# Brain Normalization with SPM12

Introduction

Brain normalization is the rigid rotation and/or deformation of a 3D brain scan so that it can match a template brain. This is a necessary step for the analysis of brain magnetic resonance data (whether it is morphological or functional) as well as of brain PET data. It allows introducing a set of spatial coordinates such that each triplet (x,y,z) identifies the same anatomical region both in the brain we are studying and in the template (Mandal P.K. et al. 2012). So, for instance, once the normalization has been performed on an fMRI of the brain of a patient and on a set of fMRIs from a suited control group, we can compare the BOLD activation of each anatomical region of the patient’s brain with the activation of the corresponding anatomical region of the control group.

Mathematical notes

This part can be skipped, it is not necessary to read these passages for the understanding of the following paragraphs. If we assume that P is a point of the brain before normalization and we call S(P) its position after the normalization, we can consider the vectorial function:

which gives the new position of each point of the brain, after normalization. If P_0 is a point of the brain before the normalization, then we can write:

and remembering the expression of the differential of a vectorial function, we have

With a few passages we can write:

From the above formula, we realize that in order to define the configuration of the brain after normalization, we have to define, for each point P, a set of 12 parameters. Of these parameters, 6 describe the rigid movement and can be considered the same for each point. The other 6 (the coefficients of the matrix) are those that describe the change of shape and size and, in general, they are different for each point. The job of SPM is to calculate these parameters.

Brain atlases

There are several templates (also called brain atlases) that have been developed across the years. The very first one was published in 1957 (Talairach J. et al. 1957). The same researcher then participated in building one of the most used brain atlas ever, based on a single female brain, published in 1988 (Talairach J. et Tournoux P. 1988). Another widely used template is the so-called MNI-152. It was built adapting the 3D MRI brain scans of 152 healthy individuals to the Talairach and Tournoux template. The adaptation was achieved using both a rigid roto-translation and a deformation (Maintz J.B. et Viergever M.A. 1988). The second step is required for overcoming the problem of differences in brain shape and dimension that we encounter within the human population.

Limitations

Available brain atlases have some limitations. One of them being the fact that despite diseased brains are the most widely studied, they are also the most difficult to register to a template built from healthy individuals, because of usually marked differences in shape and/or size. This is true for instance for brains of patients with Alzheimer’s disease (Mandal P.K. et al. 2012). Another important limitation is that registration algorithms perform poorly for the brainstem (particularly for pons and medulla) (Napadow V. et al. 2006). This might have represented a problem for the study of diseases where a possible involvement of the brainstem is suspected, like for instance ME/CFS (VanElzakker M. et al. 2019).

SPM12

The SPM software package is one of the most widely used instruments for the analysis of functional brain imaging data (web page). It is freely available for download, but it requires that you have a MatLab copy in your computer. Those who don’t have a MatLab license can request and install a standalone version of SPM12 by following the instructions of this page.

Importing a DICOM file

Once you have installed SPM12 in your computer, the first step in order to register a brain is to convert the format the series of images are written in, to a format that SPM12 can read. MRI images are usually in .dcm format (DICOM) while SPM12 reads .nii files. In order to do that, click DICOM import (figure below, on the left, red ellipse), then click on DICOM files (on the right, red ellipse), then select your .dcm file and click DONE (below, red ellipse). If you then click DISPLAY (blue ellipse, left) you will see your MRI scan in another window (see next paragraph). A video tutorial on these operations is available here.

Setting the origin

To start the normalization process, it is highly recommended to set manually the origin of the coordinates. If this is done properly, the registration will not only take less time but, even more importantly, the chances of a successful normalization will increase. The origin is set at the level of the anterior commissure (figure below). To find this anatomical structure, you can follow this video. Once you have put the cross on the right place in the sagittal, coronal and axial windows, just click SET ORIGIN (red ellipse) and then save your work clicking REORIENT (blue ellipse).

Normalization estimate

In this step, SPM12 calculates the set of distortions that have to be applied to the source brain to adapt it to the template in MNI space. On the main menu select NORMALIZE (ESTIMATE) (figure, on the left, red). This will open the batch editor where you are asked to load the subject you want to apply normalization to (figure, right, red). You have also a set of estimation options (blue), that we leave as they are.  Then you click the RUN button, the arrow on the top of the batch editor.

At this point, your PC will perform a set of calculations that will require a few minutes. At the end of this process, a new .nii file will be saved in the spm12 folder. This is the set of distortions that will allow your subject’s brain to be registered to the template.

Normalization writing

Now click on NORMALIZE (WRITE) on the main menu. The batch editor will then ask you for the deformation field, which is the file generated in the previous step, and for the images to write, which is the scan of your subject (figure below). Select them, then press the RUN button on the batch editor. A new .nii file will be written in the spm12 folder. This is the normalized brain!

In the next figure, you have the normalized brain on the left and the initial scan of the same subject on the right. As you can see, there is an overall change of shape.

Anatomical areas

Now that we have normalized our brain in the MNI space, we can easily find anatomical regions within its sections. We can, for instance, load the normalized brain with MRIcron and overlay a template with Brodmann’s areas highlighted in various colours (figure below).

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# Immunosignature analysis of a ME/CFS patient. Part 1: viruses

“Each hypothesis suggests its own criteria, its own means of proof, its own methods of developing the thruth; and if a group of hypotheses encompass the subject on all sides, the total outcome of means and of methods is full and rich.”

The purpose of the following analysis is to search for the viral epitopes that elicited – in a ME/CFS patient – IgGs against a set of 6 peptides, determined thanks to an array of 150.000 random peptides of 16 amino acids each. These peptides were used as query sequences in a BLAST search against viral proteins. No human virus was found. Three phages of bacterial human pathogens were identified, belonging to the classes Actinobacteria and γ-Proteobacteria. One of these bacteria, Serratia marcescens, was identified in a similar study on 21 ME/CFS cases.

(a commentary in Dutch is available here)

1. The quest for a pathogen

Scientists have been speculating about an infectious aetiology of ME/CFS for decades, without ever being able to link the disease to a specific pathogen. The idea that the disease might be triggered and/or maintained by an infection is due to the observation that for most of the patients the onset occurs after an infectious illness (Chu, L. et al. 2019). It has also been observed that after a major infection (whether parasitic, viral or bacterial) about 11% of the population develops ME/CFS (Mørch K et al. 2013), (Hickie I. et al. 2006).

In recent years, the advent of new technologies for pathogen hunting has given renewed impulse to the search for ongoing infection in this patient population. A 2018 study, investigating the genetic profile of peripheral blood for prokaryotic and eukaryotic organisms reported that most of the ME/CFS patients have DNA belonging to the eukaryotic genera Perkinsus and Spumella and to the prokaryotic class β-proteobacteria (alone or in combination) and that these organisms are statistically more present in patients than in controls (Ellis J.E. et al. 2018). Nevertheless, a previous metagenomic analysis of plasma by another group revealed no difference in the content of genetic material from bacteria and viruses between ME/CFS patients and healthy controls (Miller R.R. et al. 2016). Moreover, metagenomic analysis pursued in various samples from ME/CFS patients by both Stanford University and Columbia University has come empty (data not published, R, R).

2. Immunological methods

Another way of investigating the presence of current and/or past infections that might be specific of this patient population is to extract the information contained in the adaptive immune response. This can be made in several ways, each of them having their own limits. One way would be to collect the repertoire of T cell receptors (TCRs) of each patient and see if they have been elicited by some particular microorganism. This is a very complex and time-consuming method that has been developed in recent years and that I have described in details going through all the recent meaningful publications (R). The main limitation of this method is that, surprisingly, TCRs are not specific for a single epitope (Mason DA 1998), (Birnbaum ME et al. 2014), so their analysis is unlikely to reveal what agent selected them. On the other hand, the advantage of this method is that T cell epitopes are linear ones, so they are extremely suited for BLAST searches against protein databases. An attempt at applying this method to ME/CFS is currently underway: it initially gave encouraging results (R), then rejected by further analysis.

Another possible avenue for having access to the information registered by adaptive immunity is to investigate the repertoire of antibodies. The use of a collection of thousands of short random peptides coated to a plate has been recently proposed as an efficient way to study the response of B cells to cancer (Stafford P. et al. 2014), infections (Navalkar K.A. et al. 2014), and immunization (Legutki JB et al. 2010). This same method has been applied to ME/CFS patients and it has shown the potential of identifying an immunosignature that can differentiate patients from controls (Singh S. et al. 2016), (Günther O.P. et al. 2019). But what about the antigens eliciting that antibody profile? Given a set of peptides one’s antibodies react to, a possible solution for interpreting the data is to use these peptides as query sequences in a BLAST search against proteins from all the microorganisms known to infect humans. This has been done for ME/CFS, and the analysis led to several matches among proteins from bacteria, viruses, endogenous retroviruses and even human proteins (in fact, both this method and the one previously described can detect autoimmunity as well) (Singh S. et al. 2016).  There are several problems with this approach, though. First of all, the number of random peptides usually used in these arrays is not representative of the variety of possible epitopes of the same length present in nature. If we consider the paper by Günther O.P. and colleagues, for instance, they used an array of about 10^5 random peptides with a length of 12 amino acids each, with the number of all the possible peptides of the same length being  20^12 ∼ 4·10^15. This means that many potential epitopes one has antibodies to are not represented in the array. Another important limitation is that B cell epitopes are mainly conformational ones, which means that they are assembled by the folding of the proteins they belong to (Morris, 2007), the consequence of this being that the subset of random peptides one’s serum react to are in fact linear epitopes that mimic conformational ones (they are often called mimotopes) (Legutki JB et al. 2010). This means that a BLAST search of these peptides against a library of proteins from pathogens can lead to completely misleading results.

Recently an array of overlapping peptides that cover the proteins for many know viruses has been successfully used for the study of acute flaccid myelitis (AFM). This technology, called VirScan, has succeeded in linking AFM to enteroviruses where metagenomic of the cerebrospinal fluid has failed (Shubert R.D. et al. 2019). This kind of approach is probably better than the one employing arrays of random peptides, for pathogen hunting. The reason being that a set of only 150.000 random peptides is unlikely to collect a significant amount of B cell epitopes from viruses, bacteria etc. Random peptides are more suited for the establishment of immunosignatures.

3. My own analysis

I have recently got access to the results of a study I was enrolled in two years ago. My serum was diluted and applied to an array of 150.000 peptides with a length of 16 random amino acids (plus four amino acids used to link the peptides to the plate). Residues Threonine (T), Isoleucine (I) and Cysteine (C) were not included in the synthesis of peptides. Anti-human-IgG Ab was employed as a secondary antibody. The set of peptides my IgGs reacted to has been filtered with several criteria, one of them being subtracting the immune response common to healthy controls, to have an immune signature that differentiates me from healthy controls. The end result of this process is the set of the following six peptides.

 1 ALHHRHVGLRVQYDSG 2 ALHRHRVGPQLQSSGS 3 ALHRRQRVLSPVLGAS 4 ALHRVLSEQDPQLVLS 5 ALHVRVLSQKRPLQLG 6 ALHLHRHVLESQVNSL

Table 1. My immunosignature, as detected by an array of 150.000 random peptides 20-amino-acid long, four of which are used for fixing them to the plate and are not included here.

The purpose of the following analysis is to search for the viral epitopes that elicited this immune response. To overcome the limitations enumerated at the end of the previous paragraph I have decided to search within the database of viral proteins for exact matches of the length of 7 amino acids. Why this choice? A survey of a set of validated B cell epitopes found that the average B cell epitope has a linear stretch of 5 amino acids (Kringelum, et al., 2013); according to another similar work, the average linear epitope within a conformational one has a length of 4-7 amino acids (Andersen, et al., 2006). To filter the matches and to reduce the number of matches due to chance, I opted for the upper limit of this length. I excluded longer matches to limit the number of mimotopes for conformational epitopes. Moreover, I decided to look only for perfect matches (excluding the possibility of gaps and substitutions) so to simplify the analysis. It is worth mentioning that a study of cross-reactive peptides performed for previous work (Maccallini P. 2016), (Maccallini P. et al. 2018) led me to the conclusion that cross-reactive 7-amino-acid long peptides might often have 100% identity.

So, to recap, I use the following method: BLAST search (blastp algorithm) against viral proteins (taxid 10239), a perfect match (100% identity) of at least 7-amino-acid peptides (≥43% query cover), max target sequences: 1000, substitution matrix: BLOSUM62.

4. Results

Table 2 is a collection of the matches I found with the method described above. You can look at figure 1 to see how to read the table.

 ALHHRHVGLRVQYDSG (102_1_F_viruses) 9-LRVQYDS-15 QDP64279.1(29-35) Prokaryotic dsDNA virus sp. Archea, Ocean 8-GLRVQYD-14 AYV76690.1(358-364) Terrestrivirus sp Amoeba, forest soil ALHRHRVGPQLQSSGS (102_2_F_viruses) 2-LHRHRVG-8 YP_009619965.1(63-69) Stenotrophomonas phage vB_SmaS_DLP_5 Stenotrophomonas maltophilia (HP) ALHRRQRVLSPVLGAS (102_3_F_viruses) 2-LHRRQRV-8 QHN71154.1 (288-294) Mollivirus kamchatka Protozoa (R) 8-VLSPVLG-14 QDB71078.1 (24-30) Serratia phage Moabite Serratia marcescens (HP) ALHRVLSEQDPQLVLS (102_4_F_viruses) 7-SEQDPQL-13 BAR30981.1 (151-157) uncultured Mediterranean phage uvMED Archea and Bacteria, Med. sea 3-HRVLSEQ-9 AXS67723.1 (494-500) Cryptophlebia peltastica nucleopolyhedrovirus invertebrates 2-LHRVLSE-8 YP_009362111.1 (74-80) Marco virus Ameiva ameiva ALHLHRHVLESQVNSL (102_6_F_viruses) 2-LHLHRHV-8 YP_009119106.1 (510-516) Pandoravirus inopinatum Acanthamoeba 4-LHRHVLE-10 ASZ74651.1 (61-67) Mycobacterium phage Phabba Mycobacterium smegmatis mc²155 (HP)

Table 2. Collection of the matches for the BLAST search of my unique set of peptides against viral proteins (taxid 10239). HP: human pathogen. See figure 1 for how to read the table.

5. Discussion

There are no human viruses detected by this search. There are some bacteriophages and three of them have as hosts bacteria that are known to be human pathogens. Bacteriophages (also known as phages) are viruses that use the metabolic machinery of prokaryotic organisms to replicate (figure 2). It is well known that bacteriophages can elicit specific antibodies in humans: circulating IgGs to naturally occurring bacteriophages have been detected (Dąbrowska K. et al. 2014) as well as specific antibodies to phages injected for medical or experimental reasons (Shearer WT et al. 2001), as reviewed here: (Jonas D. Van Belleghem et al. 2019). According to these observations, one might expect that when a person is infected by a bacterium, this subject will develop antibodies not only to the bacterium itself but also to its phages.

If that is the case, we can use our data in table 2 to infer a possible exposure of our patient to the following bacterial pathogens: Stenotrophomonas maltophilia (HP), Serratia marcescens (HP), Mycobacterium smegmatis mc²155 (HP). In brackets, there are links to research about the pathogenicity for humans of each species. M. smegmatis belongs to the class Actinobacteria, while S. maltophila and S. marcescens are included in the class γ-Proteobacteria.

Interesting enough, Serratia marcescens was identified as one of the possible bacterial triggers for the immunosignature of a group of 21 ME/CFS patients, in a study that employed an array of 125.000 random peptides (Singh S. et al. 2016). This bacterium accounts for rare nosocomial infections of the respiratory tract, the urinary tract, surgical wounds and soft tissues. Meningitis caused by Serratia marcescens has been reported in the pediatric population (Ashish Khanna et al. 2013).

Mollivirus kamchatka is a recently discovered giant virus whose hosts are presumed to be protozoa that inhabit the soil of subarctic environment (Christo-Fourox E. et al. 2020). Not sure what the meaning might be in this context.

6. Next step

The next step will be to perform a similar BLAST search against bacterial proteins to see, among other things,  if I can find matches with the six bacteria identified by the present analysis. A further step will be to pursue an analogous study for eukaryotic microorganisms and for human proteins (in search for autoantibodies).

# Un modello matematico per la ME/CFS

La versione in inglese di questo articolo è disponibile qui.

Introduzione

Molti dei miei lettori sono probabilmente a conoscenza dei tentativi attualmente fatti per simulare matematicamente il metabolismo energetico dei pazienti ME/CFS, integrando i dati metabolici con i dati genetici. In particolare, il dr. Robert Phair ha sviluppato un modello matematico delle principali vie metaboliche coinvolte nella conversione dell’energia, dall’energia immagazzinata nei legami chimici di grandi molecole come glucosio, acidi grassi e amminoacidi, all’energia immagazzinata nell’adenosina trifosfato (ATP), pronta per l’uso. Phair, che è un ingegnere, ha determinato le equazioni differenziali che regolano questa enorme quantità di reazioni chimiche e le ha adattate al profilo genetico trovato nei pazienti ME/CFS. Ma già alcuni anni fa due fisici pubblicarono un interessante modello matematico del metabolismo energetico durante e dopo l’esercizio, nei pazienti ME/CFS . In quanto segue descriverò questo modello e le sue previsioni e vedremo da vicino queste equazioni differenziali.

Le vie metaboliche che sono state analizzate

Il modello di Lengert e Drossel estende due sistemi di equazioni differenziali precedentemente pubblicati che descrivono il comportamento della glicolisi, del ciclo di Krebs (enormemente semplificato come una singola reazione!), della catena di trasporto degli elettroni mitocondriale (descritta in dettaglio), del sistema della creatina chinasi e della conversione di adenosina difosfato (ADP) in ATP, nei muscoli scheletrici (Korzeniewski B. et Zoladz JA. 2001), (Korzeniewski B. et Liguzinski P. 2004). Gli autori hanno aggiunto equazioni per l’accumulo di lattato e il suo efflusso fuori dalla cellula, per la sintesi de novo di inosina monofosfato (IMP) durante il recupero, per la degradazione dell’adenosina monofosfato (AMP) in IMP, per la degradazione di IMP in inosina e ipoxantina. Tutte le vie coinvolte sono raccolte nella figura 1. Queste reazioni sono descritte da 15 equazioni differenziali e la soluzione è un insieme di 15 funzioni del tempo che rappresentano la concentrazione dei principali metaboliti coinvolti (come il lattato, il piruvato, l’ATP, ecc.). Diamo ora uno sguardo più da vicino a una di queste equazioni e alla struttura generale dell’intero sistema di equazioni.

Figura 1. Questa è una rappresentazione schematica dei percorsi metabolici descritti dal modello matematico sviluppato da Lengert e Drossel. In dettaglio: sintesi citosolica e degradazione di ADP, AMP e IMP (a sinistra), via della protein chinasi e glicolisi (centro), catena di trasporto degli elettroni e ciclo TCA (a destra). Da Lengert N. et Drossel B. 2015.

Equazioni differenziali per reazioni chimiche

Consideriamo l’equazione utilizzata dagli autori per la reazione catalizzata dalla lattato deidrogenasi (la trasformazione del piruvato in lattato, figura 2) dove si è anche tenuto conto dell’efflusso di lattato dal citosol. L’equazione differenziale è la seguente:

dove i tre parametri sono determinati sperimentalmente e i loro valori sono

Il primo descrive l’attività dell’enzima lattato deidrogenasi: più questo parametro è elevato, più l’enzima è attivo. Il secondo descrive la reazione inversa (dal lattato al piruvato). Il terzo è una misura di quanto lattato la cellula è in grado di trasportare al di fuori della sua membrana. Forse il lettore si è reso conto che l’equazione del lattato è una equazione differenziale ordinaria del primo ordine. Si dice “primo ordine” perché nell’equazione compare solo la derivata prima della funzione che dobbiamo determinare (lattato, in questo caso); “ordinario” si riferisce al fatto che il lattato è funzione di una sola variabile (il tempo, in questo caso). Si vede immediatamente che un’equazione come questa può essere scritta come segue:

Supponiamo ora di avere altre due equazioni differenziali di questo tipo, una per il piruvato e una per i protoni (le altre due funzioni del tempo che sono presenti nell’equazione):

Allora avremmo un sistema di tre equazioni differenziali ordinarie come questo:

I valori iniziali delle funzioni che dobbiamo determinare sono raccolti nell’ultima riga: questi sono i valori che le funzioni incognite assumono all’inizio della simulazione (t = 0). In questo caso, questi valori sono le concentrazioni di lattato, piruvato e protoni nel citosol, a riposo. Le tre funzioni del tempo sono chiamate la soluzione del sistema. Questo tipo di sistema di equazioni è un esempio di problema di Cauchy, e sappiamo dalla teoria matematica che non solo ha una soluzione, ma che questa soluzione è unica. Inoltre, mentre questa soluzione  può non essere sempre facilmente trovata con metodi rigorosi, è abbastanza facile risolvere il problema con metodi approssimati, come il  metodo di Runge-Kutta o il metodo di Heun. Detto questo, il sistema di equazioni differenziali ordinarie proposto da Lengert e Drossel per il metabolismo energetico è proprio come quello qui sopra, con l’eccezione che comprende 15 equazioni anziché tre. Quindi, la principale difficoltà in questo tipo di simulazione non è l’aspetto computazionale, ma la determinazione dei parametri (come quelli enzimatici) e dei valori iniziali, che devono essere raccolti dalla letteratura medica o devono essere determinati sperimentalmente, se non sono già disponibili. L’altro problema è come progettare le equazioni: esistono spesso diversi modi per costruire un modello matematico di una reazione chimica o di qualsiasi altro processo biologico.

Il modello matematico della ME/CFS

Come adattiamo ai pazienti ME/CFS un modello del metabolismo energetico che è stato impostato con parametri presi da esperimenti condotti su soggetti sani? Questa è un’ottima domanda, e abbiamo visto che Robert Phair ha dovuto usare i dati genetici dei pazienti ME/CFS relativi agli enzimi chiave del metabolismo energetico, al fine di impostare il suo modello. Ma questi dati non erano disponibili quando Lengert e Drossel hanno progettato le loro equazioni. E allora? I due fisici hanno cercato studi sulla fosforilazione ossidativa nei pazienti ME/CFS e hanno scoperto che qusto processo cellulare era stato misurato con diverse impostazioni sperimentali e da diversi gruppi e che il denominatore comune di tuti gli studi era una riduzione di funzione che andava da circa il 35% (Myhill S et al. 2009), (Booth, N et al 2012), (Argov Z. et al. 1997), (Lane RJ. et al. 1998) a circa il 20% (McCully KK. et al. 1996), (McCully KK. et al. 1999). Quindi l’idea degli autori è stata di moltiplicare i parametro enzimatici di ciascuna reazione appartenente alla fosforilazione ossidativa per un numero compreso tra 0,6 (grave ME / CFS) a 1,0 (persona sana). In particolare, i due fisici hanno scelto un valore di 0,7 per la ME/CFS, nei loro esperimenti in silico (cioè esperimenti virtuali condotti nel processore di un computer).

Previsioni del modello matematico

Il modello matematico è stato utilizzato per eseguire prove di esercizio in silico con varie lunghezze e intensità. Quello che Lengert e Drossel hanno trovato è stato che il tempo di recupero nel paziente ME/CFS medio era sempre maggiore se confrontato con quelli di una persona sana. Il tempo di recupero è definito come il tempo necessario affinché una cellula ripristini il suo contenuto di ATP (97% del livello in stato di riposo) dopo lo sforzo. Nella figura 3 si vedono i risultati della simulazione per un esercizio molto breve (30 secondi) e molto intenso. Come potete vedere, nel caso di una cellula sana (a sinistra) il tempo di recupero è di circa 600 minuti (10 ore) mentre una cellula di una persona con ME/CFS (a destra) richiede più di 1500 minuti ( 25 ore) per recuperare.

Un altro risultato interessante della simulazione è un aumento di AMP nei pazienti rispetto al controllo (figura 3, linea arancione). Ciò è dovuto all’uso compensativo delle due vie metaboliche in figura 4: la reazione catalizzata dall’adenilato chinasi, in cui due molecole di ADP sono utilizzate per produrre una molecola di ATP e una molecola di AMP; e la reazione catalizzata dalla deaminasi AMP, che degrada AMP in IMP (che viene quindi convertito in inosina e ipoxantina). Queste due reazioni sono utilizzate dai pazienti ME/CFS più che dal controllo sano, al fine di aumentare la produzione di ATP al di fuori dei mitocondri.

Se diamo un’occhiata più da vicino alle concentrazioni di AMP e IMP nelle 4 ore successive allo sforzo (figura 5), vediamo effettivamente una maggiore produzione di IMP (linea verde) e AMP (linea arancione) nei muscoli scheletrici dei pazienti (destra) rispetto ai controlli (sinistra).

Un’ulteriore via di compensazione utilizzata dai pazienti (secondo questo modello) è la produzione di ATP da ADP da parte dell’enzima creatina chinasi (figura 6). Questo è un altro modo che abbiamo per produrre ATP nel citosol senza l’aiuto dei mitocondri. In questo modello di ME/CFS, vi è un aumento nell’uso di questo percorso, che porta a una diminuzione della concentrazione cellulare di fosfocreatina e un aumento della concentrazione cellulare di creatina (figura 7).

# Mark Davis e il test immunitario universale

1. Introduzione

Queste sono solo alcune note raccolte dal discorso che Mark Davis ha pronunciato in occasione del Community Symposium tenutosi nell’agosto scorso (2017) a Stanford (video). Nei paragrafi 2, 3, 4 e 5 introdurrò alcune nozioni di base sui recettori delle cellule T (T cell receptors: TCR); il paragrafo 6, attraverso riferimenti  al video già menzionato e a tre articoli pubblicati da Davis et al. nel corso degli ultimi quattro anni, descrive una  nuova tecnologica sviluppata da Mark Davis e colleghi. Questi cenni preliminari dovrebbero auspicabilmente fornire i mezzi per comprendere a pieno la portata dei dati pilota presentati da Mark Davis a proposito dell’attività delle cellule T nella ME/CFS (paragrafo 7) e nella malattia di Lyme cronica (paragrafo 8), mostrando perché tale tecnologia prometta di divenire una sorta di test universale per qualsiasi tipo di infezione o patologia autoimmune, nota o sconosciuta.

2. Cellule T

I linfociti T sono una tipologia di leucociti (o globuli bianchi), vale a dire la componente cellulare del nostro sistema immunitario. La gran parte dei linfociti T in circolo è rappresentata da linfociti T helper (T helper cells: Th cells)  e da linfociti T citotossici (cytotoxic T lymphocytes: CTL). Mentre la funzione dei linfociti T helper è quella di regolare l’attività degli altri leucociti attraverso la produzione di un’ampia gamma di trasmettitori chimici (le citochine, cytokines), le CTL sono coinvolte direttamente nella soppressione delle cellule ospiti infette. I linfociti T appartengono al ramo cosiddetto adattivo del sistema immunitario, assieme alle cellule B (le fabbriche di anticorpi), e, in quanto tali, il loro compito è quello di garantire una difesa specifica, su misura, contro gli agenti patogeni: per contrastare uno specifico agente patogeno, il nostro sistema immunitario può schierare in campo non solo anticorpi specifici ma anche specifici linfociti T (Th cells e CTL). Il ramo innato del sistema immunitario, invece, (nel quale rientrano le cellule natural killer, i macrofagi, le cellule dendritiche, i mastociti…) è in grado di fornire soltanto una difesa aspecifica, una prima linea di risposta immunitaria.

3. Recettori dei linfociti T

I linfociti T sono in grado di andare alla ricerca di specifici patogeni grazie a una molecola espressa sopra la propria superficie, chiamata recettore del linfocita T (TCR). Nella figura 1 si può vedere una schematica rappresentazione del TCR e del meccanismo in virtù del quale il linfocita T riconosce il proprio target. Gli antigenti (proteine) degli agenti patogeni vengono indicati ai linfociti T da altre cellule del nostro corpo: vengono esposte sopra molecole chiamate Complesso Maggiore di Istocompatibilità (MHC), che si trova espresso sulla membrana esterna. Se un dato antigene mostra compatibilità con il TCR di uno specifico linfocita T, tale linfocita T si attiva e comincia a proliferare (espansione clonale, clonal expansion). Le due catene principali (α and β) sono assemblate combinando la trascrizione di segmenti di gene, ognuno dei quali ha copie multiple, leggermente diverse fra loro: in altre parole, i TCR vengono assemblati a partire da peptidi scelti a caso da un insieme di diverse alternative possibili. Questo comporta un repertorio di 10^15 diversi possibili TCR  (Mason DA 1998). Ogni linfocita T mostra un solo tipo di TCR.

4. Cellule T helper

Le cellule T helper sono programmate per riconoscere esclusivamente antigeni esposti dalle molecole MHC di seconda classe (II): questa classe di MHC viene espressa sulla membrana esterna di alcuni leucociti, principalmente le cellule dendritiche, le cellule B e i macrofagi (tutte assieme dette “cellule che presentano l’antigene”, antigen presenting cells: APC). Le molecole MHC II legano il TCR delle cellule T helper grazie al peptide CD4 (espresso unicamente dalle cellule T helper). L’antigene presentato dalle molecole MHC è un peptide lungo 13-17 amminoacidi (Rudensky, et al., 1991) (figura 2).

5. Linfociti T citotossici

I TCR espressi dai linfociti T citotossici (CTL) possono legare solo antigeni esposti dalle molecole MHC di prima classe (I), che si trovano nella membrana esterna di qualunque cellula del nostro corpo. La glicoproteina CD8 è la molecola che rende i TCR espressi dalle CTL specifici per il MHC I. Mentre gli antigeni esposti dalle APC appartengono a patogeni raccolti sul campo di battaglia di passate infezioni, i peptidi esposti dal MHC I di una specifica cellula appartengono a patogeni che hanno fatto ingresso nella cellula stessa, e pertanto costituiscono la prova di un’infezione intracellulare ancora in atto (figura 3). Nel momento in cui un CTL riconosce un antigene che combacia con il proprio TCR, il CTL iduce l’apoptosi (morte programmata) della cellula che mostra l’antigene. Gli antigeni esposti dal MHC I sono peptidi che vanno dagli 8 ai 10 amminoacidi (Stern, et al., 1994).

Figure 3. Una cellula infetta espone un antigene virale sul proprio MHC I. Il TCR di un CTL si lega a questo peptide ed invia un segnale interno diretto al suo proprio nucleo, il quale risponde attivando l’apoptosi (attraverso il rilascio di granzimi, ad esempio) della cellula infetta (disegno di Paolo Maccallini).6. Il test immunologico universale

Nel corso del suo discorso, Mark Davis illustra alcuni concetti base sul sistema immunitario, prima di passare a introdurre i nuovi, entusiasmanti dati riguardo alla ME/CFS e alla Lyme cronica (o post-treatment Lyme disease syndrome: PTLDS). Contestualmente, però, dedica alcuni minuti alla descrizione di un complesso nuovo test che teoricamente renderebbe possibile estrapolare tutte le informazioni contenute nel repertorio di TCR presenti – in un dato momento – nel sangue di un essere umano. Un test del genere – che chiamerei “test immunologico universale” – avrebbe la capacità di determinare se un paziente presenta un’infezione in corso (e, nel caso, indicare il patogeno coinvolto) oppure una malattia autoimmune (anche qui specificando la natura dell’autoantigene, ossia il tessuto attaccato dal sistema immunitario). A quanto mi è dato di comprendere, il test richiede tre passaggi, che elenco nelle sezioni seguenti.

6.1. Primo step: sequenziamento del TCR

Come già spiegato nel paragrafo 3, quando un linfocita T incontra un peptide a cui è compatibile, comincia a proliferare; pertanto, nel sangue di un paziente con infezione in corso (o con reazione contro il proprio organismo, cioè con reazione autoimmune) è possibile trovare molteplici copie di linfociti T che esprimono il medesimo TCR: a differenza dei controlli sani, nei quali circa il 10% delle CD8 totali è rappresentato da copie di pochi diverse linfociti T (figura 4, prima linea), nei pazienti affetti da Lyme incipiente –  un esempio di infezione attiva – o sclerosi multipla (MS) –  un esempio di malattia autoimmune – abbiamo una massiccia clonazione di alcune linee di CTL (figura 5, seconda e terza riga, rispettivamente). Il primo step del test immunologico universale starà allora nell’identificazione dell’esatta sequenza di TCR espressa dai linfociti T presenti nel sangue, come si legge in Han A et al. 2014, dove troviamo descritto il sistema per sequenziare i geni delle catene α e β di un dato linfocita T. Tale approccio permette di costruire grafici come quello in figura 4 e quindi permette di determinare se il paziente presenti in atto un’attività anomala dei linfociti T oppure no. Qualora si riscontri un fenomeno di espansione clonale, è legittimo ipotizzare che stia avendo luogo o un’infezione o una condizione autoimmune di qualche sorta.

6.2. Secondo step: raggruppamento dei TCR

Mark Davis e colleghi hanno realizzato un software capace di identificare i TCR che condividono il medesimo antigene, sia in un singolo individuo che trasversalmente a un gruppo. L’algoritmo è stato denominato GLIPH (grouping of lymphocyte interaction by paratope hotspots) ed ha dato prova di poter raggruppare – per fare un esempio – i recettori  dei linfociti T CD4 di 22 soggetti con infezione da M. tuberculosis latente in 16 gruppi distinti, ognuno dei quali comprende TCR provenienti da almeno tre individui (Glanville J et al. 2017). Cinque di questi gruppi sono riportati nella figura 5. L’idea sottostante è che TCR che appartengono allo stesso raggruppamento reagiscano allo stesso complesso peptide-MHC (pMHC).

6.3. Terzo step: ricerca degli epitopi

Come abbiamo visto, questa nuova tecnologia consente di rilevare se sia in atto un’espansione clonale di linfociti T sequenziando i TCR dal sangue periferico. Consente inoltre di raggruppare i TCR presenti in un singolo paziente o condivisi da più pazienti. Il passaggio successivo è quello di identificare a quale/i tipo/i di antigene ognuno di questi raggruppamenti reagisca. Infatti, se potessimo identificare degli antigeni comuni in un gruppo di pazienti dai sintomi comparabili nei quali si riscontri un’espansione clonale in atto e simili TCR, saremmo messi in grado di comprendere se il loro sistema immunitario stia attaccando un patogeno (e di identificare il patogeno) o se stia piuttosto attaccando un tessuto ospite e, qualora fosse questo il caso, di identificare il tessuto. Come già detto, il numero di possibili combinazioni per il materiale genetico dei TCR è calcolato attorno ai 10^15, ma il numero dei possibili epitopi di cellule Th è circa 20^15, che corrisponde a più di 10^19. Ciò implica che i TCR debbano essere in una qualche misura cross-reattivi se vogliono essere in grado di riconoscere tutti i possibili peptidi esposti dai MHC (Mason DA 1998). Il grado di tale cross-reattività e il meccanismo attraverso il quale viene ottenuta sono stati spiegati con esattezza da Mark Davis e colleghi in un recente articolo (Birnbaum ME et al. 2014), che ci fornisce il terzo step del test immunologico universale. Lo scopo di questa fase consiste nel prendere un dato TCR e trovare il repertorio dei suoi specifici antigeni (giova ripetere che, appunto, ogni TCR reagisce a più antigeni). Per comprendere come ciò sia possibile, guardiamo a uno degli esperimenti descritti nell’articolo più sopra citato. I ricercatori si sono concentrati su due TCR ben precisi (chiamati Ob.1A12 e Ob.2F3), clonati da un paziente con MS e noti per essere capaci di riconoscere i pepetidi 85-99 (figura 6) della proteina basica della mielina (MBP) esposti dall’ HLA-DR15. Hanno poi preparato un insieme di cellule di lievito che esprimono molecole HLA-DR15, ognuna caratterizzata da un diverso peptide formato da 14 amminoacidi, con amminoacidi fissi esclusivamente alle posizioni 1 e 4, dove il peptide è ancorato al MHC (figura 6, sinistra). Quando alla coltura di cellule di lievito  che esprimono complessi pMHC vengono aggiunte copie di Ob.1A12, queste legano solo con alcune di quelle e, come è possibile vedere dalla parte destra della figura 6, per ciascuna posizione degli epitopi legati dal Ob.1A12 esiste un amminoacido con maggior tasso di probabilità: ad esempio, l’epitopo Ob.1A12 tipico presenta preferibilmente alanina (A) in posizione -4, istidina (H) in posizione -3, arginina (R) in posizione -2, e così via. Da notare che istidina (H) in posizione 2 e fenilanina (F) in posizione 3 sono amminoacidi obbligatori per un epitopo di  Ob.1A12.

La tabella sulla destra della figura 6 rappresenta, infatti, una tabella di sostituzione (substitution matrix) di dimensioni 14×20, uno strumento impiegato per scansionare il database dei peptidi in modo da trovare, tra tutti i peptidi conosciuti espressi da creature viventi, tutti i possibili epitopi specifici per Ob.1A12. Le matrici di sostituzione vengono solitamente utilizzate nel cosiddetto allineamento di peptidi (peptide alignment), una tecnica che punta all’identificazione di similitudini tra peptidi. Tali matrici sono basate su considerazioni di tipo evoluzionistico (Dayhoff, et al., 1978) o sullo studio delle regioni conservate delle proteine (Henikoff, et al., 1992). Ma la ricerca degli epitopi specifici di un dato TCR richiede (come abbiamo visto per Ob.1A12) una matrice di sostituzione costruita ad hoc per quel TCR: ogni TCR richiede la propria matrice di sostituzione, ottenuta incubando cellule T esprimenti quel TCR con una coltura di lieviti che espongono sui propri MHC una grande varietà di peptidi casuali, e analizzando poi i dati ricavati dall’esperimento. Quindi, un processo piuttosto complesso! Nel caso di Ob.1A12, questo processo ha portato a 2330 peptidi (incluso MBP), mentre la matrice di sostituzione specifica per Ob.2F3 ha trovato 4824 epitopi all’interno dell’intero database di peptidi. Questi peptidi includevano sia proteine non umane (batteriche, virali…) che peptidi umani. Per 33 di loro (26 non umani e 7 proteine umane), questo gruppo di ricercatori ha eseguito un test per verificare direttamente la previsione: 25/26 dei peptidi ambientali e 6/7 dei peptidi umani hanno indotto la proliferazione di cellule T che esprimono il TCR Ob.1A12 e/o il Ob.2F3, e questa è una prova della validità di questa analisi! Questi 33 peptidi sono riportati nella figura 7. Questo è l’ultimo passaggio del test immunitario universale, quello che dal TCR conduce agli epitopi. Come avete visto, un enorme insieme di diversi peptidi da diverse fonti reagisce con un singolo tipo di TCR; in altre parole, la cross-reattività è una proprietà intrinseca del TCR. Ciò significa anche che i test di trasformazione linfocitaria (LTT), ampiamente utilizzati in Europa per l’individuazione di infezioni da Borrelia burgdorferi e altri patogeni, comportano un rischio elevato di risultati falsi positivi e richiedono un processo di validazione sperimentale e teorica, attualmente mancante.

Siamo ora pronti ad apprezzare appieno i dati pilota che Mark Davis ha presentato al Symposium sull’espansione clonale delle cellule T CD8 nella ME/CFS e nella Lyme cronica.

7. “We have a hit!”

Mark Davis, insieme a Jacob Glanville e José Montoya, hanno sequenziato TCR dal sangue periferico di 50 pazienti ME/CFS e 49 controlli (primo passo del test immunitario universale, ricordate?), quindi li hanno raggruppati usando l’algoritmo GLIPH (secondo passo). Hanno trovato 28 cluster, ciascuno costituito da più di 2500 sequenze simili, e ogni cluster raccoglie sequenze multiple dallo stesso individuo e sequenze (che sono forse più importanti) da pazienti diversi (figura 8). Il cluster che ho cerchiato in rosso, ad esempio, è una raccolta di oltre 3000 TCR simili. La presenza di questi ampi cluster nei pazienti ME/CFS, rispetto ai controlli sani, rappresenta una prova indiretta di una risposta specifica delle cellule T a un trigger comune in questo gruppo di pazienti, che potrebbe essere un agente patogeno o un tessuto del corpo (o tutti e due).

Tra questi 50 pazienti ME/CFS, Davis e colleghi hanno selezionato 6 pazienti con geni HLA simili (figura 9, sinistra), 5 femmine tra loro, e hanno studiato i loro TCR più in profondità. Nella metà destra della figura 9, è possibile vedere per ciascun paziente il grado di espansione clonale delle CTL. Ricordate che nei controlli sani solo circa il 10% dei CTL è composto da cloni di alcune cellule (figura 4, prima riga), mentre qui vediamo che circa il 50% di tutti i CTL è composto da cloni. Quindi, una “marcata espansione clonale” delle cellule T CD8, come ha detto Davis.

Le sequenze delle catene α e β dei TCR di tre dei sei pazienti (pazienti L4-02, L4-10 e L3-20) sono riportate in figura 10 (ho verificato che effettivamente si tratta di catene α e β di TCR umani, inserendole in BLAST).

Quindi, abbiamo visto finora i primi due passaggi del test immunitario universale. E il terzo passo? Nel suo discorso, Mark Davis non ha presentato alcun particolare epitopo, ha solo mostrato una diapositiva con quella che probabilmente è la selezione degli epitopi dalla libreria discussa nel paragrafo 6.3 da parte di uno dei TCR riportati in figura 10. Questa selezione è riportato in figura 11, ma da quella foto non è possibile raccogliere alcuna informazione sull’identità di questi epitopi. Come probabilmente ricorderete dal paragrafo 6.3, l’analisi dei peptidi selezionati da un TCR nella libreria di peptidi  consente l’identificazione di una matrice di sostituzione che può essere utilizzata per selezionare tutti i possibili epitopi di quel TCR specifico, dal database delle proteine. Quest’ultima fase cruciale deve essere ancora eseguita, o è già stata eseguita, ma Davis non ha comunicato i risultati preliminari durante il suo discorso. Recentemente sono state messe a disposizione nuove risorse dalla Open Medicine Foundation, affinché questa ricerca promettente possa essere ulteriormente perseguita (R). Lo scopo qui, come già detto, è di trovare l’antigene che innesca questa risposta delle cellule T. Come ha detto Mark Davis, potrebbe essere un antigene di un agente patogeno specifico (forse un patogeno comune che va e viene) che suscita una risposta immunitaria anomala che finisce per colpire alcuni tessuti ospiti (microglia, per esempio), portando così attivazione immunitaria che è stata recentemente segnalata da Mark Davis stesso e altri in ME/CFS (Montoya JG et al. 2017). L’idea di un patogeno comune che innesca una risposta immunitaria patologica non è nuova in medicina, e la febbre reumatica (RF) è un esempio di una tale malattia: la RF è una malattia autoimmune che attacca il cuore, il cervello e le articolazioni ed è generalmente innescata da uno streptococco che infetta la gola (Marijon E et al. 2012). L’altra possibilità è, naturalmente, quella di un’infezione in corso di qualche tipo, che deve ancora essere rilevata. Come detto (vedi par. 6.1), l’espansione clonale delle cellule T CD8 è presente sia nelle infezioni acute (come la malattia di Lyme) che nelle malattie autoimmuni (come la SM) (figura 4), quindi dobbiamo aspettare l’identificazione dell’antigene se vogliamo capire se l’attività del CTL è contro un agente patogeno e/o contro un tessuto del nostro corpo.

8. La Lyme cronica esiste

È stato probabilmente trascurato il fatto che nel suo discorso, Mark Davis ha riportato anche dati molto interessanti sulla sindrome della malattia di Lyme post-trattamento (PTLDS, nota anche come malattia di Lyme cronica). In particolare, ha trovato un’espansione clonale marcata nelle cellule T CD8 di 4 pazienti PTLDS (circa il 40% dei CTL totali) come riportato nella figura 12: si consideri che in questo caso le fette blu rappresentano cellule T uniche, mentre tutte le altre fette rappresentano cloni! Tutto ciò che è stato detto sull’espansione clonale CD8 nella ME/CFS si applica anche in questo caso: potrebbe essere la prova di un’infezione in corso – forse la stessa B. burgdorferi, come suggerito da diversi modelli animali (Embers ME et al. 2017), (Embers ME et al. 2012), (Hodzic E et al. 2008), (Yrjänäinen H et al. 2010) –  o una coinfezione (un virus?). Oppure potrebbe essere l’espressione di una reazione autoimmune innescata dalla infezione iniziale. Questo deve ancora essere scoperto, eseguendo il test immunitario universale completo, ma ciò che è già chiaro dalla figura 12 è che la PTLDS è una condizione reale, con qualcosa di veramente anomalo nella risposta immunitaria: la Lyme cronica esiste.

9. Conclusioni

Mark Davis e altri ricercatori hanno sviluppato un test complesso che è in grado di sequenziare i TCR dai pazienti, raggrupparli in gruppi di TCR che reagiscono agli stessi antigeni e scoprire gli antigeni che hanno attivato quella particolare risposta delle cellule T. Questo test è una sorta di test immunitario universale che è teoricamente in grado di riconoscere se una persona (o un gruppo di pazienti) presenta una risposta immunitaria contro un agente patogeno o contro uno dei loro stessi tessuti (o entrambe le cose). Questo approccio ha già fornito dati pilota su una attivazione anomala delle cellule T CD8 nei pazienti ME/CFS e nei pazienti PTLDS e, si spera, identificherà il trigger di questa risposta immunitaria nel prossimo futuro. Se la ME/CFS è causata da un’infezione attiva, da una malattia autoimmune o da entrambe le cose, il test immunologico universale potrebbe essere in grado di dircelo. Questa nuova tecnologia è per l’immunologia, ciò che il sequenziamento dell’intero genoma è per la genetica, o la metabolomica è per le malattie molecolari: non cerca un particolare agente patogeno o una particolare malattia autoimmune. No, cerca tutte le possibili infezioni e disturbi immunitari, anche quelli che devono ancora essere scoperti.

# 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.

# Convegno nazionale sulla ME/CFS, Paolo Maccallini

Quello che segue è il mio intervento durante il convegno nazionale sulla ME/CFS tenutosi a Thiene,  tappa italiana dell’End ME/CFS Worldwide Tour. L’intervento è molto denso e veloce, ho dovuto condensare 4 anni di ricerche in 30 minuti. Qualcuno ha notato che sembravo dopato. Lo ero, letteralmente: ero alla fine di un lungo trattamento cortisonico e avevo assunto modafinil per la circostanza. Altrimenti non sarei riuscito. Le slide usate per questo intervento, insieme ad altre che non ho avuto modo di far vedere al convegno, sono disponibili qui. Sotto il video c’è un sommario del contenuto dell’intervento, e il momento del video in cui i vari argomenti sono stati trattati. Ringrazio Giuseppe Pozza per aver realizzato il video.

• Note biografiche, criteri diagnostici e disturbi cognitivi (00:50)
• Intolleranza ortostatica (08:39)
• Citochine (09:45)
• Citotossicità delle NK (11:15)
• Disfunzioni metaboliche (12:40)
• Anomalie del sistema nervoso centrale (20:50)
• Anomalie del microbioma (22:40)
• Analisi genetica (24:20)
• La mia ricerca su Lyme e autoanticorpi (27:50)
• Anticorpi anti-muscarinici e anti-beta adrenergici nella M/CFS (32:20)
• Studi a cui ho partecipato come paziente, conclusioni e ringraziamenti (34:53)

Riascoltando il mio intervento ho provato stupore nel constare, forse per la prima volta, come la mia mente sia sopravvissuta. Solo io posso sapere cosa ho passato, nessuno sa che per gli ultimi 17 anni sono stato incapace di pensare per più del 90% del tempo. E non intendo incapace di risolvere sistemi di equazioni differenziali; no, intendo incapace di sostenere una conversazione o di leggere un libro.

Nonostante sia stata così colpita dalla malattia, nonostante sia stata privata di stimoli, nostante la solitudine estrema, i farmaci inutili e il consumarsi dei lustri, è sopravvissuta. Questo organo di un chilo e mezzo scarso che mi contiene tutto, che ha perso così tanto, che ho dato per spacciato tante volte, è sopravvissuto. La vita vuole vivere e non si arrende.

# ME/CFS, a mathematical model

Robert Phair and the trap

Many of my readers are probably aware of the attempts that are currently being made to mathematically simulate the energy metabolism of ME/CFS patients, integrating metabolic data with genetic data. In particular, dr. Robert Phair has developed a mathematical model of the main metabolic pathways involved in energy conversion, from energy stored in the chemical bonds of big molecules like glucose, fatty acids, and amino acids, to energy stored in adenosine triphosphate (ATP), ready to use. Phair, who is an engineer, determined the differential equations that rule this huge amount of chemical reactions and adapted them to the genetic profile found in ME/CFS patients. Genetic data are the result of the analysis of the exomes of more than 40 patients (I am among them). He found, in particular, that the enzyme ATP synthase (one step of the mitochondrial electron transport chain, called complex V) presents a damaging variant that is found in less than 30% of healthy controls while being detected in more than 60% of ME/CFS patients. He found other enzymes (I don’t know their exact number) of energy metabolism meeting these same criteria. Setting a reduced activity for these enzymes in his mathematical model, he found that, after particularly stressing events (such as infections), ME/CFS patients metabolism can fall into a low-energy state without being able to escape from it (this theory has been called the “metabolic trap hypothesis”) (R, R, R). Phair’s hypothesis is currently being experimentally tested and has not been published yet, but some years ago two physicists published an interesting mathematical model of energy metabolism during and after exercise, in ME/CFS patients compared to healthy controls (Lengert N. et Drossel B. 2015). In what follows I will describe this model and its predictions and we will also see how these differential equations look like.

Metabolic pathways that have been analyzed

The model by Lengert and Drossel extends two previously published systems of differential equations that describe the behaviour of glycolysis, Krebs cycle (hugely simplified as a single reaction!), mitochondrial electron transport chain (described in detail), creatine kinase system, and of conversion of adenosine diphosphate (ADP) in ATP, in skeletal muscles (Korzeniewski B. et Zoladz JA. 2001), (Korzeniewski B. et Liguzinski P. 2004). They added equations for lactate accumulation and efflux out of the cell, for de novo synthesis of inosine monophosphate (IMP) during recovery, for degradation of adenosine monophosphate (AMP) into IMP, for degradation of IMP to inosine and hypoxanthine. All the pathways involved are collected in figure 1. These reactions are described by 15 differential equations and the solution is a set of 15 functions of time that represent the concentration of the main metabolites involved (such as lactate, pyruvate, ATP etc). Let’s now take a closer look at one of these equations and at the general structure of the whole system of equations.

Figure 1. This is a schematic representation of the metabolic pathways described by the mathematical model developed by Lengert and Drossel. In detail: cytosolic synthesis and degradation of ADP, AMP and IMP (left), protein kinase pathway and glycolysis (centre), electron transport chain and TCA cycle (right). From Lengert N. et Drossel B. 2015.

Differential equations for chemical reactions

Let’s consider the equation used by the Authors for the reaction catalyzed by lactate dehydrogenase (the transformation of pyruvate into lactate, figure 2) where they also took into account the efflux of lactate from the cytosol. The differential equation is the following one:

where the three parameters are experimentally determined and their values are

The first one describes the activity of the enzyme lactate dehydrogenase: the higher this parameter is, the more active the enzyme is. The second one describes the inverse reaction (from lactate to pyruvate). The third one is a measure of how much lactate the cell is able to transport outside its membrane. You have probably recognized that the equation of lactate is a first-order ordinary differential one. We say “first-order” because in the equation there is only the first-order derivative of the function that we have to determine (lactate, in this case); “ordinary” refers to the fact that lactate is a function only of one variable (time, in this case). You can easily realize that an equation like that can be written as follows:

Suppose now that we had other two differential equations of this type, one for pyruvate  and one for protons (the other two functions of time that are present in the equation):

We would have a system of three ordinary differential equations like this one

The initial values of the functions that we have to determine are collected in the last row: these are the values that they have at the beginning of the simulation (t=0). In this case, these values are the concentrations of lactate, pyruvate and protons in the cytosol, at rest. The three functions of time are called the solution to the system. This kind of system of equations is an example of a Cauchy’s problem, and we know from mathematical theory that not only it has a solution, but that this solution is unique. Moreover, whereas this solution can’t be always easily found with rigorous methods, it is quite easy to solve the problem with computational methods, like the Runge-Kutta method or the Heun’s method. All that said, the system of ordinary differential equations proposed by Lengert and Drossel for energy metabolism is just like this one, with the exception that it comprises 15 equations instead of three. So, the main difficulty in this kind of simulation is not the computational aspect but the determination of the parameters (like the enzymatic ones) and of the initial values, that have to be collected from the medical literature or have to be determined experimentally, if not already available. The other problem is how to design the equations: there are several ways to build a model for a chemical reaction or for any other biological process.

The mathematical model of ME/CFS

How do we adapt to ME/CFS patients a model of energy metabolism that has been set with parameters taken from experiments performed on healthy subjects? This is a very good question, and we have seen that Robert Phair had to use genetic data from ME/CFS patients on key enzymes of energy metabolism, in order to set his model. But this data was not available when Lengert and Drossel designed their equations. So what? They looked for studies about the capacity of oxidative phosphorylation in ME/CFS patients in comparison with healthy subjects, and they found that it had been measured with different experimental settings by various groups and that the common denominator was a reduction ranging from about 35% (Myhill S et al. 2009), (Booth, N et al 2012), (Argov Z. et al. 1997), (Lane RJ. et al. 1998) to about 20% (McCully KK. et al. 1996), (McCully KK. et al. 1999). So the idea of the Authors was to multiply the enzymatic parameter of each reaction belonging to the oxidative phosphorylation by a number ranging from 0.6 (severe ME/CFS) to 1.0 (healthy person). In particular, they used a value of 0.7 for ME/CFS, in their in silico experiments.

Predictions of the mathematical model

The mathematical model was used to perform in silico exercise tests with various length and intensities. What they found was that the time of recovery in the average ME/CFS patient was always greater if compared to a healthy person. The time of recovery is defined as the time that a cell needs to replenish its content of ATP (97% of the level in resting state) after exertion. In Figure 3 you see the results of the simulation for a very short (30 seconds) and very intense exercise. As you can see, in the case of a healthy cell (on the left) the recovery time is of about 600 minutes (10 hours) whereas a cell from a person with ME/CFS (on the right) requires more than 1500 minutes (25 hours) to recover.

Another interesting result of the simulation is an increase in AMP in patients vs control (figure 3, orange line). This is due to the compensatory use of the two metabolic pathways in figure 4: the reaction catalyzed by adenylate kinase, in which two molecules of ADP are used to produce a molecule of ATP and a molecule of AMP; and the reaction catalyzed by AMP deaminase, that degrades AMP to IMP (that is then converted to inosine and hypoxanthine). These two reactions are used by ME/CFS patients more than in healthy control, in order to increase the production of ATP outside mitochondria.

If we give a closer look at the concentrations of AMP and IMP in the 4 hours following the exertion (figure 5), we actually see an increased production of IMP (green line) and AMP (orange line) in skeletal muscles of ME/CFS patients (on the right) vs controls (left).

A further compensatory pathway used by patients (according to this model) is the production of ATP from ADP by the enzyme creatine kinase (figure 6). This is another way that we have on our cells to produce ATP in the cytosol without the help of mitochondria. In this model of ME/CFS, there is an increase in the use of this pathway, which leads to a decrease in the cellular concentration of phosphocreatine and an increase in the cellular concentration of creatine (figure 7).

Comparison with available metabolic data

I am curious to see if data from the various metabolomic studies done after the publication of the model by Lengert and Drossel are in agreement with it. I will discuss this topic in another article because I still have to study this aspect. I would just point out that if we assumed true the high rate of IMP degradation proposed in this model, we would probably find a high level of hypoxanthine in the blood of patients, compared to controls, whereas this metabolite is decreased in patients, according to one study (Armstrong CW et al. 2015).

Comparison with the model by Phair

The metabolic model by Robert Phair will probably give a more accurate simulation of the energy metabolism of patients if compared with the system of ordinary differential equations that we have discussed in this article. And there are two reasons for that. The first one is that Phair has included equations also for fatty acid beta-oxidation, pentose phosphate pathway, and NAD synthesis from vitamin niacin. The other one is that, whereas the two German physicists reduced the velocities of all the enzymatic reactions that happen in mitochondria, Phair has genetic data for every enzyme involved in these reactions for a group of ME/CFS patients and thus he can determine and set the actual degree of activity for each enzyme. But there is a further level required in order to bring the mathematical simulation closer to the reality: gene expression. We know, for instance, that in ME/CFS patients there is a higher than normal expression of aconitase (an enzyme belonging to the TCA cycle) and of ATP synthase (Ciregia F et al 2016) and this should be taken into account in a simulation of ME/CFS patients energy metabolism. Note that ATP synthase is exactly the enzyme that Phair has found to be genetically damaged in patients, and this makes perfect sense: if an enzyme has reduced activity, its reaction can be speeded up by expressing more copies of the enzyme itself.

One could expect that, in a near future, genetic data and gene expression data from each of us will be used to set mathematical models for metabolic pathways, in order to build a personalized model of metabolism that might be used to define, study and correct human diseases in a personalized fashion. But we would need a writer of sci-fiction in order to tell this chapter of the future of medicine.

# Three new possible autoepitopes in ME/CFS

Paolo Maccallini

Abstract

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.

Introduction

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, I performed a bioinformatical analysis on experimental data from this study in order to extract hidden information. I 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. I 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.

Methods

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, I 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 I 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 I have used a theoretical model built using murine M3 ChR (PDB ID: 4DAJA) as a template, provided by ModBase.

Selection criteria. Our purpose is to predict to what epitopes of M3 and M4 ChRs sera from ME/CFS patients react. So I 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.

Results

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.

Discussion

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.

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 has 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.

Conclusion

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.

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.

# Is Carboxypeptidase N deficiency a contributing factor in ME/CFS and POTS?

Abstract

We present an attempt at exome analysis in two ME/CFS patients. Pt. 1 presents a mild form of carboxypeptidase N (CPN1) deficiency (a missense in exon 3) while Pt. 2 revealed two rare intronic variants in the same gene. CPN1 is an enzyme that inactivates kinins and complement proteins split products (such as C4a, a known anaphylatoxin). Therefore, CPN1 deficiency could explain C4a increase after exercise and mast cell abnormalities previously reported in ME/CFS. It could also explain the high prevalence of POTS in ME/CFS since kinins are vasodilators.

Introduction

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating disease characterized by cognitive deficits, fatigue, orthostatic intolerance with symptoms exacerbated after exertion (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 and gut flora (Blomberg J. et al. 2018), brain (Zeineh MM. et al. 2014). In this population of patients, several abnormalities have been found to be triggered by exercise, such as abnormal aerobic performance (Snell C. et al. 2013), enhanced gene expression of specific receptors (White AT. et al. 2012), abnormal gut flora translocation (Shukla SK et al. 2015) and failure in blood clearance of complement protein 4 split product A (C4a) (Sorensen B et al. 2003). An increase in C4a is part of the human physiologic response to physical exercise, but these levels return to baseline within 30 minutes to 2 hours (Dufaux B et al. 1991) while in ME/CFS there is a peak in serum C4a six hours after exertion. A possible explanation for slow C4a inactivation could be a problem in carboxypeptidase N (CPN1), an enzyme involved in the inactivation of C3a, C4a, C5a. CPN1 is required for kinins inactivation too, such as bradykinin, kalladin (Hugli T. 1978), (Plummer TH et Hurwitz MY 1978), that are vasodilators. We report on the case of a ME/CFS patient (Pt. 1) with a missense variant in CPN1 gene that is linked to reduced function of the enzyme and of another ME/CFS patient (Pt. 2) with rare variants in introns 1 and 6 of the same gene with uncertain significance (table 1, figure 1).

Materials and Methods

Whole exome sequencing (WES) has been performed on cells from the saliva of two ME/CFS patients, with an average 100X coverage (Dante Labs). The first search for pathogenic variants and insertions/deletions was performed with the software EVE, provided by Sequencing.com. A further refinement of the search was conducted by manual insertion of these SNPs in VarSome. The search for possible unknown pathogenic variants within the gene for CPN1 has been performed using Integrative Genome Viewer (IGV), an opensource tool for genetic data analysis.

Results

Results from the analysis of the two exomes performed with EVE and refined with VarSome are collected in table 2 (Pt. 1) and table 3 (Pt. 2).

Pt. 2 is carrier of a mitochondrial disease (table 3, first line): a missense in gene for medium-chain acyl-CoA dehydrogenase (MCAD) which leads to mild functional impairment of the enzyme involved in the oxidation of fatty acids (44% residual activity) (Koster KL. et al. 2014).

Pt. 2 is also homozygous for a variation in gene arylsulfatase A (ARSA) that is linked to a residual activity of only 10% of normal (Gomez-Ospina N. 2010). Arylsulfatase A deficiency (also known as metachromatic leukodystrophy or MLD) is a disorder of impaired breakdown of sulfatides (cerebroside sulfate or 3-0-sulfo-galactosylceramide), sulfate-containing lipids that occur throughout the body and are found in greatest abundance in nervous tissue, kidneys, and testes. Sulfatides are critical constituents in the nervous system, where they comprise approximately 5% of the myelin lipids. Sulfatide accumulation in the nervous system eventually leads to myelin breakdown (leukodystrophy) and a progressive neurologic disorder (Von Figura et al 2001). Nevertheless, this genotype does not cause MLD, and this benign condition of reduced ARSA activity is called ARSA pseudodeficiency. There are about 4 homozygotes in 1000 persons among non-Finnish Europeans (VarSome)

Pt. 1 is a carrier of a missense in gene CPN1 (table 2, first line) which leads to a loss of more than 60% of activity, according to a study on a single patient (Mathews KP. et al. 1980), (Cao H. et Hegele RA. 2003). The study of gene CPN1 in both patients (using IGV) has led to the identification of two rare variants (frequency less than 0.002) in intron 1 and 6 of one allele from Pt. 2 (table 1, figure 1). In MCAD no other damaging variations have been identified in these two patients by direct inspection with IGV (data not shown).

Discussion

Whole exome sequencing (WES) is a technique that aims at the sequencing of the fraction of our genome that encodes for proteins: about 30 million base pairs (1% of the all the human DNA) divided into about 20 thousand genes (Ng SB et al. 2009). It has become increasingly clear that the use of WES can positively improve the rate of diagnosis and decrease the time needed for a definitive diagnosis in patients with rare genetic diseases (Sawyer SL et al. 2016). WES also positively impacts the ability to discover new pathogenic variants in known disease genes (Polychronakos C. et Seng KC. 2011) and the discovery of completely new disease genes (Boycott KM 2013). ME/CFS seems to have a genetic component: a US study found clear evidence of familial clustering and elevated risk for the disease among relatives of ME/CFS cases (Albright F et al. 2011) and several SNPs in various genes have been reported as more prevalent in ME/CFS patients versus healthy controls (Wang T et al. 2017). And yet, no studies that analyzed whole exomes of ME/CFS patients have been published, to my knowledge.

In this study, we searched for known genetic diseases in the exomes of two ME/CFS patients who fit the IOM criteria for SEID (IOM, 2015), with postural orthostatic tachycardia syndrome (POTS) identified by positive tilt table test. We detected a missense variant in CPN1 (rs61751507) in Pt. 1 (heterozygosis) that has been associated to a loss of activity of the enzyme of at least 60% in a previous study (Mathews KP. et al. 1980), (Cao H. et Hegele RA. 2003). We then found that, although Pt. 2 was not a carrier of this SNP, she had two rare SNPs in intron 1 (rs188667294) and 6 (rs113386068) of gene CPN1 (both present in less than 1/500 alleles, table 1, figure 1). These intronic variations have not been studied, to our knowledge, so their pathogenicity can’t be excluded at present. Variations in introns can be damaging just as missense and nonsense mutations in exons; suffice to say that the main known pathogenic SNP of gene CPN1 is a substitution in intron 1 (Cao H. et Hegele RA. 2003).

Carboxypeptidase N (CPN1) is an enzyme involved in the inactivation of C3a, C4a, C5a, and of kinins (bradykinin, kalladin) (Hugli T. 1978), (Plummer TH et Hurwitz MY 1978). In ME/CFS the physiologic increase in blood of C4a (the split product of the complement protein C4) after exercise is significantly more pronounced than in healthy controls as if there was a defect in C4a inactivation (Sorensen B et al. 2003). Such a defect could very well be a loss of function in CPN1, as found in Pt 1. Moreover, CPN1 is involved in inactivation of bradykinin, which is known to induce vasodilatation (Siltari A. et al. 2016), therefore CPN1 deficiency could play a role in POTS and in orthostatic intolerance in general. Both patients have a tilt table test positive for POTS. C4a has been recently considered to play a causal role in the cognitive deficit of schizophrenia, because of its role in synapsis pruning (Sekar, A et al, 2016); therefore a failure in its inactivation could be implicated in the incapacitating cognitive defects lamented by ME/CFS patients.

Only two patients with CPN1 deficiency have been reported so far in medical literature (Mathews KP. et al. 1980), (Willemse Jl et al. 2008), and the enzymatic defect has been associated to angioedema that most often involved the face and tongue, urticaria, and hay fever and asthma precipitated by exercise. This clinical presentation could be due, at least in part, to mast cell activation: in fact, C4a is a known anaphylatoxin that induces mast cells degranulation and release of histamine (Erdei A. et al. 2004). That said, we can observe that even if the clinical presentation of the only two known cases of CPN1 deficiency doesn’t fit the clinical picture of ME/CFS, mast cell activation syndrome (MCAS) has some commonalities with ME/CFS (Theoharides, TC et al. 2005), and mast cell abnormalities have been reported among ME/CFS patients (Nguyen T. et al. 2016). So we can’t exclude that activation of mast cells by a failure in C4a inactivation may lead to ME/CFS symptoms. The role of exercise as a trigger for symptoms in CPN1 deficiency is also highly suggestive because this is a pathognomonic feature of ME/CFS.

Conclusion

CPN1 deficiency is present (even if in a mild form) in Pt. 1, while Pt. 2 presents two rare intronic variants whose pathogenic role can’t be excluded. CPN1 deficiency could explain the abnormal increase of C4a after exercise and might be a contributing factor to post-exertional malaise and cognitive symptoms in ME/CFS. A search for pathogenetic SNPs in gene CPN1 among ME/CFS patients would clarify the role (if any) of this gene.

Acknowledgments. I would like to thank Chiara Scarpellini for her careful collection of annotations for each of the 2 hundred or so variants found by EVE within the exomes of Pt. 1 and Pt. 2 (table 2 and table 3).