the US, China, and other countries; however, the outcomes were inconsistent (29). In addition, the potential risks of non-conditional pathogens in feces of donors and the attitude of patients toward FMT obstructed its development. As a new generation of bacterial therapy, BCT also has the potential for targeted restoration of the intestinal ecosystem, and the safety and manageability of this therapy suggested a significant step toward precision medicine (30). To this end, research has been carried out to test whether defined microbiota is protective in animal models infected by *C. difficile* (31) or *Salmonella* (32), and the results are promising. Our previous work also showed that a simple, defined microbial consortium can promote the recovery of intestinal microbial equilibrium in mice with ceftriaxone-induced dysbiosis (15). Transplantation of this bacterial consortium also showed effective anti-inflammatory activity on TNBS-induced colitis and upregulation of intestinal barrier function in rats (16), but the specific mechanism remains unknown.

In this study, we further analyzed the BCT effects on intestinal barrier function of DSS-treated mice and found that the tight junction protein occludin was specifically affected. Changing of the expression and the intracellular localization of occludin may contribute mainly to the improvement of the intestinal barrier function. This result suggests an emerging mechanism that the changing of intestinal microbial structure by BCT may affect the occludin regulation system. Intestinal T lymphocytes play a critical role in the mucosal immune system regulation by providing immune surveillance of the epithelium. Among them, the tissue-resident γδT cells are very important members that are considered as both protective and pathogenic T cells in IBD. The protective γδT cells were found to accumulate during DSS or pathogen-induced intestinal inflammation and produce KGF and IL-22, which promotes tissue repair and epithelial cell healing (33). The pathogenic role of γδT cells is mainly due to their production of IL-17, which can induce Th17 differentiation through the inflammatory DC-mediated production of IL-6 and IL-23. And, the Th17 cell-derived IL-17, IL-21, and IFN-γ promote MMP and NO production to induce inflammation and tissue damage (34, 35). However, a recent study demonstrated that IL-17A derived by γδT cells in the cLP promoted epithelial barrier function during DSS-mediated injury and protected the mice from excessive gut permeability (25). Given the previously suggested role of IL-17A in maintaining barrier function of epithelial tissues and the fact that the clinical trials targeting IL-17A were ineffective in treating CD (36, 37), this new finding therefore proves that IL-17A produced by γδT cells is protective, and its effects of maintaining barrier integrity may be exerted mainly through an IL-17R-ACT1-occludin pathway in epithelial cells. Our investigation into this pathway confirmed that the regulation of occludin and the enhancement of barrier function of colon mucosa after BCT are correlated with the expression changes of IL-17A, IL-17RC, and ACT1. And, to test whether γδT cells contributes to the higher levels of IL-17A in colonic tissue, we further tested the T-lymphocyte subpopulations by flow cytometry. The results confirmed that γδT cells but not the Th17 cells are the major source of IL-17A. DSS induced a compensatory accumulation of γδT17 cells in cLP, and this proportion was upregulated after transplantation of the bacterial consortium. The promotion of IL-17A producing γδT cells, therefore, highlighted the regulation mechanism of BCT on intestinal barrier function.

However, it is still unknown how BCT promoted IL-17A production from γδT cells. When tested *in vitro*, we found that the BCT-promoted IL-17A production from γδT cells was closely associated with the upregulation of TLR2 expression. This is in line with prior reports (38, 39) that the TLR2 pathway is critical for recognizing microbial products and activating the innate immune system in response to altered microbiota. Therefore, changing of the intestinal microbial structure may contribute to the mucosal immune response of γδT cells. This idea can be further supported by recent studies that commensal microbiota affects ischemic stroke outcome by regulating intestinal γδT cells (23). Gut commensal microbes were also found essential in

maintaining the homeostasis of liver-resident γδT17 cells (24). We therefore tested the intestinal microbial structure of mice from different experimental groups by 16S rDNA pyrosequencing. A dramatically dispersed intestinal microbiota in DSS-treated mice was detected, and this skewed homeostasis was corrected through transplantation of the bacterial consortium.

The deleterious roles of certain bacteria toward the intestinal damage and development of IBD have been proposed. For example, the clade of *Enterobacteriaceae*, particularly *Escherichia*/*Shigella*, has been found to be significantly increased in IBD patients and closely associated with intestinal inflammation (40). The genera *Escherichia*/*Shigella* were also found to be highly enriched in ileal CD patients above the general abundance of CD patients (41). Genera of *Mucispirillum*, which are increased during inflammation, have been suggested to be mucus-dwelling commensals that can cause disease, so calledpathobionts, because under some conditions, the immune system mounts an IgG response against them (42). Significant association of *Prevotella* with CD has been demonstrated by Said et al. (43), and their further studies supported the possibility that the increase of *Prevotella* contributes, at least partially, to the genetic susceptibility to CD (44). In addition, *Helicobacter ganmani*, which are some of the most prevalent bacterial contaminants of laboratory mice, were found associated with alterations in inflammatory cytokines in IL10-deficient mice (45). In accordance with these studies, we demonstrated that the abundance of these bacterial groups was suppressed in the gut of model mice received BCT. Importantly, we observed a significantly negative correlation between *Prevotella* spp. and *Helicobacter ganmani* with γδT17 cells. Interestingly, we also detected a negative correlation between certain *Lachnospiraceae* groups (UCG.005 and NK4A136) with γδT17 cells. The extensive diversity of this family (46) give them divergent resilience to colitis events (42), because promotion of *Lachnospiraceae* strains has been shown to restrict intestinal inflammation (47). Our results thus suggest a previously unidentified physiological difference in this group that may be responsible for the development of colitis.

(*n* = 5). The non-parametric *t*-test was performed between two groups by the assistant of GraphPad Prism 5. \**p* < 0.05, n.s., not significant.

In contrast, BCT promoted some bacterial groups, which were significantly depleted in DSS-treated mice. And, these groups have been implicated to be beneficial microbes in the gut, which may have evolved mechanisms to ameliorate intestinal inflammation and experimental colitis (48, 49). For example, IBD patients were found to have a decreased population of *Lactobacillus* compared to healthy controls. Many studies support that the colonization of *Lactobacillus* strains accounts for the resistance to DSS-induced colitis of animal models (50). Analogously, in our study, we found a decreased proportion of *Lactobacillus* in DSS-treated mice, while upon BCT, the *Lactobacillus* population was obviously increased (**Figure 5D**). Species of *Bifidobacterium* are also well-known as beneficial microbes in combating intestinal inflammation. Particularly low numbers and diversity of *Bifidobacterial* populations were found in pediatric IBD patients (51). *Bifidobacterium animalis* supsp. *lactis* was proved to have protective capacity on

acute and chronic colitis in mice (52). In our study, a positive spearman correlation between *Bifidobacterium animalis* and γδT17 cells was detected, which enhanced the important regulatory role of *Bifidobacteria* on colon mucosa. In addition, we also detected a positive correlation between *Bacillus* species with γδT17 cells. The *Bacillus* species has also been demonstrated to ameliorate DSSinduced dysbiosis and gut inflammation by balancing beneficial and harmful bacteria and associated anti- and pro-inflammatory agents (53). However, when tested *in vitro*, we found that only the *Bacillus* strain, but not the *Bifidobacterium* strain, showed promoting effects on TLR2 expression and IL-17A secretion by γδT cells, which suggested that some bacteria in the consortium may contribute to the expansion of intestinal γδT17 cells, and bacteria such as *Bifidobacterium* spp. may benefit from the re-established balance between gut microbiota and intestinal immune system and, in turn, exert beneficial effects on colon mucosa.

In conclusion, our results suggest that BCT can restore the alliance between commensal microbiota and intestinal γδT17 cells through the manipulation of intestinal dysbiosis in DSStreated mice and this contributes to the improvement of mucosal barrier function, which is closely associated with the upregulation of the IL-17A-Act1-occludin regulatory pathway. Bacteria, such as *Bacillus* and *Bifidobacterium*, may be responsible for or affected by the upregulation of γδT17 cells. However, further study is needed to clarify the underlying mechanisms.

## ETHICS STATEMENT

The animal experimental procedures were approved by the Medical Ethics Committee of Dalian Medical University, China (SYXK2015-0002). The strains used for bacterial transplantation are deposited in the Bacteria Collection of Dalian medical University (DMBC), China, and The University's Institutional Biosafety Committee (IBC) approved applications to conduct only scientific research with these microorganisms.

## AUTHOR CONTRIBUTIONS

XL and JY designed the research; ML, BW, XS, YT, XW, BG, YT, YD, and CH performed the experiments; ML and BW analyzed the data; ML and XL wrote the manuscript. All authors read and approved the final manuscript.

## REFERENCES

## FUNDING

This work was supported by the Nature Science Foundation of China (NSFC 81671606), the China Postdoctoral Science Foundation (2016M601317), the Nature Science Foundation of Liaoning Province, China<PHONE_NUMBER>), and the Research Foundation from the Department of Education, Liaoning Province, China (L2016003).

### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at http://journal.frontiersin.org/article/10.3389/fimmu.2017.00824/ full#supplementary-material.

Figure S1 | The composition of bacterial composition in different experimental groups at Class and Order levels.

Figure S2 | Heatmaps of the abundance of the dominant Phyla (A), Family (B), and Genera (C) in different groups. The relative abundance of each microbial group was normalized, and the *Z*-value was presented and depicted by the color intensity.

Figure S3 | The specific bacterial groups that are significantly manipulated by BCT. The MetaStat method was used to identify the significantly reduced or elevated microbial groups by DSS treatment and was reversed by BCT. k, kingdom; p, phylum; c, class; o, order; f, family; g, genus; All values are mean ± SEM (n = 5). \*adjusted p value <0.05; \*\*adjusted p value <0.01.

inflammatory bowel disease. *Inflamm Bowel Dis* (2005) 11:481–7. doi:10.1097/ 01.MIB.0000159663.62651.4f

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Li, Wang, Sun, Tang, Wei, Ge, Tang, Deng, He, Yuan and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Reduced Mass and Diversity of the Colonic Microbiome in Patients with Multiple Sclerosis and Their Improvement with Ketogenic Diet

Alexander Swidsinski 1, 2 \*, Yvonne Dörffel 3 , Vera Loening-Baucke1 , Christoph Gille1 , Önder Göktas 1 , Anne Reißhauer 1 , Jürgen Neuhaus 4 , Karsten-Henrich Weylandt 5 , Alexander Guschin6 and Markus Bock 7

1 Laboratory for Molecular Genetics, Polymicrobial Infections and Biofilms, Section of Gastroenterology and Hepatology, Department of Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany, 2 Institute of Molecular Medicine, Sechenov First Moscow State Medical University, Moscow, Russia, 3 Outpatient Clinic, Charité Universitätsmedizin Berlin, Berlin, Germany, 4 Faculty of Veterinary Medicine, Centre for Infectious Diseases, University of Leipzig, Leipzig, Germany, 5 Charité Universitätsmedizin Berlin, Campus Virchow, Gastroenterology Berlin, Berlin, Germany, 6 Laboratory of Molecular Diagnostics, Russian Research Center for Molecular Diagnostics and Therapy, Central Research Institute, Moscow, Russia, 7 Experimental and Clinical Research Center, A Joint Cooperation between the Charité Medical Faculty and the Max-Delbrueck Center for Molecular Medicine, Berlin, Germany

#### Edited by:

Marina I. Arleevskaya, Kazan State Medical Academy, Russia

#### Reviewed by:

Undurti Narasimha Das, UND Life Sciences, United States Paolo Puccetti, University of Perugia, Italy

\*Correspondence: Alexander Swidsinski<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Microbial Immunology, a section of the journal Frontiers in Microbiology

Received: 28 May 2017 Accepted: 06 June 2017 Published: 28 June 2017

#### Citation:

Swidsinski A, Dörffel Y, Loening-Baucke V, Gille C, Göktas Ö, Reißhauer A, Neuhaus J, Weylandt K-H, Guschin A and Bock M (2017) Reduced Mass and Diversity of the Colonic Microbiome in Patients with Multiple Sclerosis and Their Improvement with Ketogenic Diet. Front. Microbiol. 8:1141. doi: 10.3389/fmicb.2017.01141 Background: Colonic microbiome is thought to be involved in auto-immune multiple sclerosis (MS). Interactions between diet and the colonic microbiome in MS are unknown.

Methods: We compared the composition of the colonic microbiota quantitatively in 25 MS patients and 14 healthy controls.Fluorescence in situ hybridization (FISH) with 162 ribosomal RNA derived bacterial FISH probes was used. Ten of the MS patients received a ketogenic diet for 6 months. Changes in concentrations of 35 numerically substantial bacterial groups were monitored at baseline and at 2, 12, and 23/24 weeks.

Results: No MS typical microbiome pattern was apparent.The total concentrations and diversity of substantial bacterial groups were reduced in MS patients (P < 0.001). Bacterial groups detected with EREC (mainly Roseburia), Bac303 (Bacteroides), and Fprau (Faecalibacterium prausnitzii) probes were diminished the most. The individual changes were multidirectional and inconsistent. The effects of a ketogenic diet were biphasic. In the short term, bacterial concentrations and diversity were further reduced. They started to recover at week 12 and exceeded significantly the baseline values after 23–24 weeks on the ketogenic diet.

Conclusions: Colonic biofermentative function is markedly impaired in MS patients.The ketogenic diet normalized concentrations of the colonic microbiome after 6 months.

Keywords: FISH, colonic microbiota, multiple sclerosis, biofermentation, ketogenic diet

## INTRODUCTION

There is a growing awareness of the significance of the human microbiome in health and disease. Microbial colonization of the skin and epithelial surfaces protects from pathogens. The biofermentation in the colon delivers energy from digestive leftovers and synthesizes a broad spectrum of vitamins and hormone-like substances, which regulate metabolism and neuronal

**462**

activity (Galland, 2014). The enormous variety of the healthy microbiome conveys antigenic diversity to the host shaping its immunity and autoimmunity (Berer and Krishnamoorthy, 2014).

An ever growing number of studies demonstrates the involvement of the colonic microbiome in obesity, digestive, endocrine, inflammatory, and auto-immune disorders including multiple sclerosis (MS) (Berer and Krishnamoorthy, 2014; Galland, 2014; Glenn and Mowry, 2016).

Topics, methods and results of these studies are well presented and should not be repeated here. However, the previous studies of the colonic microbiome in MS were restricted to identification of microbial patterns associated with disease (Miyake et al., 2015; Jangi et al., 2016; Tremlett et al., 2016). We found no literature on the quantitative evaluation of the colonic microbiome in MS patients. Microbial concentrations, however, are an important feature, which directly measures their functional contribution to colonic fermentation. The aim of this study was to compare the concentrations of different microbial groups in MS patients and healthy controls and to follow up changes in the colonic microbiome taking place during ketogenic diet.

The option of ketogenic diet was important for the following reasons: as long as complex microbiomes cannot be reliably transferred, maintained and tested in vitro, all data raised in vivo must remain observational. Therefore, interventions simultaneously affecting the microbiome and disease are necessary to unravel possible causality.

Ketogenic diet influences brain function, inflammation, immunity and the colonic microbiome. It is increasingly applied in clinical studies (Piccio et al., 2008; Kim et al., 2012; Choi et al., 2016). Different to fasting or mono-diets, ketogenic diet can be maintained over months and is well tolerated. Diet prescriptions are often circumvented in real life. The compliance of the ketogenic diet can be reliably verified through measurement of ketone bodies in blood and urine and cannot be falsified.

## MATERIALS AND METHODS

#### Patients/Samples

Fourteen healthy volunteers from the Laboratories of Centre for Infectious Diseases, Faculty of Veterinary Medicine, University of Leipzig and 25 patients with relapsing-remitting multiple sclerosis cared for at the Charité hospital were investigated for the composition of their colonic microbiome using fluorescence in situ hybridization ribosomal RNA based FISH probes available in public resources (Loy et al., 2016).

The study was reviewed and approved by institutional review board: Ethikkomission Ethikausschuss 1 an Campus Charite Mitte EA1/130/07.

After the baseline investigation, MS patients received a ketogenic diet for 6 months. The ketogenic diet was designed (1) to achieve a modest ketosis (≥500µmol/L ß-hydroxybutyrate) in the blood, self-measured after dinner twice a week (FreeStyle Precision, Abbott Diabetes Care Ltd.), (2) to achieve a modest ketosis (≥500µmol/L acetoacetate) in the urine, self-measured after dinner once a week (Ketostix, Bayer Consumer Care AG), and (3) to maintain compliance. Patients received a booklet with meal suggestions over 28 balanced days and were encouraged to ingest fat. An average daily intake of <50 g carbohydrates, >160 g fat, and <100 g protein was recommended. Patients received detailed information about nutritional facts, glycemic load and learned how to handle carbohydrates by an experienced nutritional coach during group based workshops on 3 weekends.

Ten of the MS patients who were recruited for the ketogenic diet were randomly selected for accompanying microbiome investigations. Stools samples were collected at baseline, week 2, 12, and after 6 months (week 23–25). The evaluation of the clinical effects on MS was not performed because of the low number of patients.

None of the enclosed probands received antibiotics or probiotics in the last 6 month preceding the study.

## Fish

Colonic microbiota were investigated using FISH with ribosomal RNA derived probes. Hybridizations were performed on sections of Carnoy fixated, paraffin embedded and otherwise not manipulated stool cylinders (Swidsinski et al., 2010). Four micrometers thick sections were placed on SuperFrost plus slides.

A Nikon e600 fluorescence microscope was used. The images were photo-documented with a Nikon DXM 1200F color camera and software (Nikon, Tokyo, Japan).

Bacterial concentrations of homogeneous populations were enumerated visually in one of the 10 × 10 fields of the ocular raster corresponding to 10 × 10µm of the section surface at magnification of 1,000. This number was assigned to a concentration of × 109 bacteria/ml, which was most equivalent to the calculation formula, that we had used previously (Swidsinski et al., 2010).

In case of uneven distribution of bacteria over the microscopic field, the positive signals were enumerated in 10 fields of the ocular raster along the gradient of distribution and divided by 10.

Bacteria were quantified using group specific C3 probes. The FITC marked universal probe was used in each hybridization to evaluate the number of all bacteria, C5 marked probes with a different specificity to C3 probes were used to determine the spatial relation of different bacterial groups to each other.

Only signals that hybridized with a specific FISH probe and the universal FISH probe, but did not hybridize with specific FISH probes from unrelated bacterial groups, were enumerated (Swidsinski, 2006).

#### FISH Probes

One hundred sixty-two bacterial FISH probes available from public resources were applied for the comparative analysis of the colonic microbiome in healthy controls and MS patients (**Tables 1A,B**). The names of the FISH probes are listed according to abbreviations of the probeBase resource (http://probebase.csb. univie.ac.at/node/8) (Loy et al., 2016) and the details to FISH probe specificity and hybridization conditions are given. The Fprau probe is described in reference (Suau et al., 2001).

Probes in **Table 1** are alphabetically ordered to subgroups according to abundancy and specificity as described in the result section.

TABLE 1 | Applied FISH-probes.

#### Part A

Substantial groups Essential (N = 3) Erec482 (Eubacterium rectale, Clostridium coccoides group) Bac303 (most Bacteroidaceae) Fprau (Faecalibacterium prausnitzii)

#### Individual pioneer (N = 4)

Bif153 Genus Bifidobacterium Cdif198 Clostridium difficile Clit135 Clostridium lituseburense group including C. difficile Ebac1790 Enterobacteriaceae

Individual substantial (N = 28) ACI623 Acidaminococcaceae sp. (not the Selenomonas species) AKK406 Akkermansia Ato291 Atopobium cluster Bbif186 B. bifidum Blon1004 B. longum Bputre698 Bacteroides putredinis Burkho Burkholderia spp. Ceut705 C. eutactus, Coprococcus sp. Chis150 Clostridium histolyticum Cor653 Coriobacterium group Cvir1414 Clostridium viride group Ecyl387 Eubacterium cylindroides Ehal1469 Eubacterium hallii Eram997 Eubacterium ramulus Lab158 Lactobacillus sp., Enterococcus sp. Muc1437 Akkermansia muciniphila Myc657 Mycobacterium subdivision (mycolic acid-containing actinomycetes) Phasco741 Phascolarctobacterium faecium Pnig657 Prevotella nigrescens ProCo1264 Ruminococcus productus Rbro730 Clostridium sporosphaeroides, Ruminococcus bromii, Clostridium leptum Rfla729 Ruminococcus albus SFB1 Segmented filamentous bacteria SNA Sphaerotilus natans Strc493 most Streptococcus spp. SUBU1237 Burkholderia spp., Sutterella spp. Urobe63a Ruminococcus obeum-like Veil 223 Veillonella Ver620 Verrucomicrobium

#### Part B

Includes probes with extremely low occurrence and concentrations and probes with uncharacteristic signals:

#### Accidental groups (N = 88)

To low in occurrence and concentrations for statistical analysis MIB661 mouse intestinal bacteria AER66 Aeromonas spp. Alac1438 Anaerococcus lactolyticus ARC1430 Arcobacter Avag1280 Anaerococcus vaginalis Bbrel198 B. breve

#### TABLE 1 | Continued

Bden82 B. dentium BFV530 Bacteroides forsythus Bif1278 Bifidobacterium spp. Burcep Burkholderia cepacia CAP365 Capnoytophaga sp. Capno Capnocytophaga sputigena Cj490 Campylobacter jejuni CLOBU1022 Clostridium butyricum, Cperf 191 Clostridium perfringens Cra757 Clostridium ramosum assemblage Csac67 Clostridium saccharogumia CST440 Group 1 clones closely related to Clostridium stercorarium DSV1292 some Desulfovibrio and Bilophila wadsworthia DSV687 most Desulfovibrionales E.bar1237 Eubacterium barkeri E.bif462 Eubacterium biforme E.con1122 Eubacterium contortum E.cyl461 Eubacterium cylindroides E.cyl466 Eubacterium cylindroides E.dol183 Eubacterium dolichum E.had579 Eubacterium hadrum E.len194 Eubacterium lentum E.lim1433 Eubacterium limosum E.mon84 Eubacterium moniliforme E.mul Eubacterium multiforme E.sab Eubacterium saburreum E.ven66 Eubacterium ventriosum Enfl84 Enterococcus faecalis Enfm 93 Enterococcus faecium Fnec996 Fusobacterium necrophorum Fnuc133 Fusobacterium nucleatum GAN1237 Helicobacter ganmani Haeinf Haemophilus influenzae HEP642 Helicobacter hepaticus Hpy-1 Helicobacter pylori Hyo1210 Brachyspira hyodysenteriae Lbuc668 Leptotrichia buccalis Lis1255 Genera Listeria, Brochothrix Lis637 Genus Listeria, Lpara Lactobacillus casei Lzeae Lactobacillus zeae Pae997 Pseudomonas spp. Pamic1435 Parvimonas micra Pana134 Peptostreptococcus anaerobius Pden654 Prevotella denticola Pilosi1405 Brachyspira pilosicoli Pilosi209 Brachyspira pilosicoli Pint649 Prevotella intermedia Pint657 Prevotella intermedia Pnasa1254 Peptoniphilus asaccharolyticus Pnhar1466 Peptoniphilus harei Pnivo731 Peptoniphilus ivorii POGI Porphyromonas gingivalis

(Continued)

(Continued)

Ppu646 Pseudomonas spp. PRIN Prevotella intermedia Saga Streptococcus agalactiae SAL3 Genus Salmonella Sau Staphylococcus aureus Saur327 Staphylococcus Saur72 Staphylococcus aureus Ser1410 Genus Brachyspira SGD229 Genus Desulfotomaculum Sita649 Candidatus Sphaeronema italicum Spn Streptococcus pneumoniae Spy Streptococcus pyogenes Staaur Staphylococcus aureus Staph747 Staphylococcus spp. STEBA1426 Sterolibacterium lineage Stemal Stenotrophomonas maltophilia Strpyo (Streppyo) Streptococcus pyogenes Sval428 Some Desulfobulbaceae TM7305 subdivision 1 of candidate division TM7 Trep-D3-4 32 PT1 Treponema refringens Trep-D4-4 32 PT3 clone DDKL-20 Treponema refringens Trep-HW 170 PT6 clone DDKL-4 Treponema phagedenis Trep-T5-4 32 PT2 Treponema refringens Urobe63b Ruminococcus obeum-like VEPA Veillonella parvula VIB572a Genus Vibrio Y Yersinia FISH probes with signals which could not be definitively assigned to a

# specific group of bacteria (N = 31)

Alf 1b (Alpha) Alphaproteobacteria, some Deltaproteobacteria, Spirochaetes Arch 915 Archaea B for Bacteroides forsythus B(T)AFO Tannerella forsythensis Bact for Bacteroides forsythus Bang198 B. angulatum Bfra602 most Flavobacteria, some Bacteroidetes Bmy843 Bacillus BORR4 Genus Borrelia Bvulg1017 Bacteroides vulgatus CF319a most Flavobacteria, some Bacteroidetes CFB560 subgroup of Bacteroidetes, CFB division Clept1240 Clostridium leptum Efaec Enterococcus faecalis, Enterococcus sulfuricus Enc131 Enterococcus spp and other Ent Enterobacteriaceae except Proteus spp. FUS664 most Fusobacterium sp FUSO Fusobacterium sp. MIB724 mouse intestinal bacteria PBR2 Bifidobacterium breve PseaerA Pseudomonas aeruginosa PseaerB Pseudomonas aeruginosa

(Continued)

We performed hybridizations with all probes but excluded from analysis 31 of these probes, because they showed multiple uncharacteristic signals, in form and distribution not resembling bacteria or cross-reacting with non-related bacterial groups and eight FISH probes that were identical to related probes for the same species.

To reduce the number of unnecessary investigations, while following the impact of the ketogenic diet on the colonic microbiome, only 35 bacterial groups which were found to have substantial occurrence (in at least 20% of individuals) and concentrations (>109 in at least one of the stool samples of one individual) were applied.

#### Statistical Analysis

Differences between groups were evaluated using the two sided t-Student U-test. Data are presented as means ± SD, P <0.05 was considered statistically significant.

#### RESULTS

#### Eligibility of the FISH Probes for Analysis of the Stool Microbiome

Three bacteria detected with EREC (mainly Roseburia), Bac303 (Bacteroides), and Fprau (Faecalibacterium prausnitzii) probes were always present in healthy human controls and MS patients and contributed to about half of the colonic microbiome in each subject. We called these groups **essential bacteria**.

All other investigated bacterial groups were individual, detectable only in a subset of patients. We called them **individual bacterial** groups.

Twenty-eight of the individual bacterial groups were found in at least 30% of the probands (mostly 50–70%) in concentrations of higher than 109 bacteria/ml. They contributed substantially to the colonic microbial mass. We called them **individual substantial groups**.

Four of the **individual** bacterial groups including Bif (Bifidobacteriacae), Ebac (Enterobacteriaceae), Clit (Clostridium lituseburense), and Cdif (Clostridium difficile) are often found prevalent in newborns, after antibiotic treatment and convalescence patients and thus represent bacterial groups with pioneer function. We evaluated these groups separately.

Individual bacterial groups detected with 88 FISH probes (**Table 1B**) were observed in one, maximal two individuals in marginal concentrations of ≤0.1 ×109 ml. We called these **individual marginal** bacterial groups. Because of the uneven distribution of such bacteria over the stool cylinder, their quantification was highly unreliable.

Thirty-one of investigated FISH probes showed multiple uncharacteristic signals, in form and distribution, cross-reacting with unrelated bacterial groups. The appearance of these signals was not visually different in MS and healthy controls. Because of the uncertainty of what exactly is measured, we did not perform the quantitative analysis of the results detected with these probes.

Eight FISH probes, showed identical results with related probes for the same species/bacterial group, were also not quantitatively evaluated.

#### Microbiome in Healthy Controls and MS Patients Prior to Diet Intervention

The morphologic appearance of single bacteria detected with corresponding FISH probes was the same in MS patients and healthy controls. The distribution of bacteria over the stool cylinder surface was not noticeably different. None of the investigated bacterial groups, including groups with unspecific signals, demonstrated prevalence or absence in MS patients, which could be interpreted in terms of Koch's postulates.

As long as bacterial groups were compared pairwise, the differences between MS and healthy patients were discordant, gradual and moderate, reaching only in 9 groups statistical significance (**Table 2**). The concentrations of most investigated groups in MS were decreased. Six groups were slightly increased and included Cor653 (Coriobacterium group), Cvir1414 (Clostridium viride group) Ehal (Eubacterium hallii), Ecyl387 (Eubacterium cylindroides), Lab158 (Lactobacillus sp., Enterococcus sp.), Rfla729 (Ruminococcus albus) bacterial groups. Seven substantial individual bacterial groups had similar high concentrations in MS and healthy controls.

Due to uncertain occurrence and low concentrations, which was difficult to quantify, we did not compare the single marginal bacterial groups in MS patients and controls quantitatively. No rise of single marginal groups in MS patients was observed.

The difference in the microbiome of MS patients and healthy controls became striking, when concentrations of numerically substantial groups were summarized and considered as a whole (**Table 2**, marked in bold). The diversity of all substantial groups in MS patients was reduced by 36% (P < 0.001), the mean sum concentrations of all substantial bacterial groups were reduced by 24% (65 vs. 85.4 ×109 /bacteria/ml., P < 0.001) compared to healthy controls. The decrease in concentrations was most profound in the essential bacteria group (32%) and less impressive in individual substantial (19%) and pioneer groups (14%).

#### Changes of the Colonic Microbiome with the Ketogenic Diet in 10 MS Patients

The changes in the microbiome during the ketogenic diet were univocal. Except for Akkermansia, all groups of bacteria demonstrated consistently more or less marked decreases, leading to a reduction of the total bacterial concentrations of the substantial bacteria from 65 to 25 × 109 bacteria per ml at week 2. Some of the bacterial groups fell below detection level, resulting in further decline of the bacterial diversity from 48 to 36%.

However these tendencies were temporary.

The total bacterial concentrations in MS patients started to increase at week 12, reaching values typical for healthy controls at week 23/24, being then significantly higher than bacterial concentrations in MS patients prior to diet and statistically not different to mean bacterial concentrations in healthy controls (P = 0.7). This increase was consistent for all but the pioneer bacterial groups and Akkermansia. The concentrations of pioneer bacterial groups fell and remained low, the concentrations of Akkermansia increased initially but then declined during the ketogenic diet.

#### DISCUSSION

Previous investigation, using high throughput DNA sequencing technologies in large scale 16S rRNA or shotgun metagenomic sequencing, demonstrated miscellaneous changes in the composition of the colonic microbiome, which correlated with MS, MS-onset, -therapy or -relapse (Kim et al., 2012; Berer and Krishnamoorthy, 2014; Galland, 2014; Miyake et al., 2015; Glenn and Mowry, 2016; Jangi et al., 2016; Tremlett et al., 2016). Both single bacterial groups were found differently represented and also the whole microbiome was aberrantly composed. While the alpha diversity of MS and the healthy microbiome was similar, the beta diversity differed significantly. In ecology, alpha diversity expresses the mean species diversity in sites or habitat at a local scale, while beta diversity is the ratio between regional and local diversity. The differences indicated shifts in composition of the MS microbiome. The meaning of these observations is unclear. The pure occurrence of bacteria does not automatically mean that they are relevant or biochemically active. The vacant niches may be occupied by chance.

Although we applied publically available FISH probes as broadly as possible and included all groups covering numerically substantial components of the colonic microbiome, no conclusions to the entire biodiversity are possible, since FISH reliably detects only bacteria in concentrations of higher than 105 per ml.

However, while sequence analysis is perfect for identification of specific occurrence patterns, its information on physical abundance and contribution of bacteria to biofermentation is poor. Abundance of bacteria within the fecal mass however directly expresses their biofermenting power.

TABLE 2 | Colonic microbiome in healthy and MS patients prior to and during the ketogenic diet.

(Continued)

Our data quantifying microbial participants clearly demonstrate the impaired colonic function in patients with MS. Both concentrations and biodiversity of numerically substantial bacterial groups were markedly reduced in MS. Essential bacteria were most, individual substantial bacteria less depleted, while pioneer bacteria were nearly unchanged.

This grading of suppression matches well with the proposed role of these groups for colonic function. Essential bacteria are present in every healthy person in large concentrations, contributing roughly to approximately half of the mass of the colonic microbiome. They are obviously important for colonic fermentation and represent main fermentative groups in human. The individual substantial bacterial groups are present only in subsets of healthy persons in varying concentrations, which are each distinctly lower than those of the essential bacterial groups. Their presence is dispensable for colonic fermentation. However, their diversity is high, composition specific for each subject, indicating that they fulfill special tasks not covered by the essential bacterial groups. Despite markedly lower concentrations, when compared to essential bacterial groups, the individual substantial groups constitute together another half of the colonic biomass.

Pioneer bacteria are usually found in low concentration in healthy adults. Their concentrations are high in newborns after antibiotic treatment and in convalescence, while the colonic microbiome is reshaped (Swidsinski et al., 2016).

The fall in concentration in MS patients was gradual, decreasing from essential to individual substantial and further to pioneer bacterial groups. This suggests that the suppression is not due to the loss of responsible microbial groups or reshaping of the microbiome. It presumably results from the general downregulation of the colonic biofermentative function and affects mainly biofermentative active groups, leaving bacterial groups with other specific tasks untouched.

We observed no changes in the microbiome that could be specific for MS.

While mean concentrations of all essential bacteria detected with EREC (mainly Roseburia), Bac303 (Bacteroides), Fprau (F. prausnitzii) probes were consistently reduced in MS, the shifts in individual substantial bacterial groups were multidirectional with concentrations of some bacterial groups unchanged, increased or reduced when compared to healthy controls. Although some of the differences reached the level of statistical significance, the fluctuations in concentrations of individual substantial bacterial groups were moderate and in the range of those, when unmatched groups of subjects are compared with each other. Similar fluctuations are documented while describing microbiome composition in other pathologies such as diarrhea, irritable bowel syndrome, inflammatory bowel disease, and others (Mai et al., 2016; Swidsinski et al., 2016).

Since the microbiome is influenced by a multiplicity of racial, occupational, social, regional, and geographic factors, matching for all of them would be an impossible task. Mean values raised in small cohorts should therefore be critically evaluated, even when they occasionally prove to be statistically highly significant (Mai et al., 2016). Our data allow definitively no conclusions to whether the observed alterations in concentrations of colonic bacteria precede, result, specifically accompany MS or rely on independent coincident processes such as aberrant immunity, impaired digestion or simply changed behavior. However, they clearly demonstrate that the perturbation of the microbiome in MS is not inherent and inevitable but can be definitively corrected with diet, supplementation and other means as assumed previously (Tanca et al., 2015).

After 6 months on the ketogenic diet, the sum concentrations of the substantial microbial groups in MS patients increased significantly when compared to the period prior to intervention (83 vs. 65 × 109 bacteria/ml; P = 0.02) and became indistinguishable from the healthy group (83 vs. 85.4 × 109 bacteria/ml. P = 0.7).

Our data also demonstrate that the tracking of the changes in the microbiome should not be restricted to a single time point, but needs surveillance over longer periods of time.

The reduction of microbial concentrations observed 2 weeks after the start of the diet was dramatic, the ranges of suppression were comparable to antibiotics effects (Swidsinski et al., 2016). Concentrations of some individual substantial groups fell under detection limit, leading to further drop in the microbial diversity from mean 48 to 35 percent. However, the processes behind dietetic and antibiotic effects are principally different.

The improvement with antibiotic treatment occurred only after the end of treatment, while with the ketogenic diet, the improvement occurred in succession of the diet and the longterm effects of diet were opposite to the immediate response to the intervention. Although the diversity of the microbiome did not reach the values typical for the healthy population, at the end of the observation period, after 6 months on the ketogenic diet, it completely recovered as compared to basic values prior to treatment.

Obviously the increase in microbial concentrations was mainly due to improvement of colonic function and achieved by preexisting microbial groups. Typical for convalescence after depletion of the colonic microbiome following antibiotic use, stroke or inflammation is a temporary excess of pioneer bacterial groups. Such excess was not observed with the ketogenic diet. In contrast, the pioneer bacterial groups remained reduced over the whole duration of the ketogenic diet (p < 0.05–0.008), indicating an absence of substantial microbiome reshaping.

Although the concentrations and the biodiversity of colonic microbiota are strong markers of the intensity of the microbial metabolism, the shifts in bacterial groups per se do not reveal the exact metabolic changes taking place. The role of single substances and metabolites in neurologic disorders is still to be unraveled and follow our preliminary observations.

Summarizing, we state that colonic microbiome and neuropathology are closely interrelated. Concentrations of numerically substantial biofermentative bacteria are significantly reduced in MS patients. The microbial shifts can be reliably quantified and monitored by FISH under ambulatory conditions. The ketogenic diet for 6 months completely restored the microbial biofermentation mass and is an interesting interventional tool for prospective clinical studies.

## AUTHOR CONTRIBUTIONS

AS, MB,YD, and AG designed the study, YD, JN, AR, KW, and ÖG conducted the study, ÖG and VL critically revised the manuscript, and AS, AG, CG, and AR. performed FISH, CG, JN statistically analyzed the data. All authors contributed to conception of the work, revising of the data, shaping of the manuscript and approved the final draft submitted.

## REFERENCES

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Swidsinski, Dörffel, Loening-Baucke, Gille, Göktas, Reißhauer, Neuhaus, Weylandt, Guschin and Bock. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Associations between Viral Infection History Symptoms, Granulocyte Reactive Oxygen Species Activity, and Active Rheumatoid Arthritis Disease in Untreated Women at Onset: Results from a Longitudinal Cohort Study of Tatarstan Women

#### *Edited by:*

*Juarez Antonio Simões Quaresma, Universidade Federal do Pará, Brazil*

#### *Reviewed by:*

*Paul Proost, KU Leuven, Belgium Ali Mobasheri, University of Surrey, United Kingdom Antonio C. R. Vallinoto, Institute of Biological Sciences (ICB) of Federal University of Pará, Brazil*

> *\*Correspondence: Marina I. Arleevskaya<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Microbial Immunology, a section of the journal Frontiers in Immunology*

*Received: 17 July 2017 Accepted: 22 November 2017 Published: 05 December 2017*

#### *Citation:*

*Arleevskaya MI, Shafigullina AZ, Filina YV, Lemerle J and Renaudineau Y (2017) Associations between Viral Infection History Symptoms, Granulocyte Reactive Oxygen Species Activity, and Active Rheumatoid Arthritis Disease in Untreated Women at Onset: Results from a Longitudinal Cohort Study of Tatarstan Women. Front. Immunol. 8:1725. doi: 10.3389/fimmu.2017.01725*

*Marina I. Arleevskaya1 \*, Albina Z. Shafigullina1 , Yulia V. Filina1,2, Julie Lemerle3 and Yves Renaudineau1,3*

*1State Medical Academy, Kazan, Russia, 2Kazan Federal University, Kazan, Russia, 3 Laboratory of Immunology and Immunotherapy, INSERM U1227, Hôpital Morvan, Centre Hospitalier Régional Universitaire (CHU) de Brest, Brest, France*

To evaluate the effects of infectious episodes at early stages of rheumatoid arthritis (eRA) development, 59 untreated eRA patients, 77 first-degree relatives, from a longitudinal Tatarstan women cohort, were included, and compared to 67 healthy women without rheumatoid arthritis (RA) in their family history. At inclusion, informations were collected regarding both the type and incidence of infectious symptom episodes in the preceding year, and granulocyte reactive oxygen species (ROS) were studied at the basal level and after stimulation with serum-treated zymosan (STZ). In the eRA group, clinical [disease activity score (DAS28), health assessment questionnaire] and biological parameters associated with inflammation (erythrocyte sedimentation rate, C-reactive protein) or with RA [rheumatoid factor, anticyclic citrullinated peptide (anti-CCP2) antibodies] were evaluated. An elevated incidence of infection events in the previous year characterized the eRA and relative groups. In addition, a history of herpes simplex virus (HSV) episodes was associated with disease activity, while an elevated incidence of anti-CCP2 autoantibody characterized eRA patients with a history of viral upper respiratory tract infection symptoms (V-URI). Granulocyte ROS activity in eRA patients was quantitatively [STZ peak and its area under the curve (AUC)] and qualitatively (STZ time of peak) altered, positively correlated with disease activity, and parameters were associated with viral symptoms including HSV exacerbation/recurrence, and V-URI. In conclusion, our study provides arguments to consider a history of increased viral infection symptoms in RA at the early stage and such involvement needs to be studied further.

Keywords: rheumatoid arthritis, infection symptoms, viruses, women, reactive oxygen species

## INTRODUCTION

Rheumatoid arthritis (RA) development results from an inappropriate immune response to environmental challenges in genetically predisposed patients. Accordingly, the list of microorganisms associated with RA is still growing, and several hypotheses support their causative role, as recently reviewed (1). On one hand, the immunological hypothesis proposes that RA development results from an inappropriate immune response to infections, which can lead to loss of tolerance to self-antigens. In support of such a hypothesis, up to 40 risk factors have been associated with RA, and a significant part of the genetic contribution is associated with the major histocompatibility complex locus (2). While on the other hand, the environmental hypothesis proposes that RA development may result from a cumulative effect of microbial/ viral/environmental factors and thus explains the absence of a single defined pathogen. In this case, environmental factors may be accelerators or protective (3).

In order to decipher the respective contributions of the immune system and infections to RA, the longitudinal Tatarstan women cohort study was established in 2002 in order to include the follow-up of RA families (4). Our preliminary results from this cohort have established, first, an elevated rate of infection symptoms in the year preceding RA onset. Second, there is an elevated rate of bacterial colonization in feces, urine, and skin that remains after several years in treated patients with RA. Third, there are impaired innate (phagocyte) and acquired (antibacterial IgG response) immune responses in RA patients. In this study, our main objective was to study the infectious spectrum preceding RA onset and to test its impact on disease activity and innate immunity in comparison to controls and healthy first-degree relatives.

## MATERIALS AND METHODS

#### Subjects

In this prospective study conducted between 2002 and 2016 at the Kazan Rheumatology department, RA women were included in the Tatarstan cohort at time zero when they met RA diagnostic criteria according to the 2010 ACR/EULAR classification criteria (5, 6). In the cases of 14 of the patients who were diagnosed before 2010, the RA onset was diagnosed by consensus of three experienced rheumatologists. For this study, a total of 208 women were studied semiannually including 59 therapy naïve early stage patients [early stages of rheumatoid arthritis (eRA), defined as <6 months of RA duration, time since diagnosis 0.3 ± 0.1 years; age range: 40.8 ± 16.2 years old], 77 first-degree relatives, and a healthy control group (control) comprised of 67 women with no chronic disease and no RA among close relatives. All the individuals included in this study were described previously (4). Exclusion criteria were based on risk factors for infection as previously discussed (7). The study was approved by the Ethical Committee of the Kazan State Medical Academy, Kazan, Russia (Permit nr 1/2002). Consent was received from all patients involved in the study, including consent to participate in the study and consent to allow publication of the results.

When the RA diagnosis was verified and before starting therapy on the day of the chemiluminescent study, we also performed a standard clinical and laboratory examination including the erythrocyte sedimentation rate (ESR)-based 28 joints disease activity score (DAS28), the Health Assessment Questionnaire (HAQ), C-reactive protein (CRP), rheumatoid factor (RF), and antibodies to citrullinated peptides (CCP2), as previously described (8–10). All patients and those relatives with joint symptoms (pain and morning stiffness) in the small joints of the feet and hands underwent magnetic resonance imaging. Patients with a DAS28 ≥5.1 at the baseline were defined as highly active (11). The control group showed no symptoms of arthritis, no ESR, or CRP elevation (except during infection), and no reactivity of RF and anticyclic citrullinated peptide (anti-CCP2).

#### Infection

Information on infections within the last year and according to the criteria defined in **Table 1** was collected from the RA patients during 2-day hospital visits by a physician qualified in rheumatology (AMI). The subjects were biannually or annually questioned about any symptoms suggestive of infections experienced during the 6–12 months, and infection histories for the two semesters were compiled. For the control group, information was from the 1 year preceding enrollment. Information from documents at other clinics was sought whenever a general practitioner had been visited. All subjects were tested and were negative for serological markers of chronic viral infections including human viral hepatitis (HAV, HBV, and HCV), and human immunodeficiency virus (HIV). A history of allergic disease and/or detection of allergen-specific IgE was considered as an exclusion criterion.

The following parameters of the infectious syndrome were analyzed: no infection, viral upper respiratory tract infection symptoms (V-URI), bacterial upper respiratory tract infection symptoms requiring antibiotic therapy (B-URI), herpes simplex virus (HSV) exacerbation/reactivation, chronic tonsillitis exacerbation, and acute bronchitis. The incidence of infectious episodes and their overall duration per year were also collected. Only those episodes judged by the rheumatologist to truly indicate an infection were scored. In the case of exacerbation of a chronic infection and/or doubt regarding an infection, the diagnosis was established by a specialist in the corresponding medical area.

### Granulocyte Reactive Oxygen Species (ROS)

Peripheral blood granulocytes were collected from 59 untreated and eRA patients, 77 relatives, and 58 controls. Since an active infection may introduce a bias in the analysis, samples from all groups were collected in a period when there were no clinical symptoms of an infection and without any routine laboratory signs of inflammation. Granulocytes were isolated on a Ficoll– Urografin density gradient. The proportion of granulocytes in the cell suspension was 90–95% and the percentage of viable cells was 95–98%. The production of ROS was assessed at the basal level and after stimulation with serum-treated zymosan (STZ,

#### Table 1 | Criteria used in the study to establish infectious episodes within the last year.

Sigma) by the luminol-dependent chemiluminescence technique (chemiluminometer designed by Santalov BF, Pushchino, Russia). Real-time registration was performed every 4 s in thermostat plastic chambers with continuous mixing of granulocyte suspensions (sample volume 0.2 ml, cell density 106 cells/ml, and concentration of STZ 0.25 mg/ml). The following parameters were measured: spontaneous level of ROS production [arbitrary units (au)], peak of ROS production after STZ (in au), area under the curve (AUC, in au × min), and time of occurrence of peak ROS production (min).

#### Statistical Analysis

Continuous data are described as mean ± SEM. Differences among groups were analyzed by one-way ANOVA in a nonparametric test and the Dunn's test was used for *post hoc* comparisons, or the Fisher's exact test for categorical data. Following normality and equality of variance tests, nominal values were compared to controls using the Student's *t*-test or alternatively by using a nonparametric test (Mann–Whitney rank sum test) with Bonferroni corrections. For correlation analysis, the Pearson's coefficient *r* was calculated and Bonferroni corrections applied. *P*-values under 0.05 were considered significant. Statistical analyses were performed using GraphPad Prism 7.0 (La Jolla, CA, USA).

#### RESULTS

#### Infections

In order to evaluate the incidence of infectious episodes in the year preceding RA onset, 59 untreated eRA women and their first-degree relatives (*n* = 77) were selected and compared to 67 healthy controls. In these 203 women, the following parameters were assessed: viral upper respiratory tract infection symptoms (V-URI), bacterial upper respiratory tract infection symptoms (B-URI), acute bronchitis, HSV exacerbation or reactivation, and chronic tonsillitis exacerbation. When compared to controls and first-degree relatives (**Figure 1A**), eRA patients included in this study had more frequent infection symptoms in the previous year than healthy controls (4.2 ± 0.4 events/year in eRA versus 2.7 ± 0.2 in controls, *P* = 0.03), but less than what was observed in relatives (6.0 ± 0.4 events/year, *P* = 0.004 versus eRA, and *P* < 10−4 versus controls).

Next, and as presented in **Figure 1B**, the incidences of each specific type of infection in the subjects' histories were compared between the three groups, revealing differences for V-URI (Kruskal–Wallis test, *P* = 0.04), HSV exacerbation/reactivation (*P* = 0.01), and chronic tonsillitis reactivation (*P* = 0.001). An increase for HSV in the infection exacerbation/reactivation history was observed in eRA patients (**Figure 1C**, 1.5 ± 0.2 events/ year in eRA versus 0.7 ± 0.2 in controls and versus 0.8 ± 0.2 in relatives, *P* = 0.005 and *P* = 0.02, respectively). Chronic tonsillitis

Figure 1 | The number (nb) of infectious symptom events during the 1-year period preceding the arthritis onset (early [e]RA) or the first examination [healthy control (Cont) and first degree relatives (Rel)]. The following parameters were assessed: viral upper respiratory tract infection symptoms (V-URI), bacterial upper respiratory tract infection symptoms requiring antibiotic therapy (B-URI), acute bronchitis, herpes simplex virus (HSV) exacerbation/reactivation, and chronic tonsillitis exacerbation. (A) Total infectious symptom event incidence in the preceding year; (B) specific infectious symptom incidence history; (C) HSV exacerbation/reactivation; and (D) chronic tonsillitis exacerbation. Statistics are indicated when *P* ≤ 0.05 and (\*) denotes the calculated *P-*values \**P* ≤ 0.05, \*\**P* ≤ 0.01, \*\*\**P* ≤ 0.001.

reactivation in the history was almost absent from controls, present in eRA and the highest prevalence was observed in relatives (**Figure 1D**, *P* = 0.03 and *P* = 0.002, respectively). When considering the V-URI history, there was a trend for V-URI incidence reduction in eRA patients (data not shown). As the incidence of B-URI was similar between the three groups, and as the prevalence and incidence of acute bronchitis was modest in relatives and in eRA patients, both parameters were not considered further.

#### Clinical Data

Next, and as presented in **Table 2**, the impacts of V-URI, HSV exacerbation/reactivation, and chronic tonsillitis reactivation in the history of episodes were analyzed with regards to the clinical and biological parameters of RA. First, eRA patients who presented V-URI symptoms in the previous year were characterized by an elevated prevalence of anti-CCP2 autoantibodies (*P* = 0.006). Second, for those early stage patients who suffered from HSV infection exacerbations during the year preceding the examination, they had more active RA disease (DAS28 > 5.1, *P* = 0.0005), and no difference when considering HAQ, ESR, CRP, RF, and anti-CCP2 autoantibodies. Third, no associations were related to a chronic tonsillitis exacerbation episode history. These results support the notion that V-URI and HSV symptoms in the history influence RA activity, but probably through distinct pathways.

#### Granulocyte ROS

Granulocyte ROS production plays a crucial role in controlling viral and bacterial infections and in the pathophysiology of RA by promoting inflammation and, in turn, by inducing cartilage and joint tissue damage. Accordingly, we measured the production of ROS at basal level (spontaneous), and after stimulation by using STZ in 59 eRA patients, 77 relatives, and 58 controls. In relatives and in eRA patients (**Figures 2A,B**), differences in relation to controls did not reach significance with regards to the basal level and the maximum peak of ROS production. In contrast (**Figures 2C,D**), for relatives and even more for eRA, a reduction in ROS AUC (5,924 ± 766 au × min in eRA, 10,927 ± 2,212 au × min in relatives versus 16,184 ± 2,489 au × min in controls, *P* = 0.003 and *P* = 0.0003, respectively) and a remarkably delayed time until the peak (17.6 ± 1.0 min in eRA versus 13.2 ± 0.7 min in relative and versus 7.9 ± 0.4 min in controls, both *P* < 10−4 ) were observed.

Next, to test the impact of altered granulocyte ROS production on RA's pathophysiology, spontaneous ROS production and the ROS indexes after STZ stimulation were tested for any correlations with DAS28, HAQ, CRP, and RF in new RA patients (**Figures 3A,B**). Both STZ peaks and STZ AUC correlated with DAS28 (*P* = 4 × 10−5 and *P* = 2 × 10−7 , respectively), and CRP (*P* = 0.003 and *P* = 0.002, respectively). Regarding the time to reach the peak of ROS production, correlations were observed with CRP (*P* = 0.01). We conclude from these experiments that granulocyte ROS production upon STZ stimulation is altered in untreated eRA patients, and that a positive correlation exists between disease activity and STZ-dependent ROS production.

Table 2 | Characteristics of early (e)RA patients according their infection symptoms reported in the previous year: all studied infection symptoms (All), viral upper respiratory tract infection symptoms (V-URI), Herpes simplex virus (HSV) exacerbation or reactivation, and chronic tonsillitis (Ton) exacerbation.

*Mean* ± *SEM.*

*Statistics \*0.01* < *P* < *0.05; \*\*0.001* < *P* < *0.01; \*\*\*0.0001* < *P* < *0.001.*

## Linkage of Certain Infections and ROS Parameters

Taking into account that ROS are major pathogenic molecules produced during viral infections, including V-URI and HSV, and at the same time, important players in the immune response against bacterial infections presented by tonsillitis in our groups, we next tested whether viral and/or bacterial infection symptom episodes might influence RA through the control of the NADPH oxidase (NOX) activity. Accordingly, we further evaluated the association between V-URI, HSV, and tonsillitis exacerbation episodes reported in the previous year with the basal level and STZ-dependent ROS production in new RA patients, relatives, and controls.

When eRA patients were dichotomized according to the appearance of HSV during the year before examination (**Figure 4**), the HSV exacerbation/reactivation episode history was associated with both increased spontaneous ROS production (*P* < 10−4 ) and, upon STZ stimulation, an increase in the peak of ROS production as observed in controls (*P* = 0.05 and *P* = 0.03, respectively). In first-degree relatives, an opposite association between an HSV episode history and spontaneous ROS production (*P* = 0.01) was highlighted, while no differences were reported following STZ stimulation.

V-URI episodes during the year preceding the study were associated in new RA patients with increased spontaneous ROS production (*P*< 10−4 ) and, upon STZ stimulation, to a normalization of the time to reach the peak (*P* = 0.0006) (**Figure 5**). No difference was observed for controls and relatives when considering the V-URI history.

For chronic tonsillitis exacerbation, controls were not considered since only 2/58 controls presented episodes during the previous year. In contrast to HSV and V-URI, chronic tonsillitis exacerbation was associated in eRA patients with a negative impact on the spontaneous ROS production (*P* = 0.01) and an increase in the time to reach the peak (*P* = 0.001) (**Figure 6**). No differences were reported for the first-degree relative group.

## DISCUSSION

Results from this study suggest an important contribution of the viral infectious episodes in the year preceding RA in women on both onset and activity of the disease after excluding patients with allergy and chronic viral diseases (viral hepatitis, HIV). First, and based on the incidence of the minor infectious episode history in patients with eRA, a critical role for HSV events may be suspected. Second, a history of HSV and V-URI episodes was associated with increased granulocyte ROS production and disease activity (HSV) or specific antibody production (V-URI) in untreated eRA patients, while a decrease was observed in the case of chronic tonsillitis exacerbation. Third, the link between ROS response and disease activity in eRA was further confirmed. Fourth, firstdegree relatives of patients with eRA such as siblings, parents, and children presented an elevated incidence of viral infectious episodes with a normal immune response, characteristics that can help to better understand the mechanisms leading to RA.

#### Infections and RA

Dangerous connections exist between infections and RA since, on the one hand, defective anti-infectious activity characterizes patients with RA while, on the other hand, infections are

(AUC), time to reach peak ROS production, and rheumatoid arthritis (RA) parameters: erythrocyte sedimentation rate-based 28 joints disease activity score (DAS28), the Health Assessment Questionnaire (HAQ), C-reactive protein (CRP), and rheumatoid factor (RF) levels in RA patients. (A) Correlation table of RA parameters. Colors represent the correlation coefficients (red being the highest and blue the lowest), whereas statistical significances (\*) denotes the calculated *P* values \**P* ≤ 0.05, \*\**P* ≤ 0.01, \*\*\**P* ≤ 0.001, and \*\*\*\**P* ≤ 10−4 . (B) Significance of correlations between ROS and RA parameters, the Spearman's *P*-value is indicated for each panel.

suspected of promoting autoimmunity (1). Among viral infections associated with autoimmunity, HSVs are strongly suspected of contributing to RA development, and we have observed an elevated rate of HSV events in the history of eRA patients. We have further established an association between HSV exacerbation/reactivation in the year preceding RA onset and disease activity (DAS28). These data are in agreement with previous reports. First, with regards to the patient's personal and family history of RA, a higher personal history of HSV (OR = 2.4) is observed (12). Second, the incidence rate of HSV has already been demonstrated to be two times greater in RA than the rate observed in age matched controls (13). Third, a higher HSV viral load is also reported in RA patients (14), and this is interpreted as the result of impaired cellular immunity that characterizes RA patients. Fourth, the defective capacity of the immune system to control an HSV infection is considered to favor reactivation in patients with RA receiving biological or conventional diseasemodifying antirheumatic disease drugs (15).

Similarly, among rhinovirus, enterovirus, respiratory syncytial virus, and influenza virus known to be associated with V-URI, the incidence of the latter is reported to be higher in RA than in controls and there is a 2.75-fold increase in pneumonia complications in RA patients infected with influenza (16). RA patients with interstitial pneumonia have elevated levels of anti-CCP and RF as recently reported (17). This is in agreement with our observation that a history of V-URI is associated with higher antibody prevalence (CCP2) in eRA patients supporting the concept that V-URI may be an actor in RA activity. However, we failed to confirm the higher prevalence of V-URI in eRA and such a discrepancy may be related to the seasonality of the infections and to the criteria for selection of the control cohorts between the studies.

The most studied bacterial strain associated with RA is related to *Porphyromonas gingivalis*, which is suspected of promoting autoimmunity by inducing protein citrullination, promoting HLA-DR overexpression and citrullinated peptide presentation to CD4 T cells, and by interfering with the production of cytokines and chemokines such as Jak/STAT and NF-kappaB (18, 19). In line with this model, case reports have been published indicating that RA disease can be improved following tonsillectomy (20), and that antibodies against a carbohydrate antigen, Strep A, of *Streptococcus pyogenes* are produced during chronic tonsillitis in RA patients (21). However (22), the analysis of 1,524 RA patients

with associated antecedent tonsillectomy or appendectomy have failed to show any association suggesting that chronic tonsillitis is at the best an activator but not an inducer of RA, a conclusion that is in agreement with our observations showing that chronic tonsillitis exacerbations were increased in both new RA patients and their relatives.

## Infections and ROS

Host defense against microbial infection involves ROS production, and toll-like receptors (TLR) have been identified as a major class of pattern-recognition receptors necessary for triggering ROS. TLR family members expressed by granulocytes facilitate the recognition of pathogen-associated molecular patterns, as was demonstrated with bacterial pathogens that colonize tonsils (23). In the case of chronic inflammation and tissue injury, the host can in addition produce endogenous TLR ligands, known as damage-associated molecular patterns (DAMPs), and DAMPs have the potential to activate inflammation, initiating a vicious cycle in germ-free conditions (24). Abnormal expression of DAMPs is reported in human RA tissues, and it has been speculated that DAMPs are critical for RA pathogenesis by controlling granulocyte functions including the oxidative burst (25, 26). The DAMPs hypothesis is attractive since it provides an explanation to the major defective ROS activity observed in the basal level and after STZ activation (but not when using PMA for activation, data not shown) in RA patients. Another hypothesis is related to the fact that zymosan is a TLR2 ligand (27), and that earlier investigations have highlighted the importance of TLR2 function in RA pathogenesis (28, 29). Accordingly, the DAMP and/or TLR2 hypothesis can both explain the ROS dysregulation observed in eRA although cell samples were collected in periods without any clinical symptoms of an infection and without any routine laboratory signs of inflammation. Future experiments are necessary to test whether or not the abnormal STZ capacity to induce ROS in eRA patients is related to DAMPs and/or to an abnormal TLR2/ NF-kB pathway.

Regarding viruses and, in particular, HSV, some of them alter granulocyte functions. Indeed, HSV can directly attach to granulocytes *via* the herpes virus entry mediator (HVEM) and formyl peptide receptors, penetrate into the cells, and subsequently increase ROS production (30). RA is characterized by HVEM overexpression on various cells due to the increased levels of phagocytosis, ROS production, as well as production of interleukin-8 (neutrophil chemoattractant) and TNF-alpha (31). ROS production might inhibit the antiviral immune response since, in particular, ROS downregulates NK cell function, NK cells being the principle players in the antiviral immune response and maintenance of the latent state of HSV (32, 33). The immune response during influenza infection, being an acute upper respiratory tract viral infection, includes

such a strong granulocyte stimulation, and, in particular, the ROS production by these cells, that these cytotoxic factors become the most important drivers of the inflammatory process and its complications (34–37). In our study, we have observed an increased ROS activity when considering both spontaneous and STZ stimulated peak ROS activity of granulocytes from RA patients presenting a V-URI or HSV history in the preceding year. Since such an effect is also associated with higher RA activity (HSV) or a higher immune response (V-URI), three non-exclusive hypotheses can be proposed. First, there is a direct effect of the latent virus on granulocyte ROS production. Second, the effect is indirect and may be a consequence of the intensive inflammatory process, in this way, the HSV capacity to produce DAMPs has been reported (38). Third, since the pathogenic role of anti-Fc gamma receptor IIIb autoantibodies on granulocytes is well documented in RA, our data allow the assumption that there is some link between V-URI and RA associated autoantibodies (39–41).

While studying one of the essential mechanisms of antiinfectious activity, granulocyte ROS production in RA, a remarkable feature of NOX activity was revealed at eRA with a twofold decrease in the response to STZ stimulation. Granulocytes are known to be first responders to infections, and immediate ROS release in response to a pathogen is an important condition for curbing an infection. So, the NOX response rate against a pathogen might be essential for its successful immobilization. This is especially important during an aggressive bacterial infection. As a consequence, the slowing of the granulocyte response to STZ is suspected to play a role in the relatives' and eRA patients' susceptibility to the exacerbations of chronic tonsillitis, mainly caused by bacteria. At the same time, there was no difference in this index in the patients who underwent or did not undergo HSV event exacerbation since the rapid granulocyte ROS production does not play a significant role in the neutralization of viruses. It should be noted that previously we demonstrated the delayed monocyte NADPH oxidative response upon STZ stimulation as well (42).

#### ROS and RA

In addition, we have observed an association between spontaneous ROS production, STZ stimulated ROS parameters and disease activity (DAS28, CRP) in patients with eRA at onset. However, the role of ROS in RA is not sufficiently clear. In the initial view, increased ROS activity has been documented in synovial joints to directly contribute to the inflammation, to induce the hyperplasia of synovial tissues, and to damage cartilage, bone, and ligaments (43). In addition, it was demonstrated that abnormal ROS activity controls antigen presentation and reduces T cell responsiveness in RA through effects on cellsurface proteins such as CD4 and signal transduction proteins

such as LAT or Zap70 that are present close to the plasma membrane (44, 45). In addition, ROS, by damaging endothelial cells, increase the permeability of the vasculature and promote granulocyte migration to inflammation sites (90%). Last but not least, cytokine overproduction, including TNF-alpha and IL-1, is thought to be the main contributor to ROS in RA. However, the paradigm has changed with the discovery that allelic polymorphism in the respiratory burst oxidase component neutrophil cytosolic factor (ncf)1 in rat and ncf4 in human was associated with a more severe and a higher incidence of RA (46). The exact mechanisms are incompletely understood but may rely on an enhanced activation of autoreactive T and B cells as observed in ncf1 transgenic mice (47). In our study, a strong and positive correlation between disease activity (DAS28) and the level of ROS was observed, which is in agreement with a previous report that proposed measurement of ROS for monitoring disease severity in RA (48).

In conclusion and although patients with allergy and viral chronic infections were excluded from the study, we are aware of the limits of our study in terms of sample size, in terms of sex selection, and the fact that infections were established mostly on symptoms rather than biological parameters. Thus more studies including serological and an extended microbiota analysis are needed to support our observations and for that the longitudinal Tatarstan cohort study, which provides a longitudinal analysis of RA patients from the cohort of Tatarstan women and their first-degree relatives, is appropriate for the study of interactions between the multiple factors associated with RA.

## ETHICS STATEMENT

The study was approved by the Ethical Committee of the Kazan State Medical Academy, Kazan, Russia (Permit nr 1/2002).

## AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

## ACKNOWLEDGMENTS

We are thankful to Aida Gabdulkhakova (Central Research Laboratory, Kazan State Medical Academy, Russia) for assistance in selecting the conditions of the chemiluminescent study. The authors also express thanks to Dr. Wesley H. Brooks (University of South FL, USA) for editorial assistance, and to Simone Forest and Genevieve Michel for secretarial assistance.

## FUNDING

This study was supported by research funding from the "Russian Science Foundation" (No. 17-15-01099).

## REFERENCES

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The reviewer AV and handling Editor declared their shared affiliation.

*Copyright © 2017 Arleevskaya, Shafigullina, Filina, Lemerle and Renaudineau. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

#### *Zoe Rutter-Locher1 , Toby O. Smith2 , Ian Giles 3 and Nidhi Sofat 1 \**

*1Musculoskeletal Research Group, Institute of Infection and Immunity, St George's University of London, London, United Kingdom, 2 Faculty of Medicine and Health Sciences, University of East Anglia, Norwich, United Kingdom, 3Center for Rheumatology Research, Rayne Institute, University College London, London, United Kingdom*

Background: Systemic lupus erythematosus (SLE) is a chronic systemic inflammatory autoimmune disease, the etiology of which remains only partially characterized. Strong evidence implicates chronic infections in the development and chronicity of autoimmune conditions. Recently, an association has been demonstrated between periodontitis and rheumatoid arthritis. Such observations have led to the investigation of the possible role of periodontitis and oral dysbiosis in other systemic inflammatory conditions, including SLE. The aim of this study was to examine whether there is an association between SLE and periodontitis.

#### *Edited by:*

*Marina I. Arleevskaya, Kazan State Medical Academy, Russia*

#### *Reviewed by:*

*Maximilian F. Konig, Massachusetts General Hospital, United States Giovanni Cizza, Novo Nordisk, United States*

> *\*Correspondence: Nidhi Sofat<EMAIL_ADDRESS>
#### *Specialty section:*

*This article was submitted to Microbial Immunology, a section of the journal Frontiers in Immunology*

*Received: 02 July 2017 Accepted: 27 September 2017 Published: 17 October 2017*

#### *Citation:*

*Rutter-Locher Z, Smith TO, Giles I and Sofat N (2017) Association between Systemic Lupus Erythematosus and Periodontitis: A Systematic Review and Meta-analysis. Front. Immunol. 8:1295. doi: 10.3389/fimmu.2017.01295*

Methods: MEDLINE *via* OVID, EMBASE *via* OVID, and PsycINFO *via* OVID databases were searched to identify eligible studies, screened by two independent authors and verified by a third. Studies comparing presence of periodontitis in SLE cases to controls without SLE were included. Data were extracted using a predefined table and papers were appraised using Down's and Black tool. Mantel–Haenszel meta-analysis was performed using RevMan.

results: Eight case–control studies were included, with 487 SLE cases and a total of 1,383 participants. On meta-analysis of four studies, risk of periodontitis in SLE cases compared to controls was significantly greater with a risk ratio of 1.76 (95% CI 1.29–2.41, *p* = 0.0004). No statistical difference was found in individual measures of periodontitis, such as probing depth or clinical attachment loss, between SLE cases and controls.

conclusion: Our study found a statistically significant increased risk of periodontitis in patients with SLE compared to controls. This finding suggests a possible association between these two conditions. Larger longitudinal studies are needed to confirm this possible association.

Keywords: systemic lupus erythematosus, autoimmune and inflammatory diseases, microorganisms, periodontitis, periodontal disease, meta-analysis

## INTRODUCTION

Systemic lupus erythematosus (SLE) is a systemic, chronic inflammatory condition with diverse clinical manifestations, primarily affecting the joints, internal organs, and the skin (1).

The etiology of SLE is incompletely understood, but it is thought to occur in genetically primed individuals in whom the inflammatory response is triggered by an environmental stimulus. Immunosuppressant medications are the mainstay of treatment, but these are limited both in efficacy and by multiple side effects which lead to significant morbidity and mortality. Oral manifestations of SLE are common and typically take the form of painless oral ulcers that are frequently present during disease flares and are included in current SLE classification criteria (2).

Periodontitis is an infectious-inflammatory condition, affecting the periodontal ligament and alveolar bone (3). Most cases are due to the chronic accumulation of oral plaque which initiates inflammation, further bacterial colonization and tissue destruction. Gingivitis usually occurs first and can be reversed with oral hygiene methods. However, once the inflammation extends past the gums to the deeper tissues, the loss of periodontal attachment and bone causes progressive loosening of teeth, eventually leading to their loss (4). The "red complex" organisms, *Porphyromonas gingivalis*, *Tannerella forsythia*, and *Treponema denticola*, have a key role in the development of periodontitis (5, 6).

There is strong evidence that periodontitis, and specifically oral dysbiosis is associated with autoimmune inflammatory disease, principally rheumatoid arthritis (RA). A recent meta-analysis including 153,492 participants showed a significant association between periodontitis and rheumatoid arthritis (7). Two specific bacteria have been implicated in triggering the underlying inflammatory process, *P. gingivalis* (8, 9) and *Aggregatibacter actinomycetemcomitans* (10). Clinical trials are underway to investigate the effect of non-surgical treatment of periodontitis, such as oral hygiene and mechanical removal of plaque in RA.

There is increasing interest regarding the possible role of oral dysbiosis in the etiology of other autoimmune inflammatory conditions, including SLE. Further understanding of any potential association between periodontitis and SLE would expand current knowledge of the etiology of SLE and may lead to novel management strategies.

In this review, we hypothesized that there may be an association between SLE and periodontitis. To evaluate this hypothesis, we conducted a systematic review and meta-analysis of relevant publications.

#### MATERIALS AND METHODS

The protocol for the review was registered with PROSPERO (Registration number: CRD42016053490) an international register of systematic reviews (http://www.crd.york.ac.uk/PROSPERO/). We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2009 checklist to report the review (11).

#### Eligibility Criteria for Population

Participants in the studies needed to have a diagnosis of SLE based on internationally recognized criteria, ACR 1982/1997 revised classification criteria (12) or clinical diagnosis by a rheumatologist. Studies which included participants of all ages, gender, and disease severity were eligible.

#### Eligibility Criteria for Study

To be included, studies needed to be observational studies of cross-sectional, case–control, or cohort design. Journal articles and conference proceedings were included. Review articles, case reports, animal model studies, and those with unavailable abstracts were excluded. Non-English language papers were excluded. There were no restrictions on date of publication or publication status.

#### Eligibility Criteria for Outcome Measure

To be included, prevalence of periodontitis using standardized measures needed to be reported in both SLE population and non-SLE population.

#### Search Strategy

MEDLINE *via* OVID, EMBASE *via* OVID, and PsycINFO *via* OVID databases were searched using the following terms: systemic lupus erythematosus, SLE, lupus erythematosus, systemic or lupus nephritis, lupus vasculitis and Periodont\*, gum disease, gingivitis, tooth decay, oral health, dental health, oral plaque index (PI), probing pocket depth, bleeding on probing (BOP), and clinical attachment loss (CAL). In addition, Google Scholar was searched using the following term "Periodontitis and systemic lupus erythematosus." The searches were re-run just before the final analyses on the 03/08/2017.

#### Study Selection

The Titles and/or abstracts were screened by two independent authors (Zoe Rutter-locher and Toby O. Smith) to identify potential eligible studies. Final selection of studies was performed by two independent authors (Zoe Rutter-locher and Toby O. Smith) and verified by a third author (Nidhi Sofat) by reviewing the full text based on inclusion criteria above. Any disagreements were resolved by discussion.

#### Data Extraction

A standardized, pre-piloted form was used to extract data from the included studies. Extracted information included: (1) study design including, publication journal and date, inclusion and exclusion criteria, diagnostic criteria of SLE, definition of periodontitis, (2) study participant demographics including % females, mean age, years of SLE disease, therapies, measure of severity of SLE, and (3) periodontal measures including prevalence of periodontitis, oral plaque index (PI), probing depth (PD), clinical attachment loss (CAL), and bleeding on probing (BOP). Two authors (Zoe Rutter-locher and Toby O. Smith) extracted the data independently, discrepancies were identified and resolved through discussion (with a third author, Nidhi Sofat, where necessary). Authors were contacted by email to obtain missing information and included when received.

#### Quality Assessment

Risk of bias and quality assessment was reviewed using Downs and Black tool for non-randomized control trials (13). This 27-point tool assesses studies on five key sections: (1) study quality (10 points), (2) external validity (3 points), (3) study bias (7 points), (4) confounding and selection bias (6 points), and (5) Power of the study (1 point). Disagreements between the review authors over the risk of bias in particular studies were resolved by discussion, with involvement of a third review author where necessary.

#### Data Analysis

As there was homogeneity in participants, study design and outcome measure on visual assessment of the data extraction table, a meta-analysis was performed. Primary outcome was to calculate relative risk of periodontitis in participants with SLE compared to participants without SLE. Secondary outcomes were to calculate relative risk or mean difference for measures of periodontal disease. These measures of periodontal disease included PD, oral PI, BOP, bleeding gingival index, and CAL.

Median and interquartile range were converted to mean (SD) to allow comparison of studies, under the assumption of normal distribution. A fixed effect meta-analysis was performed when the inconsistency value (*I*-squared) was ≤50% and Chi-squared equates *p*= 0.10 and a random-effect meta-analysis when *I*-squared was >50% and Chi-squared equates to *p* < 0.10. Risk ratio with 95% confidence intervals was calculated for the prevalence rates of periodontitis and the mean difference was calculated for continuous variables. Risk of bias was identified in Down's and Black tool. Publication bias was not performed as it is convention to only present a funnel plot for 10 or more data points in a meta-analysis (14).

All analysis and forest plots were performed on RevMan Version 5.3 (Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2014).

#### RESULTS

#### Search Results

As shown in **Figure 1**, a total of 485 studies were identified using the search strategy. Of these, 454 were excluded as they were duplicates, non-English articles, review articles, or non-relevant. The 31 remaining full length articles were screened and eight were deemed appropriate to be included in the qualitative and quantitative analysis.

#### Study Design

The study characteristics are shown in **Table 1** and Table S1 in Supplementary Material. All studies included in the meta-analysis were case-control in design. Seven were published in peer review journals and one was taken from conference proceedings (15). Most studies (six of seven) were published since 2015. The remaining articles were published between 1993 and 2007. Three studies were based in Europe (two in the UK and one in Germany), two in Brazil, one in Saudi Arabia, one in Taiwan, and one in China.

The total number of study participants was 1,383 which included 487 cases of SLE. All studies defined cases as fulfilling the ACR 1982/1997 revised classification criteria for diagnosis of SLE (12). Cases were recruited mainly from Rheumatology clinics, with the exception of Meyer et al. who recruited from the "Department of Internal Medicine" (19). All studies defined controls as either "Healthy" or "Individuals without a history of rheumatic conditions or autoimmune disease." Controls were recruited from a variety of sources including dental clinics, staff at the medical school and epidemiological survey. Most studies included exclusion criteria in order to reduce the effect of confounders such as age, smoking, recent antibiotics usage, but this varied between studies.

#### Quality Assessment

Quality assessment was hindered by the limited information available for some studies, especially those that came from conference proceedings. As shown in **Table 2**, studies included in this meta-analysis had clear hypothesis, outcome measures and aims (*N* = 8, 100%). The participants were representative of the population (*N* = 7, 88%) and the outcome measures were reliable and clearly reported (*N* = 8, 100%) with estimates of random variability (*N* = 7, 88%). Particular limitations were that assessors were not blinded (*N* = 1, 13%) and sufficiently powered cohort size was present in only half of studies (*N* = 4, 50%).

#### Study Participant Demographics

**Table 3** shows the demographic of the cohorts. A total of 1,383 participants were included in the meta-analysis, with 487 participants with SLE and 896 participants without SLE. Mean age of participants with SLE was 41.3 years and controls was 42.8 years, excluding juvenile SLE. All studies had a predominance of females. Mean duration of disease was, as expected, lowest in study by Fernandes et al. looking at juvenile SLE (18). Excluding this study, mean disease duration ranged from 4.5 to 11 years. Use of immunosuppressant's varied from 36 to 100%. There was also variability in what medications were included under the term "immunosuppression." There was insufficient detailed data in order to control for immunosuppressant use in meta-analysis. Importantly, smoking status, a known risk factor for periodontitis, was the same in cases and controls in those studies with available data.

#### Meta-analysis: SLE and Periodontitis Prevalence

As shown in **Table 4**, all studies used similar measures of periodontitis. Four of the seven studies, which included 1,064 participants, defined periodontitis (15, 17, 21–23). Three studies (15, 17, 22) differentiated between mild and severe periodontitis.

The prevalence rates of periodontitis were similar in these four studies. The exceptions were the lower prevalence rate of periodontitis in the controls of the study by Wang et al. and Zhang et al. (21, 22). On meta-analysis, risk of periodontitis in SLE patients compared to controls was significantly greater with a risk ratio of 1.76 (95% CI 1.29–2.41, *p* = 0.0004, **Figure 2**). Furthermore, this greater risk ratio remained significant when the study published as a conference proceeding was excluded from the meta-analysis (RR 2.05 95% CI 1.15–3.66, *p* = 0.02) and when the study by Wang et al. was excluded from meta-analysis (RR 1.50 95% CI 1.28–1.75, *p* < 0.00001). Interestingly two studies which differentiated between mild and severe periodontitis found no difference in the prevalence of severe periodontitis between cases and controls, whereas one did (22).

#### Meta-analysis: SLE and Measures of Periodontal Disease

All studies provided data on measures of periodontal disease. Six of the eight studies reported oral PI (16, 18–20, 22, 23). Six of the studies reported on presence of gingivitis (16–20, 22, 23), with five reporting BOP and two Loe and Silness gingival index (20, 22). Six studies provided data on PD (15–17, 20–23) and four

on CAL (16, 17, 21–23) which are the gold standard measures of periodontitis. Four studies reported on residual teeth (16, 19–21), and two reported the WHO DMFT index (decayed-missing-filled teeth index) (18, 19). Meyer et al. and Zhang et al. were the only studies to use radiographs to measure bone loss, an accurate measure of periodontitis (19, 22).

Number of residual teeth was very similar in all studies. Overall, the mean residual teeth count was 23.68 for SLE and 23.65 for controls. On meta-analysis there was no significant difference in mean of PI (0.03, 95% CI −0.09–0.16, *p* = 0.62, **Table 5**, Figure S1 in Supplementary Material) or BOP (1.60, 95% CI −0.72–3.92, *p* = 0.18, **Table 5**, Figure S2 in Supplementary Material).

We also performed meta-analysis on the gold standard measures of periodontitis. Risk of PD ≥ 5 mm was greater in SLE patients compared to controls but this was not statistically significant with risk ratio of 1.34 (95% CI 0.99–1.82, *p* = 0.06, **Table 5**, Figure S3 in Supplementary Material). Risk of CAL ≥ 2 and difference in means for PD and CAL were not statistically different between cases and controls; Risk of CAL ≥ 2 risk ratio 1.09 (95% CI 0.99–1.21, *p*= 0.08, **Table 5**, Figure S4 in Supplementary Material), PD difference in means 0.08 (95% CI −0.27–0.43, *p* = 0.66, **Table 5**, Figure S5 in Supplementary Material), CAL difference in means 0.41 (95% CI −0.12–0.95, *p*= 0.13, **Table 5**, Figure S6 in Supplementary Material). Excluding the study in Juvenile SLE by Fernandes et al. did not make a significant difference to any outcome.

#### Table 2 | Down and Black's appraisal.

*S, not stated; N/A, not applicable.*

*1, Al-Mutari et al. (16); 2, Calderaro et al. (17); 3, de Pablo et al. (15); 4, Fernandes et al. (18); 5, Meyer et al. (19); 6, Mutlu et al. (20); 7, Wang et al. (21); 8, Zhang et al (22).*

#### DISCUSSION

Our report is the first systematic review to examine the association between periodontitis and SLE. On meta-analysis we found a statistically significant overall increased risk of periodontitis in patients with SLE compared to controls, suggesting an association between these two conditions. However, there was no statistical difference in individual measures of periodontitis, such as PD or CAL, between SLE cases and controls.

We found no significant difference in oral PI or BOP between SLE cases and controls. Plaque induced periodontitis is the most common form of periodontitis, and so a high oral PI would suggest

#### Table 3 | Demographic of cohorts.

*DM, data missing.*

*a Use prednisolone or immunosuppression.*

Table 4 | Definition of periodontitis and measures of periodontitis.

*PI, oral plaque index; BOP, bleeding on probing; GI, gingival index; MT, residual teeth; PD, probing depth; CAP, clinical attachment loss; DMFT, decayed-missing-filled index; CAL, clinical attachment loss.*

Figure 2 | Forest-plot representing risk ratio of periodontitis between cases with systemic lupus erythematosus (SLE) and healthy controls.

a greater risk of developing periodontitis. BOP is a measure of gingivitis, the reversible gingival inflammation which precedes periodontitis. Therefore, although these measures do highlight potential risk of periodontitis, the absence of a significant difference between cases and controls does not preclude an association between SLE and periodontitis.

There was also no difference in means of PD. PD calculates the depth of periodontal pockets and is a measure of current periodontal disease. The British Society of Periodontology delineates a healthy sulcus as <3.5 mm and a periodontal pocket as ≥3.5 mm (24). We propose that calculating differences in mean PD < 3.5 mm is, therefore, not clinically significant and risk ratio of PD above a certain level is more appropriate. In this case, the finding that PD ≥ 5 mm was higher in SLE potentially supports the evidence that periodontitis is associated with SLE. However, as this was not significant further studies with larger sample size will be needed to elucidate this further.

Clinical attachment loss is the other gold standard measurement for periodontitis (25) and is representative of cumulative destruction. Although CAL ≥ 2 mm and mean difference in CAL was higher in SLE compared to controls, this not significantly different. Again, further studies are needed to investigate this relationship.

The finding that periodontitis is associated with SLE is in agreement with other studies examining this relationship. Case studies since the 1980s have suggested a link between SLE and gum disease. Rhodus and Johnson found 93.8% of SLE patients had periodontitis (26), while a Japanese study reported a

#### Table 5 | Results from meta-analysis.

*a Mean difference analysis.*

*CI, confidence intervals.*

prevalence of periodontitis of 70% in SLE compared to 30% in the general population (27). Higher disease activity, measured by SLE Disease Activity Index (SLEDAI) (28), predicts worse periodontal disease (24, 29) and non-surgical treatment of periodontitis improves SLEDAI scores at 3 months (30). These findings suggest that oral dysbiosis may contribute to maintenance of the inflammatory process in SLE. Recently, differences in the composition of the oral microbiota, independent of periodontal status, have been elucidated using 16s ribosome sequencing in 52 cases of SLE (23). Interestingly, the periodontal pathogen *Aggregatibacter actinomycetemcomitans*, which has been identified as a potential trigger in RA (10), has also been implicated in SLE (31).

Systemic lupus erythematosus is thought to occur when an environmental stimulus triggers inflammation in a genetically primed individual. Initial studies have highlighted possible mechanisms to explain the potential association between periodontitis and SLE. Genetic variants in the Fcy receptor have been implicated in susceptibility to both SLE and periodontitis in a small Japanese study (32). Periodontitis and SLE are both inflammatory conditions, and share similar inflammatory profiles (33, 34). Specifically, a possible role for TLR-4 has been implicated. These molecules are activated by specific pathogenassociated molecular patterns produced by bacteria and stimulation of TLR-4 leads to autoimmune lupus in mice (35). However, it must be emphasized that these studies are small and much more work is needed to elucidate if there is any biological plausibility.

#### Study Limitations

A significant limitation to our meta-analysis is the lack of clear and recognized criteria to define periodontitis. Although all definitions of periodontitis used PD and CAL, they did vary in their thresholds, limiting the ability to directly compare outcomes. However, a minimum diagnostic threshold of CAL ≥ 2 mm and PD ≥ 3 mm has been suggested (25) and all studies used thresholds which surpassed these cutoffs.

Another significant limitation is the paucity of data available. Only eight studies could be included in the meta-analysis. The overall periodontitis risk ratio included only four studies, and one of these was from conference proceedings. Meta-analysis involving greater number of studies analyzing individual measures of periodontitis were not significant. This may suggest that the significant risk ratio in overall periodontitis cannot be extrapolated into larger study populations. However, previous similar meta-analysis in other conditions, which have included larger numbers of studies, have also found that overall periodontitis risk is significant, while individual markers are not (7).

There are only a small number of studies to date investigating this association and so we were unable to test for publication bias. We included data from conference proceedings try to mitigate this but it will be important in the future to ascertain the risk of small sample size publication bias.

There are a number of factors such as smoking, educational level, and immunosuppressant medications which increased risk of both SLE and periodontitis (1). Variation in the prevalence of these factors between cases and controls could, in part, be responsible for the differences seen. These factors were partly controlled for by the use of exclusion criteria in some studies. However, there was limited detailed information regarding smoking status and immunosuppressant use in most studies, and information that was available showed variations in immunosuppressant use from 36 to 100%.

Finally, all studies included were cross-sectional in nature and investigated an association at a given time point. We are, therefore, unable to make any conclusions regarding causality from this study. Further longitudinal studies are needed to delineate a temporal association and causality of periodontitis in the development of SLE.

## CONCLUSION

The results of this meta-analysis show a significant association between SLE and periodontitis. However, the meta-analysis was hampered by paucity of data and significant limitations. Therefore, these findings can only suggest a possible association and larger longitudinal studies are needed to confirm this association and investigate causality of periodontitis in SLE.

## AUTHOR CONTRIBUTIONS

ZR-l, TS, and NS conceived, analyzed, and drafted the manuscript. IG conducted literature searches and reviewed the manuscript. All authors approved the manuscript for publication.

#### FUNDING

This work was supported by an NIHR Academic Clinical Fellowship Award to ZR-L.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at http://www.frontiersin.org/article/10.3389/fimmu.2017.01295/ full#supplementary-material.

## REFERENCES

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

*Copyright © 2017 Rutter-Locher, Smith, Giles and Sofat. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.*

# Microbes and Viruses Are Bugging the Gut in Celiac Disease. Are They Friends or Foes?

Aaron Lerner 1, 2 \*, Marina Arleevskaya3 , Andreas Schmiedl 2 and Torsten Matthias 2

1 The Ruth and Bruce Rappaport Faculty of Medicine, Technion Israel Institute of Technology, Haifa, Israel, 2 Department of Research, AESKU.KIPP Institute, Wendelsheim, Germany, 3 Central Research Laboratory, Kazan State Medical Academy Kazan, Kazan, Russia

The links between microorganisms/viruses and autoimmunity are complex and multidirectional. A huge number of studies demonstrated the triggering impact of microbes and viruses as the major environmental factors on the autoimmune and inflammatory diseases. However, growing evidences suggest that infectious agents can also play a protective role or even abrogate these processes. This protective crosstalk between microbes/viruses and us might represent a mutual beneficial equilibrium relationship between two cohabiting ecosystems. The protective pathways might involve post-translational modification of proteins, decreased intestinal permeability, Th1 to Th2 immune shift, induction of apoptosis, auto-aggressive cells relocation from the target organ, immunosuppressive extracellular vesicles and down regulation of auto-reactive cells by the microbial derived proteins. Our analysis demonstrates that the interaction of the microorganisms/viruses and celiac disease (CD) is always a set of multidirectional processes. A deeper inquiry into the CD interplay with Herpes viruses and Helicobacter pylori demonstrates that the role of these infections, suggested to be potential CD protectors, is not as controversial as for the other infectious agents. The outcome of these interactions might be due to a balance between these multidirectional processes.

#### Edited by:

Kuldeep Dhama, Indian Veterinary Research Institute (IVRI), India

#### Reviewed by:

Mario M. D'Elios, University of Florence, Italy Maryam Dadar, Razi Vaccine and Serum Research Institute, Iran

\*Correspondence:

Aaron Lerner<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Microbial Immunology, a section of the journal Frontiers in Microbiology

Received: 15 May 2017 Accepted: 10 July 2017 Published: 02 August 2017

#### Citation:

Lerner A, Arleevskaya M, Schmiedl A and Matthias T (2017) Microbes and Viruses Are Bugging the Gut in Celiac Disease. Are They Friends or Foes? Front. Microbiol. 8:1392. doi: 10.3389/fmicb.2017.01392 Keywords: celiac disease, bacteria, viruses, gut, microbiome, environmental inducer, environmental protectors

## INTRODUCTION

#### Infection and Autoimmunity

The relationship between infections and autoimmunity is complex. Microbial and viral infections might act as environmental triggers inducing or propagating autoimmune and inflammatory processes, resulting in symptomatic presentation of a disease in genetically high risk individuals (Lerner, 2015; Arleevskaya et al., 2017). An autoimmune disease onset following an infectious agent exposure has been well-documented (Pordeus et al., 2008; Bogdanos et al., 2015; Sakkas and Bogdanos, 2016). At least, for CD, the following infections were suggested to be associated with the disease: viruses: enterovirus, Epstein-Barr virus (EBV), Cytomegalovirus (CMV), hepatitis C virus (HCV), hepatitis B virus (HBV), and rotavirus, microbes: Bacteroides species, Campylobacter jejuni, Pneumococcus, Mycobacterium tuberculosis, and Helicobacter pylori (Lerner, 2015). However, recent serological evidence suggests the opposite outcome, which is the protection against autoimmune conditions following bacterial/viral exposure (Christen and von Herrath, 2005). At least the suggestive protector agents for CD were CMV, EBV, Rubella, and Herpes simplex type 1 virus (HSV1) when compared to healthy people (Plot and Amital, 2009; Jansen et al., 2016). According to the "hygiene hypothesis," the excessively sterile environment leads to the enhanced incidence of autoimmune disorders, asthma, and allergies, thus, associating surge of CD incidence with decreased infectious environment (Lerner et al., 2015b,e; Bloomfield et al., 2016).

#### Celiac Disease

Celiac disease is a life-long autoimmune disease (Lerner et al., 1996) mainly of the proximal intestine, affecting genetically predisposed individuals. Gluten, the storage protein of wheat, is the environmental inducer of the disease in addition to other structurally related molecules found in barley, rye, and oat (Lerner, 2014). Many environmental factors were suggested to induce or enhance the disease: multiple infections (Lerner and Reif, 2015), early infections (Myléus et al., 2012), early gastrointestinal infections (Beyerlein et al., 2017), lack of breast feeding (Lerner and Matthias, 2016c), time and amount of gluten consumption (Chmielewska et al., 2015), microbiome/dysbiome repertoire (Lerner et al., 2015a; Lerner and Matthias, 2017a,b), mode of delivery (Decker et al., 2011), early vaccination (Kemppainen et al., 2017) or early consumption of antibiotics (Canova et al., 2014) and geo-epidemiological influences (Lerner, 1994, 2015; Reif and Lerner, 2004b; Lerner and Matthias, 2015a). The abnormal immune response is directed, in particular, against tissue transglutaminase (tTG), representing the autoantigen, (Reif and Lerner, 2004a; Lerner et al., 2015c) and the two main autoantibodies, anti-endomysium and anti-tTG antibodies, are the most prevalent serological markers used to screen for the condition (Shamir et al., 2002; Lerner and Matthias, 2015d). Recently, the list of CD serological markers was expanded by two additional autoantibodies: anti-deamidated gliadin peptide and anti- tTG neo-epitope antibodies, found to be reliable for CD diagnosis (Rozenberg et al., 2012; Lerner and Blank, 2014; Lerner et al., 2015e). As yet, HLA-DQ2 and HLA-DQ8 are known predisposing genetic factors. The sequential events in disease progression were unraveled in the last years and gave rise to multiple future therapeutic strategies (Lerner, 2010). Notably, its epidemiological, incidental, and clinical presentation are changing continuously, and new clinical pictures are reported and expand the abundance of clinical variance of the disease (Lerner et al., 2015d). In fact, age of disease onset increases and the traditional enteric presentation is more and more replaced by extraintestinal manifestations.  Those epidemiological and clinical changes can explain why the disease is diagnosed during the whole human life-span including in the elderly (Lerner and Matthias, 2015c). There is no doubt that in the last decades its incidence is constantly increasing, ranging between 1 and 3% nowadays (Lerner, 2014; Lerner et al., 2015b). The present review will concentrate, expand and update on the multiple faces of the inductive/protective roles that infectious agents might play in CD pathogenesis. This aspect is further interesting since pathogens are the major drivers of human selective genetic adaptation during evolution (Vatsiou et al., 2016), and the question of microbes that are bugging the celiac patient "are they friends or foes?" is the subject of the current review.

## Infections and CD

It should be clarified that, although the trigger role of microorganisms and viruses in the CD development was undoubtedly traced in numerous investigations, it substantially differs from other immune pathogenesis like rheumatoid arthritis (as a classic model of an autoimmune disease; Arleevskaya et al., 2016; Kemppainen et al., 2017).

The induction of rheumatoid arthritis most likely occurs under the influence of the burden of many trivial infections, influencing the patient's immune system due to the frequent and prolonged infectious episodes (Arleevskaya et al., 2014). Individuals at CD risk apparently do not have such features in their mucosal immunity, nor significant defects in systemic antiinfective protection, impacting infection susceptibility. Since all the CD studies are focused only on the disease link with various gastrointestinal infections, such association is different from what was shown in rheumatoid arthritis (Riddle et al., 2012).

The number of infectious agents related to CD is continuously increasing (**Figure 1**). Examples for viruses enterovirus, EBV, Cytomegalovirus (CMV), HCV, HBV, and rotavirus And for

microbes Bacteroides species, C. jejuni, Pneumococcus, M. tuberculosis, and H. pylori (Lerner, 2015). However, links between CD and infections were more associative and less causative, thus, far from being elucidated. Moreover, the mutually exclusive hypotheses about the provocative and protective role of a particular microorganism/virus in CD pathogenesis were suggested and discussed in various publications.

It appears that the essential condition for the CD induction is a gastrointestinal infection (Kemppainen et al., 2017). Apparently, the other major condition is an early childhood—namely immature gastrointestinal tract, immature immune system, and gastrointestinal microbiome at the early phase of formation. The beginning of gut colonization by microorganisms set the stage for the cross talks between the epithelium, enteric lymphoid tissue, and microflora, all together establishing the intestinal barrier and as a consequence, a strictly dosed delivery of macromolecules into the internal environment and shaping of mucosal tolerance to food antigens and normal flora (Makarova et al., 2014).

So, in the vulnerable infant period any gastrointestinal infection, even a transitory one, is potentially able to disturb these processes of gut microbiome maturation and the establishment of local immunity and immune tolerance, including that against food and microbial antigens. Apparently, deleterious coincidence of these circumstances leads to an error in the negative selection of gluten-reactive lymphocyte clones.

Thus, the community of microorganisms, being extremely vulnerable during the ripening period, appears to be an inert system in adults. For example, in adults it needs no more than 30– 60 days to restore gut microflora after the exposure to antibiotics (Spanhaak et al., 1998; Tannock et al., 2000). At the same time, a weekly clindamycin treatment of a newborn reduces Bacteroides diversity for the next 2 years (Jernberg et al., 2007). The same infections, capable of altering the fate of a sick infant, are merely a light ripples on the ocean surface for an adult microbiome.

In conclusion, there is an undoubted link between CD development and microorganisms, and this link looks to be rather specific. Gastrointestinal infections in predisposed infants with an immature gastrointestinal tract and immune system might shape gut microbiome in the immature and therefore labile circumstances. Such an unfortunate combination could trigger early CD development or becomes a ticking time bomb, represented by the structural features of the gut microbiome and persistence of gluten–reactive lymphocyte clones with latent basal cell proliferation without overt disruptive inflammatory activation.

#### GUT MICROBIOME SIGNATURE IN CELIAC DISEASE

Gut microbiome analysis in the healthy adult human populations revealed about 1150 bacterial species, the majority (50–75%) being represented by Firmicutes, and then Bacteroidetes (10– 50%), Actinobacteria (1–10%), with fewer than 1% being Proteobacteria (Manichanh et al., 2012). Apparently, the HLA system, to a certain extent, shapes microbiome structure. Besides, polymorphisms of some other non-HLA genes were found to correlate with a certain microbiome structure (Spor et al., 2011). Interestingly, in addition to the HLA system, microbiome composition may be due to CD-associated polymorphisms of defensin, some molecules of Toll-like receptor signaling pathways and vitamin D receptor genes (San-Pedro et al., 2005; Fernandez-Jimenez et al., 2010; Wang et al., 2016). Whole genome study of 93 individuals and 16S rRNA gene pyrosequencing of their body microflora revealed 83 alliances between genetic variance in host sequence and plethora of specific microbial taxa (Blekhman et al., 2015). In particular, the links with CD-associated host genes were revealed. In addition to the host genes related to immunity, a link was found between the microbiome composition and SNP of the genes not related to immunity. For example, the authors revealed an interesting correlation between the abundance of Bifidobacterium in the gastrointestinal tract and host genetic variation in LCT gene, encoding the lactase enzyme hydrolyzing dietary lactose. This gene SNPs are known to be associated with lactose intolerance, which is frequently associated with celiac disease (Ojetti et al., 2005). Bifidobacterium is able to metabolize lactose, and there are some strains preferring lactose instead of glucose. The authors suggested that the problems with individual's consumption of milk products might impact the richness of Bifidobacterium in the gastrointestinal tract.

The results of gut microbiome structure investigations in the infants at CD risk as well as in the therapy-naïve patients at the disease onset are somewhat contradictory. Besides the bulk of the results was obtained by study of feces, while the principal for CD microbe community in the small intestine boundary layers has its own peculiarities, although in a certain extent it is associated with fecal microbes. However, a certain tendency can be traced. A single and rather limited study of mucosa-associated microbiota in the proximal gut—using enteric samples from 45 children with CD and 18 clinical controls born during the "Swedish CD epidemic"—demonstrated only marginally differences between the groups. Enrichment with Clostridium, Prevotella, and Actinomyces was revealed in the most of the CD samples (Ou et al., 2009). Feces studies demonstrated, that infants genetically predisposed to CD, had significantly higher abundance of Firmicutes (Clostridium) and Proteobacteria (Escherichia/Shigella) and desreased proportions of Actinobacteria (Bifidobacterium) and Bacteroidetes compared to low-risk infants (Sellitto et al., 2012; Olivares et al., 2015). In a proof of concept study, Sellitto and co-workers have traced the longitudinal changes in the microbial populations colonizing from birth to 24 months in 30 genetically predisposed infants. They demonstrated that even at 2 years of age their microbiota do not resemble that of adults, while in not at-risk infants the maturation was complete at 1 year of age. The group was divided into an early and a late gluten exposure groups (17 and 13 infants, respectively). The authors showed that genetically susceptible infants may benefit from delayed gluten exposure not before 12 months of age. A hypothesis was forwarded that lack of maturity of the enteric microbiota faced with early gluten consumption can induce or accelerate the autoimmunogenetic process. Not less interesting was the infant's metabolome. When solid food was introduced at 6 months of age, succinate, acetate, propionate, and butyrate accumulated in their stools. However, by 2 years of age, butyrate, and acetate were the dominant short-chain fatty acids (SCFA). Since bacteroidetes are associated most strongly with propionate, while Firmicuters are negatively correlated to SCFAs production (Koenig et al., 2011), Sellitto's group envisioned that the high Firmicutes and low Bactriodetes abundance in CD infants results in down production of those protective SCFAs, thereby abrogating enteric health and predisposing them to autoimmune diseases. The decrease in Lactobacillus spp. associated with lower lactate production, observed in between 6 and 12 months of age, accompanied by decreased SCFAs' feces repertoire, during a vulnerable time of mucosal immune maturation and microbiome compositional changes might lead to loss of tolerance to non-self-antigens like gluten in those CD genetically predisposed infants (Sellitto et al., 2012). Once again it points to the important relations connecting nutrition to microbiome composition and diversity, its metabolome and the local maturation and functioning of the immune system.

From this perspective, it is interesting to compare the composition of the gut flora in infants at CD risk and premature babies with a priori immature gut. Arboleya and coauthors compared the gradual establishment of the intestinal microbiome in very-low birth-weight preterm infants with that of healthy full-term, vaginally born, breast-fed neonates using 16S rRNA gene profiling and quantitative PCR for the various microbial taxa. It was demonstrated that preterm neonates sheltered a higher relative proportion of Firmicutes at 2 days of age, and of Proteobacteria in the later sampling times, compared to control babies. Prematurity reflected reduced levels of Bacteroidetes at day 2 and as well as in later sampling times together with Actinobacteria (Arboleya et al., 2016). In addition, verylow birth-weight preterm infants frequently displayed a lag in establishing an adult microbiota compared to full-term children (Weng and Walker, 2013). So, the parallels to the peculiarities of the gut flora in CD prone individuals seem to be obvious. Combining the results of Arboleya et al. and Sellitto et al. about the enteric mucosal immaturity and the unbalanced microbiome found early in life and in CD high-risk infants, it can be suggested that by ingesting gluten peptides, the disease will progress in these individuals, unlike in non-CD high-risk premature infants, that will never develop CD. The features of CD gut microbiome, incorporated in early childhood seem to persist in the adulthood even despite gluten withdrawal (Nistal et al., 2012; Wacklin et al., 2014).

Caminero and co-workers demonstrated that gluten amounts in feces of healthy volunteers, CD patients and individuals under risk receiving gluten-free or normal diet depended on gluten intake. The greatest amount of gluten was found in fecal samples from healthy volunteers being on normal diet, with a significant decrease in the untreated CD patients and the individuals under risk. It is noteworthy that in all the groups fecal peptidase activity against the gluten-derived peptide 33 mer inversely correlated with gluten amount in the samples (Caminero et al., 2015). It looks like the increased functional proteolytic activity of gut microflora in CD patients can affect gluten excretion. The same research group isolated 144 strains belonging to 35 microbial species that might be involved in gluten digestion in the human intestine. Most of the strains were part of the phyla Firmicutes and Actinobacteria, mainly from the genera Lactobacillus, Streptococcus, Staphylococcus, Clostridium and Bifidobacterium (Caminero et al., 2014). Ninety-four of these strains were capable to metabolize gluten, 61 of them showed an extracellular proteolytic activity of gluten proteins, and several strains showed a peptidase activity toward the "supra-molecule" 33-mer peptide, the luminal immunogenic molecule in CD patients (Caminero et al., 2014). At the end of the day, there is a certain difference in gluten proteolysis by various bacteria, and the immunogenicity of the generated peptide fragments might be different. It should be noted that these studies were carried out in cultures, in which the glutenase activity of aggregate microbial community as a whole (biofilm) in the gut boundary layer might significantly differ from the isolated activity of the individual members of this community.

It is known that the microbiome impact gastrointestinal and systemic functions by its metabolome, the most studied one being the short chain fatty acids (SCFA). Most recently, the topic of nutrition, microbiome, and SCFA associations in CD was updated (Lerner et al., 2016). Multiple beneficial effects to the host were attributed to them. Changes in microbiota and their SCFA production is clearly related to the pathogenesis of CD. Interestingly, peculiar dysbiosis and significant changes in stool SCFA profile were described in several autoimmune diseases, one of those is in Behcet's disease where decreased butyrate production was suggested to play a role in its pathogenesis (Consolandi et al., 2015).

Taken together, the microbiota/dysbiota disbalance may present a risk factor for CD either directly by influencing the mucosal immune responses or by intensifying inflammatory responses to gluten. In contrary, several microbial species are capable to break down gliadin and perhaps therefore decrease the immunopathogenicity of consumed gliadin (Sjöberg et al., 2013; Carding et al., 2015; Lerner and Matthias, 2015a; Rostami-Nejad et al., 2015).

#### COEXISTENCE OF CERTAIN INFECTIONS AND CD: A CRITICAL LOOK AT THE ISSUE

It is of interest how CD affected individuals survived in the presence of harmful conditions (increased gluten content and toxicity in wheat, increased gluten consumption worldwide; Lerner et al., 2017) and despite them, thrived and expanded? (Lerner, 2011; Lerner et al., 2015b). Despite being underprivileged with nutritional deficiencies, failure to thrive and high morbidity and mortality, a substantial increase in the disease incidence is observed in the last decades. There are several theories explaining this paradox. One is positive evolutionary selection, in which the celiac patient accumulates protective genes (Lerner, 2011). The other is due to some pathogens which are the major drivers of human selective genetic adaptation (Vatsiou et al., 2016) that could have been beneficial environmental factors, protecting the CD populations. Indeed, in contrast to the observations that infections may induce CD, some infection agents were assumed to have a protective impact (Christen and von Herrath, 2005; Gaisford and Cooke, 2009; Kivity et al., 2009; Plot et al., 2009).

This assumption was based in particular on a lower incidence of the serum antibodies against Cytomegalovirus (CMV), EBV, and Rubella in CD patients when compared to healthy people (Plot and Amital, 2009). Another argument for the protective effect is the inverse correlation between serum anti-CMV, -EBV, and/or -Herpes simplex type 1 virus (HSV1) IgG levels and anti-tTG antibodies (Jansen et al., 2016).

#### Viruses

It should be specified that Jansen and coauthors determined the antiviral antibody levels in the sera samples of 6-year old children (Jansen et al., 2016). CMV single infection and combined CMV, EBV, and/or herpes simplex virus type 1 infection antibodies were inversely associated with strongly tTg-IgA positivity. The authors suggested that the serological profile may indicate a protective effect of herpesvirus infections in the pathogenesis of celiac disease autoimmunity. It is accepted that by this age IgG and IgM production is close to that of the adults. However, the immune system is still immature and its reaction is not fully functional at this age even in healthy children, whereas the immune system of CD prone children, at least, the local immune defense, is somewhat delayed in the maturation. Thus, Jansen et al. suggested explanation should be taken with a grain of salt.

In Plot's publication, the age profile of the studied cohorts was not presented (Plot and Amital, 2009). There are some curious details in this publication which need to be interpreted. First, while the prevalence of the anti-EBV capsid antigen and anti-EBV nuclear antigen IgG in the CD patients was significantly lower than that in the controls, the prevalence of the anti-EBV early antigen IgG was comparable in the two groups and the prevalence of anti-EBV capsid antigen IgM, though unreliably, was more than twice higher in the CD group. In general, the anti-EBV capsid antigen IgMs are known to be produced during the acute phase (the first days—6 months from the onset of the disease) or during acute exacerbation of chronic EBV infection (http://www.tiensmed.ru/news/epstein-barr-bc1.html#nov1). In addition, if the anti-EBV early antigen IgG is not concurrently revealed, it may indicate incubation period or the very beginning of the infection (up to 1 week of symptoms). So, either in the CD patients this generally latent infection is apt to more frequent exacerbations, or, given the reduced frequency of the studied IgG antiviral antibodies, the existence of some features of the antiviral antibody production in CD can be assumed. Secondly, revealed by PCR, CMV DNA presence in the samples is not always accompanied by the presence of serum specific antibodies. This situation is typical in particular for the infants with immature immune system (Dong et al., 2004, 2005).

A number of publications on the HBV vaccine nonresponsiveness in CD might attest for possible unique features of the antiviral antibody response formation (Noh et al., 2003; Park et al., 2007; Urganci and Kalyoncu, 2013), further criticizing the assumption that decrease antibodies activities in CD patients represent a protective effect.