Three new possible autoepitopes in ME/CFS

Paolo Maccallini


I have performed a set of analysis on experimental data already published about autoimmunity to muscarinic receptors in ME/CFS. My predictions are that extracellular loop 2 and 3, and also transmembrane helix 5 of both muscarinic cholinergic receptors 4 and 3, are main autoantigens in a subset of ME/CFS patients. Moreover, I have found that autoimmunity to M4 and M3 ChR is independent of autoimmunity to beta 2 adrenergic receptor, also reported in ME/CFS patients.  


Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease characterized by cognitive deficits, fatigue, orthostatic intolerance with symptoms exacerbated after exertion (post-exertional malaise, PEM) (IOM, 2015). This disease has no known cause but several abnormalities have been observed in energy metabolism (Tomas C. and Newton J. 2018), immune system, gut flora (Blomberg J. et al. 2018), brain (Zeineh MM. et al. 2014). A possible role for autoantibodies in the pathogenesis of the disease has been suggested by the finding of reactivity of patient sera to two nuclear antigens (Nishikai, et al., 1997), (Nishikai, et al., 2001), to cardiolipin (Hokama, et al., 2009), to HSP60 (Elfaitouri A. et al. 2013), and to muscarinic cholinergic (M ChR) and beta adrenergic receptors (ß AdR) (Tanaka S et al. 2003), (Loebel M et al. 2016); reactivity that was significantly elevated when compared to healthy contols. Reactivity to adrenergic and muscarinic Ch receptors has been confirmed by two independent groups, but these results have not been published yet (R). A role for autoantibodies in at least a subgroup of patients has also been suggested by a response to rituximab, a CD20 B cells depleting agent (Fluge Ø. et al. 2011), (Fluge Ø. et al. 20115), and to immunoadsorption (Scheibenbogen C. et al 2018). Sera response to muscarinic cholinergic receptors is confirmed in two studies but both of them used an immune assay with proteins coated on a plate. This kind of test does not allow to identify the exact autoepitope on the receptor and – even more importantly – it is subjected to false positive results because it exposes to sera surfaces of receptors that are hidden when they are in their physiological position (Ramanathan S et al 2016). Nevertheless, the amount of data provided in the study by Loebel et al. where reactivity of sera to 5 subtypes of muscarinic cholinergic receptors have been measured simultaneously, has – in our opinion – the potential to unveil the exact autoepitope(s). Thus, we performed a bioinformatical analysis on experimental data from this study in order to extract hidden information. We used a software for the in silico study of B cell epitope cross-reactivity (Maccallini P. et al. 2018) and a software for amino acid protrusion index calculation (Ponomarenko J. et al., 2008).  Our prediction is that patients sera mainly react to three epitopes that belong to the second and third extracellular loop of M3 and M4 ChR, but also to a hidden epitope of the same two receptors, leading to possible false positive results of this test. We have also found that the reactivity to beta 2 adrenergic receptor (ß2 AdR) found in the study by Loebel et al. is not due to the same antibody that reacts to muscarinic cholinergic receptors.


Search for cross-reactive epitopes. Cross-reactivity between muscarinic cholinergic receptors M4 and M3, and between M4 and M1 has been studied in silico using EPITOPE, a software already described (Maccallini P. et al. 2018). Briefly, EPITOPE searches for cross-reactive epitopes shared between two proteins (let’s say protein A and protein B) by comparing each possible 7-mer peptide of A with each possible 7-mer peptide of B. The comparison is made using the algorithm by Needleman and Wunsch (Needleman SB. and Wunsch CD. 1970)  with a gap model a + b·x, where a is the opening gap penalty, b is the extending one, and x is the extension of the gap. A penalty for gaps at the end of the alignment was also assumed. The choice for gap penalties and substitution matrix were done according to the theory already developed for peptide alignments (Altschul SF. 1991), (Karlin S. and Altschul SF. 1990). Available experimental data on cross-reactivity between γ enolase and α enolase (McAleese SM. et al. 1988)  have been used for EPITOPE calibration: a score >60 was considered the cut-off for cross-reactivity, a score below 50 indicates non-cross-reactive epitopes; a score between 50 and 60 defines a borderline result. A simpler version of EPITOPE has been used for single local alignments. The main program used for M4-M3 comparison, its subroutine NeWalign and the substitution matrix employed are available for download. Primary structures used in this work have been downloaded from UniProt and are the following ones: M1 ChR (P11229), M3 ChR (P20309), M4 ChR (P08173), B2 AdR (P07550).

Surface exposure. In order to select only those 7-mer peptides that are on the surface of proteins, we have considered their mean protrusion indexes. A protrusion index of at least 0.5 has been considered the cut-off for surface exposure. Protrusion indexes of single amino acids have been calculated with ElliPro. A protrusion index of 0.5 means that the amino acid is outside the ellipsoid of inertia which includes 50% of the centers of mass of all the amino acids of the protein (Ponomarenko J. et al., 2008). For M4 ChR we have used the crystal structure 5DSG (Thal DM. et al. 2016). The 3D structure of human M3 ChR has not been experimentally determined yet, so we have used a theoretical model built using murine M3 ChR (PDB ID: 4DAJA) as a template, provided by ModBase.

M ChR plot
Figure 1. The position of the first amino acid of each possible 7-mer peptide of M4 ChR is reported on the abscissa, the score for the comparison of each of these peptides with M1 ChR (blue line) and M3 ChR (orange line) is reported on the ordinate. N terminus, extracellular loop 1, 2 and 3 are also indicated. Scores above the yellow line indicate cross-reactivity, scores below the blue line indicate a lack of cross-reactivity.

Selection criteria. Our purpose is to predict to what epitopes of M3 and M4 ChRs sera from ME/CFS patients react. So we search for M4 ChRs 7-mer peptides that are cross-reactive to M3 ChR, but non-cross-reactive to M1 ChR. Moreover, they have to present surface exposure both on M4 and on M3 ChR (otherwise antibodies can’t reach them). So, selection criteria for M4 ChR epitopes are as follows:

  1. they have to be cross-reactive to M3 ChR;
  2. they have to be non-cross reactive to M1 ChR or borderline;
  3. they have to present a mean protrusion index ≥0.5;
  4. M3 ChR peptides to which thy cross-react have to present a mean protrusion index ≥0.5.

We will refer to strict criteria when we assume only non-cross-reactivity in 2, while weak selection criteria are fulfilled when M4 ChR epitopes have borderline reactivity to M3 Chr peptides.

M4 vs M1, M3
Figure 2. Distribution of the scores from the comparison of M4 ChR with M1 ChR (left) and with M3 ChR (right). M3 ChR presents a slightly higher mean score.


The search for 7-mer peptides of M4 ChR that are cross-reactive to M3 ChR found 108 sequences. We then studied cross-reactivity to M1 ChR for each of these peptides and we found that 11 of them are non-cross-reactive and that other 9  peptides have borderline reactivity. None of these 20 peptides presented a cross-reactivity to B2 AdR (Table 1S, column 1). Scores between peptides of M4 ChR and the other two muscarinic cholinergic receptors are plotted in Figure 1. The distribution of scores from the comparison of M3 ChR with M1 ChR and with M3 ChR are reported in Figure 2. For the M4 ChR 20 epitopes mentioned above, we calculated the mean protrusion indexes and we did the same calculation for their cross-reactive peptides on M3 ChR. We also indicated their position with respect to the plasma membrane. All these data are collected in Table 2S. Once we apply selection criteria on these 20 peptides, we obtain 9 epitopes (Table 1). Of these selected epitopes, one belongs to a transmembrane helix: peptide 186-192 of M4 ChR, which cross-reacts to peptide 231-237 of M3 ChR. Peptide 418-431 of M4 ChR is partially immersed in the plasmatic membrane, even though its cross-reactive peptide of M3 ChR is entirely exposed to the extracellular space, and the same applies to the other two epitopes found (figure 1). Peptide 175-181 of M4 ChR cross-reacts to peptide 211-217 of M3 ChR; peptide 186-192 of M4 Chr cross-reacts to peptide 222-228 of M3 ChR; peptide 418-431 of M4 Chr cross-reacts to peptide 513-522 of M3 ChR. Sequences that fulfill selection criteria and their respective inverted sequences are collected in  Table 2.

Table 1
Table 1. This is the collection of M4 Chr 7-mer peptides that are cross-reactive to M3 ChR; are not cross-reactive or borderline with M1 ChR; have a mean protrusion index higher than 0.5; are cross-reactive with epitopes of M3 ChR with a protrusion index higher than 0.5.


B cells autoimmunity to muscarinic cholinergic receptors in ME/CFS has been reported in two studies (Tanaka S et al. 2003), (Loebel M et al. 2016) and this finding has been recently confirmed by two other independent groups who have not published yet (R). The two studies mentioned used full-length proteins coated on a plate in order to perform the immune assay. With this kind of technique 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) as has been reported in the case of anti-MOG antibodies (Ramanathan S et al 2016). A way to solve the possible inaccuracy of these data would thus be to measure sera reactivity with a cell-based assay (CBA) which is a test where receptors are expressed by eukaryotic cells and thus they are held in their physiological position.

Figure 1. Peptides of table 1 that belong to the extracellular domain of M3 and M4 ChR are here highlighted directly on the 3D structures of their respective receptors.

Nevertheless, we can still try to extract hidden information from experimental data and predict the position of the epitope(s) ME/CFS patients sera react to. Knowing that sera from patients react to M4, M3 ChRs and that there is a low correlation between reactivity to M4 ChR and reactivity to M1 ChR (Loebel M et al. 2016) we selected 7-mer peptides of M4 ChR that cross-react (in silico) to M3 ChR but not to M1 ChR (Table 2S). We then selected, among them, only those peptides that have surface exposure on their respective proteins (Table 1). The result is that patient sera react to extracellular loops 2 and 3 of both M3 and M4 ChRs (Figure 1), but also to a hidden antigen, a peptide of transmembrane helix 5 of both M3 and M4 ChR.

Our results are of interest because extracellular loops 2 and 3 of M3 ChR are known autoepitopes in Sjögren’s syndrome (Ss) (Deng C. et al. 2915). Moreover, sera from patients with orthostatic hypotension (OH) react to extracellular loop 2 of M3 ChR, where they show an agonistic effect, thus acting as vasodilators (Li H. et al. 2012). OH, a form of orthostatic intolerance has been reported in ME/CFS patients (Bou-Holaigah et al. 1995) while fatigue similar to post-exertional malaise have been described in Ss (Segal B. et al. 2008). A pathogenic role of these antibodies in fatigue for both ME/CFS and Sjögren syndrome could perhaps be due to their vasodilatory effect.

Our analysis unveiled reactivity to a hidden autoepitope, which belongs to transmembrane helix 5 of M3 and of M4 ChR. This epitope is buried inside the plasma membrane when these two receptors are in their physiological position, so this reactivity can’t contribute to the pathogenesis of ME/CFS.

None of the 7-mer peptides of M4 ChR that cross-react to M3 ChR and at the same time don’t cross-react to M1 ChR presents in silico reactivity to B2 AdR. This means that in those patients whose sera present reactivity to both M4-M3 ChR and B2 AdR, there are two distinct autoantibodies. This prediction of our model is consistent with the low correlation found by Loebel and colleagues between anti-M4 ChR and anti-B2 AdR antibodies (Loebel M et al. 2016).

Most B cells epitopes on non-denaturated proteins (i.e. proteins that conserve their tertiary structure) are believed to be conformational (Morris, 2007), so a significant limitation of this study is due to the fact that our analysis considers only linear epitopes. Nevertheless, the main limitation of this study remains by far my encephalopathy.


This analysis of previously published data suggests a role for the second and the third extracellular loop of M4 and M3 ChR as autoantigens in ME/CFS. It also predicts the presence of a hidden autoantigen and thus a risk of false-positive results with standard ELISA.  The eight peptides found by this analysis and their inverse sequences (Table 2) should be employed as query sequences for the search for possible triggering pathogens and for other autoantigens. These predictions should be tested using both cell-based assays and ELISA tests with these 8 peptides coated on the plate.

Table 2.PNG
Table 2. Peptides belonging to M4 and M4 ChR that fulfill our selection criteria are collected on the left. On the right, their reverse sequences. These 16 peptides can be used in BLAST in order to serach for triggering pathogens and for other possible autoepitopes.


Supplementary material. The following two tables represent the first two steps of the analysis presented in this paper. M4 ChR 7-mer peptides that are cross-reactive to M3 ChR are collected in Table 1S, while those of them that are non-cross-reactive (or borderline) to M1 ChR are collected in Table 2S.

Table 1S. Peptides of M4 ChR that are cross-reactive to M3 ChR are collected in the first column. In the second column are collected the scores of these 7-mer peptides obtained from the comparison with M1 ChR. For those that obtained a score below 60, the score from the comparison with B2 AdR is reported in column 5. Positions of peptides of interest that belong to M3ChR and B2 AdR are collected in columns 4 and 6 respectively.
Table 2S.PNG
Table 2S. These 20 peptides are those M4 ChR peptides that cross-react to M3 ChR and at the same time are non-cross-reactive or borderline when compared to M1 ChR. Reactivity to B2 AdR is also indicated, as well as positions with respect to the plasma membrane and mean protrusion indexes. On the left are indicated those peptides of M4 ChR that pass the selection according to our criteria. Both a strict selection and a selection with more weak criteria are reported.







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.

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.

Figura 2. L’algoritmo di Needleman-Wunsch, con l’indicazione delle variabili che ho usato nel mio codice per implementarlo. Disegno di Paolo Maccallini.
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.


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.