Could the Microbiome be used to Determine the Onset of Autoimmunity in Adult Life?

Aims Recent discoveries suggest diagnostic, prognostic or causative relationships between autoimmunity and the make-up of the microbiome. This text seeks to review how knowledge of human microflora can be used to predict the onset of autoimmune disease in adults. Below, the current mechanism of autoimmunity has been outlined and cases of microbial discovery have been analysed with a focus on amalgamating different disciplines of big data.

1- Introduction The onset of autoimmunity appears to be driven by the interface between microbial organisms and the immune system. Early studies found that certain infections are related to autoimmune disease (AD) such as rheumatic fever; S. pyogenes cell walls have molecular mimicry with cardiac myosin resulting in autoantibodies that target cardiac tissue (1). With more sensitive methods of microbial discovery such as whole genome sequencing, meta-communities of commensal, opportunistic and pathogenic microbial flora living synergistically with humans have been discovered in areas like the gut, skin, and mouth, but also in areas once thought to be sterile such as the lungs and bladder. The compositions of these communities are specific to the physical conditions, such as pH and surface topology of the tissue to which they are attached, but are also altered between disease states which may be causative or consequential depending on the case at hand. None the less, as technology continually advances and the characterisation of one’s microbiota is becoming more accessible, scientists are exploring characteristics of the microbiome which may denote biomarkers of diagnostic and prognostic value (2).

2- Micro-Floral Effects on Tolerance Autoantigens are self-material which invoke an immunological response and are thought to be the drivers of autoimmune disease such as rheumatoid arthritis. Several processes are proposed to result in the recognition of autoantigens (3) but all share the principle lack of tolerance towards self- material (4). The current model explains that the following two mechanisms control this behaviour.

The first is peripheral tolerance whereby the regulatory lymphocyte pathways induce anergy (5,6) such as the T-reg interaction. Supporting this, when FOXP3, an essential transcription factor for T-reg cells is mutated, the resulting AD is IPEX syndrome (immunodysregulation, polyendocrinopathy, enteropathy) (7). A pivotal study found 17 strains of Clostridia (10) isolated from healthy murine (8) and human faeces (9) significantly (P<0.01) increased T-reg numbers, and induced FOXP3 in CD4 cells up to 10-fold compared to non-treated controls after oral seeding in the lamina propria of germ-free murine colons. These strains, excluding Clostridium difficile, exhibited significantly improved clinical outcomes in rats and mice when they were further induced with allergic diarrhoea and colitis. In- vitro experiments suggest they release short-chain fatty acids which cause increased TGF-β1 production in intestinal epithelial cells, which is known to induce T-regs (11). The genomes of the seeded murine microflora were compared to the MetaHIT project showing that human ulcerative colitis patients have reduced concentrations of these 17 protective strains. The study was highly thorough in its biological experimentation and other groups later reported that strains of Clostridia protected against diabetes (12) and autoimmune encephalomyelitis (13), among others in murine models, however it fell short in its genomic analysis. MetaHIT took 124 healthy and IBD volunteers from Denmark and Spain and deep sequenced their faecal microbiomes. Whole genome sequencing requires reference libraries to map short read data, which compared to the human reference genome is poorly annotated (14), limiting its power of detection, consequently reducing true positives and increasing false discovery rates. Alternative MiniION (nanopore) techniques utilising long read data to circumvent this issue have poor sensitivity of genomes present at low concentrations (15,16). Furthermore, another dimension of complexity, there is far greater variation of the microbiome between individuals (< 90% difference) than groups such as health vs given- disease (17). Adding to this, there are many conditions which alter the composition of the microbiome such as genetics, antibiotics, diet (18), caesarean birth (19), even the pH of tap water (20). This huge variability in the study of the microbiome requires genomic comparisons to use databases where the samples are highly numerous and diverse to well represent the population of interest. At the time of the study a better resource existed; the Integrated Gene Catalogue expanded the sample size to 1267 genomes across Europe, America and China and included more diseases (21). If the comparison was done on this database it may have given a clearer picture of how these 17 strains of Clostridia constitute the microbiomes of humans in health and other AD disease given experimental models in humans do not exist. So far, there are no comprehensive cohort studies which have taken infant samples and observed for future AD, however Clostridia are enriched in infants (<16 months) with cow’s milk allergy which resolves by teen years (22). Without more data it is difficult to get a clear picture here as Clostridia seeded in the first months of breastfeeding may have a different interaction with the microflora in health than in adulthood. Considering all of this and the similarity between murine and human T-regs within the context of peripheral tolerance (23,24), it is possible that the dysbiosis of commensal gut Clostridia may be a driver of T-reg related autoimmune disease meaning it could be a target for therapy. If the dysbiosis is consequential, it could be used as a marker to predict disease in adults. On the contrary, the limited data on childhood microbiomes and Clostridia in humans means no conclusion can yet be drawn about predictions to the onset of autoimmunity for later adult life.

The other mechanism is central tolerance whereby self-recognising B and T cells undergo negative selection predominantly in the bone marrow and thymus respectively (25). Similarly, dysregulation of this process also causes autoimmunity, for example APECED syndrome (autoimmune polyendocrinopathy-candidiasis-ectodermal dystrophy) is caused by a mutation in the AIRE gene which is essential for healthy thymic selection by dendritic cells (26). Interestingly, a group found that germ-free mice expressed less AIRE in thymic epithelial cells compared to specific pathogen free mice. To verify the results, they used NOD-1 deficient specific germ free mice which had similar AIRE expression to totally germ free mice (27). The findings rely on the assumption that during times of health when the body’s external barriers are intact, bacterial antigens such as peptidoglycan enter the systemic circulation and interact with the thymus. Some studies have detected circulating bacterial peptidoglycan in healthy adult mice (28) and bacterial RNA in healthy adult humans, both with no history of systemic infection (29). Though bacterial antigens have been shown to enter the circulation there is no other evidence of the microbiome affecting thymic education of CD4/CD8 FOXP3- T-cells, but other studies suggested that thymic T-reg education may be assisted by the microflora to establish healthy peripheral tolerance (30–32).

Overall there is not enough evidence to suggest the microbiome could give insight into central tolerance, however the evidence that it affects peripheral tolerance is striking and more should be done to investigate links in humans. This is paramount as all AD is related to defects in tolerance mechanisms. 3- Considering Proteomic Biomarkers Immunological pathways occur in a complex milieu where different cells express and interact with a wide range of unbound and membrane-bound signalling molecules such as interleukins (IL-n), major histocompatibility complexes (MHC), immunoglobulins (Ig), tumour necrosis factors (TNF) and transforming growth factors (TGF). Rheumatoid arthritis (RA) is an AD which causes swollen and painful joints with reduced freedom of movement. A genetic predisposition is required by mutations in genes globally associated with AD (limitations of genetics mentioned in section 4) or in a larger subset of genes specific to RA (33) but in the face of this, it is thought that environmental factors are required to initiate pathophysiology. Cigarette smoke has been strongly associated in RA patients and it is thought that on mucosal surfaces, it causes citrullination, a deamination reaction which converts arginine to citrulline on intracellular and matrix proteins such as histones, fibronectin, collagen, fibrinogen (34). Citrullinated peptides bind to MHC which causes autoantigen presentation to T-cells leading to two types of autoantibodies produced by B-cells; rheumatoid factors which target IgGs and anti-citrullinated protein antibodies (ACPA) targeting citrullinated proteins (35). These molecules are used in clinical proteomic assays to help diagnose RA and give prognostic information denoting the severity of disease with fair amounts of accuracy (36), however they cannot predict the onset of RA before symptoms have started. Though ACPA is correctly detected in ~70% of sufferer’s blood samples 6-10 years before initial symptoms, 2-5% of the healthy population also have ACPAs. On top of these proteomic assays, even considering RA genetics (limitations discussed in section 4) which have a maximum positive predictive value of 15% (35), would not raise this to a high power meaning that there would not be a tolerable balance of true and false predictions. Clinically it is difficult to determine if presentations of early inflammation would progress to chronic RA and in rarer cases of seronegative RA, there are not any ACPAs or rheumatoid factors which could be used as markers (37). This highlights the need for more accurate methods to predict onset of RA especially when one considers that earlier use of disease modifying anti rheumatic drugs (DMARDs) has been shown to give the best outcome for RA patients (38).

Peptidylarginine deiminase (PAD) is an enzyme which citrullinates its own peptides as well as those from the host (39). Within the literature it is reported to be only secreted by Porphyromonas gingivalis, one of the ‘red complex’ microorganisms which drive periodontal disease. This is most likely a result of insufficient experimental studies across other organisms. A quick search of the PAD gene on the ensemble bacterial database (release 38) returned 526 hits in alphabetical order by . For proof of concept under limited resources, piping the first 50 of these into uniprot found the following strains have been detected in human samples: pneumophila (40), jamestowniensis (41), waltersii (42), lansingensis (43), londiniensis (44); Clostridium botulinum B2 450 (45), sporogenes (46); Clostridioides difficile (47); (48). Piping all the other hits into uniprot would show more strains containing PAD with prevalence to some degree in the human microbiome.

The human PAD enzyme, unlike the prokaryotic version, requires a 100 fold cytosolic calcium increase for in-vivo activation (49) such as in fully activated neutrophils (50). One study could not detect in-vitro citrullination in neutrophils with P. gingivalis and offered an alternative explanation (51). Control neutrophils in-vivo do not contain citrullinated peptides but the periodontal pathogen Aggregatibacter actinomycetemcomitans causes citrullination by secreting leukotoxin A (LTxA) (52) which forms pores on endothelial cell membranes allowing calcium ion influx thus activating human PAD in neutrophils. The characterisation of Leukotoxin A, human PAD, and the mechanism of calcium influx and activation has been verified by various groups (references above), however similarly to the previous case only A. actinomycetemcomitans has been reported to secrete LTxA. The same ensemble/uniprot gene search described previously with search term ‘leukotoxin’ (LTxA is the only leukotoxin currently annotated) returned 13,868 hits. Of the first 10 (53), (54), Fusobacterium necrophorum (55) have been detected in human samples. It is unlikely for a gene to be present in so many genomes and not to be translated, therefore, if these strains were to be cultured from human samples, the author is confident secreted PAD and LTxA would be detected. This information would be very important as given the depth of evidence which suggests citrullination is a pathogenic driver of RA, the fact that some individuals may have increased exogenous PAD or LTxA activity causing citrullination may be the environmental factor required to one day activate disease. A genomic assessment in a cohort looking for enrichment of PAD or LTxA between those who remain healthy and develop RA would be a step in classifying the type of bacterial community most likely to cause RA.

4- Amalgamating the Microbiome to Autoimmune Genetics Aside from the direct mutations mentioned previously, the aberrant operation of the immune milieu is strongly associated with genetic predisposition (3), for example females suffer AD at a much higher incidence than males possibly due to genetic differences resulting in different hormonal control of gene expression (56). Studies of population genetics utilising high throughput technologies have found common aetiology between all AD in regions that code for MHC, T and B cell receptors, IL-23R, and IL-2R (57). Some variants are more specific, for example the rare MHC DR2 & DR3 alleles have a very strong positive correlation with heightened frequency and severity of Systemic Lupus Erythematous (SLE) (58,59). Black Africans are enriched in these mutations, but interestingly seem to exhibit increased incidence and severity of disease when they live in industrialised western countries (60) which is in-line with the ‘clean hypothesis’. These genetic predispositions only at best strongly correlate with AD as individuals carrying extensive combinations of these alleles may not always express the disease phenotype, though their odds of developing disease is elevated (61). As medical investigations are more often incorporating big data, it is worth seeing how knowledge of the microbiome could fill the gaps left out of the picture by genomic analyses.

A comparison between human Italian controls and the Hadza hunter-gatherers of Tanzania found that the Hadza had an absence of Bifidobacterium and were enriched in Prevotella, Treponema and unclassified Bacteroidetes, as well as an arrangement of Clostridiales taxa unobserved in Europe and the Americas (62).

Bifidobacterium is globally a highly abundant taxa in the microbiome and has strong associations to the development and maintenance of a healthy immune system across human studies and pre- clinical models (63). The Hazda does not have access to healthcare and is a foraging population, but a study of 491 settlers characterised them to be healthy with few instances of infectious and AD (64). This is interesting as in their natural environment, they do not benefit from these strains and are healthy, but in an industrialised environment where their microbiomes probably have Bifidobacterium, the risks of AD are highly elevated (65). Contradictory findings like this show the shortcomings of our current understanding of the interface between the immune system and bacterial communities, and the difficulty of amalgamating ambiguous fields of study such as human genetics, autoimmunity and the microbiome.

There is little data comparing Treponema to any aspect of autoimmunity but specific strains such as Treponema denticola have a high prevalence in cases of periodontitis (66) and induce the innate immune system (67). Treponema uses its fibrinolytic activities to take advantage of a highly fibrous diet with little agricultural and grain-based foods (68). In doing so, it makes available a range of nutrients which may not be present at the same quantity in the microbiomes of individuals from industrialised areas. Though it has not been reported that Treponema sustains other strains, it is now accepted that the nutrients and metabolites released by microbial strains have an impact on the survivability of other strains thus altering the composition of the microbiota (69). This and the unknown factors introduced by the uncharacterised Bacteroidetes highlights the need for detailed experimentation to generate conclusions from this data. As small groups like these are not the target of mainstream science, there is no study characterising the Hazda genomes specifically to autoimmunity. It would be interesting if some Hazda tribesmen living in industrialised areas with AD were enriched for genes of AD as it could suggest the extent the microbiome acts as an enabler of AD. One muse also consider the natural environment of the Hazda would have different parasites, pollutants, weather, and other conditions which could also be responsible for driving AD, for example a major hypothesis is that multiple sclerosis is driven by vitamin D deficiency in western countries with reduced sunlight (70). Genetic regulation is another factor which must be considered. The white cell populations of people with multiple sclerosis differ in alkylation levels to those of healthy controls (71) and so based on findings such as this, more research is being done into the role of epigenetics towards autoimmunity. Furthermore, non-coding or intronic genetic regions cluster to the whole spectrum of AD, for example rheumatoid arthritis patients have variants in leukocyte transcriptional enhancer RNAs which regulate leukocytic transcription factors. The variants of non-coding regions are poorly understood but their role in genetic regulation is becoming more apparent especially as they seem to respond to therapies (72).

5- Conclusions Reviewing current research on the topic requires considerations to the challenges of studying the microbiome. In a single sample of faeces, metagenomic techniques which use phylotypic analyses for strains differentiation (14) can detect the order of a thousand microbial strains, however many of these strains will not be cultured in vitro despite modern high throughput culturomic techniques, which have drastically increased the number of successfully cultured microorganisms by putting single samples through an array of culture conditions (73).

It is clear from the analyses of proteomic assays and of human and microbiome genomics, of which some have been discussed here, that none of these forms of big data can solely and accurately predict onset of AD. As technology is developing and medicine is starting to take a more data based approach, it is likely that the future patient’s genomes and microbiomes will be sequenced to derive useful prognostic and diagnostic information. Conclusively, there is currently not enough information for a definitive answer as to whether the microbiome could be used to predict onset of AD, however the evidence that the microbiome in involved in pathogenesis such as in RA and in tolerance is compelling enough to warrant more interest and study. For the last five years, the evidence has been consistently in the favour that the microbiome could be used to predict AD when used with other data-driven techniques and it is likely that cues will be found once the discussed limitations are addressed with the sequencing technology, the lack of full characteristic data of human populations and of bacterial phenotypes.

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