Fecal Microbiota Changes in NZB/W F1 Mice After Induction of Lupus Disease

doi:10.21203/rs.3.rs-146560/v1

Download PDF

Research Article

Fecal Microbiota Changes in NZB/W F1 Mice After Induction of Lupus Disease

https://doi.org/10.21203/rs.3.rs-146560/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 25 Nov, 2021

Read the published version in Scientific Reports →

Version 1

posted

You are reading this latest preprint version

Background: Gut microbiota is thought to regulate immune homeostasis. The association between gut microbiota and the development of systemic lupus erythematosus remains unclear. This study aims to investigate the differential alteration of gut microbiota after the induction of lupus in a murine model with viral peptide of human cytomegalovirus (HCMV), which is an established murine model of induced lupus.

Results: Specifically, we injected HCMVpp65 peptide in peritoneal space in NZB/W F1 mice to induce lupus. Three arms of animal treatment were prepared: intraperitoneal injection of HCMVpp65 peptide, intraperitoneal injection of adjuvant alone, and none injected. Feces of the study animals were collected before and every two weeks after the lupus induction for 16S rRNA sequencing. At 24 weeks of age, pathological investigation of renal tissue from sacrificed mice was conducted. Statistical analysis for dynamics and alteration of the fecal microbiota and lupus-like-activity-related functional prediction of bacterial communities was performed. HCMVpp65 peptide immunization induced lupus-like activities, with a higher level of anti-dsDNA antibodies, creatinine and proteinuria, and severe glomerular damage, compared to the mice treated with none or adjuvant only. A higher Simpson diversity value was measured in mice with injection of HCMVpp65 peptide than those with injection of adjuvant or none, but there have no difference in the species richness estimates, ACE and Chao1. Statistical Analysis of Metagenomic Profiles (STAMP) showed a higher relative abundance of the family (Saccharimonadaceae, Marinifiaceae, and Desulfovibrionaceae) and genera (Candidatus Saccharimonas, Roseburia, Odoribacter, and Desulfovibrio) that were found in HCMVpp65 peptide treated mice, compared to mice injected with adjuvant or none. The predicted metagenomic taxonomic profile showed statistically significant enrichment of flagellar assembly, bacterial motility, and chemotaxis.

Conclusion: A significant correlation between increased related genera abundance (Candidatus Saccharimonas, Roseburia, Odoribacter, and Desulfovibrio) and HCMVpp65 peptide immunization induced lupus-like activities was observed. This study confirmed significant changes in gut microbiota after the onset of lupus in a murine animal model.

General Microbiology

Applied & Industrial Microbiology

Microbiota

systemic lupus erythematosus

human cytomegalovirus phosphoprotein 65

NZB/W F1

glomerulonephritis

Systemic lupus erythematosus (SLE) is a prototype of systemic autoimmune diseases characterized by persistent chronic inflammation and various autoantibody productions, particularly anti-dsDNA antibody. It also results in dysregulation of cytokines that lead to severe and irreversible tissue injury [1, 2]. Although SLE etiology remains uncertain, genetic, environmental, hormonal, and epigenetic factors are associated with SLE development [3–5]. The mammalian gut is colonized by trillions of microorganisms that shape microbial diversity in the human intestine, are collectively known as the microbiota [6]. The reciprocal interplay between intestinal microbiota and the host immune system maintains tissue homeostasis [7–9]. Therefore, autoimmune diseases such as systemic lupus erythematosus (SLE) may be associated with changes in gut microbiota.

A recent cross-sectional study confirmed a decreased species richness diversity and a reduction in taxonomic complexity in feces of lupus nephritis patients compared with controls [10]. Other human evidence also links the gut microbiota changes to the presence of serum antinuclear antibody and changes of inflammatory cytokines associated with SLE progression [11, 12]. In animal studies, the alteration of microbial community structure and the greater bacterial diversity in the SLE have been reported [13, 14]. The increase in the relative family abundance of Lachnospiraceae and Rikenellaceae was reported to be associated with the severity of murine lupus activity, suggesting that gut microbiota significantly influences the host immune system and could effectively impact the development of SLE [15, 16]. Current data demonstrate that the pattern changes in the intestinal microorganisms or the presence of specific bacteria genus in the gut are associated with immune responses related to lupus.

Human cytomegalovirus (HCMV), a virus linked to the development of SLE in humans, has been shown to accelerate lupus-like disease in murine models [17, 18]. In the current study, we used HCMVpp65 peptide to immunize NZB/W F1 mice and to induce lupus. The objective of this study was to investigate the dynamics of fecal microbiota associated with the lupus-like activity in HCMVpp65_422-439-immunized mice compared to mice treated with none or adjuvant only.

Synthetic peptides

The purity of synthetic HCMVpp65_422-439 peptides _{(GGGAMAGASTSAGRKRKS)} was > 99%, as per the manufacturers' guarantee (GenScript Biotech Corp). The HCMVpp65 peptide was prepared and stored according to the manufacturer’s recommendations.

NZB/W F1 mice and induction of lupus-like activity

The laboratory animal experiment center was approved by the Institutional Review Board of the Chang Gung Medical Foundation (approval number #2016062804 and #2018121402). The study was carried out in compliance with the ARRIVE guidelines [19]. NZB/W F1 female mice (3–5 weeks-old) were purchased from Jackson laboratory Co., Ltd., and housed in specific-pathogen-free (SPF) conditions in the animal center at Chang Gung Memorial Hospital. After 30 days of adaptive feeding and co-housing, the NZB/W F1 mice were separated into three groups: control group (n = 3), adjuvant group (injected with adjuvant only, n = 5) and lupus group (induced by HCMVpp65_422-439, n = 7). All mice were housed in an SPF room under a regular 12-hour light/ 12-hour dark cycle and stable air humidity. The immunization schedule was performed as previously described [20]. Briefly, immunization was administered from 12 to 14 weeks of age. On day 1, the NZB/W F1 mice received an intraperitoneal injection with 100-µg HCMVpp65_422-439 emulsified with complete Freund’s adjuvant (CFA, Sigma-Aldrich, Catalog Number: F5881). Boosting of mice was performed with HCMVpp65_422-439 in incomplete Freund’s adjuvant (IFA, Sigma-Aldrich, Catalog Number: F5506) on days 14, 28, and 42.

Serum, urine, and stool collection

Stool, urine, and blood of mice were harvested once every two weeks. For blood collection, mice were bled from the retro-orbital vein sinus, and plasma was collected from the blood by centrifugation for 10 minutes at 4°C at 13,000 rpm and stored at -80°C. The fresh stool and urine samples were collected from mice and preserved in micro-tubes that were immediately stored in liquid nitrogen.

Hematoxylin and Eosin (H&E) staining

H&E staining was conducted according to the Cold Spring Harbor protocols with small modification [21]. The frozen sections of the kidney were immersed in 100% ethanol for 30 seconds after they were sectioned and then rinsed ten times in double-distilled H₂O. Slides in the rack were put into a container filled with hematoxylin for 5 minutes, and the rack was then dipped five times into a jar containing 0.1% HCl. After dipping into tap water for 5 seconds, the rack was dipped into a jar containing 0.1% NH₄OH five times and then dipped into tap water five times. The cytoplasm stain with eosin and tissue dehydration as follows: eosin for 3 minutes, 100% ethanol with 0.1% acetic acid for five dips, 100% ethanol I for five dips, 100% ethanol II for five dips, acetone I for five dips, acetone II for five dips, xylene I for five dips and xylene II for five dips. After dehydration, the slides were mounted, and a cover glass was put on the slide for further investigation by microscopy (Olympus IX73/DP72, cellSens standard software).

Evaluation for lupus-like activity in NZB/W F1 mice

The examination for lupus-like activity was performed by serum indexes and kidney pathology using the ELISA test and hematoxylin-eosin (HE) staining [21]. The serum level of creatinine was measured by using standard ELISA test kits (MyBioSource, catalog number: MBS751125 and MBS763433, Southern California, San Diego, USA). The serum titer of anti-dsDNA autoantibodies was evaluated by anti-dsDNA ELISA Kit (Inova Diagnostics, catalog number: 708510, Southern California, San Diego, USA) according to the manufacturers’ instructions. Proteinuria levels were examined by using the proteinuria strip (Medi-Test Combi 10 VET strip, MACHEREY-NAGEL, Germany).

Microbial DNA Extraction and 16S rRNA Gene Sequencing

Microbial DNA was extracted from fecal pellets of mice using the DNA Stool Mini Kit (Qiagen) according to the manufacturer’s instructions. The quality and quantity of DNA extracts were determined by agarose gel electrophoresis (0.8% w/v agarose) and NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific) followed by 16S rRNA sequencing.

Data Analysis and Bioinformatics of microbial diversity

Data analysis was carried out using the modified protocol, according to Chi-Cheng Huang [22]. Amplicon sequencing was performed by using 300-bp paired-end raw reads and assembled using FLASH v.1.2.7 [23]. De-multiplexing was carried out based on barcode identification. For quality control, Q < 20 reads were discarded from the pipeline of QIIME (v1.9.1)[24]. If three consecutive bases were < Q20, the read was classified as truncated. The read was preserved in the data set if it had more than 75% of the original length using split_libraries_fastq.py script in QIIME [25]. Using UCHIME algorithms, sequences were chimera-checked to acquire the effective tags and filtered from the data set by using the UPARSE function in the peptide of USEARCH (v.7)[26–29]. The information of operational taxonomic unit (OUT) abundance was normalized to the variation and rarefied to the minimum sequence depth using the QIIME script (single_rarefaction.py). Subsequently, analysis for alpha and beta diversities was performed using the normalized data. Alpha diversity indicated the species complexity within individual samples based on seven different criteria outputs from the QIIME pipeline, including observed-OTUs, Shannon, Simpson, Chao1, and ACE [30]. Observed-OTUs reflect the number of different species identified in the microbial community. The Chao1 and ACE indices used to evaluate community richness and the relative abundance and evenness accounting for diversity were assessed by the Shannon and Simpson indices. Beta diversity analysis was conducted to evaluate the differences across samples in terms of species complexity. Principal Coordinate Analysis (PCoA) was performed using the distance matrix to acquire principal coordinates for visualization of sophisticated and multi-dimensional data [31]. For statistical analysis, the significance of all microbial species within groups at various levels of taxonomy was detected by using the differential abundance analysis with a zero-inflated Gaussian (ZIG) log-normal model executed in the “fitFeatureModel” function of the Bioconductor metagenomeSeq package [32]. Furthermore, Welch’s t-test was carried out using the Statistical Analysis of Metagenomic Profiles (STAMP) software (v2.1.3)[33]. Anosim and MRPP analyses were used to determine whether the community structures significantly differed among and within groups. Significant biomarkers were evaluated by the linear discriminant analysis (LDA) of effect size (LEfSe) analysis with non-parametric factorial Kruskal-Wallis (KW) sum-rank test and LDA to identify the differential abundance of taxa between two groups. For functional analysis, functional abundances from 16S rRNA sequencing data were analyzed for the prediction of functional genes with PICRUSt (v1.1.1)[34]. Fisher’s two-tailed unpaired and paired student's test with graphs depicting the mean ± 3 standard error of the mean (SEM) were used for comparisons between pairs of groups. All tests of statistical hypothesis were done on the 2-sided 5% level of significance. A 5% level of significance for P values was used for all analyses (* P ≤ 0.05; ** P ≤ 0.01; *** P ≤ 0.001). Data were analyzed by using the SAS statistical software (Version 9.4, SAS Institute)

HCMVpp65_422-439immunization induces lupus-like activity in NZB/W F1 mice

Studies indicate that HCMVpp65_422-439 immunization induces anti-dsDNA autoantibodies and initiates the glomerulonephritis in non-autoimmune prone mice [35]. To investigate the differential alteration of fecal microbiota associated viral peptide-induced lupus-like activities, we conducted HCMVpp65_422-439 immunization using NZB/W F1 mice at 12 weeks of age, and their lupus-like activities were evaluated (Fig. 1). Compared with NZB/W F1 mice treated with none (control group) or adjuvant only (adjuvant group), the levels of serum anti-dsDNA antibody, creatinine, and proteinuria were increased in HCMVpp65_422-439-immunized mice (referred as lupus group) at 12 weeks post-immunization (24 weeks of mice age, Fig. 1a-c). Also, we observed enlarged spleens and more severe renal damage in the lupus group (Fig. 1d-f). These findings suggest that HCMVpp65_422-439 immunization induced lupus successfully.

Dynamics of the microbial composition in NZB/W F1 mice

To investigate the change in the microbial community in NZB/W F1 mice, we analyzed fecal samples collected from mice before immunization (Pre-disease, 12 weeks of mice age) and at the diseased time point (lupus, adjuvant, and control groups, 24 weeks of mice age). The measurement of the alpha and beta diversity was performed to evaluate the differential alteration of microbial composition between these four groups. As shown in Fig. 2a-b, the lupus group had significantly higher Shannon’s (P = 0.033) and Simpson’s (P = 0.008) diversity index values (extent of microbial diversity), but there was no difference in ACE and Chao1 indexes (extent of microbial richness) compared to the control group. A higher Simpson’s index value was found in the lupus group in comparison with the adjuvant group. The microbial diversity and richness had no significant difference between the adjuvant and control groups (Fig. 2a-d). For beta diversity analysis, the pattern variations in the microbial community structure between lupus, adjuvant, and control groups are listed in Table 1. The three-dimensional PCoA plot exhibited a separation of the community composition between the four groups (Fig. 2e). PC1, PC2, and PC3 showed 9.93% and 17% and 25.66% of the total variance in microbial species, respectively (Fig. 2E). Figure 2F-G shows the relative abundance of the top ten phyla and genera levels in the bacterial community of three groups (lupus, adjuvant, and control). At the phylum level, Bacteroidetes (59.0%) was determined to be the most abundant in the control group, followed by Firmicutes (39.5%), Tenericutes (1.0%), and Patescibacteria (0.3%) (Fig. 2f). In comparison with adjuvant group, the lupus group had an increase in Firmicutes (53.5% vs 45.7%), Patescibacteria (3.1% vs 1.7%), and Proteobacteria (0.6% vs 0.007%), as well as a decrease in Bacteroidetes (41.8% vs 52%) and Tenericutes (0.7% vs 0.9%). At the genus level, the genera (top ten) in the relative abundance of Lachnospiraceae NK4A136 group (f_ Lachnospiraceae), Oscillibacter (f_Oscillospiraceae), Ruminococcaceae UCG 014 (f_ Ruminococcaceae), Intestinimonas, Candidatus Saccharimonas (f_ Saccharimonadaceae), Ruminiclostridium, Ruminiclostridium 9 (f_ Ruminococcaceae) and Anaeroplasma (f_Anaeroplasmataceae) were observed in the four groups (Fig. 2g). The total percentage of top ten genera was 22.6%, 22.4%, 25.5%, and 31.0% in pre-disease, control, adjuvant, and lupus groups, respectively.

Table 1
Significant test of microbial community structure between two groups
	MRPP
Groups	E-Δ	P	R²	P	R	P
Adjuvant vs. Control	0.36	1.8E-2	0.21	6.3E-2	0.11	3.0E-1
Adjuvant vs. Lupus	0.45	2.0E-3	0.30	9.0E-4	0.66	3.0E-3
Control vs. Lupus	0.45	1.3E-2	0.35	1.4E-2	0.58	1.9E-2
Three different tests were performed with MRPP, Adonis and Anosim based on Bray-Curtis dissimilarity. E-Δ: Expected-delta. P: P-value.

Altered microbial families and genera associated with HCMVpp65_422-439immunization

The STAMP software was used for the comparison of lupus, adjuvant, and control groups to determine the significant alterations of microbiota in the lupus group. The significantly altered family and genera with changes in relative abundance are shown in Fig. 3 and Fig. 4, respectively. In comparison to the control group at the family level, the increased relative abundance of Saccharimonadaceae was observed in the adjuvant group (Fig. 3a-b). Meanwhile, the change in abundance of Saccharimonadaceae, Marinifiaceae, Rikenellaceae, Desulfovibrionaceae, and Eggerthellaceae was observed in the lupus group (Fig. 3c). The lupus group had a higher relative abundance of the family Saccharimonadaceae (3.1%), Marinifiaceae (2.0%), and Desulfovibrionaceae (0.7%) than the adjuvant and control group did (Fig. 3d). At the genus level, the increase in Candidatus Saccharimonas, Turicibacter, Ruminiclostridium 5, and Ruminococcaceae UCG-009 were observed in the adjuvant group (Fig. 4a). Also, the change in abundance of Lachnospiraceae_NK4A136_group, Candidatus Saccharimonas, Odoribacter, Roseburia, Ruminiclostridium_5, Alistipes, Desulfovibrio, Ruminococcaceae_UCG_005, Ruminococcaceae_UCG_010 or Enterorhabdus were found in the lupus group in comparison with the control group (Fig. 4b-c). Notably, Candidatus Saccharimonas (3.1%), Roseburia (2.3%), Odoribacter (1.9%), and Desulfovibrio (0.7%) had higher abundance in the lupus group compared to the other two groups (Fig. 4d). Linear discriminant analysis (LDA) Effect Size (LEfSe, LDA > 3) showed the differential alteration between lupus versus adjuvant and lupus versus control groups in Supplemental Fig. 1.

Functional prediction of microbial communities associated with lupus-like activities in HCMVpp65_422-439immunization

We predicted the functional potential of microbial communities using phylogenetic reconstruction of unobserved states (PICRUSt) to gain an insight into the potential functional role of fecal microbiota in the lupus group[34]. Several pathways relevant to cell motility were differentially expressed between the two groups (Fig. 5). The genetic markers with significant discriminative power in the cellular processes, including lysosome, flagellar assembly, cytoskeleton proteins, bacterial motility proteins, and bacterial chemotaxis, were detected. Spearman’s rank correlation method was conducted to infer the association of fecal microbial genera and lupus-like activity. Statistically significant positive correlations between microbial genera and lupus-like activity were identified (Fig. 6). Noticeably, all four lupus-like activities correlate positively with Odoribacter, Desulfovibrio, and Roseburia. Candidatus Saccharimonas showed a statistically significant positive correlation with creatinine, anti-dsDNA IgG titer and severity of glomerulonephritis (Fig. 6).

Accumulating evidence suggests that alterations in microbiota composition are related to the development of SLE [36]. In the current study, we report that the fecal microbial alteration is associated with lupus-like activity in NZB/W F1 mice. The HCMVpp65_422-439 immunization accelerated lupus progression and exacerbated glomerulonephritis. Among these animals, a significantly altered microbiota composition was observed, and had a differential bacterial community, compared to the adjuvant or control group. The abundance of significantly altered microbial families (Saccharimonadaceae, Marinifilaceae, and Desulfovibrionaceae) and genera (Candidatus Saccharimonas, Roseburia, Odoribacter, and Desulfovibrio) in lupus group were suggested to be involved in the HCMVpp65 peptide induced lupus-like activity in NZB/W F1 mice.

Previous studies reported altered microbiota composition in lupus as well. A significant relative increase in the family Clostridiaceae, Lachnospiraceae, Ruminococcaceae, Desulfovibrionaceae, and Rikenellaceae, and a decrease in Akkermansia muciniphila were described [37]. Moreover, the same group has been reported the increased levels of Lachnospiraceae, Ruminococcaceae, and Rikenellaceae, as well as decreased levels of Lactobacillaceae in MRL/lpr mice at the disease stage [16]. A higher relative abundance of the Rikenellaceae family in SNF1 lupus mice is believed to be associated with more severe lupus symptoms [15]. The Lactobacillaceae and Akkermansiaceae families are reported to have an anti-inflammatory effect and play a crucial role in microbiota remodeling [38–40]. In the current study, the family Saccharimonadaceae, Marinifilaceae, Rikenellaceae, and Desulfovibrionaceae were more abundant in the lupus group in comparison to the control group. The significant decrease in Akkermansiaceae family was found in the three groups at 12 weeks post-immunization, but the decreased level of Lactobacillaceae did not reach the statistical significance (Supplemental Fig. 2a). Of note, the increase in Saccharimonadaceae and Marinifilaceae family has been reported to positively correlate with levels of pro-inflammatory cytokines such as TNF-α, IL-1β, and IL-6 [41]. The enrichment in the relative abundance of the Desulfovibrionaceae family associated with Th17 expression was found in patients with multiple sclerosis [42]. The higher phylogenetic diversity and a significant change in bacterial community structure were found in the lupus group that is prone to inflammation and potentially related to the increased risk of inflammatory disease.

At the genus level, we observed an increased relative abundance of Candidatus Saccharimonas, Roseburia, Odoribacter, and Desulfovibrio genera in the lupus group. These microbial genera were found to link to host immune regulation or autoimmune diseases such as proinflammatry cytokine IL-17. The increased relative abundance of Candidatus Saccharimonas and Roseburia in colitis has been reported. They were found to associate with the reduction of IL-17 expression [43, 44]. Roseburia may promote the regulatory T cell differentiation and reduce the expression of IL-17 in inflammatory bowel disease [43]. In contrast, the abundance of Odoribacter and Desulfovibrio genera are found in several autoimmune diseases, including rheumatoid arthritis, ankylosing spondylitis, and inflammatory bowel disease, possibly due to the increased expression of IL-17 and Th17 cells [45–47]. The parallel changes in the abundance of microbial genera in the lupus group were suggested to mediate a synergistic or antagonistic interaction in response to the induction of lupus in NZB/W F1 mice.

In the present study, we found that the average Firmicutes and Bacteroidetes (F/B) ratio increased in the lupus group (1.72 ± 0.49) compared with control (0.72 ± 0.22) and adjuvant (0.89 ± 0.23) groups (Supplemental Fig. 2b). Our results showed significant bacterial diversity and an increasing trend of F/B ratio in NZB/W F1 mice after the induction of lupus. Both Firmicutes and Bacteroidetes are dominant microbial phyla in the human gut and have critical roles in modulating inflammation and immune status in the host [48, 49]. The functional roles of Bacteroidetes that mediate the intestinal mucosal barrier function and reduce inflammation have been described [50, 51]. Firmicutes can lead to the increased production of lipopolysaccharides (LPS) that enter the bloodstreams and result in chronic inflammation [52, 53]. The elevated F/B ratio in an abundance of fecal microbiota is suggested to promote a pro-inflammatory environment and exhibits characteristics of multiple sclerosis [42, 54]. Recently, a research work to study the difference in gut microbiota between SLE patients and non-SLE controls reported that the value of F/B ratio was no significant difference between the two groups [37]. By contrast, Hevia et al. showed an opposite finding that the remissive SLE patients had significantly lower F/B ratios than did healthy individuals [55]. The F/B ratio has been examined extensively for gut microbiota in human or animal studies, but the correlation between the F/B ratio and diseases seems far from conclusive.

There are some limitations to this study. Long term co-housing of mice may result in hybrid-microbiota animals or cage effects. Taking advantage of the coprophagy of mice, co-housing gnotobiotic animals shortly before mice received immunization can reduce the bias in gut microbiota analysis [56]. However, we were unable to eliminate the cage effect, even though co-housing of mice before induction of lupus in mice. Moreover, the small population of mice and disease severity of mice after induction are two possible factors causing bias in this study. However, our results are not merely consistent with previously reported alterations in microbiota composition in spontaneous murine lupus model, but also observed significantly higher species richness and microbial community. These findings suggest that the orchestration of specific bacterial compositions may play a part in the development of autoimmune diseases.

In our study, we attempted to investigate the interplay between the changes in the fecal microbiota and lupus-like activity in NZB/W F1 mice. The lupus group has higher microbial diversity and an increase in the family (Rikenellaceae Desulfovibrionaceae. Saccharimonadaceae and Marinifilaceae) and genera (Candidatus Saccharimonas, Roseburia, Odoribacter, and Desulfovibrio). The current study showed that the fecal microbiota composition of the viral peptide induced lupus-like activity in NZB/W F1 mice markedly differed from their controls, which may be of value for future microbiota-targeted work in the similar disease induction scheme.

Ethics approval and consent to participate

The laboratory animal experiment was approved by the Institutional Review Board of the Chang Gung Medical Foundation (approval number #2016062804 and #2018121402). All experiments were performed with full compliance with the Guide of the Care and the Use of Laboratory Animals in Taiwan. The study was carried out in compliance with the ARRIVE guidelines.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. All sequences used in this study are available in the NCBI Sequence Read Archive. https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA693398 (Reference number PRJNA693398)

Competing interests

The authors declare that they have no competing interests.

Funding

The current study was supported by the Chang Gung Memorial Hospital (CMRPG3G0851, CMRPG3H1393 & CMRPG3F2142), and the Taiwan Ministry of science and technology (108-2314-B-182A-064). The funders had no role in study design, data collection, analysis, decision to publish, or preparation of the manuscript.

Authors’ contributions

AHH, LCW, WYT and CFK designed the project. AHH, LCW, YFC, YJH, and YCT performed the experiments, acquired the data and interpreted the results. AHH, YLK, KHY, CFK and SFL participated in the interpretation of the results and drafted and revised the manuscript. All authors reviewed the manuscript, approved the final version to be published and agreed to be accountable for all aspects of the work.

Acknowledgments

We greatly appreciate Wan-Ting Su and Wei-Ling Chang for their technical support.

Authors' information

Affiliations

Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Taoyuan, Taiwan

Yen-Fu Chen, Ao-Ho Hsieh, Lian-Chin Wang, Yun-Ju Huang, Yun-Chen Tsai, Shue-Fen Luo, Kuang-Hui Yu, Chang-Fu Kuo

Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Keelung, Taiwan Wen-Yi Tseng

Biotools Co., Ltd, New Taipei City 221, Taiwan

Yu-Lun Kuo

Arbuckle MR, McClain MT, Rubertone MV, Scofield RH, Dennis GJ, James JA, et al. Development of autoantibodies before the clinical onset of systemic lupus erythematosus. N Engl J Med. 2003;349(16):1526–33; doi: 10.1056/NEJMoa021933.
Wahren-Herlenius M, Dorner T. Immunopathogenic mechanisms of systemic autoimmune disease. Lancet. 2013;382(9894):819–31; doi: 10.1016/S0140-6736(13)60954-X.
Tektonidou MG, Wang Z, Dasgupta A, Ward MM. Burden of Serious Infections in Adults With Systemic Lupus Erythematosus: A National Population-Based Study, 1996–2011. Arthritis Care Res (Hoboken). 2015;67(8):1078–85; doi: 10.1002/acr.22575.
Sun C, Molineros JE, Looger LL, Zhou XJ, Kim K, Okada Y, et al. High-density genotyping of immune-related loci identifies new SLE risk variants in individuals with Asian ancestry. Nat Genet. 2016;48(3):323–30; doi: 10.1038/ng.3496.
Scharer CD, Blalock EL, Mi T, Barwick BG, Jenks SA, Deguchi T, et al. Epigenetic programming underpins B cell dysfunction in human SLE. Nat Immunol. 2019;20(8):1071–82; doi: 10.1038/s41590-019-0419-9.
Ley RE, Peterson DA, Gordon JI. Ecological and evolutionary forces shaping microbial diversity in the human intestine. Cell. 2006;124(4):837–48; doi: 10.1016/j.cell.2006.02.017.
Stecher B, Hardt WD. Mechanisms controlling pathogen colonization of the gut. Curr Opin Microbiol. 2011;14(1):82–91; doi: 10.1016/j.mib.2010.10.003.
He B, Xu W, Santini PA, Polydorides AD, Chiu A, Estrella J, et al. Intestinal bacteria trigger T cell-independent immunoglobulin A(2) class switching by inducing epithelial-cell secretion of the cytokine APRIL. Immunity. 2007;26(6):812–26; doi: 10.1016/j.immuni.2007.04.014.
Kuwahara T, Yamashita A, Hirakawa H, Nakayama H, Toh H, Okada N, et al. Genomic analysis of Bacteroides fragilis reveals extensive DNA inversions regulating cell surface adaptation. Proc Natl Acad Sci U S A. 2004;101(41):14919–24; doi: 10.1073/pnas.0404172101.
Azzouz D, Omarbekova A, Heguy A, Schwudke D, Gisch N, Rovin BH, et al. Lupus nephritis is linked to disease-activity associated expansions and immunity to a gut commensal. Ann Rheum Dis. 2019;78(7):947–56; doi: 10.1136/annrheumdis-2018-214856.
Gaudreau MC, Johnson BM, Gudi R, Al-Gadban MM, Vasu C. Gender bias in lupus: does immune response initiated in the gut mucosa have a role? Clin Exp Immunol. 2015;180(3):393–407; doi: 10.1111/cei.12587.
Lopez P, de Paz B, Rodriguez-Carrio J, Hevia A, Sanchez B, Margolles A, et al. Th17 responses and natural IgM antibodies are related to gut microbiota composition in systemic lupus erythematosus patients. Sci Rep. 2016;6:24072; doi: 10.1038/srep24072.
Mu Q, Tavella VJ, Kirby JL, Cecere TE, Chung M, Lee J, et al. Antibiotics ameliorate lupus-like symptoms in mice. Sci Rep. 2017;7(1):13675; doi: 10.1038/s41598-017-14223-0.
Silverman GJ. The microbiome in SLE pathogenesis. Nat Rev Rheumatol. 2019;15(2):72–4; doi: 10.1038/s41584-018-0152-z.
Johnson BM, Gaudreau MC, Al-Gadban MM, Gudi R, Vasu C. Impact of dietary deviation on disease progression and gut microbiome composition in lupus-prone SNF1 mice. Clin Exp Immunol. 2015;181(2):323–37; doi: 10.1111/cei.12609.
Zhang H, Liao X, Sparks JB, Luo XM. Dynamics of gut microbiota in autoimmune lupus. Appl Environ Microbiol. 2014;80(24):7551–60; doi: 10.1128/AEM.02676-14.
Chang M, Pan MR, Chen DY, Lan JL. Human cytomegalovirus pp65 lower matrix protein: a humoral immunogen for systemic lupus erythematosus patients and autoantibody accelerator for NZB/W F1 mice. Clin Exp Immunol. 2006;143(1):167–79; doi: 10.1111/j.1365-2249.2005.02974.x.
Sebastiani GD, Iuliano A, Canofari C, Bracci M. Cytomegalovirus infection in Systemic Lupus Erythematosus: report of four cases challenging the management of the disease, and literature review. Lupus. 2019;28(3):432–7; doi: 10.1177/0961203319825570.
Percie du Sert N, Hurst V, Ahluwalia A, Alam S, Avey MT, Baker M, et al. The ARRIVE guidelines 2.0: updated guidelines for reporting animal research. J Physiol. 2020;598(18):3793–801; doi: 10.1113/JP280389.
Hsieh AH, Wang CM, Wu YJ, Chen A, Chang MI, Chen JY. B cell epitope of human cytomegalovirus phosphoprotein 65 (HCMV pp65) induced anti-dsDNA antibody in BALB/c mice. Arthritis Res Ther. 2017;19(1):65; doi: 10.1186/s13075-017-1268-2.
Fischer AH, Jacobson KA, Rose J, Zeller R. Hematoxylin and eosin staining of tissue and cell sections. CSH Protoc. 2008;2008:pdb prot4986; doi: 10.1101/pdb.prot4986.
Huang CC, Shen MH, Chen SK, Yang SH, Liu CY, Guo JW, et al. Gut butyrate-producing organisms correlate to Placenta Specific 8 protein: Importance to colorectal cancer progression. J Adv Res. 2020;22:7–20; doi: 10.1016/j.jare.2019.11.005.
Magoc T, Salzberg SL. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27(21):2957–63; doi: 10.1093/bioinformatics/btr507.
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7(5):335–6; doi: 10.1038/nmeth.f.303.
Bokulich NA, Subramanian S, Faith JJ, Gevers D, Gordon JI, Knight R, et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat Methods. 2013;10(1):57–9; doi: 10.1038/nmeth.2276.
Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics. 2011;27(16):2194–200; doi: 10.1093/bioinformatics/btr381.
Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10(10):996–8; doi: 10.1038/nmeth.2604.
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26(19):2460–1; doi: 10.1093/bioinformatics/btq461.
Haas BJ, Gevers D, Earl AM, Feldgarden M, Ward DV, Giannoukos G, et al. Chimeric 16S rRNA sequence formation and detection in Sanger and 454-pyrosequenced PCR amplicons. Genome Res. 2011;21(3):494–504; doi: 10.1101/gr.112730.110.
Lozupone CA, Knight R. Species divergence and the measurement of microbial diversity. FEMS Microbiol Rev. 2008;32(4):557–78; doi: 10.1111/j.1574-6976.2008.00111.x.
Jiang XT, Peng X, Deng GH, Sheng HF, Wang Y, Zhou HW, et al. Illumina sequencing of 16S rRNA tag revealed spatial variations of bacterial communities in a mangrove wetland. Microb Ecol. 2013;66(1):96–104; doi: 10.1007/s00248-013-0238-8.
Paulson JN, Stine OC, Bravo HC, Pop M. Differential abundance analysis for microbial marker-gene surveys. Nat Methods. 2013;10(12):1200–2; doi: 10.1038/nmeth.2658.
Parks DH, Tyson GW, Hugenholtz P, Beiko RG. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics. 2014;30(21):3123–4; doi: 10.1093/bioinformatics/btu494.
Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31(9):814–21; doi: 10.1038/nbt.2676.
Hsieh AH, Kuo CF, Chou IJ, Tseng WY, Chen YF, Yu KH, et al. Human cytomegalovirus pp65 peptide-induced autoantibodies cross-reacts with TAF9 protein and induces lupus-like autoimmunity in BALB/c mice. Sci Rep. 2020;10(1):9662; doi: 10.1038/s41598-020-66804-1.
Collison J. Gut microbiota linked to kidney disease in SLE. Nat Rev Rheumatol. 2019;15(4):188; doi: 10.1038/s41584-019-0196-8.
Luo XM, Edwards MR, Mu Q, Yu Y, Vieson MD, Reilly CM, et al. Gut Microbiota in Human Systemic Lupus Erythematosus and a Mouse Model of Lupus. Appl Environ Microbiol. 2018;84(4); doi: 10.1128/AEM.02288-17.
Klaenhammer TR, Kleerebezem M, Kopp MV, Rescigno M. The impact of probiotics and prebiotics on the immune system. Nat Rev Immunol. 2012;12(10):728–34; doi: 10.1038/nri3312.
Hanninen A, Toivonen R, Poysti S, Belzer C, Plovier H, Ouwerkerk JP, et al. Akkermansia muciniphila induces gut microbiota remodelling and controls islet autoimmunity in NOD mice. Gut. 2018;67(8):1445–53; doi: 10.1136/gutjnl-2017-314508.
Zhai R, Xue X, Zhang L, Yang X, Zhao L, Zhang C. Strain-Specific Anti-inflammatory Properties of Two Akkermansia muciniphila Strains on Chronic Colitis in Mice. Front Cell Infect Microbiol. 2019;9:239; doi: 10.3389/fcimb.2019.00239.
Zhai Z, Zhang F, Cao R, Ni X, Xin Z, Deng J, et al. Cecropin A Alleviates Inflammation Through Modulating the Gut Microbiota of C57BL/6 Mice With DSS-Induced IBD. Front Microbiol. 2019;10:1595; doi: 10.3389/fmicb.2019.01595.
Tremlett H, Fadrosh DW, Faruqi AA, Zhu F, Hart J, Roalstad S, et al. Gut microbiota in early pediatric multiple sclerosis: a case-control study. Eur J Neurol. 2016;23(8):1308–21; doi: 10.1111/ene.13026.
Zhu C, Song K, Shen Z, Quan Y, Tan B, Luo W, et al. Roseburia intestinalis inhibits interleukin17 excretion and promotes regulatory T cells differentiation in colitis. Mol Med Rep. 2018;17(6):7567–74; doi: 10.3892/mmr.2018.8833.
Ren W, Chen S, Zhang L, Liu G, Hussain T, Hao X, et al. Interferon Tau Affects Mouse Intestinal Microbiota and Expression of IL-17. Mediators Inflamm. 2016;2016:2839232; doi: 10.1155/2016/2839232.
Liu X, Zeng B, Zhang J, Li W, Mou F, Wang H, et al. Role of the Gut Microbiome in Modulating Arthritis Progression in Mice. Sci Rep. 2016;6:30594; doi: 10.1038/srep30594.
Stebbings S, Munro K, Simon MA, Tannock G, Highton J, Harmsen H, et al. Comparison of the faecal microflora of patients with ankylosing spondylitis and controls using molecular methods of analysis. Rheumatology (Oxford). 2002;41(12):1395–401; doi: 10.1093/rheumatology/41.12.1395.
Fox JG, Dewhirst FE, Fraser GJ, Paster BJ, Shames B, Murphy JC. Intracellular Campylobacter-like organism from ferrets and hamsters with proliferative bowel disease is a Desulfovibrio sp. J Clin Microbiol. 1994;32(5):1229–37.
Turnbaugh PJ, Ley RE, Mahowald MA, Magrini V, Mardis ER, Gordon JI. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 2006;444(7122):1027–31; doi: 10.1038/nature05414.
Wong ML, Inserra A, Lewis MD, Mastronardi CA, Leong L, Choo J, et al. Inflammasome signaling affects anxiety- and depressive-like behavior and gut microbiome composition. Mol Psychiatry. 2016;21(6):797–805; doi: 10.1038/mp.2016.46.
Delday M, Mulder I, Logan ET, Grant G. Bacteroides thetaiotaomicron Ameliorates Colon Inflammation in Preclinical Models of Crohn's Disease. Inflamm Bowel Dis. 2019;25(1):85–96; doi: 10.1093/ibd/izy281.
Fan H, Chen Z, Lin R, Liu Y, Wu X, Puthiyakunnon S, et al. Bacteroides fragilis Strain ZY-312 Defense against Cronobacter sakazakii-Induced Necrotizing Enterocolitis In Vitro and in a Neonatal Rat Model. mSystems. 2019;4(4); doi: 10.1128/mSystems.00305-19.
Cani PD, Amar J, Iglesias MA, Poggi M, Knauf C, Bastelica D, et al. Metabolic endotoxemia initiates obesity and insulin resistance. Diabetes. 2007;56(7):1761–72; doi: 10.2337/db06-1491.
Mehta NN, McGillicuddy FC, Anderson PD, Hinkle CC, Shah R, Pruscino L, et al. Experimental endotoxemia induces adipose inflammation and insulin resistance in humans. Diabetes. 2010;59(1):172–81; doi: 10.2337/db09-0367.
Tremlett H, Fadrosh DW, Faruqi AA, Hart J, Roalstad S, Graves J, et al. Associations between the gut microbiota and host immune markers in pediatric multiple sclerosis and controls. BMC Neurol. 2016;16(1):182; doi: 10.1186/s12883-016-0703-3.
Hevia A, Milani C, Lopez P, Cuervo A, Arboleya S, Duranti S, et al. Intestinal dysbiosis associated with systemic lupus erythematosus. mBio. 2014;5(5):e01548-14; doi: 10.1128/mBio.01548-14.
Caruso R, Ono M, Bunker ME, Nunez G, Inohara N. Dynamic and Asymmetric Changes of the Microbial Communities after Cohousing in Laboratory Mice. Cell Rep. 2019;27(11):3401-12 e3; doi: 10.1016/j.celrep.2019.05.042.

Additionalfile1.docx

Download PDF

Journal Publication

published 25 Nov, 2021

Read the published version in Scientific Reports →

Version 1

posted

You are reading this latest preprint version

Fecal Microbiota Changes in NZB/W F1 Mice After Induction of Lupus Disease

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Methods

Synthetic peptides

NZB/W F1 mice and induction of lupus-like activity

Serum, urine, and stool collection

Hematoxylin and Eosin (H&E) staining

Evaluation for lupus-like activity in NZB/W F1 mice

Microbial DNA Extraction and 16S rRNA Gene Sequencing

Data Analysis and Bioinformatics of microbial diversity

Results

Discussion

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1