can be integrated to provide a holistic depiction of the human and microbial genetic and proteomic changes underlying physiological and pathophysiological phenomena. We highlight the potential pitfalls and alternatives to help avoid the common errors in study design, execution, and analysis. We focus on the application, integration, and analysis of big data in gastroenterology and hepatobiliary research.

Systems biology approach delineates critical pathways associated with disease progression in rheumatoid arthritis

Rheumatoid Arthritis (RA) is a chronic systemic autoimmune disease leading to inflammation, cartilage cell death, synoviocyte proliferation, and increased and impaired differentiation of osteoclasts and osteoblasts leading to joint erosions and deformities. Transcriptomics, proteomics, and metabolomics datasets were analyzed to identify the critical pathways that drive the RA pathophysiology. Single nucleotide polymorphisms (SNPs) associated with RA were analyzed for the functional implications, clinical outcomes, and blood parameters later validated by literature. SNPs associated with RA were grouped into pathways that drive the immune response and cytokine production. Further gene set enrichment analysis (GSEA) was performed on gene expression omnibus (GEO) data sets of peripheral blood mononuclear cells (PBMCs), synovial macrophages, and synovial biopsies from RA patients showed enrichment of Th1, Th2, Th17 differentiation, viral and bacterial infections, metabolic signalling and immunological pathways with potential implications for RA. The proteomics data analysis presented pathways with genes involved in immunological signaling and metabolic pathways, including vitamin B12 and folate metabolism. Metabolomics datasets analysis showed significant pathways like amino-acyl tRNA biosynthesis, metabolism of amino acids (arginine, alanine aspartate, glutamate, glutamine, phenylalanine, and tryptophan), and nucleotide metabolism. Furthermore, our commonality analysis of multi-omics datasets identified common pathways with potential implications for joint remodeling in RA. Disease-modifying anti-rheumatic drugs (DMARDs) and biologics treatments were found to modulate many of the pathways that were deregulated in RA. Overall, our analysis identified molecular signatures associated with the observed symptoms, joint erosions, potential biomarkers, and therapeutic targets in RA. Communicated by Ramaswamy H. Sarma.

ReDisX, a machine learning approach, rationalizes rheumatoid arthritis and coronary artery disease patients uniquely upon identifying subpopulation differentiation markers from their genomic data

Diseases originate at the molecular-genetic layer, manifest through altered biochemical homeostasis, and develop symptoms later. Hence, symptomatic diagnosis is inadequate to explain the underlying molecular-genetic abnormality and individual genomic disparities. The current trends include molecular-genetic information relying on algorithms to recognize the disease subtypes through gene expressions. Despite their disposition toward disease-specific heterogeneity and cross-disease homogeneity, a gap still exists in describing the extent of homogeneity within the heterogeneous subpopulation of different diseases. They are limited to obtaining the holistic sense of the whole genome-based diagnosis resulting in inaccurate diagnosis and subsequent management. Addressing those ambiguities, our proposed framework, ReDisX, introduces a unique classification system for the patients based on their genomic signatures. In this study, it is a scalable machine learning algorithm deployed to re-categorize the patients with rheumatoid arthritis and coronary artery disease. It reveals heterogeneous subpopulations within a disease and homogenous subpopulations across different diseases. Besides, it identifies granzyme B (GZMB) as a subpopulation-differentiation marker that plausibly serves as a prominent indicator for GZMB-targeted drug repurposing. The ReDisX framework offers a novel strategy to redefine disease diagnosis through characterizing personalized genomic signatures. It may rejuvenate the landscape of precision and personalized diagnosis and a clue to drug repurposing.

Integrative serum metabolomic analysis for preventive effects of Yaobitong capsule in adjuvant-induced rheumatoid arthritis rat based on RP/HILIC-UHPLC-Q-TOF MS

Yaobitong capsule (YBTC) has been used for the prevention and treatment of inflammation-related lumbago and leg pain. However, its intervention mechanism still remains unclear. This study was aimed to evaluate the control efficiency of YBTC on adjuvant-induced rheumatoid arthritis (RA) rats by metabonomic method and to explore its possible anti-arthritis mechanism. Taking into account the complexity of endogenous metabolites in serum samples, an integrated metabolomics method based on RP/HILIC-UHPLC-Q-TOF MS was developed, to overcome the limitations of a single chromatographic in this study. The results showed that 32 potential biomarkers of arthritis were identified, primarily related to amino acid metabolism, glucose metabolism, lipid metabolism and nucleotide metabolism. Further receiver operating characteristic analysis revealed that the area under the curve of two down-regulated metabolites (3-Hydroxy-hexadecanoic acid, 2-Oxoarginine) and one up-regulated metabolite (l-Glutamic acid) among 32 biomarkers were 0.906, 0.969 and 1.000, respectively, indicating that high predictive ability of this method for RA. In this study, an integrated serum metabolomics method based on high-resolution mass spectrometry was successfully established for the first time to study the intervention mechanism of YBTC in RA, providing evidence regarding the clinical application of YBTC and a new insight for the prevention of RA in the future.

Developing a Personalized Integrative Obesity-Coaching Program: A Systems Health Perspective

Current obesity management strategies are failing to achieve sustainable and favorable long-term results. We propose a more personalized, dynamic, and systemic perspective on the interactions of key determinants and coaching advice on obesity. The aim of this study was to use a systems view on overweight, complexity science, and a transdisciplinary process to develop a five-year personalized integrative obesity-coaching and research program. Managers, medical specialists, clinical psychologists, dieticians, physical- and psychomotor therapists, and lifestyle coaches aligned their perspectives and objectives with experts in systems thinking and systems biology. A systems health model of obesity was used to identify the causal relations of variables with the most influence on obesity. The model helped to align and design a personalized integrative obesity-coaching program and to identify the key variables to monitor the progress and to adjust the personalized program, depending on the goals and needs of the participant. It was decided to use subtyping of participants by a systems biologist, based on traditional Chinese medicine symptoms, as a novel method to personalize the intervention. The collaborative transdisciplinary approach based upon a systems view on obesity was successful in developing a personalized and adaptive five-year obesity-coaching and research program.

Metabolomics profiling predicts outcome of tocilizumab in rheumatoid arthritis: an exploratory study

INTRODUCTION

To study metabolic signatures can be used to identify predictive biomarkers for a patient's therapeutic response.

OBJECTIVES

We hypothesized that the characterization of a patients' metabolic profile, utilizing one-dimensional nuclear magnetic resonance (¹H-NMR), may predict a response to tocilizumab in patients with rheumatoid arthritis (RA).

METHODS

40 active RA patients meeting the 2010 ACR/EULAR classification criteria initiating treatment with tocilizumab were recruited. Clinical outcomes were determined at baseline, and after six and twelve months of treatment. EULAR response criteria at 6 and 12 months to categorize patients as responders and non-responders. Blood was collected at baseline and after six months of tocilizumab therapy. ¹H-NMR was used to acquire a spectra of plasma samples. Chenomx NMR suite 8.5 was used for metabolite identification and quantification. SPSS v.27 and MetaboAnalyst 4.0 were used for statistical and pathway analysis.

RESULTS

Isobutyrate, 3-hydroxybutyrate, lysine, phenylalanine, sn-glycero-3-phosphocholine, tryptophan and tyrosine were significantly elevated in responders at the baseline. OPLS-DA at baseline partially discriminated between RA responders and non-responders. A multivariate diagnostic model showed that concentrations of 3-hydroxybutyrate and phenylalanine improved the ability to specifically predict responders classifying 77.1% of the patients correctly. At 6 months, levels of methylamine, sn-glycero-3-phosphocholine and tryptophan tended to still be low in non-responders.

CONCLUSION

The relationship between plasma metabolic profiles and the clinical response to tocilizumab suggests that ¹H-NMR may be a promising tool for RA therapy optimization. More studies are needed to determine if metabolic profiling can predict the response to biological therapies in RA patients.

Fatty Acid Metabolism in Immune Cells: A Bioinformatics Analysis of Genes Involved in Ulcerative Colitis

Many immune cells participate in the pathogenesis of ulcerative colitis (UC), and fatty acid metabolism (FAM) is reported to supporting their cell-specific functions and proliferation, but the underlying mechanism is unclear. This study aimed to investigate the relationship between FAM and inflammation in colon tissues and identify potential therapeutic targets for regulating immune response. A total of 870 different expression genes (DEGs), 304 immunity-related DEGs, and 11 FAM-related DEGs were obtained, gene ontology analysis results showed that immune DEGs were significantly enriched in neutrophil migration, positive regulation of T cell activation. Fifteen types of immune cells were identified in inflamed colon tissues. Five FAM-related DEGs (ACOX1, ACSL4, ELOVL5, FADS2, and SCD) were highly correlated with immunity-related DEGs, and ACSL4, ELOVL5, and FADS2 were significantly upregulated in immune cells, while SCD is downregulated. Five FAM-related DEGs were highly correlated with immune cells. The study promotes the understanding of the pathogenesis of FAM in UC immune cells.

Metabolomics in the development and progression of rheumatoid arthritis: A systematic review

OBJECTIVE

A systematic review and analysis of data from several rheumatoid arthritis metabolomics studies attempts to determine which metabolites can be used as potential biomarkers for the diagnosis of rheumatoid arthritis and to explore the pathogenesis of rheumatoid arthritis.

METHODS

We searched all the subject-related documents published by EMBASE, PubMed, Web of Science, and Cochrane Library from the database to the September 2019 publication. Two researchers independently screened the literature and extracted the data. QUADOMICS tool was used to assess the quality of studies included in this systematic review.

RESULTS

A total of 10 studies met the inclusion criteria of systematic review, including 502 patients with rheumatoid arthritis and 373 healthy people. Among them, the biological samples utilised for metabolomic analysis include: serum (n=8), urine (n=1) and synovial fluid (n=1). Some metabolites play an important role in rheumatoid arthritis: glucose, lactic acid, citric acid, leucine, methionine, isoleucine, valine, phenylalanine, threonine, serine, proline, glutamate, histidine, alanine, cholesterol, glycerol, and ribose.

CONCLUSIONS

Metabolomics provides important new opportunities for further research in rheumatoid arthritis and is expected to elucidate the pathogenesis of rheumatoid arthritis that has not been fully understood before.

Fatty Acid and Carnitine Metabolism Are Dysregulated in Systemic Sclerosis Patients

Systemic sclerosis (SSc) is a rare chronic disease of unknown pathogenesis characterized by fibrosis of the skin and internal organs, vascular alteration, and dysregulation of the immune system. In order to better understand the immune system and its perturbations leading to diseases, the study of the mechanisms regulating cellular metabolism has gained a widespread interest. Here, we have assessed the metabolic status of plasma and dendritic cells (DCs) in patients with SSc. We identified a dysregulated metabolomic signature in carnitine in circulation (plasma) and intracellularly in DCs of SSc patients. In addition, we confirmed carnitine alteration in the circulation of SSc patients in three independent plasma measurements from two different cohorts and identified dysregulation of fatty acids. We hypothesized that fatty acid and carnitine alterations contribute to potentiation of inflammation in SSc. Incubation of healthy and SSc dendritic cells with etoposide, a carnitine transporter inhibitor, inhibited the production of pro-inflammatory cytokines such as IL-6 through inhibition of fatty acid oxidation. These findings shed light on the altered metabolic status of the immune system in SSc patients and opens up for potential novel avenues to reduce inflammation.

Metabolite Markers for Characterizing Sasang Constitution Type through GC-MS and 1H NMR-Based Metabolomics Study

Sasang constitutional medicine classifies human beings into four types based on their physical and psychological characteristics. Despite its potential value in achieving personalized medicine, the diagnosis of sasang constitution (SC) type is complex and subjective. In this study, gas chromatography–mass spectrometry and ¹H nuclear magnetic resonance–based metabolic analyses were conducted to find maker metabolites in serum and urine according to different SC types. Although some samples were overlapped on orthogonal projection to latent structure discriminant analysis score plots, serum samples showed separation between different SC types. Levels of lactate, glutamate, triglyceride, and fatty acids in serum and glycolic acid in urine of Tae-Eum type were higher than those of So-Eum and So-Yang type. Fatty acids, triglyceride, and lactate levels were found to be metabolites related to body mass index, indicating that marker metabolites for the diagnosis of SC type could be associated with obese. However, Tae-Eum type showed higher lactate levels in serum than So-Yang type for both normal weight and overweight groups, suggesting that the contents of serum lactate might be dependent on the SC type regardless of body weight. These results suggest that metabolomics analysis could be used to determine SC type.

Metabolite assignment of ultrafiltered synovial fluid extracted from knee joints of reactive arthritis patients using high resolution NMR spectroscopy

Currently, there are no reliable biomarkers available that can aid early differential diagnosis of reactive arthritis (ReA) from other inflammatory joint diseases. Metabolic profiling of synovial fluid (SF)-obtained from joints affected in ReA-holds great promise in this regard and will further aid monitoring treatment and improving our understanding about disease mechanism. As a first step in this direction, we report here the metabolite specific assignment of ¹ H and ¹³ C resonances detected in the NMR spectra of SF samples extracted from human patients with established ReA. The metabolite characterization has been carried out on both normal and ultrafiltered (deproteinized) SF samples of eight ReA patients (n = 8) using high-resolution (800 MHz) ¹ H and ¹ H─¹³ C NMR spectroscopy methods such as one-dimensional ¹ H CPMG and two-dimensional J-resolved¹ H NMR and homonuclear ¹ H─¹ H TOCSY and heteronuclear¹ H─¹³ C HSQC correlation spectra. Compared with normal SF samples, several distinctive ¹ H NMR signals were identified and assigned to metabolites in the ¹ H NMR spectra of ultrafiltered SF samples. Overall, we assigned 53 metabolites in normal filtered SF and 64 metabolites in filtered pooled SF sample compared with nonfiltered SF samples for which only 48 metabolites (including lipid/membrane metabolites as well) have been identified. The established NMR characterization of SF metabolites will serve to guide future metabolomics studies aiming to identify/evaluate the SF-based metabolic biomarkers of diagnostic/prognostic potential or seeking biochemical insights into disease mechanisms in a clinical perspective.

Metabolomic serum profiling after ACL injury in rats: A pilot study implicating inflammation and immune dysregulation in post-traumatic osteoarthritis

ACL rupture is a major risk factor for post-traumatic osteoarthritis (PTOA) development. Little information exists on acute systemic metabolic indicators of disease development. Thirty-six female Lewis rats were randomized to Control or noninvasive anterior cruciate ligament rupture (ACLR) and to three post-injury time points: 72 h, 4 weeks, 10 weeks (n = 6). Serum was collected and analyzed by ¹ H nuclear magnetic resonance (NMR) spectroscopy and combined direct injection and liquid chromatography (LC)-mass spectrometry (MS)/MS (DI-MS). Univariate and multivariate statistics were used to analyze metabolomic data, and predictive biomarker models were analyzed by receiver operating characteristic (ROC) analysis. Topological pathway analysis was used to identify perturbed pathways. Two hundred twenty-two metabolites were identified by ¹ H NMR and DI-MS. Differences in the serum metabolome between ACLR and Control were dominated by medium- and long-chain acylcarnitine species. Further, decreases in several tryptophan metabolites were either found to be significantly different in univariate analysis or to play important contributory roles to multivariate model separation. In addition to acylcarnitines and tryptophan metabolites, glycine, carnosine, and D-mannose were found to differentiate ACLR from Control. Glycine, 9-hexadecenoylcarnitine, trans-2-Dodecenoylcarnitine, linoelaidyl carnitine, hydroxypropionylcarnitine, and D-Mannose were identified as biomarkers with high area under ROC curve values and high predictive accuracies. Our analysis provides new information regarding the potential contribution of inflammatory processes and immune dysregulation to the onset and progression of PTOA following ACL injury. As these processes have most commonly been associated with inflammatory arthropathies, larger-scale studies elucidating their involvement in PTOA development and progression are necessary. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1969-1979, 2018.

Metabolomic Analysis of Biochemical Changes in the Serum and Urine of Freund's Adjuvant-Induced Arthritis in Rats after Treatment with Silkworm Excrement

Silkworm excrement (SE), is used as a traditional antirheumatic medicine in China. The present study was designed to investigate the therapeutic efficacy of water fraction of SE (ST) and ethanol fraction of SE (CT) at two different doses on adjuvant induced arthritis (AA) rats. Arthritis severity was evaluated by body weight, paw thickness, histological changes and index of paws oedema and spleen. Serum samples were collected for estimation of biochemical indicators and cytokines. In addition, a metabonomic method based on the ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) had been established to investigate the holistic efficacy of SE by serum and urine. Multivariate statistical approaches, such as partial least-squares discriminant analysis (PLS-DA) and orthogonal projection to latent structures squares-discriminant analysis (OPLS-DA) were built to evaluate the therapeutic effects of SE and find potential biomarkers and metabolic pathways. Administration with SE significantly ameliorated the AA severity, including body weight loss, paw swelling, histological changes and the levels of biochemical index. 33 endogenous metabolites had been identified (10 in serum and 23 in urine) in the AA rats. Urinary and serum metabolic profiling revealed that the metabolites underpin the metabolic pathway including nicotinate and nicotinamide metabolism; pentose and glucuronate interconversions; TCA cycle; beta-Alanine metabolism; purine metabolism and glycolysis or gluconeogenesis. The altered metabolites could be regulated closer to normal level after SE intervention. The results suggested SE possesses substantial anti-arthritic activity and demonstrated that metabonomics is a powerful tool to gain insight in the mechanism of SE formula in therapy.

Metabolomic profile of systemic sclerosis patients

Systemic sclerosis (SSc) is an autoimmune disease of unknown aetiology characterized by vascular lesions, immunological alterations and diffuse fibrosis of the skin and internal organs. Since recent evidence suggests that there is a link between metabolomics and immune mediated disease, serum metabolic profile of SSc patients and healthy controls was investigated by ¹H-NMR and GC-MS techniques. The results indicated a lower level of aspartate, alanine, choline, glutamate, and glutarate in SSc patients compared with healthy controls. Moreover, comparing patients affected by limited SSc (lcSSc) and diffuse SSc (dcSSc), 6 discriminant metabolites were identified. The multivariate analysis performed using all the metabolites significantly different revealed glycolysis, gluconeogenesis, energetic pathways, glutamate metabolism, degradation of ketone bodies and pyruvate metabolism as the most important networks. Aspartate, alanine and citrate yielded a high area under receiver-operating characteristic (ROC) curves (AUC of 0.81; CI 0.726–0.93) for discriminating SSc patients from controls, whereas ROC curve generated with acetate, fructose, glutamate, glutamine, glycerol and glutarate (AUC of 0.84; CI 0.7–0.98) discriminated between lcSSc and dcSSc. These results indicated that serum NMR-based metabolomics profiling method is sensitive and specific enough to distinguish SSc from healthy controls and provided a feasible diagnostic tool for the diagnosis and classification of the disease.

Understanding Chinese Medicine Patterns of Rheumatoid Arthritis and Related Biomarkers

Background: A considerable number of Rheumatoid Arthritis (RA) patients only experience side effects from treatment, with little to no actual pain relief. The combination of disease diagnosis in biomedicine and multi-disciplinary integrative approaches such as Chinese Medicine (CM), can help to identify different functional diagnosis of RA in the context of biomarker discovery. We aimed to analyse CM patterns in RA and their biomarker profiles. Methods: Four electronic databases (web of science, CINAHL, Scopus and PubMed) were searched. The reference list of all identified reports and articles were searched for additional studies. All study designs were included and no date limits were set. Studies were considered if they were published in English and explored the possible biomarkers profiles in RA patients, classified according to the American College of Rheumatology and categorized in CM as either cold, heat/hot or deficiency patterns. Methodological quality of included studies was assessed using checklists adapted from the ©Critical Appraisal Skills Programme by two independent reviewers. A narrative synthesis was conducted, using thematic analysis. Results: A total of 10 articles were included. The studies examined 77 healthy volunteers and 1150 RA patients categorized as cold, heat/hot or deficiency pattern and related biomarkers were identified individually or concomitantly. Conclusions: CM pattern differentiation based on clinical signs and symptoms showed a diverse range of biomolecules, proteins and genes from RA patients correlated well with cold, heat/hot or deficiency phenotype-based CM patterns and could be used as diagnostic biomarkers for early detection, disease monitoring and therapeutic targets.

Urinary Metabolomic Profiling to Identify Potential Biomarkers for the Diagnosis of Behcet's Disease by Gas Chromatography/Time-of-Flight-Mass Spectrometry

Diagnosing Behcet's disease (BD) is challenging because of the lack of a diagnostic biomarker. The purposes of this study were to investigate distinctive metabolic changes in urine samples of BD patients and to identify urinary metabolic biomarkers for diagnosis of BD using gas chromatography/time-of-flight-mass spectrometry (GC/TOF-MS). Metabolomic profiling of urine samples from 44 BD patients and 41 healthy controls (HC) were assessed using GC/TOF-MS, in conjunction with multivariate statistical analysis. A total of 110 urinary metabolites were identified. The urine metabolite profiles obtained from GC/TOF-MS analysis could distinguish BD patients from the HC group in the discovery set. The parameter values of the orthogonal partial least squared-discrimination analysis (OPLS-DA) model were R²X of 0.231, R²Y of 0.804, and Q² of 0.598. A biomarker panel composed of guanine, pyrrole-2-carboxylate, 3-hydroxypyridine, mannose, l-citrulline, galactonate, isothreonate, sedoheptuloses, hypoxanthine, and gluconic acid lactone were selected and adequately validated as putative biomarkers of BD (sensitivity 96.7%, specificity 93.3%, area under the curve 0.974). OPLS-DA showed clear discrimination of BD and HC groups by a biomarker panel of ten metabolites in the independent set (accuracy 88%). We demonstrated characteristic urinary metabolic profiles and potential urinary metabolite biomarkers that have clinical value in the diagnosis of BD using GC/TOF-MS.

Diagnosing eosinophilic asthma using a multivariate prediction model based on blood granulocyte responsiveness

BACKGROUND

The identification of inflammatory asthma phenotypes, using sputum analysis, has proven its value in diagnosis and disease monitoring. However due to technical limitations of sputum analysis, there is a strong need for fast and noninvasive diagnostics. This study included the activation state of eosinophils and neutrophils in peripheral blood to phenotype and monitor asthma.

OBJECTIVES

To (i) construct a multivariable model using the activation state of blood granulocytes, (ii) compare its diagnostic value with sputum eosinophilia as gold standard and (iii) validate the model in an independent patient cohort.

METHODS

Clinical parameters, activation of blood granulocytes and sputum characteristics were assessed in 115 adult patients with asthma (training cohort/Utrecht) and 34 patients (validation cohort/Oxford).

RESULTS

The combination of blood eosinophil count, fractional exhaled nitric oxide, Asthma Control Questionnaire, medication use, nasal polyposis, aspirin sensitivity and neutrophil/eosinophil responsiveness upon stimulation with formyl-methionyl-leucyl phenylalanine was found to identify sputum eosinophilia with 90.5% sensitivity and 91.5% specificity in the training cohort and with 77% sensitivity and 71% specificity in the validation cohort (relatively high percentage on oral corticosteroids [OCS]).

CONCLUSIONS

The proposed prediction model identifies eosinophilic asthma without the need for sputum induction. The model forms a noninvasive and externally validated test to assess eosinophilic asthma in patients not on OCS.

Immune biomarkers in the spectrum of childhood noncommunicable diseases

A biomarker is an accurately and reproducibly quantifiable biological characteristic that provides an objective measure of health status or disease. Benefits of biomarkers include identification of therapeutic targets, monitoring of clinical interventions, and development of personalized (or precision) medicine. Challenges to the use of biomarkers include optimizing sample collection, processing and storage, validation, and often the need for sophisticated laboratory and bioinformatics approaches. Biomarkers offer better understanding of disease processes and should benefit the early detection, treatment, and management of multiple noncommunicable diseases (NCDs). This review will consider the utility of biomarkers in patients with allergic and other immune-mediated diseases in childhood. Typically, biomarkers are used currently to provide mechanistic insight or an objective measure of disease severity, with their future role in risk stratification/disease prediction speculative at best. There are many lessons to be learned from the biomarker strategies used for cancer in which biomarkers are in routine clinical use and industry-wide standardized approaches have been developed. Biomarker discovery and validation in children with disease lag behind those in adults; given the early onset and therefore potential lifelong effect of many NCDs, there should be more studies incorporating cohorts of children. Many pediatric biomarkers are at the discovery stage, with a long path to evaluation and clinical implementation. The ultimate challenge will be optimization of prevention strategies that can be implemented in children identified as being at risk of an NCD through the use of biomarkers.

Role of amino acids in rheumatoid arthritis studied by metabolomics

BACKGROUND

Rheumatoid arthritis (RA) is a complex, chronic autoimmune disease characterized by various inflammatory symptoms, including joint swelling, joint pain, and both structural and functional joint damage. The most commonly used animal model for studying RA is mice with collagen-induced arthritis (CIA); the wide use of this model is due primarily to many similarities with RA in human patients. Metabolomics is used increasingly in biological studies for diagnosing disease and for predicting and evaluating drug interventions, as a large number of disease-associated metabolites can be analyzed and interpreted from a biological perspective.

AIM

To profile free amino acids and their biogenic metabolites in CIA mice plasma.

METHOD

Ultra-high-performance liquid chromatography/tandem mass spectrometry coupled with multiple reaction monitoring (MRM) was used for metabolomics study.

RESULTS

Profile of 45 amine metabolites, including free amino acids and their biogenic metabolites in plasma was obtained from CIA mice. We found that the plasma levels of 20 amine metabolites were significantly decreased in the CIA group.

CONCLUSION

The results suggest that a disordered amine response is linked to RA-associated muscle wasting and energy expenditure.

Systems Health: A Transition from Disease Management Toward Health Promotion

To date, most of the chronic diseases such as cancer, cardiovascular disease, and diabetes, are the leading cause of death. Current strategies toward disease treatment, e.g., risk prediction and target therapy, still have limitations for precision medicine due to the dynamic and complex nature of health. Interactions among genetics, lifestyle, and surrounding environments have nonnegligible effects on disease evolution. Thus a transition in health-care area is urgently needed to address the hysteresis of diagnosis and stabilize the increasing health-care costs. In this chapter, we explored new insights in the field of health promotion and introduced the integration of systems theories with health science and clinical practice. On the basis of systems biology and systems medicine, a novel concept called "systems health" was comprehensively advocated. Two types of bioinformatics models, i.e., causal loop diagram and quantitative model, were selected as examples for further illumination. Translational applications of these models in systems health were sequentially discussed. Moreover, we highlighted the bridging of ancient and modern views toward health and put forward a proposition for citizen science and citizen empowerment in health promotion.

Prediction of cold and heat patterns using anthropometric measures based on machine learning

OBJECTIVE

To examine the association of body shape with cold and heat patterns, to determine which anthropometric measure is the best indicator for discriminating between the two patterns, and to investigate whether using a combination of measures can improve the predictive power to diagnose these patterns.

METHODS

Based on a total of 4,859 subjects (3,000 women and 1,859 men), statistical analyses using binary logistic regression were performed to assess the significance of the difference and the predictive power of each anthropometric measure, and binary logistic regression and Naive Bayes with the variable selection technique were used to assess the improvement in the predictive power of the patterns using the combined measures.

RESULTS

In women, the strongest indicators for determining the cold and heat patterns among anthropometric measures were body mass index (BMI) and rib circumference; in men, the best indicator was BMI. In experiments using a combination of measures, the values of the area under the receiver operating characteristic curve in women were 0.776 by Naive Bayes and 0.772 by logistic regression, and the values in men were 0.788 by Naive Bayes and 0.779 by logistic regression.

CONCLUSIONS

Individuals with a higher BMI have a tendency toward a heat pattern in both women and men. The use of a combination of anthropometric measures can slightly improve the diagnostic accuracy. Our findings can provide fundamental information for the diagnosis of cold and heat patterns based on body shape for personalized medicine.

NMR-based metabolomic approach to study urine samples of chronic inflammatory rheumatic disease patients

The nuclear magnetic resonance (NMR)-based metabolomic approach was used as analytical methodology to study the urine samples of chronic inflammatory rheumatic disease (CIRD) patients. The urine samples of CIRD patients were compared to the ones of both healthy subjects and patients with multiple sclerosis (MS), another immuno-mediated disease. Urine samples collected from 39 CIRD patients, 25 healthy subjects, and 26 MS patients were analyzed using ¹H NMR spectroscopy, and the NMR spectra were examined using partial least squares-discriminant analysis (PLS-DA). PLS-DA models were validated by a double cross-validation procedure and randomization tests. Clear discriminations between CIRD patients and healthy controls (average diagnostic accuracy 83.5 ± 1.9%) as well as between CIRD patients and MS patients (diagnostic accuracy 81.1 ± 1.9%) were obtained. Leucine, alanine, 3-hydroxyisobutyric acid, hippuric acid, citric acid, 3-hydroxyisovaleric acid, and creatinine contributed to the discrimination; all of them being in a lower concentration in CIRD patients as compared to controls or to MS patients. The application of NMR metabolomics to study these still poorly understood diseases can be useful to better clarify the pathologic mechanisms; moreover, as a holistic approach, it allowed the detection of, by means of anomalous metabolic traits, the presence of other pathologies or pharmaceutical treatments not directly connected to CIRDs, giving comprehensive information on the general health state of individuals. Graphical abstract NMR-based metabolomic approach as a tool to study urine samples in CIRD patients with respect to MS patients and healthy controls.

Targeted Metabolic Profiling of the Tg197 Mouse Model Reveals Itaconic Acid as a Marker of Rheumatoid Arthritis

Rheumatoid arthritis is a progressive, highly debilitating disease where early diagnosis, enabling rapid clinical intervention, would provide obvious benefits to patients, healthcare systems, and society. Novel biomarkers that enable noninvasive early diagnosis of the onset and progression of the disease provide one route to achieving this goal. Here a metabolic profiling method has been applied to investigate disease development in the Tg197 arthritis mouse model. Hind limb extract profiling demonstrated clear differences in metabolic phenotypes between control (wild type) and Tg197 transgenic mice and highlighted raised concentrations of itaconic acid as a potential marker of the disease. These changes in itaconic acid concentrations were moderated or indeed reversed when the Tg197 mice were treated with the anti-hTNF biologic infliximab (10 mg/kg twice weekly for 6 weeks). Further in vitro studies on synovial fibroblasts obtained from healthy wild-type, arthritic Tg197, and infliximab-treated Tg197 transgenic mice confirmed the association of itaconic acid with rheumatoid arthritis and disease-moderating drug effects. Preliminary indications of the potential value of itaconic acid as a translational biomarker were obtained when studies on K4IM human fibroblasts treated with hTNF showed an increase in the concentrations of this metabolite.

Differential Amino Acid, Carbohydrate and Lipid Metabolism Perpetuations Involved in a Subtype of Rheumatoid Arthritis with Chinese Medicine Cold Pattern

Pattern classification is a key approach in Traditional Chinese Medicine (TCM), and it is used to classify the patients for intervention selection accordingly. TCM cold and heat patterns, two main patterns of rheumatoid arthritis (RA) had been explored with systems biology approaches. Different regulations of apoptosis were found to be involved in cold and heat classification in our previous works. For this study, the metabolic profiling of plasma was explored in RA patients with typical TCM cold or heat patterns by integrating liquid chromatography/mass spectrometry (LC/MS) and gas chromatography/mass spectrometry (GC/MS) platforms in conjunction with the Ingenuity Pathway Analysis (IPA) software. Three main processes of metabolism, including amino acid, carbohydrate and lipid were focused on for function analysis. The results showed that 29 and 19 differential metabolites were found in cold and heat patterns respectively, compared with healthy controls. The perturbation of amino acid metabolism (increased essential amino acids), carbohydrate metabolism (galactose metabolism) and lipid metabolism, were found to be involved in both cold and heat pattern RA. In particular, more metabolic perturbations in protein and collagen breakdown, decreased glycolytic activity and aerobic oxidation, and increased energy utilization associated with RA cold pattern patients. These findings may be useful for obtaining a better understanding of RA pathogenesis and for achieving a better efficacy in RA clinical practice.

NMR based serum metabolomics reveals a distinctive signature in patients with Lupus Nephritis

Management of patient with Lupus Nephritis (LN) continues to remain a challenge for the treating physicians because of considerable morbidity and even mortality. The search of biomarkers in serum and urine is a focus of researchers to unravel new targets for therapy. In the present study, the utility of NMR-based serum metabolomics has been evaluated for the first time in discriminating LN patients from non-nephritis lupus patients (SLE) and further to get new insights into the underlying disease processes for better clinical management. Metabolic profiling of sera obtained from 22 SLE patients, 40 LN patients and 30 healthy controls (HC) were performed using high resolution 1D ¹H-CPMG and diffusion edited NMR spectra to identify the potential molecular biomarkers. Using multivariate analysis, we could distinguish SLE and LN patients from HC and LN from SLE patients. Compared to SLE patients, the LN patients had increased serum levels of lipid metabolites (including LDL/VLDL lipoproteins), creatinine and decreased levels of acetate. Our results revealed that metabolic markers especially lipids and acetate derived from NMR spectroscopy has high sensitivity and specificity to distinguish LN among SLE patients and has the potential to be a useful adjunctive tool in diagnosis and clinical management of LN.

Non-Esterified Fatty Acids Profiling in Rheumatoid Arthritis: Associations with Clinical Features and Th1 Response

Objectives

Since lipid compounds are known to modulate the function of CD4⁺ T-cells and macrophages, we hypothesize that altered levels of serum non-esterified fatty acids (NEFA) may underlie rheumatoid arthritis (RA) pathogenesis.

Methods

Serum levels of NEFA (palmitic, stearic, palmitoleic, oleic, linoleic, γ-linoleic, arachidonic –AA–, linolenic, eicosapentaenoic –EPA– and docosahexaenoic –DHA–) were quantified by LC-MS/MS after methyl-tert-butylether (MTBE)-extraction in 124 RA patients and 56 healthy controls (HC). CD4⁺ phenotype was studied by flow cytometry. TNFα, IL-8, VEGF, GM-CSF, IFNγ, IL-17, CCL2, CXCL10, leptin and resistin serum levels were quantified by immunoassays. The effect of FA on IFNγ production by PBMC was evaluated in vitro.

Results

Lower levels of palmitic (p<0.0001), palmitoleic (p = 0.002), oleic (p = 0.010), arachidonic (p = 0.027), EPA (p<0.0001) and DHA (p<0.0001) were found in RA patients, some NEFA being altered at onset. Cluster analysis identified a NEFA profile (hallmarked by increased stearic and decreased EPA and DHA) overrepresented in RA patients compared to HC (p = 0.002), being associated with clinical features (RF, shared epitope and erosions), increased IFNγ expression in CD4⁺ T-cells (p = 0.002) and a Th1-enriched serum milieu (IFNγ, CCL2 and CXCL10, all p<0.005). In vitro assays demonstrated that imbalanced FA could underlie IFNγ production by CD4⁺ T-cells. Finally, changes on NEFA levels were associated with clinical response upon TNFα-blockade.

Conclusion

An altered NEFA profile can be found in RA patients associated with clinical characteristics of aggressive disease and enhanced Th1 response. These results support the relevance of lipidomic studies in RA and provide a rationale for new therapeutic targets.

Altered levels of acylcarnitines, phosphatidylcholines, and sphingomyelins in peritoneal fluid from ovarian endometriosis patients

Endometriosis is a complex, polygenic, and estrogen-dependent disease that affects 6% to 10% of women of reproductive age, and 30% to 50% of women with infertility and/or pelvic pain. Surgical diagnosis of endometriosis is still the gold standard, as there are currently no diagnostic biomarkers available. Due to the invasive diagnostics, it can take up to 11 years before affected women are diagnosed and receive the appropriate treatment. We performed a targeted metabolomics study to search for potential semi-invasive biomarkers in peritoneal fluid from endometriosis patients. Our case-control study comprised 29 ovarian endometriosis patients and 36 healthy control women. The 148 metabolites included acylcarnitines, glycerophospholipids, and sphingolipids, which were quantified by electrospray ionization tandem mass spectrometry. The strength of association between the metabolites and the metabolite ratios and disease was assessed using crude and adjusted odds ratios. The best combination of biomarkers was then selected by performing step-wise logistic regression. Our analysis reveals significantly decreased concentrations of 10 metabolites, of carnitine and acylcarnitines (C0, C8:1, C6C4:1 DC, C10:1), phosphatidylcholines (PC aa C38:3, PC aa C38:4, PC aa C40:4, PC aa C40:5), and sphingomyelins (SM C16:1, SM C18:1), and 125 significantly altered metabolite ratios in patients versus control women. The best model includes two ratios: a carnitine to a phosphatidylcholine (C0/PC ae C36:0); and between two phosphatidylcholines (PC aa C30:0/PC ae C32:2). When adjusted for age, this provides sensitivity of 82.8% and specificity of 94.4%, with AUC of 0.944. Our study supports the importance of carnitine, phosphatidylcholine, and sphingomyelin metabolites in the pathophysiology of endometriosis, and confirms the potential for the combination of individual metabolite ratios to provide biomarkers for semi-invasive diagnostics.

Metabolomics in rheumatic diseases: desperately seeking biomarkers

Metabolomics enables the profiling of large numbers of small molecules in cells, tissues and biological fluids. These molecules, which include amino acids, carbohydrates, lipids, nucleotides and their metabolites, can be detected quantitatively. Metabolomic methods, often focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry, have potential for early diagnosis, monitoring therapy and defining disease pathogenesis in many therapeutic areas, including rheumatic diseases. By performing global metabolite profiling, also known as untargeted metabolomics, new discoveries linking cellular pathways to biological mechanisms are being revealed and are shaping our understanding of cell biology, physiology and medicine. These pathways can potentially be targeted to diagnose and treat patients with immune-mediated diseases.

Lipid and Metabolic Changes in Rheumatoid Arthritis

While the most obvious manifestations of rheumatoid arthritis (RA) involve inflammation and damage in the synovial joints, the systemic effects of the condition are widespread and life-threatening. Of particular interest is the 'lipid paradox' of RA, where patients with a numerically equivocal starting lipid profile have a significantly raised risk of cardiovascular (CV) events and response to therapy results in a 'normalization' of lipid levels and reduction in events. Changes in lipids can be seen before overt disease manifestations which suggest that they are closely linked to the more widespread inflammation-driven metabolic changes associated with tumour necrosis factor (TNF). Cachexia involves a shift in body mass from muscle to fat, which may or may not directly increase the cardiovascular risk. However, since TNF inhibition is associated with reduction in cardiovascular events, it does suggest that these widespread metabolic changes involving lipids are of importance. Analysis of single lipids or metabolites have been used to identify some of the key changes, but more recently, metabolomic and lipidomic approaches have been applied to identify a broad spectrum of small molecule changes and identify potentially altered metabolic pathways. Further work is needed to understand fully the metabolic changes in lipid profiles and identify novel ways of targeting desired profile changes, but work so far does suggest that a better understanding may allow management of patients to downregulate the systemic effects of their disease that puts them at risk of cardiovascular complications.

High-throughput metabolic profiling for discovering metabolic biomarkers of sepsis-induced acute lung injury

Sepsis-induced acute lung injury (ALI) remains a leading cause of death in intensive care units and early detection is very important. This work showed that metabolite phenotype profiling might be a useful tool for the effective diagnosis and further understanding of ALI.

The gut microbiome in autoimmunity: Sex matters

Autoimmune diseases like rheumatoid arthritis are multifactorial in nature, requiring both genetic and environmental factors for onset. Increased predisposition of females to a wide range of autoimmune diseases points to a gender bias in the multifactorial etiology of these disorders. However, the existing evidence to date has not provided any conclusive mechanism of gender-bias beyond the role of hormones and sex chromosomes. The gut microbiome, which impacts the innate and adaptive branches of immunity, not only influences the development of autoimmune disorders but may interact with sex-hormones to modulate disease progression and sex-bias. Here, we review the current information on gender bias in autoimmunity and discuss the potential of microbiome-derived biomarkers to help unravel the complex interplay between genes, environment and hormones in rheumatoid arthritis.

Herb Network Analysis for a Famous TCM Doctor's Prescriptions on Treatment of Rheumatoid Arthritis

Traditional Chinese Medicine (TCM) doctors always prescribe various herbal formulae tailored to individual patients. However, there is still a lack of appropriate methods to study the rule and potential biological basis underlying the numerous prescriptions. Here we developed an Herb-Compound-Target-Disease coherent network approach to analyze 871 herbal prescriptions from a TCM master, Mr. Ji-Ren Li, in his clinical practice on treatment of rheumatoid arthritis (RA). The core herb networks were extracted from Mr. Li's prescriptions. Then, we predicted target profiles of compounds in core herb networks and calculated potential synergistic activities among them. We further found that the target sets of core herbs overlapped significantly with the RA related biological processes and pathways. Moreover, we detected a possible connection between the prescribed herbs with different properties such as Cold and Hot and the Western drugs with different actions such as immunomodulatory and hormone regulation on treatment of RA. In summary, we explored a new application of TCM network pharmacology on the analysis of TCM prescriptions and detected the networked core herbs, their potential synergistic and biological activities, and possible connections with drugs. This work offers a novel way to understand TCM prescriptions in clinical practice.

The role of hypoxia in inflammatory disease (review)

Mammals have developed evolutionarily conserved programs of transcriptional response to hypoxia and inflammation. These stimuli commonly occur together in vivo and there is significant crosstalk between the transcription factors that are classically understood to respond to either hypoxia or inflammation. This crosstalk can be used to modulate the overall response to environmental stress. Several common disease processes are characterised by aberrant transcriptional programs in response to environmental stress. In this review, we discuss the current understanding of the role of the hypoxia-responsive (hypoxia-inducible factor) and inflammatory (nuclear factor-κB) transcription factor families and their crosstalk in rheumatoid arthritis, inflammatory bowel disease and colorectal cancer, with relevance for future therapies for the management of these conditions.

Review of the regulations for clinical research in herbal medicines in USA

In 2012, USA Food and Drug Administration (FDA) approved 39 new drugs, however, there are only two botanical drugs (one topical and one oral) approved by FDA since the publication of the FDA's industry guidelines for the botanical drug product in June 2004. The approval shows the Western guideline can be used for herbal medicines, authors investigate current regulation on herbal medicine clinical research, identify challenges conducting clinical trials, and seek to produce some guidance for potential investigators and sponsors considering a clinical trial in this area. Key words were formulated for searching on Medline and FDA website to locate relevant regulations for clinical research in herbal medicines to understand current environment for herbal medicine usage and examine the barriers affecting herbal medicine in clinical trials. Authors critically explore case study of the 1st FDA approved botanical drugs, Veregen (sinecatechins), green tea leaves extract, a topical cream for perianal and genital condyloma. In consideration of current regulation environment in USA, based on the findings and analysis through the literature review and Veregen case study, authors produce and propose a Checklist for New Drug Application of Herbal Medicines for potential investigators and sponsors considering in a herbal medicine clinical trial.

Nontargeted modification-specific metabolomics study based on liquid chromatography-high-resolution mass spectrometry

Modifications of genes and proteins have been extensively studied in systems biology using comprehensive analytical strategies. Although metabolites are frequently modified, these modifications have not been studied using -omics approaches. Here a general strategy for the nontargeted profiling of modified metabolites, which we call "nontargeted modification-specific metabolomics", is reported. A key aspect of this strategy was the combination of in-source collision-induced dissociation liquid chromatography-mass spectrometry (LC-MS) and global nontargeted LC-MS-based metabolomics. Characteristic neutral loss fragments that are specific for acetylation, sulfation, glucuronidation, glucosidation, or ribose conjugation were reproducibly detected using human urine as a model specimen for method development. The practical application of this method was demonstrated by profiling urine samples from liver cirrhosis patients. Approximately 900 features were identified as modified endogenous metabolites and xenobiotics. Moreover, this strategy supports the identification of compounds not included in traditional metabolomics databases (HMDB, Metlin, and KEGG), which are currently referred to as "unknowns" in metabolomics projects. Nontargeted modification-specific metabolomics opens a new perspective in systems biology.

Metabolomics approach in allergic and rheumatic diseases

Metabolomics is the analysis of the concentration profiles of low molecular weight compounds present in biological fluids. Metabolites are nonpeptide molecules representing the end products of cellular activity. Therefore, changes in metabolite concentrations reveal the range of biochemical effects induced by a disease or its therapeutic intervention. Metabolomics has recently become feasible with the accessibility of new technologies, including mass spectrometry and high-resolution proton nuclear magnetic resonance, and has already been applied to several disorders. Indeed, it has the advantage of being a nontargeted approach for identifying potential biomarkers, which means that it does not require a preliminary knowledge of the substances to be studied. In this review, we summarize the main studies in which metabolomic approach was used in some allergic (asthma, atopic dermatitis) and rheumatic diseases (rheumatoid arthritis, systemic lupus erythematosus) to explore the feasibility of this technique as a novel diagnostic tool in these complex disorders.