Alterations of the intestinal microbiome and metabolome in women with rheumatoid arthritis

doi:10.21203/rs.3.rs-3089847/v1

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Alterations of the intestinal microbiome and metabolome in women with rheumatoid arthritis

https://doi.org/10.21203/rs.3.rs-3089847/v1

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published 12 Aug, 2023

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Objectives: Rheumatoid arthritis (RA) is more common in women, and many reports of sex differences have been reported in various aspects of RA. However, there has been a lack of specific research on women's gut flora. To assess the association between the gut flora and RA patients, this study combined the microbiome with metabolomics.

Methods: Fecal samples from RA patients and healthy controls were collected for 16S rRNA sequencing. Nontargeted liquid chromatography‒mass spectrometry was used to detect metabolites in fecal samples. We comprehensively used various analytical methods to reveal changes in intestinal flora and metabolites in female patients.

Results: The gut flora of RA patients was significantly different from that of healthy women. The abundance of Bacteroides, Megamonas and Oscillospira was higher in RA patients, while the abundance of Prevotella, Gemmiger and Roseburia was lower than that of healthy women. Gemmiger, Bilophila and Odoribacter represented large differences in microflora between RA and healthy women and could be used as potential microorganisms in the diagnosis. Fatty acid biosynthesis was significantly different between RA patients and healthy women in terms of metabolic pathways. There were different degrees of correlation between the gut flora and metabolites. Lys-Phe-Lys and heptadecasphin-4-enine can be used as potential markers for RA diagnosis. There was an extremely significant positive correlation between Megamonas, Dialister and rheumatoid factors (RF), which was found for the first time.

Conclusions: These findings indicated that alterations of these gut microbiome and metabolome may contribute to the diagnosis and treatment of RA patients.

Gut flora

Rheumatoid arthritis

Fecal

Microbiome

Metabolome

Microbes in the patients' guts, including Roseburia, Sutterella and Gemmiger, caused differences from healthy women.
Lys-Phe-Lys and heptadecasphing-4-enine had high abundance in patients and can be regarded as potential biomarkers.
The intestinal microbiota of patients was positively or negatively correlated with metabolites and laboratory indicators.

Autoimmune diseases are characterized by the body's production of antibodies and immune complexes against its own tissues, which ultimately lead to systemic and organ-specific inflammation and associated pathology^[1]. Autoimmune diseases affect 5–10% of the world's population, and the majority of autoimmune diseases occur more often in women^[2]. Rheumatoid arthritis (RA) is characterized by chronic erosive arthritis with synovitis and destruction of articular cartilage and bone, leading to joint deformities. It is more common in women than men (3:1)^[3–6] and musculoskeletal pain, swelling, and stiffness particularly afflict women of child-bearing period.

Although the disease difference between men and women is clear, the exact mechanism is not yet clear. However, the general consensus in the academic world is that sex hormones play a crucial role in the gender differences in this disease. Estrogen is the stimulus and activator of autoimmunity, while androgen plays a protective and inhibitory role^[7]. Some studies have shown that IL-4 is the key molecule of sex differences in RA patients, and it is involved in cytokine‒cytokine receptor interactions and Th1 and Th2 cell differentiation^[8]. Empirical evidence illustrates that there are differences in outcomes between women and men in the treatment of RA with biologics or immunotherapy, and women tend to experience more adverse reactions than men^[9]. Sexual dysfunction is a new problem in both male and female patients, with questionnaire assessments indicating that orgasm and sexual dysfunction are more frequent in women than in men^[10].

Many studies have shown that intestinal microbiome imbalance is related to autoimmune disease. According to the ‘host-gut’ concept, the gut microbiome is involved in the regulation of multiple metabolic pathways, signal transduction, and the immune inflammation axis^[11]. Some studies have suggested that the intestinal microbiome is the main environmental risk factor for central nervous system (CNS) demyelinating diseases in the study of multiple sclerosis^[12]. Kun Xiang et al. found that actinomycetes may reduce the risk of systemic lupus erythematosus (SLE) based on a Mendelian randomization study. Coprobacter was negatively correlated with SLE^[13].

It is obvious that existing studies specifically aimed at RA women are scarce and often difficult to draw convincing conclusions due to small sample sizes. In addition, the mechanisms of RA in women from the perspective of intestinal microflora have rarely been revealed. Our research combines intestinal microbiome and metabolomics in single-sex study of RA, with a relatively large sample size, which is important for filling the gap in this field.

2.1 Study Design

40 women with RA and 7 healthy women were recruited from November 2021 to November 2022 in the inpatient and outpatient departments of the rheumatology department of Suzhou Hospital of Traditional Chinese Medicine. All patients and healthy volunteers signed informed consent forms, which were approved by the ethics committee of Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine. Key inclusion criteria were as follows: (i) Patients were diagnosed with the RA criteria of the European League Against Rheumatism (EULAR). (ii) Patients ranged in age from 18 to 75. (iii) Patients and healthy volunteers who participated in the study did not use probiotics, antibiotics, immunosuppressants or biologics for at least one month.

The following types of patients were excluded: (i) RA patients that had severe complications or other autoimmune diseases. (ii) Patients with malignant tumors, infectious diseases or serious primary diseases of other systems (such as heart, liver and kidney). (iii) Pregnant, expectant and lactating women. After fasting for more than 8 hours on an empty stomach, approximately 2 g of uncontaminated feces was collected from the middle part of the flask using a sterile bacterial culture tube with stable liquid and frozen in a -80℃ refrigerator within 2 hours for subsequent treatment.

2.2 DNA extraction and 16S rRNA sequencing

Total genomic DNA samples were extracted using the OMEGA Soil DNA Kit (M5636-02) (Omega Bio-Tek, Norcross, GA, USA) following the manufacturer’s instructions and stored at -20°C prior to further analysis. PCR amplification of the bacterial 16S rRNA gene V3-V4 region was performed using the forward primer 338F (5'-ACTCCTACGGGAGGCAGCA-3') and the reverse primer 806R (5'-GGACTACHVGGGTWTCTAAT-3'). PCR amplicons were purified with Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA). After the individual quantification step, amplicons were pooled in equal amounts, and paired-end 2×250 bp sequencing was performed using the Illumina NovaSeq platform with a NovaSeq 6000 SP Reagent Kit (500 cycles) at Shanghai Personal Biotechnology Co., Ltd. (Shanghai, China).

2.3 Analysis of 16S rDNA Gene Sequences

Microbiome bioinformatics was performed with QIIME2 2019.4^[14] with slight modification according to the official tutorials (https://docs.qiime2.org/2019.4/tutorials/). Sequences were then quality filtered, denoised, merged and chimera removed using the DADA2 plugin^[15]. ASVs were aligned with mafft^[16] and used to construct a phylogeny with fasttree2^[17].

2.4 Metabolite Extraction and LC-MS Analysis

We removed the fecal mixture from the refrigerator and slowly thawed it. It was then mixed by shaking at 4°C. We used a pipette to take 100 µL feces and mixed them in a centrifuge tube. After shaking, we drained them with a vacuum dryer. The protein was precipitated with precooled acetonitrile methanol aqueous solution, mixed by vortexing for 30 min, and then placed in a -20°C refrigerator for 10 min to precipitate the protein more thoroughly. After centrifugation at 4°C (14000 rpm) for 15 min, the supernatant was taken and drained with a vacuum dryer. One hundred microliters of acetonitrile solution was added for resolution and centrifugation (13000 rpm) for 15 min. The supernatant was absorbed for analysis.

After intestinal metabolites were extracted, an Agilent 1290 Infinity LC ultrahigh-performance liquid chromatography system (UHPLC) HILIC column was used for separation and chromatographic analysis, and an AB Triple TOF 6600 mass spectrometer was used for mass spectrometry.

2.5 Metabolomics Analysis

Proteowizard software was used to convert the original data in Wiff format into mzXML format. We used the XCMS package of R language for peak identification, peak filtering, peak alignment, and retention time correction. The metabolite structure was first identified, and the data were preprocessed. Then, the experimental data quality was evaluated, and the data were exported to Excel for subsequent analysis. Batch normalization of the peak area of the data enables comparison of data of different orders of magnitude.

2.6 Statistical Analysis

We plotted rank abundance curve at level of each sample based on the distribution of ASV in different samples and measured the beta diversity difference. We combined PCA, LEfSe analysis, OPLS-DA and random forest analysis for species difference analysis and marker species screening. For data processing, we used QIIME2, R language, ggplot2 package, python LEfSe package and other analysis tools. We conducted nested stratified cross-check analysis and then scored all the samples according to the importance of the microbes. Correlation analysis was used to assess the relationship between gut flora and metabolites.

3.1 Summary of clinical characteristics

A total of 47 people were enrolled in this study, and RA patients consisted of 40 women, and normal volunteers (N) consisted of 7 women. The results showed that ESR, CRP, RF and anti-CCP were significantly different between the two groups (P<0.05) (Table 1). The DAS28 score, VAS score, number of tender joints, number of swollen joints and HAQ score of the control group were all 0 in healthy volunteers. Inflammatory indicators such as ESR and CRP in the RA patients were significantly higher than those in normal people (P<0.001). Immunological indices such as RF and anti-CCP antibodies were significantly higher in the RA patients than in the healthy subjects (P<0.001).

3.2 Species Diversity of the Gut microbiome

To evaluate gut microbial community richness, we combined rank abundance analysis with beta diversity analysis. The flatness of the broken line reflects the evenness of the community composition. The flatter the broken line is, the smaller the difference in community abundance and the higher the evenness of community composition (Figure 1A). In measuring beta diversity, UniFrac distances were used to estimate differences between samples or taxa. The PCoA analysis of the two-dimensional ranking chart showed the difference between the two groups, with Axis 1 as the main factor and Axis 2 as the secondary factor. Combined with the results before and after weighting, RA had a significant impact on the abundance and composition of the intestinal flora (Figure 1B).

3.3 Alterations in Microbial Composition Associated with RA

We analyzed which species mainly affected the differential distribution of the above differences (Figure 2). We drew a Venn diagram, and the results showed that the number of Nonsingleton amplicon sequence variants (ASVs) jointly owned by the two groups was 2193 (Figure 2A). Group RA and N had a certain degree of difference and similarity. More unique ASVs were detected in Group RA than in Group N, indicating that RA had a great influence on the number of gut flora of patients.

To further explore the taxonomic distribution of species in each region of the Venn diagram, we counted the ASV abundance at the phylum and genus levels. At the phylum level, the total abundance values of the two groups were similar. In particular, compared with Group N, the abundance of Proteobacteria and Fusobacteria in Group RA was relatively increased, while the abundance of Firmicutes and Bacteroidetes was relatively decreased. At the genus level, the total value of ASV abundance in Group RA was higher than that in Group N. In Group RA, the abundance of Bacteroides, Megamonas and Oscillospira increased, while the abundance of Prevotella, Faecalibacterium and Roseburia decreased (Figure 2B).

Bidirectional clustering was also used to further analyze the species abundance between groups to study more different species between Group RA and N (Figure 2C). By selecting different species, a heatmap of different species between the two groups was drawn. Lactococcus, Butyricicoccus, Epulopiscium, Megasphaera and Desulfovibrio were highly abundant in women with RA. In addition, we also studied the phylogenetic relationships of species at the genus level. Multiple sequence alignments were used to obtain phylogenetic trees constructed by the first 100 genera (Figure 2D), with branches representing their corresponding phyla. Stacked histograms outside the ring represent the abundance distribution information of the genus in different groups.

We used linear discriminant analysis (LDA) effect size analysis (LEfSe) to estimate the magnitude of the influence of each species abundance on the differential effect and then identified the species with significant differences between the two groups (Figure 3A). 49 taxa (LDA>2, P<0.05) were identified as significant discriminants. Roseburia, Sutterella, Gemmiger, Parabacteroides, Alistipes, Bifidobacterium and Barnesiella were the main taxa enriched in healthy people. RA patients had significantly fewer of these bacteria in their gut. Figure 3B shows the taxonomic hierarchy of the sample community from phylum to genus.

3.4 Random forest analysis and nested stratified cross-check analysis

Random forest analysis is a supervised machine learning algorithm based on the idea of integrated learning. It integrates more models, avoids limitations, and combines them to predict the final outcome. It is often used to screen biomarkers for intestinal flora^[¹⁸^-²¹^]. We used it to provide further support for distinguishing RA from N groups. We used undrawn ASV tables and taxon abundance tables to conduct nest analysis cross-check analysis and 5-fold cross-check to score according to the importance of output species. Figure 3C shows that species descended in order of importance, so it can be considered that the most important species were markers of differences between groups. The genera with importance scores greater than 0.05 were Gemmiger, Bilophila, Odoribacter and Phascolarctobacterium.

The color shading in each row group identifies the predicted proportion of markup samples. The values for each row in the top half of the table were the proportion of the sample in the corresponding group that was correctly or incorrectly divided into each group. We evaluated the models according to model accuracy and baseline accuracy. Baseline accuracy was correlated with sample size in each group. Figure 3D shows that the comprehensive evaluation ability of this model was ideal.

3.5 Differentially Abundant Metabolites Between the RA and N Groups

Principal component analysis (PCA) can select a few important variables through linear transformation of multiple variables. To obtain high-quality metabolomics data, PCA diagram (Figure 4A) was drawn in this study. The dense distribution of samples indicated that the data measured in this study were reliable. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) can effectively reduce the complexity of the model without reducing the predictive power of the model while enhancing the explanatory power of the model (Figure 4B). Most of the 40 samples in Group RA were concentrated in the left quadrant, and all 7 samples in Group N were concentrated in the right quadrant, indicating good separation between the two groups. There were significant differences in metabolites between RA and N group, indicating that the fitting accuracy of this model was high. It is necessary to conduct a displacement test on the model to observe whether the model exhibits overfitting. As shown in Figure 4C, there was no overfitting of the model.

3.6 Screening and identification of differential metabolites in patients with RA

As a nontargeted metabolomics project based on LC-MC, we identified and qualitatively analyzed metabolites based on retention time, primary mass spectrometry, secondary mass spectrometry and other information.

Variable importance in the projection (VIP) of OPLS-DA and P values of univariate statistical analysis (T test) were used to screen differential metabolites. VIP>1 and P<0.05 were used as reference criteria for screening differential metabolites. In the positive ion mode, phytolaccasaponin G, Lys-Phe-Lys, Pyroglu-Arg-Arg and sucralose were upregulated. In the negative ion mode, cinchonine, heptadecasphing-4-enine, N-acetyl-l-glutamate, deoxyinosine and 5,9-tetracosadienoic acid were upregulated (Figure 5A).

Receiver operating characteristic (ROC) curves were plotted for further analysis. The results ultimately showed that Lys-Phe-Lys and heptadecasphing-4-enine can be used as potential biomarkers for RA. The AUC of heptadecasphing-4-enine was 0.811 and that of Lys-Phe-Lys was 0.796. The P value of them was less than 0.05, indicating that the two biological indicators were strongly significant (Figure 5C, D).

3.7 Differential analysis of metabolic pathways

For KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis, Fisher's exact test was used to analyze and calculate the significance level of metabolite enrichment in each pathway. Fatty acid biosynthesis was found to be significantly different between RA patients and healthy women (Figure 5B).

3.8 Correlation analysis of intestinal differential metabolites, laboratory and intestinal differential flora

We performed Spearman correlation analysis of gut bacteria and metabolites at a subordinate level in women. From Figure S1 A, Gemmiger had an extremely strong positive correlation with Pyroglu-Arg-Arg, betonicine, benzoylmesaconine and protoporphyrinix (P<0.001). Gemmiger can produce short-chain fatty acids such as butyric acid, and we consider that these four metabolites may exert stronger anti-inflammatory effects with the help of Gemmiger. There was a strong positive correlation between L-pipecolic acid, 2 s-amino-4e-pentadecene-1, phytolaccasaponin G, neoline/bullatine b, Lys-Phe-Lys, poncirin and Parabacteroides. Afridi R et al. found that poncirin had good analgesic potential under the condition of inflammatory pain^[²²^]. Parabacteroides, as beneficial intestinal bacteria, could have synergistic anti-inflammatory effects with poncirin^[²³^-²⁵^].

As shown in Figure S1 B, probucol had a negative correlation with Faecalibacterium, Oscillospira, Ruminococcus, and Parabacteroides. Faecalibacterium can produce butyrate, and studies have confirmed that the abundance of Faecalibacterium decreases in RA patients^[²⁶^,²⁷^], which can further affect our immune system through the gut. Since Oscillospira can produce short-chain fatty acids, we speculate that probucol may interfere with the function of Oscillospira in protecting the intestine and indirectly affect the occurrence of RA^[²⁸^-³⁰^]. Many studies have shown that the abundance of Ruminococcus significantly increased in the intestinal tract of patients with systemic lupus erythematosus, Crohn's disease and other immune diseases^[³¹^,³²^]. Studies have shown that Ruminococcus is positively correlated with RF-IgA and anti-CCP antibodies^[³³^]. Ruminococcus abundance was significantly increased in female RA patients, while the specific relationship between probucol and Ruminococcus was not clear. Roseburia has a positive effect in maintaining homeostasis in the gut and may help 5,9-tetracosadienoic acid play an anti-inflammatory role.

Figure S1 C shows that RF had an extremely strong positive correlation with Megamonas, Dialister and SMB53 (P<0.001). ESR had a relatively strong positive correlation with Streptococcus, Lactobacillus and SMB53 (P<0.01). There was an extremely strong negative correlation between Sutterella and DAS28.

The "gut-joint" hypothesis suggests that RA transitions from intestinal mucosal immunity to abnormal local microbiome interactions to synovium and joint sites. Some studies have also shown that intestinal ecological disorders may lead to intestinal inflammation and promote activation of innate and adaptive immune responses^{[34, 35]}. The abundance of intestinal bacteria such as Roseburia, Sutterella, Gemmiger, Parabacteroides, and Alistipes in RA patients was greatly less than that in healthy women, and these bacteria often produce acetic acid, butyric acid and other short-chain fatty acids that can protect the intestinal mucosal barrier.

There have been disputes over the influence of Bacteroides on RA. Some studies have suggested that Bacteroides can weaken the metabolism of glycosaminoglycan in rheumatoid patients, leading to sustained damage to articular cartilage^[36]. Some scholars propose that Bacteroides, as a probiotic bacterium, can be reduced in the intestinal tract of RA patients and that increasing Bacteroides can prevent the development of RA^{[37, 38]}. However, our research results tend to support the view that Bacteroides was increased in the intestinal tract of RA patients, which may aggravate the condition of RA. In this study, the abundance of Megamonas was higher in the guts of female RA patients than in the guts of healthy women. Similarly, the abundance of Megamonas increases significantly in patients with ankylosing spondylitis and Sjogren's syndrome^{[39, 40]}. Therefore, we believe that Megamonas is a microbe that negatively affects RA. We hypothesized that the loss of protection from these bacteria in RA patients resulted in increased intestinal permeability, which may lead to inflammatory immune cells entering the joint, exacerbating the process of RA. However, whether the changes in intestinal flora caused by RA or the disorder of intestinal flora caused by RA needs further study.

In the analysis of significant differences between groups, we used multivariate statistical analysis to screen differential metabolites. The results of this study showed a total of 27 metabolites with significant differences, most of which have not been previously reported by researchers. These metabolites had both positive and negative effects on RA under the influence of the intestinal flora. Most of the metabolites were lipids and lipid-like molecules and organic acids and derivatives. We screened them according to the ROC curve and finally identified heptadecasphing-4-enine and Lys-Phe-Lys as potential markers of RA, which was of great clinical significance in diagnosis.

At the level of metabolic pathways, the most significant differential pathway after screening was fatty acid biosynthesis. Short-chain fatty acids such as acetic acid, propionic acid, and butyric acid are known to be anti-inflammatory and can maintain the intestinal barrier and health^[41]. SCFAs can regulate B-cell differentiation through the FFA2 receptor to alleviate RA^{[42, 43]}. In the process of fatty acid biosynthesis, palmitic acid and myristic acid were involved in the pathway.

We considered that the activity of Megamonas, Dialister and SMB53 was closely related to the increase in RF. The proliferation of Blautia may lead to the rise of CCP. Among the bacteria that cause abnormal ESR, the effect of Coprococcus, Streptococcus, Lactobacillus and SMB53 is more significant. DAS28 is a clinical method of measuring rheumatic range of motion scores in 28 joints^{[44, 45]}. Many bacteria are not directly related to DAS28 other than SMB53 as a possible cause. Gut flora may contribute to the development of RA by interacting with the host immune system. This confirms the validity of the ‘gut-joint’ theory^[46]. Therefore, we hypothesized that the regulation of these gut bacteria, which are closely related to rheumatoid indicators, might be a way to treat RA women in the future. These findings were found for the first time in our study, which was not reported by previous researchers.

The advantages of our study include the use of a multigroup approach to conduct an analysis combining microbiome with fecal metabolomics. Due to the lack of specific studies on female RA patients in the existing research literature, we have a large sample size in this respect, so this paper has valuable diagnostic and clinical significance for female patients with RA. Limitations of this study include the fact that it is still a discovery study and the associations and markers shown do not directly indicate their mechanism of action in RA. The pattern of intestinal flora, metabolites, laboratory indicators, and biomarkers we found in women with RA needs to be further studied in the future for their applicability to other autoimmune diseases. Since our patients were from a single hospital and region, our findings still need to be confirmed in other multicenter, multiethnic, and multicountry cohort studies.

In this study, we successfully distinguished female patients with RA from healthy women using LC-MS based on an untargeted metabolomics approach. Lys-Phe-Lys and heptadecasphing-4-enine could be potential biological diagnostic markers of RA. The laboratory index and metabolites were correlated with the gut flora to varying degrees, which requires further study and verification.

Consent for publication

Not applicable.

Ethics approval and consent to participate

All patients and healthy volunteers signed informed consent forms, which were approved by the ethics committee of Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine (ethics approval number: 2021-266), and written informed consent was obtained from all participants. The study complied with the ethical standards of the Declaration of Helsinki (https://www.wma.net/what-we-do/medical-ethics/declaration-of-helsinki/).

Author contributions

Conception and design: Rui Liu, Dawei Cui

Methodology and interpretation: Haifeng Yun

Data collection: Lixian Yan, Na Li

Statistical analysis: : Xinxin Wang, Changjiang Wei

Writing – Original Draft: Xinxin Wang, Rui Liu

Writing – Review & Editing: Guoxing Zhang, Qiuhong Liu, Dawei Cui

All authors read and approved the final manuscript.

Acknowledgments

The authors thank all investigators and supporters involved in this study. The manuscript had been polished by one or more of the highly qualified native English speaking editors at AJE, and this certificate was verified on the AJE website using the verification code 4OEA-BCC5-9OED-CD19-9754.

Availability of data and material

The data used to support the findings of this study are included within the article.

Funding

This work was supported by Natural Science Foundation Project of Nanjing University of Traditional Chinese Medicine (XZR2020039), and National Natural Science Foundation of China (81871709).

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

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Table 1 Comparison of clinical data between the two groups

Group	RA (N=40)	N (N=7)	t/z/x²	P
Gender	female	female	-	-
Age	53.20 (21.00-11.80)	56.00 (33.00-65.00)	-	-
DAS28 score	4.22±0.55	0.00±0.00	-	-
VAS score（cm）	5.70±1.34	0.00±0.00	-	-
ESR（mm/H）	48.00 (20.00-133.00)	10.00 (1.00-18.00)	-4.185	<0.001
CRP（mg/L）	22.78 (1.44-201.00)	3.02 (1.74-4.68)	-3.855	<0.001
RF（IU/ml）	175 (20.00-2850.00)	9.3 (3.4-15.7)	-4.183	<0.001
Anti-CCP antibody（RU/L）	116.45 (0.70-1200.00)	2.46 (0.18-3.94)	-4.005	<0.001
Number of tender joints	7.55±2.35	0.00±0.00	-	-
Number of swollen joints	6.65±2.44	0.00±0.00	-	-
HAQ score	13.35±4.43	0.00±0.00	-	-

Abbreviations: DAS28 Score:Disease activity score 28 Score; VAS score:Visual analogue score; ESR:Erythrocyte sedimentation rate; CRP:C-reactive protein; RF:Rheumatoid factors; HAQ:health assessment question score

No competing interests reported.

FigureS1.docx
Figure S1. Correlation heatmaps (A) Positive ion mode of metabolites and microorganism (B) Negative ion mode of metabolites and microorganism (C) Correlation heatmaps between metabolites and microorganism. Red means positive correlation and blue means negative correlation; *P<0.05,**P<0.01.

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Alterations of the intestinal microbiome and metabolome in women with rheumatoid arthritis

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1. Introduction

2. Materials and methods

2.1 Study Design

2.2 DNA extraction and 16S rRNA sequencing

2.3 Analysis of 16S rDNA Gene Sequences

2.4 Metabolite Extraction and LC-MS Analysis

2.5 Metabolomics Analysis

2.6 Statistical Analysis

3. Results

4. Discussion

5. Conclusion

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References

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