Impedance, the biomarker you wouldn’t expect

Impedance, the biomarker you wouldn’t expect

1. Introduction

In this article, I report on the results from two research groups in which different experimental settings were used to measure electric impedance in blood samples from ME/CFS patients vs healthy controls. One of these studies comes from Stanford University and has been just published in PNAS: it is freely available here. The other one has been presented by Alan Moreau during the NIH conference on ME/CFS, and it is unpublished (R). In paragraph 2 I introduce the definition of impedance, in paragraph 3 you will learn something about the electric behaviour of cells, in paragraph 4 there is a description of the device used by the Stanford University group, in paragraph 5 there are the results of the experiment from Stanford University, in paragraph 6 there is a discussion of these results, in paragraph 7 the results from the other group are reported, and these two studies are compared in paragraph 8. In paragraph 9 I reported on two drugs that have shown the promise to be of therapeutic use in ME/CFS. Other notes follow in the last two paragraphs. If you are not interested in technical details on impedance (or if you don’t need them), go directly to paragraph 5.

2. Impedance

In this paragraph, I try to give a very simple and short introduction to circuits in a sinusoidal regime in general, and to impedance in particular. The main definition that we need, for that purpose, is the so-called Steinmetz transform for a sinusoidal function. Let’s consider the sinusoid

sinusoid function.JPG

where A is called amplitude and is the maximal value that the function can reach, ω is the angular frequency (also called pulsatance) which is an indication of how fast the value of the function changes in time, α is the phase and it gives the indication of what the value of the function a(t) was for t = 0. The Steinmetz transform consists of the univocal association of the sinusoid a(t) with the complex number

phasor.JPG

also called phasor (which stands for phase vector), where j=√(-1) is the imaginary unity. A complex number can then be easily represented as a vector in the complex plane (see figure 1).

Steinmetz.JPG
Figure 1. According to the Steinmetz transform, a sinusoidal function can be univocally associated with a complex number which, in turn, can be represented as a vector on the complex plane. Diagrams by Paolo Maccallini.
Circuit.JPG
Figure 2. Left. A generator of voltage is linked to a system (represented by a box) whose electrical properties, as a whole, are described by its electrical impedance. This can be seen as a simplified scheme of the device used by Ron Davis and its team if we assume that the box is the sample (white blood cells incubated with plasma). Right. The impedance of the sample can be seen as a series of a resistor (an electrical component whose only relevant property is its resistance) with a resistance equal to the real part of impedance, and a second electrical component completely characterized by a reactance with a value equal to the imaginary component of impedance. Sketch by Paolo Maccallini.

Let’s now consider the elementary circuit in figure 2 (which is also a simplified model of the device in the study by Ron Davis), where a generator of electrical potential is linked to another circuit (depicted as a box in the figure, on the left) that in our case is represented by the sample of peripheral blood white cells incubated in plasma. But it could be an arbitrarily complex net made up of conductors and what follows would still hold. Let’s assume that the electric current and the voltage of the generator are given respectively by

current and voltage.JPG

We can associate to these sinusoids their respective phasors with the Steinmetz transform, which gives

phasors.JPG

That said, we define impedance of the sample, the complex number that we obtain dividing the phasor of u(t) by the phasor of i(t):

impedance.JPG

Impedance describes several physical properties of the box in figure 2. Without going into details (this is beyond the scope of this article) just consider what follows.

  • The real part of impedance represents the resistance of whatever is inside the box of figure 2, which can be seen as its ability to transform electric energy into heat, i.e. kinetic energy at a molecular level. The higher the value of the resistance, the more the ability to generate heat.
  • The imaginary part of the impedance (called reactance) can be positive or negative. When it is positive it indicates the ability of whatever is inside the box to translate a magnetic field into voltage. The higher the positive reactance, the more its ability to generate a voltage from a magnetic field. A positive reactance is also called inductive reactance.
  • When reactance is negative, it means that whatever is inside the box, it has the ability to store energy in an electric field: the higher the absolute value of the reactance, the more the energy stored in an electric field within the box. A negative reactance is also called capacitive reactance.

No matter how complex the system in the box is, its external electrical behaviour is completely characterized by its impedance, which means that the system can also be simplified in a series of an electrical component whose only relevant property is a resistance equal to the real component of impedance, and a second component completely characterized by a reactance with a value equal to the imaginary component of impedance (figure 2, on the right).

3. Impedance of cells

The study of the impedance of cellular cultures is a field that started probably in the early nineties. In a paper from the Rensselaer Polytechnic Insititute (NY), it was demonstrated that the measure of electrical impedance of a single cell layer was more sensitive than optical microscopy for the measure of changes of nanometers in the cell diameter or subnanometer changes in the distance between the cell layer and the electrodes (Giaever I. & Keese CR. 1991). In that pivotal paper, a mathematical model for the impedance of a layer of cells was also proposed and solved, but it is beyond the scope of this article. A simplified electrical model of a cell layer is provided by a parallel of a capacitance due to dielectric properties of the cell membrane, and a resistance due to the cell membrane, to the cytoplasm and to the layer between cells (Voiculescu I. et al. 2018). We can add a resistance for the solution in which cells are incubated and we obtain the circuit in figure 3.

Circuit 2.JPG
Figure 3. A simplified model for the impedance of a cell culture, where a layer of cells is incubated in a substrate. The electrical properties of the layer of cells are described by a parallel of a resistance (due to the cell membrane, cytoplasm and the layer between cells) and a capacitance (due to the dielectric properties of the cell membrane). We then add a resistance for the substrate. Sketch by Paolo Maccallini.

Remember now that the only electrical property that we can directly measure is the total impedance (both the real component and the imaginary one). So we have to find the relationships between these two components and the physical parameters introduced in figure 3. For the equivalent impedance of the sample (see the last paragraph for the mathematical passages) we have:

impedance 3.JPG

The dependence of the real part of Z_cl and of its imaginary component to R_cl and C_cl can be got from figure 4. The absolute value of Z_cl is represented in figure 5.

diagrams.JPG
Figure 4. The real part of Z_cl (left) and its imaginary component (right) as functions of the resitance and the capacitance of the sample introduced in the model in figure 3.
module.JPG
Figure 5. The absolute value of Z_cl.

The capacitance in this formula is due – as said – to the dielectric properties of the plasma membrane. We can see a cell as a spherical capacitor, where two conductive layers (one in the cytoplasm and the other one in the extracellular space) are separated by the outer membrane. The insulating portion of a phospholipid membrane is of about 4.5 nm and it has been found that the capacitance per square cm of the cell membrane is one μF (Matthews GG, 2002). Since the permittivity constant ε is known, we can calculate the dielectric constant κ of a lipid membrane quite easily (see the last paragraph), and we find κ=5.

4. The nanoneedle

The device used for the measurement of the impedance of blood samples from ME/CFS patients is an array of thousands of sensors. Each sensor is made up of two conductive layers, separated by a dielectric material (figure 6). Each sensor is a sinusoid circuit that operates at a frequency of 15 kHz and at a voltage with an amplitude of about 350 mV. In figure 6, I have added the electric scheme for the circuit made up by the sensor itself and the sample, according to what seen in the previous paragraph. I have added some resistances and capacitors for the electrodes, according to (Esfandyarpour R et al. 2014).

nanoneedle2.png
Figure 6. The nanoneedle is made up of two conductive layers separated by a dielectric layer. A voltage with an amplitude of about 350 mV and a frequency of 15 kHz is applied to the electrodes. Sketch by paolo Maccallini.

As you can see from the picture, one of the dimensions of the sensor is below one micron, while the other is of about 3 microns. Keep in mind that the diameter of the average white blood cell is of about 15 microns… To me, such a small size makes it difficult the application to this system of both the electrical model by Ivar Giaever and Charles Keese and of the simplified one presented in the previous paragraph, which have been designed to describe the behaviour of a layer of cells that grow above an electrode that can harbour many cells on its surface. And in fact, in their paper, Esfandyarpour R. and his colleagues have sketched a different model (R, B), even though they haven’t used it to draw any conclusion or interpretation from the experimental data, yet.

5. The experiment

The measurement of the impedance of samples from ME/CFS patients and controls has been made with an array of thousands of electrodes, each one like the one in figure 6. The system took 5 measures of impedance for second and the experiment on each sample lasted for about 3 hours. The researchers measured, for each point in time, both the real and the imaginary component of the impedance of the sample. They also measured the module of the impedance.

Each sample consisted of peripheral blood mononuclear cells (PBMC) incubated in patient’s own plasma (plasma is blood without erythrocytes, platelets and white blood cells), at a concentration of 200 cells per μL. It might be useful to remember that PBMCs are basically all the white blood cells that are present in peripheral blood but granulocytes, which have multi-lobed nuclei and, as such, are not “monuclear”.

The researchers drew blood from 5 severe patients, 15 moderate patients (diagnosed by a physician according to the Canadian Consensus Criteria) and 20 healthy controls, with 5 of them age- and gender-matched to 5 of the ME/CFS patients.

About 20 minutes were required for the impedance to reach a steady state (the baseline level, characterized by swings in impedance below 2% of its value). The measures for each sample have been divided by the value of impedance at the baseline. This is the reason why the baseline has a value of 1 in the diagrams. After the steady state was reached, the researchers added 6 μL of NaCl to the samples. After a transient reduction in impedance, the samples from healthy controls returned to the baseline value. In samples from patients, the initial reduction in impedance after NaCl introduction was followed by a dramatic change in both the real component and the imaginary component of impedance. The normalized real part, in particular, had an increase of 301.67% ± 3.55 (see figure 7 and R).

zre.jpg
Figure 7. The real part of the impedance. ME/CFS patients are in red, while controls are in blue. From the speech by Ron Davis during the NIH conference on ME/CFS (R).

6. What does it mean?

In the experiment by Stanford University, they added NaCl to the samples and this likely led to the activation of the sodium-potassium pump that requires a molecule of ATP in order to transport 3 Na ions outside the cells (and two K ions inside). This would be the only way for these cells to maintain the correct intracellular concentration of sodium, pumping out those Na ions that found their way to the cytoplasm from the plasma. This is like putting a cell on a stationary bike. What the experiment says is that this effort made by the cells to maintain homeostasis leads to huge changes in the electrical properties of the samples from ME/CFS patients, while producing virtually no changes in the samples from healthy controls. But what is the origin of the change in impedance?

If we consider the electrical model that I have proposed in figures 3 and 6 and looking at figure 4 (left), we might hypothesise that the change comes from a reduction in the capacitance C_cl  which is due to the dielectric properties of cell membranes. A change in composition in these membranes could lead to a reduction in C_cl and thus to the observed increase in the real component of the total impedance. This might perhaps be linked to the reduction in the metabolism of the main components of the plasma membrane (sphingolipid, phospholipid and glycosphingolipid) in patients vs controls previously reported in a metabolomic study (Naviaux R et al. 2016). A reduction in the dielectric properties of cell membranes could also explain the increase in the module of impedance observed in this study (see figure 5). But it is worth noting again that the model I used for the description of the electrical properties of the sample is a hugely simplified version of the one proposed in (Giaever I. & Keese CR. 1991) and it has been developed for electrodes that are many times larger than the one used by Esfandyarpour R and colleagues. As said elsewhere, the authors have proposed a different, more complex, electric circuit (R, B) and they wrote that the process of using it to interpret the experimental data is currently on-going. But they did note that a change in plasma membrane composition might be responsible for the observed change in impedance, in one point of the article, among other possible explanations.

A release of molecules (cytokines?) from the PBMCs into the plasma might also be the cause of the change in impedance, but if we assume that our model in figure 3 is reliable, these molecules would only change the value of R_su, so the imaginary component of the impedance would not be affected, while we know that there is a change in that component too. But again, our model is a very simplistic one.

A change in the shape or size of the cells would lead to a change in C_cl. But the authors observed the samples in standard live microscopy imaging and they were not able to record any significant cell size difference in samples from ME/CFS patients vs samples from healthy controls.

7. Canadian impedance

During the NIH conference on ME/CFS, the Canadian group led by Alan Moreau, presented, at the end of a speech about microRNAs, a measure of impedance on immortalized T cells incubated with plasma from healthy controls, plasma from ME/CFS patients, and plasma from patients with idiopathic scoliosis (figure 8) and, as you can see, there is an increase in impedance with the increase in plasma concentration only in the second group (R). This measure has been made with the CellKey system, after stimulation of cells with G-coupled protein receptors agonists (Garbison KE et al. 2012). It is also worth mentioning that this impedance is the one due to the flow of charges in the extracellular space and that it seems to be the module of impedance, rather than the real or the imaginary part.

Cattura18
Figure 8. Measure of electrical impedance in immortalized T cells incubated in plasma from healthy controls (CTRL), from ME/CFS patients (EM) and from patients with idiopathic scoliosis, at increasing concentrations of plasma (R).

Alan Moreau also noted that if we subgroup ME/CFS patients according to differences in circulating microRNA, we find that plasma from two of these groups leads to an increase in impedance while plasma from three other groups induces a decrease in impedance, if compared with T cells incubated with plasma from healthy controls (figure 9).

Cattura18
Figure 9. The same experiment as in figure 8, but here patients are divided into subgroups according to the content in microRNA in their blood (R).

8. The X factor

Even though the Canadian experiment is not directly comparable to the one from the Stanford University group, nevertheless it is a partial confirmation of that result. Moreover, since in the Canadian experiment the cells are the same for all the groups (it is a line of immortalized T cells) and what changes is only the plasma they are incubated in, we can say that the origin of the electrical shift in these samples is something that is present in the plasma of patients (an X factor) and it might be due to the interaction between this X factor and cells. This interpretation is in agreement with a previous observation from a Norwegian group who incubated muscular cells in serum from 12 patients and from 12 healthy donors: they found an increase in oxygen consumption and in lactic acid production in cells incubated with sera from patients vs cells incubated with sera from healthy controls. This experiment was performed using the Seahorse instrument (Fluge et al. 2016). It is worth noting that in this case only serum was used, and serum is plasma without clotting factor.

table.JPG
Figure 10. When PBMCs from patients are incubated in plasma from healthy controls, the impedance is normal; when, on the other hand, cells from healthy controls are incubated in plasma from ME/CFS patients, the impedance increases. So, the increase is due to an interaction between plasma from patients and cells, no matter if the cells come from healthy individuals or from patients.

The idea of an X factor present in plasma (or serum) of patients is even more suggestive if we take into account the unpublished result presented by Ron Davis during the NIH conference, called the “plasma swap experiment”, performed with the nanoneedle device (R). As you can see from figure 10, the increase in impedance happens only when cells are incubated with plasma from ME/CFS, no matter whether the cells are from healthy controls or from ME/CFS patients.

It is extremely important here to note that several filtrations of the plasma from patients have been made by the Stanford Group in order to discover what the X factor is: they have concluded that it is neither a metabolite nor a cytokine. Alan Moreau noted also that it is probably not an antibody. It turned out that it might be an exosome, a vesicle released by cells which contains – among other molecules – microRNA molecules. As Ron Davis said: “I guess that the signal is coming from damaged mitochondria, but it is only a guess” (R).

9. Drug testing

The authors of the study on the nanoneedle device are interested in using it for drug testing. Ron Davis reported during the last Emerge Australia conference (R) that two compounds are able to reduce the alteration in impedance seen in PBMCs incubated with plasma from patients: Copaxone, a peptide currently used in the treatment of multiple sclerosis, and SS31, a molecule not available yet, that can scavenge mitochondrial reactive oxygen species (ROS), thereby promoting mitochondrial function (Escribano-Lopez I. et al. 2018), (Thomas DA et al 2007).

drugs.jpg
Figure 11. Two drugs seem to reduce the increase in impedance in cells incubated with plasma from ME/CFS patients (R).

10. Limitations of the study from Stanford University

Even though the differences observed in the electric properties of the samples from ME/CFS patients vs controls, after the addition of the osmotic stressor, are striking, there are some potential limitations that ought to be mentioned.

  • Only 5 of the 20 healthy controls were age and gender-matched to 5 ME/CFS patients. So the difference observed might be due, at least in part, to age or gender.
  • The difference in impedance might be due to some consequence of the disease, like deconditioning, since the healthy control was not a sedentary one.

I presented the content of this blog post after the screening of Unrest in Turin (Italy) in May 2019 (video in Italian).

11. Mathematical notes

The calculation of the impedance Z_cl of the sample (figure 3) is as follows:

impedance 2Then you have to add the resistance R_su to the real part and you obtain Z_tot. In order to calculate the dielectric constant of the lipid membrane just follow these passages:

dielectric constantIn order to choose the range of variation for C_cl and R_cl in the diagrams in figures 4 and 5, I calculated the capacitance of a cell, assuming a spheric shape, a radius of 5 μm, a capacitance for square cm of 1 μF, a thickness of the plasma membrane of 4.5 nm, and a dielectric constant κ=5. This gives

cell-capacitance.jpg

I then found the value of the imaginary component of the impedance of a culture of yeast cells measured by the nanoneedle, which is 800 kΩ and I set the angular frequency at 2π·15 kHz (which is the frequency of the generator of voltage of the nanoneedle). Then we have a reference value for resistance too:

Cellular resistance.JPG

The simple code (Matlab) that I used to plot the diagrams in figure 4 and 5 is the following one.


% file name = impedance
% date of creation = 4/05/2019
clear all
% we define the angular frequency
w = 2*pi*15*(10^3)
% we register the array of the capacitance axis (pico Farad)
c_span = 4.;
delta_c = c_span/30.;
n_c = c_span/delta_c;
% we register the array
c(1) = 0.;
for i = 2:30+1
c(i) = c(i-1) + delta_c;
end
% we define the array of resistance (mega Ohm)
r_span = 9.;
delta_r = r_span/30.;
n_r = r_span/delta_r;
r(1) = 0.;
for i = 2:30+1
r(i) = r(i-1) + delta_r;
end
% we register the array of the real part and of the imaginary part of impedance and its module
for i=1:n_c
for j=1:n_r
Rcl = r(j)*(10^6);
Ccl = c(i)*(10^(-12));
Z_r (i,j) = Rcl/( 1 + ( (Rcl^2)*(w^2)*(Ccl^2) ) );
Z_i (i,j) = (-1)*( w*Ccl )/( ( 1/(Rcl^2) ) + (w*Ccl)^2 );
Z_m (i,j) = sqrt( (Z_r (i,j)^2)+(Z_i (i,j)^2) );
endfor
endfor
% we plot the real part of the impedance
figure(1)
mesh(r(1:n_r), c(1:n_c), Z_r(1:n_c,1:n_r));
grid on
ylabel('capacitance (pico Farad)');
xlabel('resistance (Mega Ohm)');
zlabel('Ohm');
legend('Real part of Impedance',"location","NORTHEAST");
% we plot the imaginary part of the impedance
figure(2)
mesh(r(1:n_r), c(1:n_c), Z_i(1:n_c,1:n_r));
grid on
ylabel('capacitance (pico Farad)');
xlabel('resistance (Mega Ohm)');
zlabel('Ohm');
legend('Imaginary part of Impedance',"location","NORTHEAST");
figure(3)
mesh(r(1:n_r), c(1:n_c), Z_m(1:n_c,1:n_r));
grid on
ylabel('capacitance (pico Farad)');
xlabel('resistance (Mega Ohm)');
zlabel('Ohm');
legend('Module of Impedance',"location","NORTHEAST");
Annunci

Parvovirus B19 e Sindrome da Fatica Cronica

Parvovirus B19 e Sindrome da Fatica Cronica

La versione in inglese di questo articolo è disponibile qui.

Introduzione

Il parvovirus B19 è un virus a singolo filamento di DNA con un tropismo per i precursori degli eritrociti di Homo sapiens. Fu scoperto nel 1975 (Cossart YE et al. 1975) ma fu classificato come patologico per gli esseri umani solo nel 1981 (Pattison JR. et al. 1981). Il suo genoma consiste in un DNA lineare a filamento singolo con una lunghezza di 5,600 basi che include i geni per le due proteine ​​del capside VP1 e VP2 e per la proteina non strutturale NP1 (Trösemeier JH. et al. 2014). La sua classificazione linneana è quella riportata nella tabella sottostante. Il parvovirus B19 ha un diametro di soli 25 nm, e a questo deve il suo nome: parvum è un aggettivo latino che significa piccolo. Nei bambini, l’infezione acuta è associata a erythema infectiosum (noto anche come quinta malattia). Negli adulti immunocompetenti può causare poliartrite simmetrica acuta, mentre nell’ospite immunodepresso l’infezione persistente da B19 si manifesta come aplasia eritroide pura e anemia cronica (Heegaard ED et Brown KE 2002). Il parvovirus B19 si diffonde attraverso le secrezioni respiratorie, come la saliva, l’espettorato o il muco nasale, quando una persona infetta tossisce o starnutisce [R]. Il virus può persistere nei globuli bianchi (Saal JG. et al. 1992).

Family: Parvoviridae
Subfamily: Parvovirinae
Genus: Erythroparvovirus
Species: Primate erythroparvovirus 1

Parvovirus B19 e sindrome da fatica cronica

Una forma di affaticamento cronico è stata descritta sia durante l’infezione acuta da parvovirus che durante la convalescenza (Kerr JR et al. 2001) ed è risultata associata a livelli elevati di TNF-α e INF-γ. Uno studio ha seguito 39 pazienti con infezione da Parvovirus B19 acuta per una media di due anni e ha riferito che 5  di loro (13%) hanno sviluppato la CFS. La maggior parte di loro aveva una PCR positiva e/o IgG positive nel sangue per B19. Il deterioramento nella memoria e nella concentrazione, il malessere post-sforzo e la mialgia erano presenti in tutti e cinque i soggetti. La prevalenza di IgG anti-VP1/2 era pressappoco la stessa nei pazienti e nei controlli, mentre le IgG anti-NS1 e il DNA nel siero erano più prevalenti nei pazienti che nei controlli (Kerr JR. et al. 2002). Nel 2009 Frémont e colleghi hanno cercato il DNA virale nelle biopsie intestinali (sia dell’antro gastrico che del duodeno) e hanno riscontrato una maggiore prevalenza di risultati positivi nei pazienti rispetto ai controlli. Eppure, i pazienti con PCR positiva per il DNA di Parvovirus B19 nelle biopsie avevano una PCR negativa nel sangue (Frémont M. et al. 2009). Un altro studio ha riscontrato una maggiore prevalenza di IgG anti-NS1 nei pazienti rispetto ai controlli, mentre il DNA del siero, l’IgG anti-VP1/2, l’IgM anti-VP1, l’IgM anti-NS1 non differivano tra pazienti e controlli. Gli anticorpi anti-NS1 erano associati a artralgia, tra i pazienti (Kerr JR. et al. 2010). Recentemente, un altro gruppo ha confermato una normale prevalenza di IgG anti-VP1/2 nei pazienti CFS, ma un contestuale un aumento di IgM anti-VP 1 e DNA sierico nei pazienti rispetto ai controlli (Rasa S. et al. 2016). Questi dati sono riassunti nella tabella sottostante.

Tipologia di test
ME/CFS
Controlli sani
valore p
Riferimenti
IgM o DNA 3/200 Chia JK. et Chia A. 2003
DNA in biopsie¹ 19/48 (40%) 5/35 (14%) 0.008 Frémont M. et al. 2009
DNA nel siero 3/5 (60%) 0/50 Kerr JR. et al. 2002
11/200 (5,5%) 0/200 NS Kerr JR. et al. 2010
34/200 (17%) 2/104 (1.9%) <0.0001 Rasa S. et al. 2016
0/32 Frémont M. et al. 2009
WBC DNA 1/5 (20%) 0/50 Kerr JR. et al. 2002
Anti-VP 1 IgM 4/200 0/200 NS Kerr JR. et al. 2010
16/200 (8%) 0/89 0.0038 Rasa S. et al. 2016
Anti-NS1 IgM 3/200 1/200 NS Kerr JR. et al. 2010
Anti-VP 1/2 IgG 4/5 (80%) 37/50 (74%) Kerr JR. et al. 2002
150/200 (75%) 156/200 (78%) NS Kerr JR. et al. 2010
140/200 (70%) 60/89 (67.4%) NS Rasa S. et al. 2016²
Anti-NS1 IgG 2/5 (40%) 8/50 (16%) Kerr JR. et al. 2002
83/200 (41.5%) 14/200 (7%) <0.0001 Kerr JR. et al. 2010

1: biopsie dell’antro gastrico e del duodeno. 2: il kit usato è questo. WBC, white blood cells.

In letteratura sono descritti almeno quattro casi di pazienti ME/CFS con infezione B19  attiva (DNA positivo nel sangue) trattati con successo con immunoglobuline per via endovenosa, con rapida risoluzione dei sintomi e eliminazione dell’infezione. In tre casi il trattamento è stato il seguente: 400 mg/kg/giorno per cinque giorni (Kerr JR. et al. 2003). Nel paziente rimanente la posologia non viene riportata (Jacobson SK. et al. 1997).

Discussione

L’infezione acuta da Parvovirus può evolvere in ME/CFS in più del 10% dei casi (Kerr JR et al. 2001), (Kerr JR. et al. 2002). Questa prevalenza è in accordo con la percentuale di coloro che sviluppano ME/CFS dopo infezioni sintomatiche da Giardia duodenalis (Mørch K et al. 2013), virus Epstein-Barr, Coxiella burnetii e Ross River virus (Hickie I. et al. 2006) (vedi anche questo post). Ciò suggerirebbe che diversi patogeni possono innescare un percorso comune che alla fine porta alla ME/CFS. Eppure, i marcatori di infezione attiva da Parvovirus B19 sono più comuni tra i pazienti ME/CFS rispetto ai controlli sani: questo è il caso del DNA virale nella mucosa gastrica (Frémont M. et al. 2009) e nel siero (Rasa S. et al. 2016) e delle IgM anti-VP 1 (Rasa S. et al. 2016). Inoltre, la sintesi di IgG specifiche per NS1 è significativamente più prevalente nei pazienti rispetto ai controlli, e questo tipo di anticorpi è stato documentato essere più frequente in caso di decorso più grave e persistente dell’infezione da B19 (von Poblotzki A. et al. 1995). Quattro casi di ME/CFS con infezione attiva da B19 sono stati trattati con successo con IVIG (Jacobson SK. et al. 1997), (Kerr JR. et al. 2003). Allo stesso tempo, la sieroprevalenza di B19 rispetto alle IgG contro le proteine VP 1/2 è la stessa nei pazienti e nei controlli (Kerr JR. et al. 2002), (Kerr JR. et al. 2010), (Rasa S. et al. 2016), il che significa che il numero di individui che contraggono il virus nella loro vita è lo stesso nei pazienti e nei controlli.

Conclusione

La sieroprevalenza del parvovirus B19 è la stessa nei pazienti ME/CFS e nei controlli, ma l’infezione attiva è più prevalente nei casi rispetto ai controlli. Inoltre, i pazienti hanno maggiori probabilità di avere anticorpi contro la proteina NS1, un marcatore di decorso persistente dell’infezione da B19. Le immunoglobuline in vena potrebbero essere un’opzione terapeutica nei pazienti ME/CFS con infezione attiva da B19.

 

Why we can’t use LTTs, yet

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

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

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

 


 

 

 

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False positive IgM tests in infectious and autoimmune diseases

False positive IgM tests in infectious and autoimmune diseases

Introduction

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

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

Nombre virus IgM

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

(OspC and/or BmpA)

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

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

Cross-reactivity with other pathogens

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

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

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

Latent infections reactivation

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

Rheumatoid factor interference

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

Cross-reactivity with autoantigens

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

The effect of prevalence on the rate of false positive results

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

PPV.PNG

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

PPV 2.png

 

 

Il dr. Systrom e l’intolleranza all’esercizio

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

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

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

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

 


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Mark Davis and the search for the universal immune test

Mark Davis and the search for the universal immune test

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

1. Introduction

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

2. T cells

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

3. T cell receptor

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

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

4. T helper cells

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

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

5. Cytotoxic T lymphocytes

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

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

6. The universal immune testing

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

6.1. First step: TCR sequencing

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

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

6.2. Second step: TCR clustering

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

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

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

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

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

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

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

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

7. We have a hit!

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

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

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

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

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

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

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

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

8. Chronic Lyme does exist

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

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

9. Conclusions

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


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Mitocondri belgi

Mitocondri belgi

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

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

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

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

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

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

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

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

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