Why we can’t use LTTs, yet

A line of T cells (called Ob.2F3) expressing the same T cell receptor (TCR) from an MS patient was studied in 2014 and it was found to proliferate when incubated with 4824 different peptides. Thirty-three of them were further studied (see figure) and found to belong to both Homo sapiens and several different, unrelated microbes (Birnbaum ME et al. 2014). The taking home message here is that T cells are not specific to a single pathogen, they are highly cross-reactive, as it was already pointed out in this pivotal study: (Mason DA 1998). And this means that we can’t use lymphocyte transformation tests (LTTs) the way we do now. 

I feel really frustrated when patients send me their LTTs and ask me to comment the results. I have to say that they have vasted their money and that these results are useless. I do hope that my blog can make a difference and stop this unfair commerce at the expenses of desperate folks.

Crossreactive epitopes
Figure. 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).

 


 

 

 

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False positive IgM tests in infectious and autoimmune diseases

False positive IgM tests in infectious and autoimmune diseases

Introduction

IgM tests present a high rate of false positive results. This can lead to misdiagnosis, inappropriate treatments, and lack of treatment for the true aetiology (Landry ML. 2016). In the following table, I have collected several well-documented cases of IgM tests falsely positive for an infectious disease. In most of these studies, the cause of the false positive result was found to be either another acute infection or autoantibodies, including rheumatoid factor (RF), an IgM that binds to the Fc region of IgG. So it might be important to determine the exact origin of a false positive IgM test, in cases where a diagnosis is hard to find: it could be the clue that ultimately leads to the true aetiology.

IgM falsely positive for: True aetiology N. of cases Reference
Hantavirus IgM

Nombre virus IgM

Adenovirus 1 Landry ML. 2016
Measles virus IgM Sulfa drug allergy 1 Landry ML. 2016
HAV IgM CHF 1 Landry ML. 2016
HEV IgM HAV (IgM+) 1 Landry ML. 2016
HSV IgM VZV (IgM+) 1 Kinno R. et al. 2015
VZV (IgM+) 11/50 Ziegler T. et al. 1989
Parvovirus B19 (acute) 5/65 Costa E. et al. 2009
RF 9/50 Ziegler T. et al. 1989
RF 1 Pan J. et al. 2018
Anti-HDF 2/50 Ziegler T. et al. 1989
HSV-2 IgM HSV-1 1 Landry ML. 2016
VZV Anti-HDF 5/74 Ziegler T. et al. 1989
HSV (IgM+) 8/54 Ziegler T. et al. 1989
RF 8/54 Ziegler T. et al. 1989
EBV VCA IgM CMV (IgM+) 1 Landry ML. 2016
CMV (IgM+) 7/50 Aalto MS et al. 1998
B. burgdorferi (IgM+) 2 Pavletic A. Marques AR. 2017
HEV (IgM+) 33,3% Hyams C et al. 2012
CMV IgM HEV (IgM+) 24,2% Hyams C et al. 2012
Anti-HDF 10/75 Ziegler T. et al. 1989
RF 3/75 Ziegler T. et al. 1989
WNV IgM HSV-2 1 Landry ML. 2016
B. burgdorferi IgM

(OspC and/or BmpA)

HSV 2 (IgM+) 1 Strasfeld L. et al. 2005
VZV (acute) 5/12 Feder HM. et al. 1991
EBV (acute) 14/58 Goossens HA. et al. 1998
CMV (acute) 13/58 Goossens HA. et al. 1998
Mycoplasma IgM WNV (IgM+) 1 Landry ML. 2016

List of abbreviations. CHF, congestive heart failure; CHIK virus, Chikungunya virus; CMV, cytomegalovirus; EBV, Epstein-Barr virus; HAV, hepatitis A virus; HDF, human diploid fibroblast cells; HEV, hepatitis E virus; HIV, human immunodeficiency virus; HHV-6, human herpesvirus type 6; HSV, herpes simplex virus; RF, rheumatoid factor; VZV, varicella-zoster virus; WNV, West Nile virus.

Cross-reactivity with other pathogens

One possible cause for false positive results is cross-reactivity between antigens that belong to different pathogens. A little-known example of this phenomenon comes from the research on ME/CFS: in the study that ultimately ruled out the involvement of XMR virus in the pathogenesis of ME/CFS, antibodies to that pathogen were found in about 6% of both cases and healthy controls, whereas the molecular testing turned out to be negative in all participants (Alter HJ. et al. 2012). So, sera reactivity to XMRV is likely due to a relatively common pathogen that has an antigen similar to another one belonging to XMRV.

In one case of false positive HSV IgM due to VZV infection, the serum/CSF IgM ratio as a function of time had the same profile for both the viruses, suggesting cross-reactivity (Kinno R. et al. 2015). Cross-reactivity between HSV IgM and VZV IgM seems quite common, with both false positive HSV samples due to reactivity to VZV and false positive VZV IgMs due to IgM against HSV (Ziegler T. et al. 1989).

Interestingly enough, although OspC is considered to be a highly specific antigen of B. burgdorferi, OspC IgM is often positive in patients with active EBV or CMV infections (Goossens HA. et al. 1998). If cross-reactivity was responsible for false positive OspC IgM in infectious mononucleosis, we would expect false positive IgM for EBV and CMV in early Lyme disease. And this is is exactly what has been found in two cases of acute Lyme disease, where falsely positive IgM to VCA has been documented (Pavletic A. Marques AR. 2017).

Latent infections reactivation

It has been described a rise of EBV VCA IgM titers in CMV primary infections. This is likely due to EBV reactivation in many cases. This can lead to a misdiagnosis of a primary EBV infection, instead of a primary CMV infection. This error can have serious consequences during immune suppression or pregnancy, when CMV infections are health threatening (Aalto MS et al. 1998).

Rheumatoid factor interference

As mentioned in the introduction, rheumatoid factor (RF) is an autoantibody – mainly of the IgM subclass – that is found in most of the patients with rheumatoid arthritis (Hermann E. et al. 1986). It binds the constant region (Fc region) of human IgGs and thus can bind the enzyme-linked immunoglobulins often used in serologic assays, leading to falsely positive results (Pan J. et al. 2018).

Cross-reactivity with autoantigens

Another possible cause of falsely positive IgM tests to a pathogen is the presence of autoantibodies other than RF. Autoantibodies to fibroblast cells have been found to be the cause of a false positive IgM test for HSV and CMV (Ziegler T. et al. 1989). This kind of reactivity to self-antigens is probably non-specific of a particular autoimmune disease, and it has been found for instance in pretibial myxedema, Graves’ disease, and Hashimoto’s thyroiditis (Arnold K. et al. 1995).

The effect of prevalence on the rate of false positive results

The likelihood of a false positive result is inversely correlated with the prevalence of the pathogen in the specific population considered. In other words, the rarer the disease, the more likely a positive test for that disease is a false positive. This can be easily seen introducing the predictive positive value (PPV), which is the probability that a positive test is really a true positive (Lalkhem AG. et McCluskey A. 2008). PPV is given by

PPV.PNG

In the following figure, you can see how PPV increases as the prevalence increases. This diagram has been plotted considering a sensitivity of 67% and a specificity of 53%.

PPV 2.png

 


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Il dr. Systrom e l’intolleranza all’esercizio

Con una versione più sofisticata (e invasiva) del consueto test cardiopolmonare con la cyclette, il dr. Systrom riporta che nella maggioranza dei pazienti ME/CFS la capacità delle vene delle gambe e dell’addome di contrarsi e spingere il sangue verso l’atrio destro è ridotta. Cioè la pressione del sangue all’imbocco dell’atrio destro – durante esercizio – è inadeguata. Altra anomalia è l’uso inadeguato di ossigeno da parte dei muscoli scheletrici. E questo difetto può essere dovuto a due cause: una disfunzione dei vasi e/o una disfunzione dei mitocondri. In altre parole, l’ossigeno non è utilizzato perché il sistema che lo trasporta non funziona e/o perché gli organelli che lo usano non riescono a svolgere la loro attività.

Poco meno della metà dei pazienti di Systrom presenta una biopsia cutanea positiva per la neuropatia delle piccole fibre, una patologia ben nota che si riscontra nel 40% dei pazienti con fibromialgia, nel 40% dei pazienti con POTS, e in varie altre patologie.

Systrom riporta risultati positivi con la piridostigmina, un inibitore dell’acetilcolinesterasi (l’enzima che degrada l’acetilcolina). Ha trattato 300 pazienti e il farmaco sembra funzionare per l’intolleranza all’esercizio.

La particolarità di questo test – rispetto al consueto test da sforzo con cyclette – è l’uso di un catetere nella arteria del polso destro e di un secondo catetere che raggiunge l’imbocco della arteria polmonare. I cateteri permettono di misurare la pressione del fluido e di prelevare sangue da utilizzare per misure dei gas in miscela e di altri parametri.

 


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Mark Davis and the search for the universal immune test

Mark Davis and the search for the universal immune test

A traslation of this blog post to Spanish can be downloaded here. I would like to thank Humbert.Cat for the translation.

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.


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Mitocondri belgi

Mitocondri belgi

Un piccolo studio (10 pazienti) senza gruppo di controllo, da parte di un prof. di endocrinologia del Ghent University Hospital, Belgio (Frank Comhaire, 2017). E’ stato somministrato un insieme di integratori contenente anche un ingrediente X (estratto da un’alga, senza altre indicazioni) che dovrebbero inibire le piruvato deidrogenasi chinasi (PDK) (figura 1). Gli altri ingredienti sono vit. B1, acido alfa lipoico, acetil-L-carnitina e ossidoriduttasi ubiqiunone Q10.

glucose test.jpg
Figura 1. Il farmaco proposto in questo studio dovrebbe attivare il piruvato deidrogenasi, inibendo le piruvato deidrogenasi chinasi .

Ricordo che le PDK sono state chiamate in causa nell’ultimo studio norvegese sulla ME/CFS in cui si è potuto documentare una riduzione della attività dell’enzima piruvato deidrogenasi, verosimilmente riconducibile alla iperattività di alcune PDK (in particolare PDK1, PDK2 e PDK4) (vedi qui).

Cinque dei dieci pazienti hanno risposto al farmaco, normalizzando la propria funzionalità, per gli altri 5 sono state trovate diagnosi alternative (ipogonadismo, burn-out, osteoporosi, CMV attivo, focolaio batterico nei seni nasali) e sono stati avviati i trattamenti del caso, con beneficio.

Nel complesso lo studio è quantomeno stuzzicante, una lettura edificante. Ma alcune cose lasciano perplessi.

Per esempio, come è possibile che siano state fatte le diagnosi di ME/CFS a livello universitario per poi scoprire che i pazienti avevano altro, tra cui un palese ipogonadismo in un ragazzo di 29 anni? Secondo, in un paziente si confonde apparentemente la fibromialgia con la ME/CFS. Terzo, le indagini che hanno portato a diagnosi alternative sono state fatte solo a coloro che non rispondevano al nuovo farmaco.

glucose test
Figura 2. La linea rossa indica il livello di lattato nel sangue dopo assunzione di glucosio, nei pazienti esaminati in questo studio. Le misure sono state fatte sul sangue, prima dell’ingestione di 75 g di glucosio (tempo 0) e dopo 30, 60, 90, 120, 180 e 240 minuti.

Da segnalare anche che l’autore propone 3 possibili test per rilevare la ridotta attività del piruvato deidrogenasi nella ME/CFS:

  1. un test che prevede la misura del piruvato e dell’acetil-coenzima A nei monociti (che l’autore indica come in fase di sviluppo);
  2. un test in cui si misura il lattato dopo somministrazione di glucosio: se c’è un blocco nel piruvato deidrogenasi, il lattato dovrebbe aumentare in questo test (come viene anche indicato da alcune misure fatte sui pazienti dello studio, figura 2);
  3. testare il farmaco sui pazienti, se rispondono allora le PDK erano iperattive.

Antibodies to adrenergic and muscarinic receptors in ME/CFS

Antibodies to adrenergic and muscarinic receptors in ME/CFS

A translation to Dutch of this article is available here.

Latest news

During the Community Symposium on the molecular basis of ME/CFS (R) two different groups of researchers reported on an increased level of antibodies to beta adrenergic and muscarinic receptors in sera from ME/CFS patients vs healthy controls (Figure 1). These new data have been collected independently by Alan Light (University of Utah) and Jonas Bergquist (Uppsala Universitet). Bergquist also reported that these autoantibodies can’t be found in cerebrospinal fluid from ME/CFS patients.

autoantibodies
Figure 1. Two slides from the symposium: on the left data from Uppsala Universitet, on the right data from a group of patients studied by Alan Light (University of Utah).

What was already known on these autoantibodies

The presence of a higher than normal reactivity of sera from ME/CFS patients to muscarinic receptors was reported for the first time by a Japanese group, more than a decade ago (Tanaka S et al. 2003) and it has been confirmed recently in a work by Osaka City University Graduate School of Medicine (Yamamoto S et al. 2012) and in another paper by University of Bergen (Norway) and Charité University (Germany) (Loebel M et al. 2016). In particular, while Tanaka and colleagues measured an increased level of autoantibodies against muscarinic cholinergic receptor 1 (CHRM1) in about half of patients, the European group described an increase in reactivity of sera to subtypes M3 and M4, in a subset of patients (Figure 2). They used two completely different assays, as we will see later, and this might be the reason for the different results.

autoantibodies 2.png
Figure 2. An increase in reactivity of sera from ME/CFS patients to M1 cholinergic receptors was reported by Tanaka and colleagues in 2003 (left). Loebel and colleagues found an increase in reactivity to M3, M4 cholinergic receptors and beta 2 adrenergic receptors in 2016 (right).

As you can see from figure 2, the study by Loebel et al. also indicated an increase in antibodies to beta adrenergic receptors (subtype 2), in agreement with the latest data from Light and Bergquist. In this regard, it is worth noting that autoantibodies to muscarinic receptors M2 and M3, and to beta adrenergic receptors (subtype 1 and 2) have been already reported in orthostatic hypotension (OH) (Yu X et al. 2012), (Li H et al. 2012) and that antibodies to beta 2 adrenergic receptors have been identified in patients with postural-orthostatic tachycardia syndrome (POTS) (Li et al. 2014). This means that this group of autoantibodies is associated with orthostatic intolerance (POTS and/or OH), but orthostatic intollerance is part of the clinical picture of ME/CFS (IOM 2015) and those patients who have a diagnosis of POTS often have many features in common with ME/CFS patients, see for instance (Okamoto L et al. 2012), (Wise S et al. 2015). So, it might be conceivable that these autoantibodies play a role in the pathogenesis of some symptoms in a subgroup of patients, although this has not been proven, so far.

Molecular mimicry?

We don’t know the reason why the immune system of some ME/CFS patients reacts with these receptors, but Alan Light suggested, during the symposium, that a possible source for these antibodies might be a mechanism known with the name of molecular mimicry (MM). The basic idea behind MM is that B cells can erroneously produce antibodies to human proteins when epitopes of an infectious agent closely resemble epitopes found in the host (Rose NR 1998). MM is currently believed to explain the pathogenesis of Guillain-Barré syndrome, where lipo-oligosaccharides on the Campylobacter jejunii outer membrane seems to elicit (in predisposed individuals) an immune response to human gangliosides, due to the similarity between these antigens (Van den Berg B et al. 2014). Now, if molecular mimicry was involved in the origin of antibodies to beta 2 adrenergic receptors, which could be the epitope on the receptor? And which the pathogen-borne antigen? In order to provide a possible answer to this question we have to consider that the regions of a receptor that can be involved in B cell autoimmunity are only those that have extracellular exposure; the other regions are immersed in plasma membrane and in cytoplasm, so they can’t interact with antibodies. As you can see from Figure 3, beta 2 adrenergic receptor (ADRB2) has four extracellular regions, in particular peptides 1-34, 96-106, 175-196, 299-305. In general, epitopes are mainly conformational and that means that they are regions of the protein surface, produced by the folding of the protein itself. Nevertheless, in our example we will search only for linear epitopes.

Beta 2
Figure 3. Schematic representation of ADRB2, from (Rasmussen G et al. 2007). You can notice the extracellular peptides 1-34 (the N-terminus), 96-106 (loop 1), 175-196 (loop 2), 299-305 (loop 3).

I have used QuickBLASTP provided by NCBI, with default settings (E=100, a word of 6 letters, BLOSUM62 as substitution matrix) and I have considered for each of the four extracellular peptides both the sequence of residues from the N-terminus to the C-terminus, and the inverted sequence. We obtain as the only match the protein sensor histidine kinase MtrB belonging to Pseudonocardia sp. Ae331_Ps2 (R) (Figure 4). I can’t find this particular protein in UniProt, but if it was a membrane protein and if peptide 67-77 was exposed to the extracellular space, this peptide could perhaps be a candidate as a trigger for anti-ADRB2, according to the MM theory. It is important to note here that although molecular mimicry is a popular theory (perhaps because of its simplicity) it has been proven to be a cause of autoimmnity only in Guillain-Barré syndrome.

molecular mimicry
Figure 4. Peptide 2-12 of the ADRB2 receptor resembles peptide 67-77 of sensor histidine kinase MtrB (from Pseudonocardia sp. Ae331_Ps2).

So, what about a test for these autoantibodies?

If antibodies to adrenergic and muscarinc receptors were involved in the pathogenesis of some cases of ME/CFS, it would be interesting for patients to test for them. In this regard, it is worth noting that the measure of antibodies to membrane receptors should be done using an assay in which these receptors are expressed by living cells in their physiological position (CBA, cell based assay). In fact, with assays in which receptors are coated on plates we may have both false positives (due to the fact that sera react with peptides that are not in the extracellular domain) and false negatives (due to protein denaturation, which leads to the formation of epitopes that would not be present if the protein were correctly folded). The superiority of CBA over the other kind of test is well accepted in the case of anti-MOG antibodies (Ramanathan S et al 2016). It is worth noting that both the study by Loebel et al. and the previous one (Tanaka et al.) used recombinat proteins coated on plates. As far as I know, there are no commercial CBA assays for anti-muscarinic cholinergic receptors and beta adrenergic receptors, at present. The only assay available does not seem to be a CBA, from the provided documentation (R).

ADRB2_2R4R_231_242
Figure 5. I have reported in yellow the epitope predicted by DiscoTope 2.0 on the 3D structure of ADRB2 (PDB ID: 2R4R chain A). I have also indicated what part of the molecule is outside the cell, what is inside the membrane and what is inside the cell.

In silico experiment

We will now try to simulate what could happen with a test for the search of anti-ADB2R antibodies, if the protein was coated on a plate. We will use the prediction of DiscoTope 2.0, which is a software that calculates all possible B cell epitopes of a given protein, using both the geometry of the protein (in particular a parameter called protrusion index, calculated from the protein’s ellipsoid of inertia) and statistical data on known B cell epitopes (Kringelum, et al., 2012). If we use the 3D structure of ADB2R experimentally determined in (Rasmussen et al. 2007) with standard settings, DiscoTope predicts peptide 231-242 as the only possible epitope (consider that the experimental 3D structure of ADB2R is incomplete). As you can see from figure 5 this peptide belongs to the intracellular domain of the receptor and so it by no means could be a B cell epitope, in vivo. In conclusion, according to this simulation, there is a risk of false positive results with any test that uses recombinat ADB2R coated on a plate.


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I dati grezzi dello studio Hanson

I dati grezzi dello studio Hanson

Introduzione

Il gruppo di Maureen Hanson (Cornell University) ha pubblicato alcuni mesi fa uno studio in cui 361 metaboliti sono stati quantificati nel sangue di 17 donne con ME/CFS (e 15 controlli sani, corrispondenti per sesso ed età) (Germain A et al. 2017). La tecnica utilizzata è la spettroscopia di massa, e questo studio si aggiunge ad altri 3 lavori analoghi sulla ME/CFS pubblicati in questi ultimi 11 mesi (Naviaux R et al. 2016), (Øystein Fluge .et al. 2017), (Yamano E et al. 2016). Lo studio Hanson e lo studio Naviaux sono per ora i due con il maggior numero di metaboliti esaminati e i loro risultati sono coerenti con un complessivo ipometabolismo: circa l’85% dei metaboliti esaminati nei due studi sono ridotti in modo significativo rispetto al controllo sano. I percorsi metabolici coinvolti sono numerosi, dalla ossidazione degli acidi grassi (beta-ossidazione), alla ossidazione degli amminoacidi, alla sintesi di fosfolipidi (i componenti delle membrane cellulari). In figura 1 trovate un confronto fra lo studio Naviaux e lo studio Hanson con analogie e differenze.

Hanson vs Naviaux.png
Figura 1. Confronto fra lo studio Naviaux e lo studio Hanson, mostrato dalla stessa Hanson durante un webinar.

I dati grezzi

In questo post non esaminerò lo studio Hanson nel dettaglio, piuttosto voglio proporre una rianalisi statistica di una piccola parte dei dati grezzi, ovvero della misura dei 361 metaboliti nelle 32 persone complessivamente esaminate. I dati sono stati resi disponibili al pubblico (cosa lodevole) in formato .XLSX. Il file è qui.

Glicolisi-Krebs
Figura 2. Riduzione significativa di oxaloacetato e succinato nei pazienti ME/CFS rispetto ai controlli sani. L’analisi statistica e i grafici delle distribuzioni sono di Paolo Maccallini.

La mia rianalisi statistica della glicolisi e del ciclo di Krebs

Per la mia analisi statistica dei dati grezzi mi sono concentrato sui percorsi metabolici della glicolisi (piruvato, lattato) e del ciclo di Krebs (aconitato, succinato, fumarato, oxaloacetato). L’analisi si basa sulla assunzione di una distribuzione normale dei valori, utilizzanto il t-test (one-tailed) per il calcolo del valore p. I valori p e le distribuzioni dei dati sono riportati in figura 2. Come si vede, c’è una tendenza all’aumento dei prodotti finali della glicolisi in alcuni pazienti (piruvato, lattato) che tuttavia non è significativa nel complesso. Si apprezza altresì una tendenza alla riduzione dei metaboliti intermedi del ciclo di Krebs, ma solo il succinato e l’oxaloacetato sono ridotti in modo significativo. E’ interessante notare che una tendenza alla riduzione dei metaboliti del ciclo di Krebs è coerente con quanto riportato in (Yamano E et al. 2016) con una metodica simile, e quanto riportato in questo blog, utilizzando la spettroscopia di massa su urine in tre pazienti, due maschi e una femmina (vedi qui).