Introduction

Autoimmune diseases (ADs) are chronic pathologies triggered by the loss of immunological tolerance to self-antigens, which can cause systemic or organ specific damage. ADs are common, with an estimated prevalence of 3,225/100,000. They are also a frequent cause of morbidity and mortality (1,2). Genetic and environmental factors are the main ones involved in the pathogenesis of ADs. Infections and exposure to pathogens or opportunistic organisms are among the environmental factors and may induce the initiation or exacerbation of ADs (3-5). Many types of infection may influence one or more of these diseases, and a single organism may be able to trigger more than one AD (1,6). In this chapter, the evidence indicating a causal role of infections in the development of ADs is reviewed.

Infectious agents and ADs

Autoimmune response is mediated by autoreactive T and B lymphocytes responsible for the production of soluble mediators (e.g., cytokines, nitric oxide, etc.) and autoantibodies. These will lead to tissue damage that may be systemic in the case of systemic lupus erythematosus (SLE) or organ specific such as in type I diabetes (T1D) (7).

Infections can be triggers of ADs as has been shown in animal models (1,3,6,7). Moreover, infections can participate in the activation and later clonal proliferation of autoreactive T and B lymphocytes that are crucial for the development of an AD. Almost all ADs have been associated with at least one infection (1). In addition, patients diagnosed with ADs have a higher risk of infections as a consequence of their treatment (1,4). For example, SLE patients under treatment who have a low grade vaccination against varicella zoster virus have an increased frequency of reactivation of the virus (8). According to the “hygiene hypothesis,” infections may act as a protective mechanism for autoimmune development. However, it is well known that they also trigger autoimmune manifestations (9). Moreover, one or more microorganisms can be associated with the same AD (1,6). Note that, this association may depend on the population under study given the evolutionary effect exerted by some infectious agents to induce resistance in the host (see Chapter 15), which in turn, influences the risk of ADs (Table 1) (9–40). Autoantibodies may also be seen in infectious diseases in patients without ADs (41,42), indicating that the presence of a pathogen may lead to the occurrence of autoimmune phenomena.

Table 1

Pathogens associated with most common autoimmune diseases.

Antiphospholipid syndrome (APS). High levels of IgM antibodies against Rubella, Toxoplasma gondii, Cytomegalovirus (CMV), and hepatitis C virus have been found. Moreover, cross-reactivity has been demonstrated between proteins of bacteria such as Haemophilus influenzae, Neisseria gonorrhoeae, tetanus toxoid, and CMV with anti β2 glycoprotein 1 (β2-GPI) antibodies, which is one of the typical antibodies of this AD (1,22,43).

SLE. Studies in animal models of SLE have shown that Epstein-Barr virus (EBV) can trigger the production of autoreactive antibodies with subsequent development of manifestations similar to those presented in the human disease (1,19,44). In patients, a high EBV seroprevalence has been observed as compared to healthy controls (1,19,45). Additionally, it has been suggested that Rubella (1) and CMV may induce the production of autoantibodies in patients with SLE (10,12,13). Toxoplasma gondii and Helicobacter pylori are also risk factors for ADs (46).

Rheumatoid arthritis (RA). It has been reported that the IgM response to some bacterial infections, e.g., Escherichia coli, Klebsiella pneumonia, and Proteus mirabilis is associated with rheumatoid factor (1,27). In the case of P. Miriabilis, a relationship with RA was established through structural similarities between self-epitopes and bacterial molecules, e.g., the shared epitope alleles (mainly HLA-DRB1*01:01, *04:01, *04:04, *04:05 – See Chapter 17) and the bacterial haemolysin as well as the human type XI collagen and the urease from P. Miriabilis (28–30). Additionally, RA has also been associated with the presence of Hepatitis B virus which is higher in RA patients than in healthy controls (47). Similar findings have been reported for EBV in RA as well as for Sjögren’s syndrome (1,31,48).

T1D. CMV infection may trigger the clinical manifestations associated with this disease. However, there are conflicting results concerning this matter (24,25). Associations between Coxsackie B4 virus, Rubella, and T1D have been reported (49). Furthermore, a homology has been shown between sequences of glutamic acid decarboxylase (GAD), a self antigen recognized by autoantibodies in T1D, and some protein sequences of several viruses, e.g., adenovirus, CMV, EBV, and rotavirus (50). There is, in addtion, some evidence of the association between islet autoantibodies and H. pylori (51).

Multiple sclerosis (MS). While some animal models suggest a protective effect of viral infections against myelin loss, other evidence suggests that murine CMV and EBV may favor the development of the disease (52). In humans, there is no agreement about the risk of developing MS confered by infection (53,54). There are different types of viruses and bacteria associated with the induction of autoantibodies to myelin basic protein (MBP) (55) (e.g., Acinetobacter sp. and P. aeruginosa). These infections are associated with the occurrence of autoantibodies against MBP and myelin glycoprotein oligodendrocyte due to their similarity with some bacterial molecules (37). In a series of 593 Colombian individuals including 143 healthy controls, 99 MS patients, 152 RA patients, 120 SLE patients, and 82 SS patients, an evaluation for antibodies to EBV infection was done (Figure 1). An association between ADs and anti IgG antibodies wih the early antigen of EBV was observed in all ADs except for MS.

Figure 1

EBV infection in ADs. The percentage of individuals with IgG against EA-EBV is shown. (* OR=3.35 95%CI: 1.63–6.92, p<0.001, *+ OR= 4, 95+CI: 1.82 - 8.83, p<0.001, *** OR= 15.22, 95%CI: 7.45–31.1, p<0.001). EA-EBV, (more...)

Infections: A trigger mechanism for ADs

The AD definition has been framed by various direct and indirect criteria as well as circumstantial evidence about the disease (56–58). Infectious agents can trigger some ADs through different mechanisms (Table 2). Reconciling the criteria for the AD definition with Koch’s postulates provides a better understanding of the relationship between ADs and infections. Koch’s postulates are the criteria for establishing a causal link between a microbe and a disease. However, these four criteria are based on a simplistic view of ADs because they do not take into account the multifactorial nature of autoimmunity (Table 3). Since environmental, epigenetic, and genetic factors also influence ADs, these factors should be taken into consideration. In other words, the inner characteristics of a population may influence the development of an AD when they are exposed to an infection. This explains why, despite the association of certain pathogens with a specific disease, there is still a considerable group of healthy individuals who, after exposure to a microorganism, does not develop the disease [e.g., EBV and SLE (14)].

Table 2

Classification criteria for autoimmune diseases in humans and role of infection.

Table 3

Koch’s postulates in autoimmune diseases.

There are different mechanisms by which infection may trigger ADs (Figure 2). First, there is molecular mimicry in which B and T cells are activated as a result of an infection, but they are able to recognize self-molecules or proteins that are similar to those from infectious agents. This cross reaction may generate an autoimmune response which has been reported in several ADs (1,3,19,52,59–61). The second is epitope spreading, which is also known as antigenic determinant. In this case, the autoimmune response is due to the spatial proximity and similarities between several self epitopes and epitopes from the microorganism (1,61). A third mechanism is bystander activation. This makes reference to the activation and clonal expansion of auto reactive T cells by the cytokine profile generated during the inflammatory process and swelling in response to an infection. This inflammatory cocktail activates cells with different specificity and which may be auto reactive. Finally, all these circumstances favor the inflammatory process and the development of an aggressive response leading to exacerbations in patients with ADs (1,3,52,61). In contrast with molecular mimicry and epitope spreading where cells are activated by specific antigens, the bystander activation mechanism assumes that pathogens may break immune tolerance by the induction of a non-specific polyclonal response. This mechanism is achieved through several pathways including the release of intracellular antigens after cell death known as cryptic antigens. A characteristic of this type of antigens is that they are not presented to T cells during central tolerance. Therefore, as a consequence of cell death, these self-antigens may increase their visibility and abundance thus attracting and enhancing antigen-presenting cells or disturbing the cytokine balance (both locally and long distance) (1,3,52,61).

Figure 2

Infectious mechanisms of autoimmune disease. Molecular mimicry: Activation of self-reactive cells that may recognize pathogen and self-epitopes. Antigenic peptides are processed by antigen presenting cells (APC). As a result, peptides are presented to (more...)

Fourth, the persistence of infection and a polyclonal response can trigger the appearance of ADs as a consequence of the constant activation of the immune response. In ADs, it is possible to find an accumulation of circulating immune complexes, which may affect specific tissues, for example, the case of mixed cryoglobulinemia caused by hepatitis C virus infection (1,61). Furthermore, the study of the relationship between infection and autoimmunity has been advanced by the description of superantigens They are bacterial, viral, or retroviral proteins that can activate a large quantity of T cells by binding the T cell antigen receptor to MHC molecules expressed on other cells. All this is done without antigen processing (61,62). The Vβ region is sufficient for superantigen recognition in contrast to conventional epitopes that require a very specific interaction with the third hypervariable region of the TCR. Superantigens are not restricted to MHC class II, and as a result, they may cause T cytotoxic activity without antigen presentation. Therefore, superantigens are extremely potent immunostimulatory substances (61,62).

One of the consequences of the chronic inflammatory process is apoptosis or necrosis of the cells within the location where the infection took place. Some abnormalities such as deficiencies in the clearance of apoptotic bodies can cause a secondary necrotic step in which there may be an exposure of cryptic antigens. Consequently, these antigens may generate the autoimmune process. It is common in autoimmunity to find clearance deficiency of dying cell particles associated with disease development (Figure 3) (61,63). For example, there are deficiencies in the complement system in SLE patients. As a result, there are alterations of phagocytosis, clearance of apoptotic cells, and the recognition of potential pathogenic microorganisms. This causes the accumulation of substances which induces a pro-inflammatory microenvironment that includes the exposure of self antigens and the generation of autoantibodies against them (Figure 3) (61,63).

Figure 3

Clearance deficiency. Microorganisms may induce the apoptosis of cells as part of their pathogenic process. In systemic lupus erythematosus (SLE) there are defects in the mechanisms of clearance of apoptotic bodies. As a consequence, there is an accumulation (more...)

β2-GPI and tetanus toxoid: Molecular mimicry

APS is an autoimmune multisystemic disease mainly characterized by recurrent fetal loss and thromboembolic phenomena together with the presence of antiphospholipid antibodies. These antibodies recognize negatively charged phospholipids, mainly through cryptic antigens of the β2-glycoprotein I (β2-GPI) that are exposed after the protein is unfolded. Molecular mimicry is one of the mechanisms that may induce experimental APS when it is associated with certain pathogens, e.g., Haemophilus influenzae, Neisseria gonorrhoeae, CMV, and the tetanus toxoid (22,23).

The β2-GPI molecule is involved in many important processes in the human body such as coagulation where it is related to induction of platelet aggregation, inhibition of prothrombinase activity, and production of platelet factor IX. β2-GPI has been identified as one of the most important antigens in APS and consists of 326 amino acids organized in five domains. The first four domains have conserved sequences, and the fifth contains a sequence that consists of 20 positively charged amino acids in the C-terminal loop ending. This loop constitutes the patch that determines the affinity for anionic phospholipids. It has been demonstrated that the interaction between β2-GPI and phospholipids induces conformational changes resulting in the exposure of cryptic epitopes within other domains that will be recognized by antibodies. Indeed, this is essential to the exposure of a potential auto-antigen (23).

Tetanus toxoid, in turn, is a potent exotoxin produced by the bacteria Clostridium tetani. The active form of the toxoid is made up of two chains. It affects motor neurons by binding to cell membranes with its carboxyl terminal on the heavy chain. The infection may be prevented through a vaccine, which is composed of the tetanus toxoid and an adjuvant (usually aluminum hydroxide). However, the vaccine may induce the production of antibodies against the toxoid that can react to other auto-antigens, e.g., cardiolipins and antiphospholipids (23,64).

A human hybridome (H3) was produced from a boosted healthy individual with diphtheria and tetanus toxoid. The monoclonal antibody demonstrated a cross reaction with human β2-GPI. Consequently, the analysis of potential epitopes by a phage display library proved that the isolated monoclonal antibody recognizes a linear epitope (TLRVYK) that is found in the third domain of the β2-GPI (23,65–67). The TLRVYK was found to be located once on the β2-GPI and three times as conformational epitopes on the tetanus toxoid by bioinformatic analysis (Figure 4) (23,68). Therefore, molecular mimicry is a mechanism that favors APS development. In addition, studies with animal models have shown the induction of symptoms and the disease in the animals immunized with H3. Furthermore, murine model treated with this peptide did not develop the disease.

Figure 4

Molecular mimicry and anti-β2-GPI: Production of antibodies against β2-GPI by the presence of the tetanus toxoid. As a consequence of the recognition and destruction of the bacteria, there is presentation of bacterial peptides. The peptide (more...)

ADs prevention: Helminths and induction of TH2

Infections may prevent the development of autoimmunity or even withdrawal of an autoimmune process. This happens as a result of the interaction between microorganisms and the host (1,69). Part of the evolutionary goal of infectious agents is to stay in their host as long as possible. Thus, parasites such as helminths are a clear example of co-evolution. These microorganisms modulate the immune response towards an anti-inflammatory profile which favors their survival in the host. Thus, helminths are suppressors of the immunological pro-inflammatory process (69,70).

Over the last few decades, quality of life has improved in the developed countries. This has decreased the prevalence of infectious diseases, especially those related to parasites. According to the hygiene hypothesis, the increase in atopic disorders, allergies, and even ADs are the result of the reduction in exposure to microorganisms and parasites during childhood (71,72). This was demonstrated by the geographic relationship observed worldwide. For instance, in places where there is a low prevalence of parasite infections, the prevalence of ADs is rising (71–74). Many inversed associations have been reported between parasitic infections and protection from ADs (Table 4). For example, the Schistosoma mansoni infection shows a protective effect against the development of T1D (70,75). Therefore, microorganisms should be recognized as not only a cause of infection and disease, but also potent immune system modulators.

Table 4

Helminth anti-inflammatory effect in ADs.

Helminths may stay in their host for long periods of time making themselves successful pathogens. Essentially, parasites are able to change the cytokine profile from a pro-inflammatory to an anti-inflammatory profile. This change creates the perfect environmental conditions for them to survive and extend their lives within the host. Research using both human and animal models has shown that helminths can modulate the innate and the adaptive immune response. In the case of autoimmune and inflammatory diseases, the presence of parasites may induce an anti-inflammatory profile that prevents the pathological inflammatory process. First of all, they may promote the inhibition of IFNα, IL-1β, and IL-17 to suppress the Th1 and Th17 response (see Chapter 9). Secondly, they promote the production of IL-4, IL-10, TGF-β, and the activation of regulatory cells including Treg, Breg, regulatory dendritic cells, and macrophages (74,76–78).

Helminths infect their host mainly through the gut. Characteristically, there is an abundance of macrophages in the intestine which may be activated by the parasites many different ways. In a helminth infection, parasites induce the production of IL-10 and Th2 cytokine profile including IL-4, IL-5, IL-9, and IL-13 (79). Furthermore, this cytokine profile stimulates macrophages to become alternative activated macrophages (AAM). AAM express specific molecules such as arginase-1, RELMa, YM11, and TGF-β. At the same time, they have a decreased expression of IL-12 and IFNγ (Th1 cytokines) (76).

There is production of different molecules during helminth infections. Some of them are essential for the parasite life cycle or structure. These molecules include proteins, lipids, glycoproteins, and glycolipids which probably have a regulatory effect on the host immune system (Table 5). Helminths may also produce molecules that mimic host cytokines due to structural similarities. There are two clear examples of this mechanism. The first one is the transforming growth factor homologue-2 (TGH-2) which is produced by Brugia malayi and binds to the TGF-β receptor (77,78). Secondly, some helminths produce macrophage migration inhibition factors (MIFs) which activate AAM. The result is that the anti-inflammatory profile (Th2) is induced which favors the survival of the parasite and the eosinophilic response (77).

Table 5

Helminth derived – molecules with immunoregulatory properties.

The dendritic cell is the pivotal point to determine the adaptive immune response. This response will depend on the stimulation of a specific receptor (TLR, NLR, NOD-like, or CLRs). CLRs, in particular, recognize specific glycan parasite profiles, and after their activation, the result is Th2 cytokine polarization and the anti-inflammatory process (79,80). For example, the phosphorylcholine (PC) has been shown to be a potent immunomodulator. It contains the glycoprotein ES-62 from Acanthocheilonema viteae, which suppresses murine arthritis in vivo and improves the maturation of DC towards a Th2 cytokine profile (80).

Finally, regulatory cells are activated due to all these interactions and have a direct effect on the auto reactive B and T cells (Figure 5). T regulatory cells increase after a helminthic infection. This has been reflected in higher amounts of CD4+CD25+FOXP3+ cells that can release IL-10 and TGF-β.

Figure 5

Immune response to helminths and protection from autoimmunity. The exposure to helminths induces changes in the innate immune system—mainly dendritic cells (DC) and macrophages that become alternative activated macrophages (AAM). These changes (more...)

Microbiota and ADs

There are different microorganisms that populate the gut known as intestinal microbiota. This process starts at the time of delivery and breastfeeding, and it plays an important role in the homeostasis of the human body. Indeed, there are microorganisms in the microbiota that produce enzymes and molecules which are not generated by human beings. Therefore, microbiota is important to normal metabolism because it is capable of degrading the different components in food (81,82). However, microbiota may also influence other systems that do not seem to be related to the gut such as in the case of the immune system. Moreover, it actively participates in the education of the immune system. For example, the development of Th17 and Treg lymphocytes is highly dependent on the interactions of commensal bacteria with host cells in the intestine. That is why it is possible to establish a connection between microbiota and autoimmunity or inflammatory disorders (81–84).

Studies of germ free (GF) animal models have demonstrated deficiencies in their immune system. It is noteworthy that microbiota is the first barrier against pathogenic microorganisms. They may produce molecules against the pathogens during infection because they occupy the same niche, thus competing for the same place. GF mice show deficiencies in T lymphocyte differentiation within the lamina propria in the presence of IgA in mucosal layers and alterations in the homeostasis of Th populations (Th1, Th17, and Treg). Furthermore, GF mice spleens show few germinal centers which indicates abnormal development and maturation of cells in the lymphoid follicles (81–83). As a result, the cytokine production and the normal maturation process of immune cells are greatly affected (82).

Microbiota varies from one individual to another and even in the same individual over the course of his life. However, there are many factors that influence the composition of microbiota. Initially, newborns are sterile, and their microbiota depends on maternal transfer at the time of delivery, breastfeeding, and skin contact with the mother. For example, there are differences between children that were born by natural delivery and caesarean section, and also between children that were breastfed and those fed with formula. The latter group in both cases is colonized by potential pathogens and, in contrast with first group, they present a lack of beneficial commensal microorganisms (82,85). Nevertheless, their own genetic background plays an important role in determining the microbiota before contact with the mother’s microbiota. For example, animal models with gene alterations related to the innate immune system have problems in the signaling pathways associated with PRR recognition (e.g., TLRs and NODs). These models also have important differences in the pattern of microbiota microorganisms compared to wild type animals (81–83).

In addition, the evolution of medical treatment, stress, and quality of life influence microbiota development. For example, antibiotics are the treatment for infectious diseases, but their use is linked with the loss of beneficial microorganisms. Moreover, they alter the microbiota ecology because they affect the equilibrium between different bacterial species in the gut, which allows opportunistic pathogens to attack the body (82). Finally, diet plays a central role in the homeostasis of the microbiota because it defines which microorganisms can survive in the gut due to differences in the preferences of microorganisms for energy sources. Thus, diet composition is extremely important in microbiota maintenance. Components from plants are the energy source for beneficial microbes and promote their growth over other microbes. It has been suggested that differences in the modern western diet could be causing the rapid increase in diseases such as asthma (82,86). For example, one study shows how a switching from a low fat, vegetable rich diet to a high fat, high sugar diet could alter the microbiota within one day (82,87).

Altogether, there is a lot of variability in microbiota between individuals, and it also varies based on the anatomic area of the human body (Table 6) (88). Fluctuations in microbiota population have been described in patients with ADs (81). In addition, most of the studies have established a relationship between the microbiota inhabiting the gut and its influence on health. Usually, microorganisms that live in the gut are not pathogenic under healthy conditions, and they have a positive effect on the host (83). Nevertheless, some commensal bacteria may drive the preferential development of Treg while others promote Th17 response and inflammation. These bacteria favor the production of regulatory molecules and cytokines, e.g., Foxp3 and IL-10, which characterize the regulatory cells, Treg in particular. Specifically, species such as Bacteroides fragilis and the genus Lactobacillus and Bififobacterium greatly promote the presence of Treg in the gut. In contrast, a pathogenic phenotype characterized by Th17 response and pro-inflammatory cytokines is promoted by segmented filamentous bacteria such as Firmicutes (Figure 6). Moreover, it has been demonstrated that this kind of bacteria is able to induce the production of IgA in the small intestine. Th17 response certainly has its own positive role in the case of infection control, but it is also critical in the development of inflammatory and autoimmune diseases (81–83,88).

Table 6

Anatomic Site Microbiota Composition.

Figure 6

Factors influencing gut microbiota. A balancing microbiota in symbiosis influences the equilibrium between T regulatory cells (Treg) and effector T cells (Th1, Th17). As a consequence, the gut can respond to an infection but this also leads to the permanence (more...)

Therefore, any change in their environment may induce a dysbiosis and a pathological event (Figure 6). At present, the use of antibiotics such as ampicillin, gentamicin, and vancomycin may affect the balance toward bacteria that stimulate the Th17 response. At the same time, the consumption of a diet rich in sugar and fat favors a rise in the population of Firmicutes and a reduction in the Bacteroidetes (81,82,88). Finally, the influence of microbiota on the immune balance does not depend exclusively on the presence of the microorganisms but also on molecules that are produced by microbes and can stimulate specific pathways associated with immune tolerance (82). An example is the anti-inflammatory effect of the short-chain fatty acids, e.g., acetate, propionate, and butyrate. These fatty acids can bind to the G protein/coupled receptor (GPR43 and GPR120) which is expressed in most of the innate immune system cells and thus produce an anti-inflammatory action (82). The anti-inflammatory events induce the maintenance of the epithelial barrier, regulate apoptosis, diminish oxidative DNA damages, and regulate cytokine production, phagocytosis and neutrophil recruitment. At the same time, the peptidoglycan and the polysaccharide A teach the immune system to recognize potential pathogenic bacteria, and they also help in the correct development of balanced T cell response in the gut. This balanced response promotes cell-cell interaction by modification of protein expression in immune cells (82).

Metagenomics

Metagenomics is the study of the genomes in a specific environment. Human beings can be considered either a superorganism or an ecosystem. For instance, there are more microorganisms living in human bodies than cells themselves. Therefore, commensal and pathogenic microorganisms are considered biomarkers for specific conditions, and they may be a therapeutic option due to their potential immunoregulatory roles. As a consequence, different studies in the last few years described the human microbiome as a changing ecosystem that has many factors in the model (Figure 7a). Most of these studies try to determine what these microorganisms are and how they vary over time. However, the study of these microorganisms represents a challenge in terms of methodology because their identification was done by culture, and most of them cannot be grown in laboratory conditions. Therefore, most of them are now being studied with the help of DNA high-throughput analysis. Indeed, analysis of the entire DNA allows the study of all the organisms present in a sample (89,90).

Figure 7

Metagenomic approximations. A. Factors that influence the human microbiome composition. B. Metagenomic analysis of a human sample allows species, function, and phylogeny identification. C. Metatranscriptomic, Metaproteomics and Metametabolomics: Other (more...)

There are different strategies for analyzing data. Currently, the sequencing of the 16S rRNA gene has made it possible to describe prokaryote taxonomic diversity. This gene has been used for this type of analysis for three main reasons. Primarily, it is present in every population member due to its essential role in protein translation in all prokaryotes. Secondly, 16S rRNA always and only differs between individuals with different genomes. Thirdly, it is considered an evolution marker between species because its function is extremely important, so any change in its sequence can be potentially lethal. Therefore, the 16S rRNA sequence analysis brings us to the construction of OTUs (operational taxonomic units) based on the percentage of similarity between sequences. Then, comparison between multiple databases leads to the identification of species within a sample. Finally, the population becomes a 16S rRNA sequence collection where the number of unique sequences represents the number of microorganisms. At the same time, the quantity of each one can show their distribution within the sample (89–92) (Figure 7b).

Furthermore, high-throughput DNA sequencing technologies have recently become very useful for metagenomic analysis (i.e., pyrosequenciation). Thus, different strategies for different types of genome assembly and annotation within a sample are used to ensure an accurate description (93). In addition, potential proteins encoded in each genome can be analyzed based on DNA sequence. As a result, it is possible to assign a role or function for a specific microorganism within the microbiome based only on their genome capabilities (89) (Figure 7b).

In the end, due to the current metagenomic results, the analysis of the microbiome is moving forward and new OMICS have been identified. However, the real involvement in host systems does not depend exclusively on the presence of a specific microbiome. For instance, metagenomics would be just a representation of what these microorganisms are. Moreover, molecules produced by microorganisms can interact with the host at different levels leading to diverse outcomes. In consequence, it is important to study gene expression (metatranscriptomics), proteins (metaproteomics), and metabolites (metametabolomics) from a microbiome. These analyses including possible interactions within the host system and under certain conditions can settle the real mechanisms by which specific microorganisms (present in a particular microbiome) may influence the host system and thus lead to complex disease modulation such as ADs (90) (Figure 7c).

Conclusion

The relationships between infection and ADs and their main mechanisms have been outlined (i.e., host-guest interaction). Nonetheless, most of the interactions and mechanisms that influence this relationship are still unknown. Microorganisms may alter and deregulate gene transcription, translation, and human metabolic processes. This means that the effect induced on the host by a microorganism is not caused by the presence of the microorganism itself but also by the metabolic and genetic polymorphism of the microorganism. In particular, intracellular pathogens may have a direct influence on gene regulation and protein expression inside host cells (85,94).

In the last few years, most of the large cohort studies have evaluated genetic factors that may predispose to ADs. In addition, expression and proteomic analysis have also been done, and most of them have tried to find a way to establish predictor genetic factors for the diseases. However, these studies do not take into consideration the DNA, RNA, and proteins from microorganisms that could be considered potential “contamination,” and which should be considered a source of information that would help complete the overall picture of molecular interactions between infection and ADs and make it understandable (85,95,96).

Last but not least, familial autoimmunity and polyautoimmunity should be incorporated into the study of infection and ADs (97–99). ADs may be associated with a specific family group exposed to a particular environment. Within this environment, family members will be in contact with the same microorganism, and they will develop the same microbiota. As a new common mechanism for ADs, microbiota can be “inherited” by the child from parent, and it can also be shared among siblings (88).

Abbreviation list

AAM:

Alternative activated macrophages

AdES:

Adult excretory-secretory antigen

ADs:

Autoimmune diseases

ALT:

Abundant larval transcript

APC:

Antigen presenting cell

APS:

Antiphospholipid syndrome

β2-GPI:

β2 glycoprotein 1

Bc:

B cell

Breg:

B regulatory cell

CD:

Crohn’s disease

CLR:

C-type lectin receptor

CMV:

Cytomegalovirus

Cystatins:

cysteine protease inhibitors

DC:

Dendritic cell

DNA:

Deoxyribonucleic acid

EBV:

Epstein-Barr virus

EA-EBV:

Early antigen of EBV

FOXP3:

Forkhead box P3 transcription factor

GF:

Germ free

GPR:

G protein/coupled receptor

H3:

Human hybridome 3

HES:

Excretory-secretory antigen

IBD:

Inflammatory bowel disease

IFNα:

Interferon alpha

IFNγ:

Interferon gamma

IL:

Interleukin

LNFPIII:

Lacto-N-fucopentaoseIII

LPS:

Lipopolysaccharide.

MBP:

Myelin basic protein

MIF:

Macrophage migration inhibition factor

MLCr:

Crude muscle larvae antigen.

MS:

Multiple sclerosis

Mφ:

Macrophages

NBL:

Newborn larvae antigen

NES:

Excretory-secretory antigen

NLR:

Nod-like receptor

NOD:

Nucleotide-binding oligomerization domain receptor

OTUS:

Operational taxonomic units

PC:

Phosphorylcholine

RA:

Rheumatoid arthritis

RNA:

Ribonucleic acid

rRNA:

Ribosomal ribonucleic acid

SEA:

Soluble egg antigen

Serpins:

Serine protease inhibitors

SLE:

Systemic lupus erythematosus

SS:

Sjögren’s syndrome

T1D:

Type I diabetes

Tc:

T cytotoxic cells

TGF-β:

Transforming growth factor beta

TGH-2:

Transforming growth homologue-2

Th:

T helper cells

TLR:

Toll like receptor

Treg:

T regulatory cell

References

1.

Kivity S, Agmon-Levin N, Blank M, Shoenfeld Y. Infections and autoimmunity-friends or foes? Trends Immunol. 2009;30:409–14. [PubMed: 19643667]

2.

Kim B, Kaistha SD, Rouse BT. Viruses and autoimmunity. Autoimmunity. 2006;39:71–7. [PubMed: 16455584]

3.

Wucherpfennig KW. Mechanisms for the induction of autoimmunity by infectious agents. J Clin Invest. 2001;108:1097–104. [PMC free article: PMC209539] [PubMed: 11602615]

4.

Chakravarty EF. Viral infection and reactivation in autoimmune disease. Arthritis Rheum. 2008;58:2949–57. [PubMed: 18821704]

5.

Anaya JM. The autoimmune tautology. Arthritis Res Ther. 2010;12:147. [PMC free article: PMC3046506] [PubMed: 21092150]

6.

Rose NR. Infection, mimics, and autoimmune disease. J Clin Invest. 2001;107:943–4. [PMC free article: PMC199561] [PubMed: 11306595]

7.

Pollard KM, Hultman P, Kono DH. Toxicology of autoimmune diseases. Chem Res Toxicol. 2010;23:455–66. [PMC free article: PMC3076021] [PubMed: 20078109]

8.

Chakravarty E, Michaud K, Katz R, Wolfe F. Increased incidence of herpes zoster among patients with systemic lupus erythe-matosus. Lupus. 2013;22:238–44. [PubMed: 23257402]

9.

Sironi M, Clerici M. The hygiene hypothesis: an evolutionary perspective. Microbes Infect. 2010;12:421–7. [PubMed: 20178858]

10.

Hachfi W, Laurichesse J-J, Chauveheid M-P, et al. [Acute cytomegalovirus infection revealing systemic lupus erythematosus] Rev Med Interne. 2011;32:e6–8. [PubMed: 20880615]

11.

Nawata M, Seta N, Yamada M, Sekigawa I, Lida N, Hashimoto H. Possible triggering effect of cytomegalovirus infection on systemic lupus erythematosus. Scand J Rheumatol. 2001;30:360–2. [PubMed: 11846056]

12.

Pérez-Mercado AE, Vilá-Pérez S. Cytomegalovirus as a trigger for systemic lupus erythematosus. J Clin Rheumatol. 2010;16:335–7. [PubMed: 20859222]

13.

Newkirk MM, Van Venrooij WJ, Marshall GS. Autoimmune response to U1 small nuclear ribonucleoprotein (U1 snRNP) associated with cytomegalovirus infection. Arthritis Res. 2001;3:253–8. [PMC free article: PMC34115] [PubMed: 11438044]

14.

James JA, Kaufman KM, Farris AD, Taylor-Albert E, Lehman TJ, Harley JB. An increased prevalence of Epstein-Barr virus infection in young patients suggests a possible etiology for systemic lupus erythematosus. J Clin Invest. 1997;100:3019–26. [PMC free article: PMC508514] [PubMed: 9399948]

15.

Moon UY, Park SJ, Oh ST, et al. Patients with systemic lupus erythematosus have abnormally elevated Epstein-Barr virus load in blood. Arthritis Res Ther. 2004;6:R295–302. [PMC free article: PMC464871] [PubMed: 15225364]

16.

Yu S-F, Wu H-C, Tsai W-C, et al. Detecting Epstein-Barr virus DNA from peripheral blood mononuclear cells in adult patients with systemic lupus erythematosus in Taiwan. Med Microbiol Immunol. 2005;194:115–20. [PubMed: 15378356]

17.

Gross AJ, Hochberg D, Rand WM, Thorley-Lawson DA. EBV and systemic lupus erythematosus: a new perspective. J Immunology. 2005;174:6599–607. [PubMed: 15905498]

18.

Deshmukh US, Sim DL, Dai C, et al. HLA-DR3 restricted T cell epitope mimicry in induction of autoimmune response to lupus-associated antigen SmD. J Autoimmun. 2011;37:254–62. [PMC free article: PMC3372418] [PubMed: 21868195]

19.

Poole BD, Gross T, Maier S, Harley JB, James JA. Lupus-like autoantibody development in rabbits and mice after immunization with EBNA-1 fragments. J Autoimmun. 2008;31:362–71. [PMC free article: PMC2852321] [PubMed: 18849143]

20.

Wilcox MH, Powell RJ, Pugh SF, Balfour AH. Toxoplasmosis and systemic lupus erythematosus. Anna Rheum Dis. 1990;49:254–7. [PMC free article: PMC1004049] [PubMed: 2339908]

21.

Esen BA, Yılmaz G, Uzun S, et al. Serologic response to Epstein-Barr virus antigens in patients with systemic lupus er-ythematosus: a controlled study. Rheumatol Int. 2012;32:79–83. [PubMed: 20661740]

22.

Blank M, Asherson RA, Cervera R, Shoenfeld Y. Antiphospholipid syndrome infectious origin. J Clin Immunol. 2004;24:12–23. [PubMed: 14997029]

23.

Cruz-Tapias P, Blank M, Anaya JM, Shoenfeld Y. Infections and vaccines in the etiology of antiphospholipid syndrome. Curr Opin Rheumatol. 2012;24:389–93. [PubMed: 22617823]

24.

Zanone MM, Favaro E, Quadri R, et al. Association of cytomegalovirus infections with recurrence of humoral and cellular autoimmunity to islet autoantigens and of type 1 diabetes in a pancreas transplanted patient. Transpl Int. 2010;23:333–7. [PubMed: 19906032]

25.

Aarnisalo J, Veijola R, Vainionpää R, Simell O, Knip M, Ilonen J. Cytomegalovirus infection in early infancy: risk of induction and progression of autoimmunity associated with type 1 diabetes. Diabetologia. 2008;51:769–72. [PubMed: 18278478]

26.

Sakly W, Mankaï A, Sakly N, et al. Anti-Saccharomyces cerevisiae antibodies are frequent in type 1 diabetes. Endocr Pathol. 2010;21:108–14. [PubMed: 20387011]

27.

Newkirk MM, Zbar A, Baron M, Manges AR. Distinct bacterial colonization patterns of Escherichia coli subtypes associate with rheumatoid factor status in early inflammatory arthritis. Rheumatology (Oxford). 2010;49:1311–6. [PubMed: 20360042]

28.

Wilson C, Thakore A, Isenberg D, Ebringer A. Correlation between anti-Proteus antibodies and isolation rates of P. mi-rabilis in rheumatoid arthritis. Rheumatol Int. 1997;16:187–9. [PubMed: 9032817]

29.

Ebringer A, Rashid T. Rheumatoid arthritis is an autoimmune disease triggered by Proteus urinary tract infection. Clin Dev Immunol. 2006;13:41–8. [PMC free article: PMC2270745] [PubMed: 16603443]

30.

Ebringer A, Rashid T. Rheumatoid arthritis is caused by Proteus: the molecular mimicry theory and Karl Popper. Front Biosci (Elite Ed). 2009;1:577–86. [PubMed: 19482674]

31.

Toussirot E, Roudier J. Pathophysiological links between rheumatoid arthritis and the Epstein-Barr virus: an update. Joint Bone Spine. 2007;74:418–26. [PubMed: 17625943]

32.

Freimanis G, Hooley P, Ejtehadi HD, et al. A role for human endogenous retrovirus-K (HML-2) in rheumatoid arthritis: investigating mechanisms of pathogenesis. Clin Exp Immunol. 2010;160:340–7. [PMC free article: PMC2883104] [PubMed: 20345981]

33.

Santiago O, Gutierrez J, Sorlozano A, De Dios Luna J, Villegas E, Fernandez O. Relation between Epstein-Barr virus and multiple sclerosis: analytic study of scientific production. Eur J Clin Microbiol Infect Dis. 2010;29:857–66. [PubMed: 20428908]

34.

Ferraccioli G, Tolusso B. Infections, B cell receptor activation and autoimmunity: different check-point impairments lead to autoimmunity, clonal B cell expansion and fibrosis in different immunological settings. Autoimmun Rev. 2007;7:109–13. [PubMed: 18035319]

35.

Pender MP. CD8+ T-Cell Deficiency, Epstein-Barr Virus Infection, Vitamin D Deficiency, and Steps to Autoimmunity: A Unifying Hypothesis. Autoimmune Dis. 2012;2012:189096. [PMC free article: PMC3270541] [PubMed: 22312480]

36.

Tracy SI, Kakalacheva K, Lünemann JD, Luzuriaga K, Middeldorp J, Thorley-Lawson D. Persistence of epstein-barr virus in self-reactive memory B cells. J Virol. 2012;86:12330–40. [PMC free article: PMC3486485] [PubMed: 22951828]

37.

Hughes LE, Smith PA, Bonell S, et al. Cross-reactivity between related sequences found in Acinetobacter sp., Pseudomo-nas aeruginosa, myelin basic protein and myelin oligodendrocyte glycoprotein in multiple sclerosis. J Neuroimmunol. 2003;144:105–15. [PubMed: 14597104]

38.

Triantafyllopoulou A, Tapinos N, Moutsopoulos HM. Evidence for coxsackievirus infection in primary Sjögren’s syndrome. Arthritis Rheum. 2004;50:2897–902. [PubMed: 15457458]

39.

Padalko EY, Bossuyt X. Anti-dsDNA antibodies associated with acute EBV infection in Sjögren’s syndrome. Ann Rheum Dis. 2001;60:992. [PMC free article: PMC1753389] [PubMed: 11589186]

40.

Arango MT, Anaya JM. Infección y enfermedad autoinmune. In: Anaya JM, Rojas-Villarraga A, editors. La Tautología Autoinmune. Primera. Bogotá: Editorial Univesidad del Rosario; 2012. pp. 131–44.

41.

Berlin T, Zandman-Goddard G, Blank M, et al. Autoantibodies in Nonautoimmune Individuals during Infections. Ann N Y Acad Sci. 2007;1108:584–93. [PubMed: 17894023]

42.

Franssila R, Hedman K. Infection and musculoskeletal conditions: Viral causes of arthritis. Best Pract Res Clin Rheumatol. 2006;20:1139–57. [PubMed: 17127201]

43.

Gharavi AE, Pierangeli SS, Harris EN. Viral origin of antiphospholipid antibodies: endothelial cell activation and throm-bus enhancement by CMV peptide-induced APL antibodies. Immunobiology. 2003;207:37–42. [PubMed: 12638901]

44.

Christen U, Von Herrath MG. Infections and autoimmunity-good or bad? J Immunol. 2005;174:7481–6. [PubMed: 15944245]

45.

Navarra S V, Leynes MSN. Infections in systemic lupus erythematosus. Lupus. 2010;19:1419–24. [PubMed: 20947551]

46.

Francis L, Perl A. Infection in systemic lupus erythematosus: friend or foe? Int J Clin Rheumtol. 2010;5:59–74. [PMC free article: PMC2830655] [PubMed: 20209114]

47.

Toussirot E, Roudier J. Epstein-Barr virus in autoimmune diseases. Best Pract Res Clin Rheumatol. 2008;22:883–96. [PubMed: 19028369]

48.

Pope JE, Stevens A, Howson W, Bell DA. The development of rheumatoid arthritis after recombinant hepatitis B vaccination. J Rheumatol. 1998;25:1687–93. [PubMed: 9733447]

49.

Adeghate E. Diabetes mellitus - multifactorial in aetiology and global in prevalence. Arch Physiol Biochem. 2001;109:197–9. [PubMed: 11880920]

50.

Atkinson MA. Molecular mimicry and the pathogenesis of insulin-dependent diabetes mellitus: still just an attractive hypothesis. Ann Med. 1997;29:393–9. [PubMed: 9453286]

51.

Sterzl I, Hrdá P, Matucha P, Cerovská J, Zamrazil V. Anti-Helicobacter Pylori, anti-thyroid peroxidase, anti-thyroglobulin and anti-gastric parietal cells antibodies in Czech population. Physiol Res. 2008;57:S135–41. [PubMed: 18271683]

52.

McCoy L, Tsunoda I, Fujinami RS. Multiple sclerosis and virus induced immune responses: autoimmunity can be primed by molecular mimicry and augmented by bystander activation. Autoimmunity. 2006;39:9–19. [PubMed: 16455578]

53.

Pender MP. Preventing and curing multiple sclerosis by controlling Epstein-Barr virus infection. Autoimmun Rev. 2009;8:563–8. [PubMed: 19254880]

54.

Lünemann JD, Münz C. EBV in MS: guilty by association? Trends Immunol. 2009;30:243–8. [PubMed: 19428300]

55.

Wucherpfennig KW, Strominger JL. Molecular mimicry in T cell-mediated autoimmunity: viral peptides activate human T cell clones specific for myelin basic protein. Cell. 1995;80:695–705. [PMC free article: PMC7133435] [PubMed: 7534214]

56.

Rose NR, C B. Defining criteria for autoimmune diseases (Witebsky’s postulates revisited). Immunol Today. 1993;14:426. [PubMed: 8216719]

57.

Cervera R, Gomez-Puerta J-A, Blank M, Asherson R, Shoenfeld Y. Autoinmunidad e infección: Hipótesis del mimetismo molecular. In: Anaya JM, Shoenfeld Y, Correa PA, Garcia-Carrasco M, Cervera R, editors. Autoinmunidad y enfermedad autoinmune. Medellin: Corporación para Investigaciones Biológicas; 2005. pp. 183–90.

58.

Pérez-Fernández OM, Mantilla RD, Cruz-Tapias P, Rodriguez-Rodriguez A, Rojas-Villarraga A, Anaya JM. Spondyloar-thropathies in autoimmune diseases and vice versa. Autoimmune Dis. 2012;2012:736384. [PMC free article: PMC3286883] [PubMed: 22400103]

59.

Poole BD, Scofield RH, Harley JB, James JA. Epstein-Barr virus and molecular mimicry in systemic lupus erythematosus. Autoimmunity. 2006;39:63–70. [PubMed: 16455583]

60.

Perl A, Nagy G, Koncz A, et al. Molecular mimicry and immunomodulation by the HRES-1 endogenous retrovirus in SLE. Autoimmunity. 2008;41:287–97. [PMC free article: PMC5294745] [PubMed: 18432409]

61.

Sfriso P, Ghirardello A, Botsios C, et al. Infections and autoimmunity: the multifaceted relationship. J Leukoc Biol. 2010;87:385–95. [PubMed: 20015961]

62.

Samarkos M, Vaiopoulos G. The role of infections in the pathogenesis of autoimmune diseases. Curr Drug Targets Inflamm Allergy. 2005;4:99–103. [PubMed: 15720242]

63.

Schulze C, Munoz LE, Franz S, et al. Clearance deficiency--a potential link between infections and autoimmunity. Autoim-mun Rev. 2008;8:5–8. [PubMed: 18790092]

64.

Cook TM, Protheroe RT, Handel JM. Tetanus: a review of the literature. Br J Anaesth. 2001;87:477–87. [PubMed: 11517134]

65.

Sutjita M, Hohmann A, Comacchio R, Boey ML, Bradley J. A common anti-cardiolipin antibody idiotype in autoimmune disease: identification using a mouse monoclonal antibody directed against a naturally-occurring anti-phospholipid antibody. Clin Exp Immunol. 1989;75:211–6. [PMC free article: PMC1542132] [PubMed: 2784733]

66.

Sutjita M, Hohmann A, Comacchio R, Bradley J. Polyspecific human and murine antibodies to diphtheria and tetanus toxoids and phospholipids. Clin Exp Immunol. 1988;73:191–7. [PMC free article: PMC1541618] [PubMed: 3263226]

67.

Blank M, Shoenfeld Y, Cabilly S, Heldman Y, Fridkin M, Katchalski-Katzir E. Prevention of experimental antiphospholipid syndrome and endothelial cell activation by synthetic peptides. Proc Natl Acad Sci U S A. 1999;96:5164–8. [PMC free article: PMC21834] [PubMed: 10220436]

68.

Blank M, Krause I, Fridkin M, et al. Bacterial induction of autoantibodies to beta2-glycoprotein-I accounts for the infectious etiology of antiphospholipid syndrome. J Clin Invest. 2002 Mar;109:797–804. [PMC free article: PMC150905] [PubMed: 11901188]

69.

Zandman-Goddard G, Shoenfeld Y. Parasitic infection and autoimmunity. Lupus. 2009;18:1144–8. [PubMed: 19880560]

70.

Zaccone P, Fehérvári Z, Jones FM, et al. Schistosoma mansoni antigens modulate the activity of the innate immune re-sponse and prevent onset of type 1 diabetes. Eur J Immunol. 2003;33:1439–49. [PubMed: 12731071]

71.

Strachan DP. Hay fever, hygiene, and household size. BMJ. 1989;299:1259–60. [PMC free article: PMC1838109] [PubMed: 2513902]

72.

Strachan DP. Family size, infection and atopy: the first decade of the “hygiene hypothesis” Thorax. 2000;55:S2–10. [PMC free article: PMC1765943] [PubMed: 10943631]

73.

Youinou P, Pers J-O, Gershwin ME, Shoenfeld Y. Geo-epidemiology and autoimmunity. J Autoimmun. 2010;34:J163–7. [PubMed: 20056534]

74.

Ben-Ami Shor D, Harel M, Eliakim R, Shoenfeld Y. The Hygiene Theory Harnessing Helminths and Their Ova to Treat Autoimmunity. Clin Rev Allergy Immunol. 2013. in press. [PubMed: 23325330]

75.

Cooke A, Tonks P, Jones FM, et al. Infection with Schistosoma mansoni prevents insulin dependent diabetes mellitus in non-obese diabetic mice. Parasite Immunol. 1999;21:169–76. [PubMed: 10320614]

76.

Elliott DE, Weinstock J V. Helminth-host immunological interactions: prevention and control of immune-mediated diseases. Ann N Y Acad Sci. 2012;1247:83–96. [PMC free article: PMC3744090] [PubMed: 22239614]

77.

Maizels RM, Hewitson JP, Smith KA. Susceptibility and immunity to helminth parasites. Curr Opin Immunol. 2012;24:459–66. [PMC free article: PMC3437973] [PubMed: 22795966]

78.

Aranzamendi C, Sofronic-Milosavljevic L, Pinelli E. Helminths: Immunoregulation and Inflammatory Diseases-Which Side Are Trichinella spp. and Toxocara spp. on? J Parasitol Res. 2013. In press. [PMC free article: PMC3556843] [PubMed: 23365718]

79.

Allen JE, Maizels RM. Diversity and dialogue in immunity to helminths. Nat Rev Immunol. 2011;11:375–88. [PubMed: 21610741]

80.

Kuijk LM, Van Die I. Worms to the rescue: can worm glycans protect from autoimmune diseases? IUBMB life. 2010;62:303–12. [PubMed: 20101628]

81.

Kosiewicz MM, Zirnheld AL, Alard P. Gut microbiota, immunity, and disease: a complex relationship. Front Cell Infect Microbiol. 2011;2:1–11.

82.

Kranich J, Maslowski KM, Mackay CR. Commensal flora and the regulation of inflammatory and autoimmune responses. Semin Immunol. 2011;23:139–45. [PubMed: 21292499]

83.

Ivanov II, Honda K. Intestinal commensal microbes as immune modulators. Cell Host Microbe. 2012;12:496–508. [PMC free article: PMC3516493] [PubMed: 23084918]

84.

Blander JM, Torchinsky MB, Campisi L. Revisiting the old link between infection and autoimmune disease with commensals and T helper 17 cells. Immunol Res. 2012;54:50–68. [PMC free article: PMC5797425] [PubMed: 22460741]

85.

Proal AD, Albert PJ, Marshall TG. The human microbiome and autoimmunity. Curr Opin Rheumatol. 2013;25:234–40. [PubMed: 23370376]

86.

Devereux G. The increase in the prevalence of asthma and allergy: food for thought. Nat Rev Immunol. 2006;6:869–74. [PubMed: 17063187]

87.

Turnbaugh PJ, Ridaura VK, Faith JJ, Rey FE, Knight R, Gordon JI. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci Transl Med. 2009;1:6–14. [PMC free article: PMC2894525] [PubMed: 20368178]

88.

Cho I, Blaser MJ. The human microbiome: at the interface of health and disease. Nat Rev Genet. 2012;13:260–70. [PMC free article: PMC3418802] [PubMed: 22411464]

89.

Morgan XC, Huttenhower C. Chapter 12: Human microbiome analysis. Lewitter F, Kann M, editors. PLoS Comput Biol. 2012;8:e1002808. [PMC free article: PMC3531975] [PubMed: 23300406]

90.

Morgan XC, Segata N, Huttenhower C. Biodiversity and functional genomics in the human microbiome. Trends in genetics: TIG. 2013;29:51–8. [PMC free article: PMC3534939] [PubMed: 23140990]

91.

Turnbaugh PJ, Ley RE, Hamady M, Fraser-Liggett CM, Knight R, Gordon JI. The human microbiome project. Nature. 2007;449:804–10. [PMC free article: PMC3709439] [PubMed: 17943116]

92.

Gosalbes MJ, Abellan JJ, Durbán A, Pérez-Cobas AE, Latorre A, Moya A. Metagenomics of human microbiome: beyond 16s rDNA. Clin Microbiol Infect. 2012;18:S47–9. [PubMed: 22647049]

93.

Thomas T, Gilbert J, Meyer F. Metagenomics - a guide from sampling to data analysis. Microb Inform Exp. 2012;2:3. [PMC free article: PMC3351745] [PubMed: 22587947]

94.

Smyk D, Rigopoulou EI, Baum H, Burroughs AK, Vergani D, Bogdanos DP. Autoimmunity and environment: am I at risk? Clin Rev Allergy Immunol. 2012;42:199–212. [PubMed: 21337133]

95.

Homer N, Merriman B, Nelson SF. BFAST: an alignment tool for large scale genome resequencing. PloS one. 2009;4:e7767. [PMC free article: PMC2770639] [PubMed: 19907642]

96.

Schmieder R, Edwards R. Fast identification and removal of sequence contamination from genomic and metagenomic datasets. PloS one. 2011;6:e17288. [PMC free article: PMC3052304] [PubMed: 21408061]

97.

Cardenas-Roldan J, Rojas-Villarraga A, Anaya JM. How do autoimmune diseases cluster in families? A systematic review and meta-analysis. BMC Med. 2013;11:73. [PMC free article: PMC3655934] [PubMed: 23497011]

98.

Anaya JM, Tobon GJ, Vega P, Castiblanco J. Autoimmune disease aggregation in families with primary Sjögren’s syndrome. J Rheumatol. 2006;33:2227–34. [PubMed: 17086607]

99.

Anaya JM, Corena R, Castiblanco J, Rojas-Villarraga A, Shoenfeld Y. The kaleidoscope of autoimmunity: multiple autoim-mune syndromes and familial autoimmunity. Expert Rev Clin Immunol. 2007;3:623–35. [PubMed: 20477166]