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.

Spontaneous ultra-weak photon emission in correlation to inflammatory metabolism and oxidative stress in a mouse model of collagen-induced arthritis.

The increasing prevalence of rheumatoid arthritis has driven the development of new approaches and technologies for investigating the pathophysiology of this devastating, chronic disease. From the perspective of systems biology, combining comprehensive personal data such as metabolomics profiling with ultra-weak photon emission (UPE) data may provide key information regarding the complex pathophysiology underlying rheumatoid arthritis. In this article, we integrated UPE with metabolomics-based technologies in order to investigate collagen-induced arthritis, a mouse model of rheumatoid arthritis, at the systems level, and we investigated the biological underpinnings of the complex dataset. Using correlation networks, we found that elevated inflammatory and ROS-mediated plasma metabolites are strongly correlated with a systematic reduction in amine metabolites, which is linked to muscle wasting in rheumatoid arthritis. We also found that increased UPE intensity is strongly linked to metabolic processes (with correlation co-efficiency |r| value >0.7), which may be associated with lipid oxidation that related to inflammatory and/or ROS-mediated processes. Together, these results indicate that UPE is correlated with metabolomics and may serve as a valuable tool for diagnosing chronic disease by integrating inflammatory signals at the systems level. Our correlation network analysis provides important and valuable information regarding the disease process from a system-wide perspective.

Characterization of the therapeutic properties of Chinese herbal materials by measuring delayed luminescence and dendritic cell-based immunomodulatory response.

Based on the traditional Chinese medicine theory, the Chinese pharmacopeia assigns a therapeutic description of "taste" to all herbs; thus, an herb's "taste" is valued in traditional Chinese medicine as a major ethnopharmacological category and reflects the herb's therapeutic properties. These properties guide the practitioner with respect to preparing a specific herbal formula in order to provide each patient with a personalized intervention. The key challenge in evidence-based medicine is to characterize herbal therapeutic properties from a multi-target, multi-dimensional systems pharmacology perspective. Here, we used delayed luminescence (DL, the slowly decaying emission of photons following excitation with light) as a rapid, direct, highly sensitive indicator to characterize the properties of herbal medicines. The DL parameters were able to reliably identify a specific category of herbal materials with the so-called "sweet" taste. To support the DL results and provide biological relevance to the DL results, we used a murine bone marrow-derived dendritic cell-based assay to examine the immunomodulatory effects of herbal extracts from various "taste" categories. Our results indicate that DL may serve as a robust and sensitive tool for evaluating the therapeutic properties of herbs based on the traditional Chinese medicine classification of "taste". Thus, DL provides a promising technological platform for investigating the properties of Chinese herbal medicines both qualitatively and quantitatively.

Measuring ultra-weak photon emission as a non-invasive diagnostic tool for detecting early-stage type 2 diabetes: A step toward personalized medicine.

The global prevalence of type 2 diabetes is estimated to reach 4.4% by 2030, placing a significant burden on our healthcare system. Therefore, the ability to identify patients in early stages of the disease is essential for both prevention and effective management, and diagnostic methods based on traditional Chinese medicine (TCM) may be suitable for identifying patients with early-stage type 2 diabetes. Here, a panel of three physicians trained in TCM classified 44 pre-diabetic subjects into three syndrome subtypes using TCM-based diagnostics. In addition, ultra-weak photon emission (UPE) was measured at four anatomical sites in each subject. Ten properties encompassing 40 parameters were then extracted from the UPE time series. Statistical analyses, including multinomial logistic regression, were performed using the results of each parameter measured at the four sites. Sixteen UPE parameters were then selected and used to discriminate between the three subtypes of pre-diabetic subjects. Next, Spearman's correlation coefficient was used to quantify the correlation between the 16 UPE parameters and the TCM-based diagnoses. The resulting correlation networks accurately reflected the differences between the three syndrome subtypes. These results suggest that UPE is a suitable tool for detecting subtypes in early-stage type 2 diabetes. In addition, our results provide evidence that TCM may represent an important step toward personalized medicine.

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis.

In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA) respond insufficiently to TNF-α inhibitors (TNFis). The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients' response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124). The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI). The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28), erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP). Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6), sn1-LPC(15:0), ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01). The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23) and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05). Our study established an accurate prediction model for response to TNFi therapy, containing metabolites and clinical parameters. Associations between metabolites and disease activity may help elucidate additional pathologic mechanisms behind RA.

Differences between serum polar lipid profiles of male and female rheumatoid arthritis patients in response to glucocorticoid treatment.

OBJECTIVE: As there are pharmacological differences between males and females, and glucocorticoid (GC) treatment is associated with increased cardiovascular mortality rate in rheumatoid arthritis (RA) patients, it is important to study serum polar lipid profiles of male and female patients in response to GC therapy. Gender differences may require an adjustment to the treatment strategy for a selection of patients. METHODS: Serum samples from 281 RA patients were analysed using a targeted lipidomics platform. The differences in GC use and gender on polar lipid profiles were cross sectionally examined by multiple linear regressions, while correcting for confounding factors. RESULTS: Differences in polar lipids between GC users and non-GC users in females and males were merely restricted to lysophospholipids (lysophosphatidylcholines and lysophosphatidylethanolamines). Lysophospholipids in female patients treated with GCs were significantly higher than female patients not treated with GCs (p = 6.0 E-6), whereas no significant difference was observed in male GC users versus non-users (p = 0.397). CONCLUSION: The lysophospholipid profiles in response to GCs were significantly different between male and female RA patients, which may have implications for the cardiovascular risk of GC treatment.

Tracking biochemical changes correlated with ultra-weak photon emission using metabolomics.

Ultra-weak photon emission (UPE) is light emitted spontaneously by biological systems without the use of specific luminescent complexes. UPE is emitted in the near-UV/UV-Vis/near-IR spectra during oxidative metabolic reactions; however, the specific pathways involved in UPE remain poorly understood. Here, we used HL-60 cells, a human promyelocytic cell line that is often used to study respiratory burst, as a model system to measure UPE kinetics together with metabolic changes. HL-60 cells were differentiated into neutrophil-like cells by culturing in all-trans-retinoic acid for 7days. We then used a targeted metabolomics approach with capillary electrophoresis-mass spectrometry to profile intracellular metabolites in HL-60 cells and to investigate the biochemical changes based on the measured UPE profile. Our analysis revealed that the levels of specific metabolites, including putrescine, creatine, ß-alanine, methionine, hydroxyproline, serine, and S-adenosylmethionine, were significantly altered in HL-60 cells after inducing respiratory burst. A comparison with recorded UPE data revealed that the changes in putrescine, glutathione, sarcosine, creatine, ß-alanine, methionine, and hydroxyproline levels were inversely correlated with the change in UPE intensity. These results suggest that these metabolic pathways, particular the methionine pathway, may play a role in the observed changes in UPE in HL-60 cells and therefore demonstrate the potential for using UPE to monitor metabolic changes.

Effects of growth altitude on chemical constituents and delayed luminescence properties in medicinal rhubarb.

To improve the quality control of herbal drugs, there has been a major shift from evaluating individual chemicals to evaluating multiple-constituent chemicals, given the multi-pharmacology nature of herbal drugs. Therefore, rapid, systematic assays are needed in order to assess the quality of medicinal herbs using a comprehensive, integrated approach. Light-induced delayed luminescence (DL) is used to measure decaying long-term ultra-weak photon emissions following excitation with light. DL is considered to be a sensitive indicator for characterizing the properties of biological systems and herbal medicines with various therapeutic properties. The aim of this study was to examine the feasibility of using DL as a novel quality-assessment tool using rhubarb material as a model system, and to establish the correlation between DL parameters and the chemical constituents of rhubarb. Raw roots and rhizomes were collected from rhubarb (Rheum palmatum L.) at various elevations in western China. HPLC analysis was used to identify fourteen bioactive constituents. Five DL parameters were calculated from the DL decay curves of the rhubarb samples. Statistical tools, including principal component analysis, were used to classify the rhubarb samples using data obtained using two different assays. Finally, Spearman's correlation coefficient was calculated to quantify the correlation between the bioactive compounds and corresponding DL parameters. We found that both the chemical analysis and DL measurements reflect variations in the quality of rhubarb due to environment factor. The DL parameters were correlated significantly with the bioactive chemical constituents. Our results indicate that DL is a promising tool for evaluating multiple constituents and for assessing the therapeutic properties of herbal medicines. Thus, DL may be used as part of a comprehensive system for assessing the quality and/or therapeutic properties of herbal medicines.

Integrator complex regulates NELF-mediated RNA polymerase II pause/release and processivity at coding genes.

RNA polymerase II (RNAPII) pausing/termination shortly after initiation is a hallmark of gene regulation. Here, we show that negative elongation factor (NELF) interacts with Integrator complex subunits (INTScom), RNAPII and Spt5. The interaction between NELF and INTScom subunits is RNA and DNA independent. Using both human immunodeficiency virus type 1 promoter and genome-wide analyses, we demonstrate that Integrator subunits specifically control NELF-mediated RNAPII pause/release at coding genes. The strength of RNAPII pausing is determined by the nature of the NELF-associated INTScom subunits. Interestingly, in addition to controlling RNAPII pause-release INTS11 catalytic subunit of the INTScom is required for RNAPII processivity. Finally, INTScom target genes are enriched in human immunodeficiency virus type 1 transactivation response element/NELF binding element and in a 3' box sequence required for small nuclear RNA biogenesis. Revealing these unexpected functions of INTScom in regulating RNAPII pause-release and completion of mRNA synthesis of NELF-target genes will contribute to our understanding of the gene expression cycle.