Mark Davis and the search for the universal immune test

Mark Davis and the search for the universal immune test

1. Introduction

These are some notes about the talk that Mark Davis gave during the Community Symposium held in August at Stanford (video). I will introduce some basic notions about T cell receptors (TCR) in paragraphs 2, 3, 4, and 5. Paragraphs 6 is a description of an innovative technology developed by Mark Davis and his colleagues, based on information gathered from the video itself and three research papers published by Davis and others in the last 4 years. This background should be hopefully enough to allow a good understanding of the exciting pilot data presented by Mark Davis on T cell activity in ME/CFS (paragraph 7), and in chronic Lyme (paragraph 8), and to realize why this technology promises to be some sort of universal test for any kind of infectious and autoimmune diseases, known or unknown.

2. T cells

T cells are a type of leukocytes (also known as white blood cells), the cellular component of our immune system. Most of our circulating T cells are represented by T helper cells (Th cells) and cytotoxic T lymphocytes (CTL). While the function of Th cells is to regulate the activity of other leukocytes through the production of a wide range of chemicals (cytokines), CTLs are directly involved in the killing of host cells infected by pathogens. T cells belong to the adaptive arm of the immune system, along with B cells (the factories of antibodies), and as such, they are meant to provide a defence tailored to specific pathogens: our immune system can provide not only antibodies specific for a given pathogen but also specific T cells (both Th cells and CTLs). The innate arm of the immune system (which includes natural killer cells, macrophages, dendritic cells, mast cells…) on the other hand can provide only a one-fits-all type of defense, which represents the first line of immune response, during an infection.

3. T cell receptor

T cells search for their specific pathogens thanks to a molecule expressed on their surface, called T cell receptor (TCR). In figure 1 you can see a schematic representation of the TCR and of the mechanism by which T cells recognize their targets. Antigens (proteins) from pathogens are presented to T cells by other cells of our body: they are displayed on molecules called major histocompatibility complex (MHC), expressed on the outer membrane; if the antigen fits the TCR of a specific T cell, then this T cell is activated and proliferates (clonal expansion). The two chains (α and β) are assembled using the transcription of gene segments with several copies each: in other words, TCRs are assembled with peptides chosen randomly within a set of several possible choices. This leads to a repertoire of 10^15 possible different TCRs (Mason DA 1998). Each T cell displays only one type of TCR.

TCR
Figure 1. Upper half. Th cells and CTLs share the same TCR: in both cases this molecule is the assembly of two peptides (chain α and chain β), but while the TCR of Th cells (on the right) is expressed next to the molecule CD4 (which binds to class II MHC), the TCR of CTL is associated with the molecule CD8 (on the left), which is specific for MHC I. Black bars represent four chains (a complex called CD3) that are involved in the signaling of the TCR with the nucleus of the cell (by Paolo Maccallini). Lower half. A beautiful structural representation of the TCR, bound to the peptide-MHC complex (pMHC), from (Gonzàlez PA et al. 2013). In green the peptide, in blue the β chain, in dark green the α chain. CDRs (complementarity determining regions, orange) are composed of those residues of the α and β chains that directly bind the pMHC.

4. T helper cells

Th cells can recognize only antigens presented by class II MHC: this class of MHC is expressed on the outer membrane of some leukocytes, mainly dendritic cells, B cells, and macrophages (referred to as antigen presenting cells, APCs). MHC II engages the TCR of Th cells thanks to peptide CD4 (expressed exclusively by Th cells). The antigen presented by MHC II is a peptide with a length of 13-17 amino acids (Rudensky, et al., 1991) (figure 2).

MHC II.JPG
Figure 2. The TCR expressed by a Th cell binds an epitope presented by a class II MHC expressed on the plasma membrane of an APC. Chains α and β of MHC II are also represented (by Paolo Maccallini).

5. Cytotoxic T lymphocytes

TCRs expressed by CTLs can bind only antigens displayed by class I MHC, which can be found on the outer membrane of any cell of our body. CD8 is the molecule that makes the TCR expressed by CTLs specific for MHC I. While antigens presented by APCs belongs to pathogens that have been collected on the battlefield of the infection, peptides displayed by class I MHC of a specific cell belong to pathogens that have entered the cell itself, therefore they are the proof of an ongoing intracellular infection (figure 3). When a CTL recognizes an antigen that fits its TCR, then the CTL induces apoptosis (programmed death) of the cell that displays it. Antigens presented by MHC I are peptides in the range of 8 to 10 amino acids (Stern, et al., 1994).

MHC I.JPG
Figure 3. An infected cell displays a viral antigen on its MHC I. The TCR of a CTL binds this peptide and send a signal to the nucleus of the CTL itself, that responds with the induction of apoptosis (releasing granzymes, for instance) of the infected cell (by Paolo Maccallini).  

6. The universal immune testing

In his talk, Mark Davis presents an overview of some basic concepts about the immune system, before introducing his exciting new data about ME/CFS and post-treatment Lyme disease syndrome (PTLDS, also known as chronic Lyme). But he also describes a few details of a complex new assay that is theoretically able to read all the information packed in the repertoire of TCRs present – in a given moment – in the blood of a human being. As such, this test – that I have named the universal immune testing – seems to have the potential to determine if a given patient has an ongoing infection (and the exact pathogen) or an autoimmune disease (and the exact autoantigen, i.e. the tissue attached by the immune system). To my understanding, this assay requires three steps, described in the following sections.

6.1. First step: TCR sequencing

As said in paragraph 3, when T cells encounter their specific peptide presented by MHC, they proliferate so that in blood of patients with an ongoing infection (or with a reaction against self, i.e. autoimmunity) we can find several copies of T cells expressing the same TCR: while in healthy controls about 10% of total CD8 T cells is represented by clones of a few different T cells (figure 4, first line), in early Lyme disease – an example of active infection – and in multiple sclerosis (MS) – an example of autoimmune disease – we have a massive clonation of a few lines of CTLs (figure 5, second and third line, respectively). The first step of the universal immune testing is represented by the identification of the exact sequence of TCRs expressed by T cells in blood, as reported in (Han A et al. 2014) where it is described how to sequence genes for the α and the β chain of a given T cell. This approach allows to build graphs of the kind in figure 4, and therefore to determine whether the patient has an abnormal ongoing T cell activity or not. If a clonal expansion is found, then we can speculate that either an active infection is present or some sort of autoimmune condition.

Clonal expansion CD8.png
Figure 4. Each circle represents a patient. In the first line, we have four healthy controls, with no clonal expansion of CD8 T cells (the first one, left) or with only a low-level clonal expansion (slices in blue, white, and grey). In the second line, we have four patients with active Lyme disease (early Lyme) and all of them present a massive expansion of only three different T cells (slices in red, blue and orange). In the third line, we have four MS patient with most of their CD8 T cells represented by clones of a bunch of T cells. From the talk by Mark Davis.

6.2. Second step: TCR clustering

Mark Davis and his group have been able to code a software that allows to identify TCRs that share the same antigen, either within an individual or across different donors. This algorithm has been termed GLIPH (grouping of lymphocyte interaction by paratope hotspots) and has been found capable – for instance – to cluster T CD4 cell receptors from 22 subjects with latent M. tuberculosis infection into 16 distinct groups, each of which comprises TCRs from at least 3 different donors (Glanville J et al. 2017). Five of these groups are reported in figure 5. The idea here is that TCRs that belong to the same cluster, react to the same peptide-MHC complex (pMHC).

GLIPH.jpg
Figure 5. Five group of TCRs from 22 different donors with latent tuberculosis, clustered by GLIPH. The first column on the left has TCRs IDs, the second one reports donors IDs. Complementarity determining regions (CDR) for the β and the α chains are reported in the third and fifth column, respectively. From (Glanville J et al. 2017).

6.3. Third step: quest for the epitope(s)

As we have seen, this new technology allows to recognize if T cell clonal expansion is an issue in a given patient, by sequencing TCRs from his peripheral blood. It also allows to cluster TCRs either within an individual or across different patients. The next step is to identify what kind of antigen(s) each cluster of TCRs reacts to. In fact, if we could recognize these antigens in a group of patients with similar symptoms, with T cell clonal expansion and similar TCRs, we would be able to understand whether their immune system is fighting a pathogen (and to identify the pathogen) or if it is attacking host tissues and, if that was the case, to identify what tissue. As mentioned, the number of possible TCR gene rearrangement is supposed to be about 10^15, but the number of possible Th cell epitope is about 20^15 which is more than 10^19. This implies that TCRs have to be cross-reactive to some extent, in order to recognize all possible peptides presented by MHCs (Mason DA 1998). The exact extent of this cross-reactivity and the mechanism by which it is obtained has been elucidated by Mark Davis and his colleagues in a recent paper (Birnbaum ME et al. 2014) that gives us the third step of the universal immune testing. The aim of this phase is to take a given TCR and to find the repertoire of his specific antigens (as said, one TCR reacts to several antigens). In order to understand how this is possible let’s consider one of the experiments described in the paper mentioned above. The researchers considered two well-defined TCRs (named Ob.1A12 and Ob.2F3), cloned from a patient with MS and known to recognize peptide 85-99 (figure 6) of myelin basic protein (MBP) presented by HLA-DR15. They then prepared a set of yeast cells expressing HLA-DR15 molecules, each presenting a different peptide of 14 amino acids, with fixed residues only at position 1 and 4, where the peptide is anchored to MHC (figure 6, left). When copies of Ob.1A12 are added to this culture of yeast cells expressing pMHC complexes, they bind only some of them, and as you can see in the right half of figure 6, for each position of the epitopes bound by Ob.1A12, there is an amino acid that is more likely: for instance, the typical epitope of Ob.1A12 preferentially has alanine (A) at position -4, histidine (H) at position -3, arginine (R) at position -2, and so forth. As you can see, histidine (H) at position 2 and phenylalanine (F) at position 3 are obligate amino acids for a Ob.1A12 epitope.

ob-1a121.jpg
Figure 6. On the left: peptide 85-99 of myelin basic protein (first row) is known to be an epitope for Ob.1A12. At position 1 and 4 it has two residues that allow its binding to the MHC molecule. At position -2, -1, 2, 3, and 5 we find those residues that bind the TCR. The second row represents the generic epitope of the peptide library used to identify the degree of crossreactivity between all the possible Ob.1A12 specific epitopes. On the right: the likelihood of amino acids for each position of Ob.1A12 specific epitopes is represented by shades of violet. As you can see, histidine (H) at position 2 and phenylalanine (F) at position 3 are obligate amino acids for a Ob.1A12 epitope. From (Birnbaum ME et al. 2014).

The table on the right side of figure 6 is, in fact, a substitution matrix with dimension 14×20, a tool that can be used to scan the peptide database in order to find, among all the known peptides expressed by living creatures, all the possible Ob.1A12 specific epitopes. Substitution matrices are commonly used for the so-called peptide alignment, a technique that aims at the identification of similarities between peptides. These matrices are based on evolutionary considerations (Dayhoff, et al., 1978) or on the study of conserved regions in proteins (Henikoff, et al., 1992). But the search for specific epitopes of a given TCR requires (as we have seen here for Ob.1A12) a substitution matrix built ad hoc for that TCR: each TCR requires its own substitution matrix that is obtained adding clones of that TCR on a culture of yeast cells presenting a huge peptide library on their MHCs, and analyzing data from this experiment. So, quite a complex process! In the case of Ob.1A12, this process led to 2330 peptides (including MBP), while the Ob.2F3 specific substitution matrix found 4824 epitopes within the whole peptide database. These peptides included both non-human proteins (bacterial, viral…) and human peptides. For 33 of them (26 non human and 7 human proteins), this group of researchers performed a test in order to directly verify the prediction: 25/26 of environmental peptides and 6/7 of the human peptides induced proliferation of T cells expressing Ob.1A12 and/or Ob.2F3, and this is a huge proof of the validity of this analysis! These 33 peptides are reported in figure 7. This is the last step of the universal immune testing, the one that from the TCR leads to the epitopes. As you have seen, a huge set of different peptides from different sources is linked to each single TCR, in other words, crossreactivity is an intrinsic property of TCR. This also means that lymphocyte transformation tests (LTTs), widely used in Europe for the detection of infections like Borrelia burgdorferi and others, bear a high risk of false-positive results and require a process of experimental and theoretical validation, that is currently lacking (see also this post on this issue).

Crossreactive epitopes.JPG
Figure 7. A set of 33 peptides (both human and environmental) predicted to be specific epitopes for both Ob.1A12 and Ob.2F3. From (Birnbaum ME et al. 2014).

We are now ready to fully appreciate the pilot data that Mark Davis presented at the Symposium on CD8 T cell clonal expansion in ME/CFS and in chronic Lyme.

7. We have a hit!

Mark Davis, along with Jacob Glanville and José Montoya, has sequenced TCRs from the peripheral blood of 50 ME/CFS patients and 49 controls (first step of the universal immune testing, remember?), then they have clustered them using the GLIPH algorithm (second step). They have found 28 clusters with more than 2500 similar sequences each, where each cluster collects multiple sequences from the same individual as well as (which is perhaps more important) sequences from different patients (figure 8). The cluster that I have circled in red, for instance, is a collection of more than 3000 similar TCRs. The presence of this wide clusters in ME/CFS patients, compared to healthy controls, represents an indirect proof of a specific T cell response to some common trigger in this group of patients, which might be a pathogen or a tissue of the body (or both).

Clustered TCR
Figure 8. In ME/CFS, TCRs sequences from 50 patients form 28 clusters with more than 2500 similar sequences, and this is clearly not the case in healthy controls. This point to some specific immune response to a pathogen or to a human tissue (or both). This slide is from the talk by Mark Davis.

Among these 50 ME/CFS patients, Davis and colleagues selected 6 patients with similar HLA genes (figure 9, left), 5 females among them, and studied their TCRs deeper. In the right half of figure 9, you can see for each patient the degree of CTL clonal expansion. Remember that in healthy controls only about 10% of CTLs is composed by clones of a few cells (figure 4, first raw), while here we see that about 50% of all CTLs is composed by clones. So, a “marked clonal expansion” of CD8 T cells, as Davis said.

ME subjects CD8
Figure 9. On the left: 6 MECFS patients with similar HLA genes have been selected. Patient ID is reported in the first column on the left, the second column indicates the age of each patient, the third indicates the gender, the fourth column is about exposure to cytomegalovirus, the third one is on MHC I genes. On the right: analysis of clonal expansion of CD8 T cells for each of the six patients. There is a marked clonal expansion (about 50%) compared to healthy controls (about 10%).

Sequences of α and β chains of TCRs from three of the six patients (patients L4-02, L4-10, and L3-20) are reported in figure 10 (I have verified that in fact these are sequences of α and β chains of human TCRs using them as query sequences in standard protein BLAST).

TCR epitope.png
Figure 10. Beta chains (first column) and respective α chains (fifth column) from 3 ME/CFS patients (L4-02, L4-10, and L3-20, last column).

So, we have seen so far the first two steps of the universal immune testing in ME. What about the third step? In his talk, Mark Davis didn’t present any particular epitope, he just showed a slide with what likely is the selection of the epitopes from the peptide library (see paragraph 6.3) by one of the TCRs reported in figure 10. This selection is reported in figure 11, but from that picture, it is not possible to gather any information about the identity of these epitopes. As you probably remember from paragraph 6.3, the analysis of the peptides selected by a TCR among the peptide library allows the identification of a substitution matrix that can be used to select all the possible epitopes of that specific TCR, from the peptide database. This last crucial step has to be performed yet, or it has been already performed, but Davis has not communicated the preliminary results during his talk. Recently new resources have been made available by Open Medicine Foundation, for this promising research to be further pursued, among other projects (R). The aim here, as already said, is to find the antigen that triggers this T cell response. As Mark Davis said, it might be an antigen from a specific pathogen (perhaps a common pathogen that comes and goes) that elicits an abnormal immune response which ends targeting some host tissue (microglia, for instance), thus leading to the kind of immune activation that has been recently reported by Mark Davis himself and others in ME/CFS (Montoya JG et al. 2017). The idea of a common pathogen triggering a pathologic immune response is not new in medicine, and rheumatic fever (RF) is an example of such a disease: RF is an autoimmune disease that attacks heart, brain and joints and is generally triggered by a streptococcal throat infection (Marijon E et al. 2012). The other possible avenue is, of course, that of an ongoing infection of some kind, that has yet to be detected. As said (see par. 6.1), CD8 T cell clonal expansion is present in both acute infections (like early Lyme disease) and autoimmune diseases (like MS) (figure 4), so we have to wait for the antigen identification if we want to understand if the CTLs activity is against a pathogen and/or against a host tissue.

peptide-library.png
Figure 11. In this picture, we can see the selection, through several rounds, of a bunch of peptides by a particular TCR from a ME patient. The selection takes place among a huge collection of peptides presented by HLA-A2 (MHC I) expressed by yeast cells. At each round the number of possible peptides is smaller.

8. Chronic Lyme does exist

It has probably been overlooked that in his talk, Mark Davis reported also very interesting data on post-treatment Lyme disease syndrome (PTLDS, also known as chronic Lyme disease). In particular, he found a marked clonal expansion in CD8 T cells of 4 PTLDS patients (about 40% of total CTLs) as reported in figure 12: consider that in this case, blue slices represent unique T cells, while all the other slices represent clones! All that has been said about CD8 clonal expansion in ME/CFS does apply in this case too: it might be the proof of an ongoing infection – perhaps the same B. burgdorferi, as suggested by several animal models (Embers ME et al. 2017), (Embers ME et al. 2012), (Hodzic E et al. 2008), (Yrjänäinen H et al. 2010) – or a coinfection (a virus?) or it could be the expression of an autoimmune reaction triggered by the initial infection. This has still to be discovered, running the complete universal immune testing, but what is already clear from figure 12 is that PTLDS is a real condition, with something really wrong going on within the immune response: chronic Lyme does exist.

ptlds-cd8.jpg
Figure 12. CD8 T cells clonal expansion in four chronic Lyme patients: there is a marked clonal expansion that stands for T cell activity against a pathogen or against host tissue.

9. Conclusions

Mark Davis and other researchers have developed a complex assay that is able to sequence TCRs from patients, cluster them into groups of TCRs that react to the same antigens, and discover the antigens that triggered that particular T cell response. This assay is a kind of universal immune testing that is theoretically able to recognize if a person (or a group of patients) presents an immune response against a pathogen or against one of his own tissues (or both). This approach has already given pilot data on an ongoing CD8 T cell activity in ME/CFS patients and in chronic Lyme patients and will hopefully identify the trigger of this immune response in the near future. Whether ME/CFS is an ongoing infection, an autoimmune disease or both, the universal immune testing might be able to tell us. This new technology is for immunology, what whole genome sequencing is for genetics, or metabolomics is for molecular diseases: it doesn’t search for a particular pathogen or a particular autoimmune disease. No, it searches for all possible infections and immune disorders, even those that have yet to be discovered.


As mentioned, the Open Medicine Foundation is funding Mark Davis’ research, among other research projects. Please consider a donation to the Open Medicine Foundation: donate.

Annunci

Il cavallo marino e l’energia

Il cavallo marino e l’energia

Le misure metaboliche in vitro dello studio norvegese  in cui fu evidenziato un possibile blocco al livello del piruvato deidrogenasi nei pazienti ME/CFS, sono state effettuate con il dispositivo Seahorse XFe96 della Agilent. Questo apparecchio (grande come una stampante da tavolo) permette di misurare in tempo reale – in vitro – il metabolismo energetico di cellule prelevate da pazienti (ad esempio linfociti). Come è spiegato nel video che segue, il tutto si riduce a due misure:

  1. una misura del consumo di ossigeno, che fornisce una stima del funzionamento mitocondriale;
  2. una misura della concentrazione di protoni, che fornisce una stima del funzionamento della glicolisi.

Mi risulta che l’Università degli Studi di Firenze sia in possesso di questo apparecchio (vedi qui).

Il Seahorse è attualmente impiegato nello studio di Avindra Nath (NIH) su 40 pazienti con ME/CFS post-infettiva (PI-ME/CFS). Come gruppo di controllo per questa ricerca, oltre a 20 persone sane, sono state selezionate anche 20 persone che hanno avuto la Lyme e sono completamente guarite.

seahorse

L’algoritmo di Needleman-Wunsch

L’algoritmo di Needleman-Wunsch

Allineamento di proteine

Cercare similitudini fra sequenze di peptidi è estremamente utile per stabilire rapporti evolutivi fra proteine (e quindi fra gli esseri viventi che le sintetizzano), per progettare vaccini, per studiare fenomeni di autoimmunità e altro. In questo post presento un software che si occupa proprio del confronto fra due sequenze proteiche. La scrittura di questo programma mi ha tenuto compagnia durante molti mesi, segnandomi la rotta fra le costanti ricadute e riacutizzazioni della mia malattia, malattia che fin’ora non mi ha ancora abbandonato per un solo giorno. Mentre scrivevo questo e altri codici, che ne sono lo sviluppo, inventavo anche un modo per aspettare il domani.

dihedral-angle-psi-2
Figura 1. Una sequenza di tre amminoacidi, con in evidenza il legame peptidico e l’angolo psi del legame fra il carbonio C-alpha e il C-beta di un amminoacido. Disegno di Paolo Maccallini.

Needleman e Wunsch

L’algoritmo di Needleman-Wunsch descrive un procedimento automatico che consente di calcolare il migliore allineamento possibile fra due sequenze di amminoacidi (Needleman SB, Wunsch CD, 1969). Questo metodo permette di svolgere in modo relativamente veloce e ingegnoso il confronto fra tutti gli allineamenti fra le due sequenze, considerando ogni possibile numero di lacune, in ogni possibile posizione. Il suo scopo è quello di scegliere fra questi allineamenti il migliore, ovvero quello che garantisce il ‘punteggio’ più alto, essendo tale punteggio calcolato utilizzando delle matrici quadrate di dimensione 20, dette ‘matrici di sostituzione’. Questo compito è non banale e comporta l’esame di un numero di allineamenti pari a

NeW 1.png

dove k e m sono le lunghezze dei due peptidi. Si tratta di numeri molto elevati, infatti posto ad esempio k=4 e m=6, si ottengono circa 215 allineamenti diversi. In genere però si ha a che fare con peptidi di centinaia di amminoacidi, il che comporta milioni di possibili allineamenti tra cui cercare il migliore. Ebbene, l’algoritmo di Needleman e Wunsch permette di effettuare questa analisi senza dover considerare direttamente ogni allineamento possibile. In figura 2 e 3 si ha una descrizione grafica di questo algoritmo.

new
Figura 2. L’algoritmo di Needleman-Wunsch, con l’indicazione delle variabili che ho usato nel mio codice per implementarlo. Disegno di Paolo Maccallini.
new2
Figura 3. La matrice TBM (trace back matrix) per uno specifico allineamento. Disegno di Paolo Maccallini.

Il mio programma

Il mio software per l’allineamento globale fra due proteine si chiama NeW_6 ed è scritto in Octave. Il programma presenta la stessa funzionalità di due analoghi prodotti di largo impiego, che sono LALIGN dello Swiss Institute of Bioniformatics e EMBOSS Needle dell’Europen Bioinformatic Institute. In particolare il mio programma ha le seguenti caratteristiche:

  • permette all’utente di scegliere fra un set di comuni matrici di sostituzione;
  • ha un modello di lacuna del tipo a+b(x), dove a è la penalità della lacuna iniziale, b quella delle lacune di estensione e x è il numero di lacune;
  • prevede lacune alla fine delle sequenze.

Lo sviluppo del programma, nonché il codice, si trovano in questo PDF, dove è possibile seguire vari esempi applicativi e varie versioni del programma stesso, nonché dei test in cui ne confronto l’output con i programmi attualmente in uso.

Applicazioni

Perché scrivere un programma che esiste già? Mi è servito per penetrare i metodi di allineamento fra due sequenze di amminoacidi, ma soprattutto mi ha permesso di costruire programmi più complessi (non inclusi nel PDF di cui sopra) che sto attualmente utilizzando per risolvere problemi di immunologia. In cerca di una soluzione.

Testing the lymphocyte transformation test for Lyme disease

Testing the lymphocyte transformation test for Lyme disease

In questo articolo dimostro che un test LTT per malattia di Lyme che utilizzi come uno degli antigeni la OspC (proteina integra) di B. burgdorferi sensu stricto può teoricamente risultare positivo (falso positivo) in soggetti con aumentata permeabilità intestinale.

Abstract

Some lymphocyte transformation tests (LTT) popular in Europe for the diagnosis of Lyme disease, use full length OspC of B. burgdorferi as one of their antigens and request a positive stimulation index against only one or two antigens, in order to be considered positive. In what follows, we demonstrate that, in case of patients with gut bacteria translocation, such a test has a theoretical risk of false positive results.

Lymphocyte transformation test

Lymphocyte transformation test (LTT) is an assay which allows to measure the activity of peripheral blood Th cells against specific antigens. T cell activation starts shortly after infection, with T cells proliferation and the production of cytokines (such as INF-γ) which regulate the adaptive immune response (Sompayrac, 2012). As T cell response vanishes after the resolution of the infection (Kaech, et al., 2007), LTT may be useful in providing a proof of active infection. When a LTT assay is performed, Th cells from peripheral blood of a patient are exposed to proteins from a particular pathogen. If a significant reaction is noted, which could be either Th cells proliferation or INF-γ expression, the assay is considered positive and suggestive of an active infection by that particular pathogen. The response is expressed through a number, often referred to as stimulatory index (SI). In Lyme disease, several attempts have been made in order to obtain such a tool, either by T cells proliferation assays or by INF-γ measures (Dressler, et al., 1991), (Chen, et al., 1999), (Valentine-Thon, et al., 2007), (von Baehr, et al., 2012), (Callister, et al., 2016 May). Nevertheless, this procedure has not been fully recognized as useful at present and neither the European guidelines (Stanek, et al., 2011) nor the CDC (Centers for disease control and prevention, 2015) recommend the use of this kind of test.

TCR.png
Figure 1. Presentation of an antigen to a helper T cell by MHC II molecule.

Th cells activation and cross-reactive T cell epitopes

Th cells are activated when their T cell receptors (TCR) recognize a complementary antigen presented by MHC II molecules (see Figure 1) (Sompayrac, 2012). Peptides presented by MHC II to T helper cells are exclusively linear epitopes, and they have a length between 13 and 17 amino acids (Rudensky, et al., 1991). Various experiments have demonstrated that peptides with 5 identical amino acids in a sequence of 10 have good chances to represent cross-reactive T cell epitopes (Root-Bernstein, 2014). That said, the algorithm described above for the LTT test is not free from the risk of false positive results, as each protein used as antigen could present one or more linear epitopes of 10 amino acids which share at least 5 amino acids with some epitope of 10 amino acids from another pathogen. This risk is particularly high when the assay uses complete proteins as antigens, and when a high SI for only a few antigens is required in order to have a positive result of the test.

OspC and Pseudomonas aeruginosa

We have used BLAST from NCBI (National Library of Medicine), with OspC from Borrelia burgdorferi (strain ATCC 35210 / B31 / CIP 102532 / DSM 4680) identified by the swiss-prot ID Q07337 () as query sequence, settings being as follows: expected threshold of 10, BLOSUM62 as substitution matrix, and a word of 3 amino acids. We have built a custom database with the main Phyla of the human gut microbiome observed in a healthy population, which are Bacteroides, Firmicutes, Proteobacteria, Verrucomicrobia, Actinobacteria, Tenericutes, and Euryarchaeota (Giloteaux, et al., 2016). One of the possible matches that BLAST gives back is the following alignment between the query sequence and the outer membrane protein G (OprG) of Pseudomonas aeruginosa (PDB ID: 2X27):

OspC_OmpG.png

As you can see, we have 6 identical amino acids in a peptide 10 amino acids long. This means that this peptide from Borrelia burgdorferi could theoretically binds a Th cell previously activated by P. aeruginosa. Peptide 111-120 from OspC is reported in Figure 2. Peptide 51-60 of OrpG is in Figure 3.  The 3D structure of OspC from B. burgdorferi strain B31 used for that picture has been experimentally determined with X rays and a resolution of 2,51 Å in 2001 (Kumaran, et al., 2001) and its MMDB ID is 15958 (). The conclusion from this data is that Th cells from a patient with an active infection by P. aeruginosa could proliferate and produce INF-γ when exposed to OspC from B. burgdorferi. In other words, a patient with an active P. aeruginosa infection would come out to have a positive LTT test for OspC.

OspC.png
Figure 2. Peptide 111-120 (in yellow) of OspC from B. burgdorferi (B31) is surface exposed.
OprG_29-39
Figure 3. Peptide 51-60 of OrpG from Pseudomona aeruginosa.

Gut bacteria translocation

A disrupted mucosal barrier of the bowel, with consequent translocation of bacteria from the gut to the peripheral blood, has been described in patients with liver diseases (Zhu, et al., 2013), chronic HIV infection (Openshaw, 2009), Crohn’s disease (Wyatt, et al., 1993), and in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) (Giloteaux, et al., 2016). In ME/CFS it has been possible, in particular, to demonstrate the translocation of Pseudomonas aeruginosa, among other gram-negative enterobacteria. In fact serum concentration of IgA against lipopolysaccharides from P. aeruginosa and other enterobacteria has been found to be significantly greater in ME/CFS patients than in normal volunteers (Maes, et al., 2007). Thus in ME/CFS patients the adaptive immune system usually reacts against pathogens which exit from the gut, and in particular we know that it reacts against P. auruginosa.

Conclusion

ME/CFS patients are among the main users of this kind of tests, because of the similarities between Lyme disease and the clinical picture of ME/CFS (Gaudino, et al., 1997). ME/CFS patients have a high prevalence of increased gut permeability and gut microbiome translocation (Giloteaux, et al., 2016), and their immune system reacts against P. aeruginosa in many cases (Maes, et al., 2007). Thus, each LTT for Lyme disease which uses full length OspC from B. burgdorferi ss as antigen, could theoretically lead to a high rate of false positive results in this population of patients. The Lyme disease LTT discussed above, which is currently popular in Europe, is one of such tests. More researches are warranted in order to confirm or exclude the theoretical danger of cross reaction of Lyme disease LTT with gut microbiome. Moreover, on the basis of what here presented, it might be possible to develop an LTT specific for the diagnosis of gut bacteria translocation.