Abstract: The theory of autoimmunity was developed at a time when the human body was regarded as largely sterile. Antibodies in patients with chronic inflammatory disease could consequently not be tied to persistent human pathogens. The concept of the "autoantibody" was created to reconcile this phenomenon. Today, however, the discovery of the human microbiome has revolutionized our understanding of human biology. Humans are superorganisms that harbor trillions of persistent microbial cells. Indeed, vast human microbiomes have been detected in human tissue and blood. These microbial ecosystems harbor thousands of newly identified bacteria, viruses, and other microorganisms -- most of which can act as pathogens under conditions of immunosuppression. The theory of autoimmunity must be revised to account for the human microbiome. Here, we propose a model in which "autoantibodies" are created in response to chronic, persistent microbiome pathogens. The structural homology (molecular mimicry) between pathogen and host proteins can result in "collateral damage" to surrounding human tissue. This calls for a paradigm shift in autoimmune disease treatment. Immunosuppressive medications palliate inflammatory symptoms at the expense of microbiome health and balance. In contrast, treatments that support the immune system in autoimmune disease could allow patients to target pathogens at the root of the disease process.

Introduction

At the turn of the century, German immunologist and Nobel Laureate Paul Ehrlich coined the term “horror autotoxicus” to describe the body’s aversion to immunological self-destruction. Ehrlich argued this “horror of self-toxicity” in an effort to counter the emerging theory of autoimmunity (Silverstein, 2001).

The theory of autoimmunity contends that the immune system can “lose tolerance” and generate antibodies in response to antigens associated with the body’s own tissues. Despite Ehrlich’s resistance, the bulk of the scientific community adopted the concept of autoimmunity. By the 1970s, the theory was widely accepted and a growing number of chronic inflammatory conditions were associated with the presence of specific “autoantibodies” in serum. Immunosuppressive medications aimed at curbing the resulting inflammation became the standard of care for patients with “autoimmune disease.”

Today, in 2018, the theory of autoimmunity continues to guide the study and treatment of chronic inflammatory conditions ranging from rheumatoid arthritis (RA) to multiple sclerosis (MS). Indeed, the immunosuppressive biologics used to treat autoimmune disease are the top-selling medications worldwide. At the same time, however, the incidence of most autoimmune conditions is on the rise (Lerner et al., 2015) (Figure 1). In addition, patients administered immunosuppressive biologics for autoimmune disease often require higher doses of these medications over time. Comorbidity rates are often high: patients with one autoimmune disease have a higher risk of developing a second or even a third inflammatory/autoimmune condition (Eaton et al., 2007).

There is a clear explanation for this disparity. The theory of autoimmunity was developed at a time when the human body was believed to be largely sterile. It subsequently fails to account for one of the most important breakthroughs in the history of human health: the discovery of the human microbiome. Humans are now understood to harbor vast ecosystems of bacteria, viruses, fungi, bacteriophages, and other microorganisms (Lloyd-Price et al., 2017). This microbiome persists in/on all human body sites including tissue and blood (Kowarsky et al., 2017).

New genome-based technologies have identified thousands of previously uncharacterized pathogens and pathobionts capable of persisting in the human microbiome. Many of these organisms promote severe dysbiosis or imbalance of microbiome ecosystems. A growing number of autoimmune conditions are associated with this dysbiosis (de Oliveira et al., 2017).

The following paper re-examines the “autoantibody” in light of these new findings and proposes a model in which “autoantibodies” are actually created in response to chronic, persistent microbiome pathogens. The structural homology (molecular mimicry) between pathogen and host proteins can result in “collateral damage” to surrounding human tissue.

The Concept of the “Autoantibody”

In the early 1900s, antibody production was identified in a range of acute infectious diseases such as diphtheria, tuberculosis, and polio. It was quickly apparent that the human immune system produces antibodies to target pathogen-associated proteins. This knowledge improved diagnostics for acute infection and has formed the basis of vaccine development.

However, antibodies were additionally detected in patients with a range of chronic inflammatory conditions including Grave’s disease, Hashimoto’s thyroiditis, RA, and MS. These antibodies might have been tied to the presence of chronic human pathogens, but at the time, culture-based laboratory techniques could detect only a handful of the microbes now understood to persist in Homo sapiens. The human body was consequently regarded as largely sterile.

The theory of autoimmunity was developed to reconcile this phenomenon. Central to the theory is the concept of the “autoantibody.” It was postulated that the immune system “loses tolerance” in patients with certain chronic inflammatory conditions. This dysregulated immune system then generates antibodies in response to proteins associated with the body’s own tissues.

But if the theory of autoimmunity is correct, what causes the immune system to fail so dramatically that it turns against self? A number of hypotheses attempting to explain this dilemma have been proposed. The most common — the “hit and run” model — contends that certain well-characterized pathogens can “trigger” the immune system to induce autoimmunity. The pathogen itself is eliminated, but autoreactive B cells persist and drive lifelong “autoantibody” production and autoimmune disease (Ding and Yan, 2007).

Discovery of the Human Microbiome

The “hit and run” model is challenged by the discovery of the human microbiome. Around the year 2000, research teams began using novel DNA-based sequencing technologies to identify and characterize microbes in the human body. The results of these molecular analyses were astounding: entire ecosystems of microbes were identified in the human body that had been missed by previous laboratory testing methods (Gilbert et al., 2018). These vast microbial communities are collectively known as the human microbiome.

The sheer number of non-human genes represented by the human microbiome — tens of millions of unique genes compared to the 20,500 that comprise the human genome — has re-defined the human condition (Lloyd-Price et al., 2017). Homo Sapiens is best described as a superorganism, in which the human and microbial genomes continually interact to regulate metabolism.

While early analyses of the human microbiome characterized microbial ecosystems in the gut and on mucosal surfaces, further studies soon identified extensive microbial communities in human tissue and blood. A “diversified microbiome” was identified in the blood of healthy donors (Paisse et al., 2016). Hundreds of bacteria and bacteriophage-derived samples were detected in brain tissue obtained from patients with epilepsy (Branton et al., 2013). In fact, the microbiome extends to nearly every human body site - including the bladder (Thomas-White et al., 2016), the liver (André et al., 2017), and the lungs (Dickson and Huffnagle, 2015).

The microbiome is vastly more extensive than first estimated. Nguyen et al. (2017) calculated that ~31 billion bacteriophages are continually present in human tissue and blood. The gut microbiome alone harbors thousands of bacterial species and over 100 trillion microbial cells in total (Ursell et al., 2012). Persistent viruses also inhabit all blood and tissue microbiomes. These include polyomaviruses, adenoviruses, and anelloviruses but also hundreds of newly identified viruses whose genomes have yet to be fully characterized. For example, the JGI IMG/VR Database catalogs viruses in many of Earth’s ecosystems including the human body. Viral diversity in the database has tripled since January 2017 (Figure 2). Nevertheless, the vast majority of the gene content (over 15 million genes in total) remains hypothetical or unknown (Paez-Espino et al., 2017; 2016).

A 2017 analysis of the human gut, skin, mouth, and vaginal microbiomes uncovered millions of previously unknown microbial genes (Lloyd-Price et al., 2017). Kowarsky et al. (2017) recently detected 3,761 previously unidentified bacteria, viruses, and bacteriophages in human blood samples obtained from immunocompromised patients. The team was forced to add new branches to the “tree of life” in order to classify many of these organisms. They concluded that the newly identified microbes “may prove to be the cause of acute or chronic diseases that, to date, have unknown etiology.”

The microbiome is inherited and evolves with the host throughout life. Babies are seeded in the womb, during birth, and after birth by extensive microbiome populations in the placenta (Aagaard et al., 2014), the amniotic fluid (Urushiyama et al., 2017), breast milk, and the vaginal canal, among others (Gomez-Gallego et al., 2016; Ma et al., 2012).

In addition, microbes traffic between human body sites via several newly discovered pathways. Louveau et al. (2015) recently demonstrated the existence of a previously undiscovered central nervous lymphatic system. These fluid pathways connect the cerebrospinal fluid and cervical lymph nodes directly to the brain. Benias et al. (2018) documented a previously unrecognized fluid-filled space connecting all human tissues. This human interstitium spans the entire body and drains directly into the lymph nodes.

Microbiome Dysbiosis in Autoimmune Disease

Most of the bacteria and viruses identified in the human microbiome are pathobionts: they are capable of acting as pathogens under conditions of imbalance and immunosuppression (Hornef, 2015). For example, commensal E. coli can evolve into virulent clones that escape phagocytosis in less than 500 generations (Miskinyte et al., 2013). S. pneumoniae can persist as a highly adapted commensal or a virulent pathogen depending on its ability to evade the host immune response (Weiser et al., 2018).

Persistent keystone pathogens can drive community-wide changes in microbial gene expression that promote microbiome virulence and dysbiosis (Yost et al., 2015). This dysbiosis is characterized by massive shifts in microbial ecosystem structure, often resulting in decreased species diversity (Belizario and Napolitano, 2015). Many pathogens associated with microbiome dysbiosis persist inside macrophages, astrocytes, and other cells of the immune system.

A growing number of autoimmune conditions are now tied to microbiome dysbiosis. Gut microbiome dysbiosis is associated with nearly all well-studied autoimmune diseases including type 1 and 2 diabetes, MS, RA, psoriasis, psoriatic arthritis, sarcoidosis, and system lupus erythematosus (de Oliveira et al., 2017; Li et al., 2018).

For example, Moustafa et al. (2018) found that gut microbiome bacterial diversity was significantly reduced in Crohn’s disease (CD) and ulcerative colitis patients compared to healthy controls. This reflected the destruction of a core and diversified microbiome, the composition of which facilitates pathogenic bacteria overgrowth. Indeed, the CD microbiome was characterized by expansion of virulence factors over time. Virulence factors expressed by pathogens such as E.coli, Klebsiella, and Clostridium perfringens were identified in 51% of CD patients as compared to 14% of healthy controls.

Microbiome dysbiosis in autoimmune disease extends beyond the gastrointestinal tract. Amar et al. (2011) measured bacterial 16S rDNA in the blood of 3,280 participants without diabetes or obesity at baseline. 16S rDNA concentration was higher in subjects who later developed diabetes. The composition of the bronchoalveolar lavage microbiome has been shown to change in sarcoidosis. Zimmermann et al. (2017) found that pathogenic species such as Atopobium and Fusobacterium were significantly more abundant in sarcoidosis samples than control samples.

In addition, pathogens and/or the metabolites they express can dysregulate human pathways linked to autoimmune disease. Liu et al. (2018) found that eLatS, a protein expressed by S. aureus, prevented insulin from correctly binding its target receptor. Pathogens including Epstein-Barr virus (EBV), Mycobacterium tuberculosis, and Cytomegalovirus (CMV) can dysregulate the activity of the human VDR nuclear receptor (Proal et al., 2013). This alters the expression of human genes connected to autoimmune disease.

Re-evaluating the “Autoantibody”

It follows that patients with autoimmune disease harbor thousands, if not more, pathogens and/or pathobionts whose presence might be tied to “autoantibody” production. This strongly suggests that “autoantibodies” may be normal antibodies created in response to microbiome pathogens.

Indeed, “autoantibodies” are regularly detected in non-autoimmune patients suffering from infection. Berlin et al. (2007) analyzed sera from patients with a range of bacterial, viral, parasitic, and rickettsial infections. Elevated titers of “autoantibodies” including anti-annex in-V, prothrombin, antinuclear antibodies (ANA), anti-Saccharomyces cerevisiae (ASCA), and antiphospholipid antibodies (Anti-PL) were identified in a high percentage study subjects. Indeed, ~39% of subjects harbored elevated titers of at least two of these “autoantibodies.”

Patients in the study by Berlin et al. (2007) presented with acute disease. However, most of the pathogens in the study also survived in persistent forms. For example, anti-annexin-V “autoantibodies” were detected in 80% of patients with hepatitis A, a virus tied to chronic liver cirrhosis. “Autoantibody” production was also associated with H. pylori, S. aureus, Enterobacter, S. pneumonia, Stomatococcus, E.coli, and Klebsiella pneumonia. These pathogens are repeatedly detected in genome-based analyses of human microbiome communities.

It is much more likely that such “autoantibodies” are created in response to these and other chronic, persistent microbiome pathogens as opposed to transient pathogens postulated to “trigger” autoreactive B cells (the hit and run model). Indeed, a growing number of studies demonstrate that acute human pathogens persist in the human microbiome. For example, EBV causes acute mononucleosis, but can additionally persist inside long-lasting human lymphoblastic B cells (Yenamandra et al., 2009). Ebola virus RNA has been detected in men’s semen for up to two years after “recovery” (Fischer et al., 2017). Brodin et al. (2015) found that the lifelong need to control persistent CMV infection caused 10% of T cells in CMV+ individuals to be directed against the virus.

This fits with data obtained by Kriegel/Vieira et al. (2018), who recently tied “autoantibody” production to yet another persistent member of the human microbiome. The team identified pathobiont E. gallinarum in the mesenteric veins, mesenteric lymph nodes, liver, and spleens of mice made genetically prone to autoimmunity. In these mice, E. gallinarum initiated the production of “autoantibodies,” activated T cells, and inflammation. This “autoantibody” production stopped when E. gallinarum’s growth was suppressed with the antibiotic vancomycin. Indeed, anti-dsDNA, anti-RNA autoantibodies, anti-b2GPI immunoglobulin G, hepatic and serum ERV gp70, and anti-ERV gp70 immune complexes were all suppressed by vancomycin treatment. Similar results were obtained when an intramuscular vaccine was used to suppress E. gallinarum’s growth. In addition, E. gallinarum-specific DNA was recovered from liver biopsies of human autoimmune patients, and co-cultures with human hepatocytes replicated the murine findings.

Molecular Mimicry

The chronic inflammation associated with “autoantibodies” can damage human tissue. This can be explained by molecular mimicry: the structures of many microbial proteins/metabolites are identical or very similar to those expressed by their human hosts. For example, humans and E. coli metabolize glucose with very similar metabolites, so the human superorganism may have trouble distinguishing the microbial proteins from its own. Antibodies themselves are also notoriously polyspecific. Even the T cells that help direct antibody responses are characterized by flexible specificity (Davis, 2015). It follows that antibodies created to target pathogens can then additionally target human proteins, resulting in “collateral damage” and inflammation.

Molecular mimicry is extremely common. Kusalik et al. (2007) found that 19,605 pentamers from the hepatitis C virus polyprotein have a high level of similarity to the human proteome. Altindis et al. (2018) found that viruses carry sequences with significant homology to human peptide hormones including insulin, insulin-like growth factors, endothelin-1, inhibin, adiponectin, and resistin. Among the strongest homologies were those for four viral insulin/IGF-1-like peptides (VILPs), each encoded by a different member of the bacterial family Iridoviridae.

Lekakh et al. (1991) found that autoantibodies in the serum of healthy donors cross-reacted with DNA and lipopolysaccharides (LPS) of widespread bacterial species including Salmonella, E. coli, P. aeruginosa, and Shigella boydii. Another study tested healthy women for the presence of IgG or IgA autoantibodies directed against 14 key regulatory peptides including leptin, ghrelin, vasopressin, and insulin. Numerous cases of sequence homology were identified between these peptides and the protein structures of over 30 microbes including Listeria monocytogenes, E. coli, Lactobacilli, H. pylori, and Yersinia pseudotuberculosis (Fetissov et al., 2008). B cells infected with EBV secreted antibodies capable of reacting with dozens of self and non-self antigens including albumin, renin, and thyroglobulin (Seigneurin et al., 1988).

In fact redundancy between human and microbial proteins, pathways, and products is so great that the potential for molecular mimicry to confuse the human immune system is almost infinite. For example, Hoen et al. (2016) recently discovered that human extracellular vesicles (EVs) have incredible structural and functional similarity to viral vesicles. These similarities are so broad that it is almost impossible to distinguish and separate EVs from (noninfectious) viruses. For example, the neuronal gene Arc encodes a protein that forms virus-like capsids. This Arc protein exhibits similar molecular properties to retroviral Gag proteins, suggesting they may share a common evolutionary history (Pastuzyn et al., 2018).

Chronic Immune/Microbiome Disorders vs. “Autoimmune Disease”

The above suggests that the “theory of autoimmunity” and the concept of the “autoantibody” no longer provide an accurate model of the immune processes. The prefix “auto” could be removed from both terms. Patients with conditions such as Grave’s disease, MS, and RA are most likely suffering from immune disorders in which inflammation is generated in response to microbiome-associated antibodies.

This paradigm shift helps explain other phenomena associated with these conditions. For example, “autoantibodies” are often detected in patients months/years before a full presentation of inflammatory symptoms (Nicola et al., 2007). This reflects the gradual nature of microbiome dysbiosis, in which associated pathogens evolve towards virulence as the human immune system weakens over time.

Chronic inflammatory disease is also polymicrobial in nature (Proal et al., 2017). Microbiome pathogens interact to drive inflammation and other disease processes. Since different microbes in a community often perform the same biochemical functions, different interacting pathogens can drive similar patterns of chronic inflammatory symptoms (Louca et al., 2018). This helps explain why exactly the same “autoantibodies” are never detected in 100% of patients with a given disease, and why “autoantibodies” are often detected in patients who lack a concrete inflammatory diagnosis.

Treatment Based on the Microbiome Model

Immunosuppressive medications are the standard of care in “autoimmune” disease. These medications temporarily palliate inflammatory symptoms, but allow pathogens to proliferate with much greater ease. For example, Earn et al. (2014) recently found that using antipyretic medications to suppress fever (and subsequently the immune response) in patients with influenza allowed viral particles to spread more easily among individuals. Thus, while subjects taking the antipyretic medications felt fewer symptoms, they were actually more contagious.

It is common knowledge that immunosuppressive medications allow Mycobacterium tuberculosis to proliferate. This same pattern (increased virulence in the face of immunosuppression) extends to most microbiome pathogens (Finlay and McFadden, 2006). Indeed, immunosuppressive medications are drivers of the microbiome dysbiosis now associated with nearly every autoimmune condition (Diaz et al., 2013; Nellore and Fishman, 2016). This contributes to the high rates of relapse and comorbidity observed in patients administered immunosuppressive therapies.

If “autoantibodies” are created in response to microbiome pathogens, then treatments that support the immune system could better address the root cause of the disease process. This would be a paradigm shift in “autoimmune disease” treatment.

In concert with our clinical collaborators, we have applied this model to develop an immunostimulative treatment for patients with autoimmune/inflammatory disease (Proal et al., 2011). Many patients report improvement, but must first endure temporary immunopathology — a cascade of reactions including inflammation, cytokine release, and endotoxin release that occur as part of the immune response to microbial death.

A similar form of immunopathology has been documented in HIV/AIDS following treatment with Highly Active Antiretroviral Therapy (HAART). HAART enables the immune system to better target pathogens acquired during periods of severe immunosuppression. This results in Immune Reconstitution Inflammatory Syndrome (IRIS): a temporary increase in inflammatory symptoms driven by the activated immune system (Bosamiya, 2011).

New cancer immunotherapies also generate immunopathology. These immunotherapies activate patient T cells, allowing them to better target malignant tumors. The “cytokine release syndrome” resulting from this immunostimulation leads to massive, temporary symptom increases, and has even resulted in death (Lee et al., 2014). However, this risk is considered acceptable, as patients who survive this severe immunopathology are more likely to enter a state of remission. Since many forms of cancer are also tied to microbiome dysbiosis, at least part of this immunopathology may result from the death of associated pathogens.

Treatments that address the microbiome itself in “autoimmune” disease may also prove helpful. Fecal microbiome transplantation and certain probiotics have been shown to alleviate symptoms in patients with a range of “autoimmune” conditions (Xu et al., 2015).

Discussion

The discovery of the human microbiome has forever changed our understanding of human biology. Humans are superorganisms colonized by trillions of persistent microbes. This human microbiome extends to nearly every human body site, including tissue and blood. Thousands of newly discovered pathogens and/or pathobionts have been detected in these microbiome ecosystems. Even then, the genomes of most bacteria, viruses, and bacteriophages in the human microbiome have yet to be fully characterized. For example, it is estimated that only 2% of viruses on Earth have been sequenced (Anthony et al., 2013).

This increases the likelihood that “autoantibodies” are created in response to chronic microbiome pathogens. Under such conditions, molecular mimicry or structural homology between pathogen and host proteins can result in “collateral damage” towards human tissue. Indeed, a growing body of research has documented “autoantibody” production in response to a range of pathogens/pathobionts. These pathogens are not “triggers” but persist as members of complex microbiome communities.

This opens up new possibilities in “autoimmune” disease research. The concept of the “autoantibody” should be retired in favor of a paradigm that seeks to foster microbiome health, diversity, and balance. Research teams should seek to better characterize and identify microbiome pathogens capable of driving antibody production. The survival mechanisms and activity of such pathogens should also be investigated.

Additionally, there is no need to segregate “autoimmune” diseases from other chronic inflammatory conditions tied to the human microbiome. The “autoimmune” disease research community should collaborate with these related research communities to better identify common trends associated with microbiome dysbiosis.

The situation also calls for a paradigm shift in “autoimmune” disease treatment. The immunosuppressive medications currently prescribed for these conditions palliate symptoms but allow pathogens in the microbiome to spread with greater ease. Indeed, these medications can drive the very microbiome dysbiosis now connected to most “autoimmune” conditions. This helps explain the poor long-term outcomes associated with their use.

In contrast, treatments that support the immune system in “autoimmune” disease would target microbiome pathogens at the root of the disease process. While such treatments induce temporary immunopathology, patients may eventually reach a state of stable remission. Indeed, the cancer community has introduced T cell immunotherapies with great success, despite the fact that patients administered these immunostimulative treatments must endure an immunopathology “cytokine-storm.”

Paradigm shifts often take decades to implement. We cannot afford the luxury of such a timeline. The incidence and prevalence of nearly every chronic inflammatory condition are on the rise, to a point where we are facing a global epidemic of chronic disease. RAND Health estimates that 60% of Americans adults are taking a drug for at least one chronic diagnosis and 12% suffer five or more comorbidities (Buttorff, 2017). We must revise the study and treatment of these diseases to account for the human microbiome. Otherwise, this trend may further accelerate at a significant cost to patients and society as a whole.

Acknowledgments

We thank Michael Eason Kirkpatrick for his help with technical editing of the manuscript and graphic design.

Disclosure

The authors report no conflicts of interest.

Corresponding Author

Amy D. Proal, Ph.D., Autoimmunity Research Foundation, Thousand Oaks, CA 91360, USA.

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