Preprint Comparison of T-cell Receptor Diversity of people with Myalgic Encephalomyelitis versus controls, 2023, Dibble, Ponting et al

[Tom Kindlon]

Pre-print

Free fulltext:
https://www.researchsquare.com/article/rs-3164397/v1

Comparison of T-cell Receptor Diversity of people with Myalgic Encephalomyelitis versus controls

Joshua J Dibble1

Ben Ferneyhough2

Matthew Roddis2

Sam Millington2

Michael D Fischer2

Nick J Parkinson2

Chris P Ponting1

Email

1 University of Edinburgh,

2 Systems Biology Laboratory UK


Objective:

Myalgic Encephalomyelitis (ME; sometimes referred to as Chronic Fatigue Syndrome or CFS) is a chronic disease without laboratory test, detailed aetiological understanding or effective therapy. Its symptoms are diverse, but it is distinguished from other fatiguing illnesses by the experience of post-exertional malaise, the worsening of symptoms even after minor physical or mental exertion. Its frequent onset after infection might indicate that it is an autoimmune disease or that it arises from abnormal T-cell activation.

Results:

To test this hypothesis, we sequenced the genomic loci of a/d, b and g T-cell receptors (TCR) from 40 human blood samples from each of four groups: severely affected people with ME/CFS; mildly or moderately affected people with ME/CFS; people diagnosed with Multiple Sclerosis, as disease controls; and, healthy controls. Seeking to automatically classify these individuals’ samples by their TCR repertoires, we applied P-SVM, a machine learning method. However, despite working well on a simulated data set, this approach did not partition samples into the four subgroups, beyond what was expected by chance alone. Our findings do not support the hypothesis that blood samples from people with ME/CFS frequently contain altered T-cell receptor diversity.

 

[Hutan]

This is my naive understanding, there will be members who can correct any mistakes:

When T-cells are sampled from blood, most TCR sequences are observed only once, although some are found multiply in part due to identical recombination events occurring in the thymus [3] and in part due to clonal expansion of T-cells whose TCR binds to an antigen-bound major histocompatibility complex protein.

This paper is looking at T-cells sharing receptors with sequences that bind to particular antigens. They say this increase from production of such T-cells in the thymus, or from clonal expansion, where a T-cell that has bound to a particular antigen proliferates. (I'll call the outcome where there is an increase in T-cells sharing the same antigen receptor 'clonal expansion'.)

Clonal expansion happens in a range of diseases, including autoimmune diseases, and as a response to pathogens. I guess it happens whenever an antigen binds to a T-cell.

So, if there is a pathogen outside cells, or it inside cells and causing the cells to put bits of the pathogen on their surface to signal for help, then we might expect to see clonal expansion. If there was a persistent infection causing ME/CFS, T-cell clonal expansion would be an important clue, although there are some reasons why that clonal expansion might not be detected.

This paper is a result of the work Joshua Dibble did for his PhD thesis, discussed here:
Thesis: Investigating the Genetic and Immunological Aetiology of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome 2022 Dibble

 

[Hutan]

Samples covering mild and severe ME/CFS, MS and healthy controls, with the ME/CFS samples probably as well-characterised as in any study.

Forty human peripheral blood mononuclear cell (PBMC) samples were received from each of four groups: (i) Severely affected people with ME/CFS (either house- or bed-bound; MEsa); (ii) Mildly or moderately affected people with ME/CFS (MEmm); (iii) people diagnosed with Multiple Sclerosis (MS; disease controls); and, (iv) Healthy controls (HC). Samples were sourced from the CureME Biobank [21] from female donors aged 40-60 years, chosen to reduce possible age- or sex-dependent confounding effects, although their limited availability among MEsa required us to source samples from younger donors

Cytomegalovirus seropositivity didn't differ by group.

We chose to adopt Rényi entropy as our T-cell receptor diversity metric.

It's important to note that this study looked at the diversity of T- cell receptors. There are different ways of measuring diversity. Some of us will have heard of Shannon diversity, from ecology. So, the method chosen could result in something important being missed, (and it's something to be watched for in analyses like this) but the chosen metric here was consciously chosen to be robust.

Deciding exactly what part of the T-cell to evaluate and then sorting the receptors into clonal groups doesn't sound to be as straightforward as might have expected. The Methods section is after the Discussion, so I haven't got to it yet, and I still might not understand.

For each cell type and a/b/g-chain combination we defined a vector of clonotype counts in a sample. Here, a clonotype is defined as the CDR3 region, plus the full V, D and J gene segments without considering pairing between a-b or g-d chains. Note that -chain data is not included, because recombination of the locus, which occurs first, preferentially removes the TCR-δ locus [30].

Next, we constructed a matrix of Euclidean distances between each vector pair, ensuring that the pair contained the same number of recombinants by randomly down-sampling the more-populous sample 1,000-times (Methods). We needed to down-sample because the TCR recombinant count varied over two orders of magnitude among study samples. Distances were calculated over a pre-set optimised range of a, the order of Rényi entropy. Next, for cell type and a/b/g-chain combinations we partitioned samples by their clonotypes, adapting the machine learning approach described in Greiff et al. [31] (Methods). Once distances were precomputed, investigators were unblinded to the group identity (e.g. MEmm or MS) of each CD8+ sample. CD4+ data were acquired following unblinding.

A unique pseudo-anonymised identifier was assigned to each sample upon receipt to allow the study to be blinded.

Good to see this happening.

 

[Hutan]

The distance matrix for all three CD8+ chain types (TCR a-, b-, and g-chains) considered together was visualised using a multidimensional scaling (MDS) plot (Figure 1). The four groups (MEsa, MEmm, MS and HC) are not clearly separated in this plot’s two dimensions.

Figure 1 is below, for CD8+ chains with data for each group in a different colour.


It is noted that the outliers out to the right with higher diversity are explained by the participants being of younger age. Because it wasn't possible to get enough ME severe samples in the target are range of 40 to 60 years, they included samples from younger people. So, clearly age is a huge confounder for T-cell diversity measures.

That makes me wonder about the utility of this diversity measure. If even the MS group (which is known to have T-cell clonal expansion) isn't showing up as different, then can we expect it to find issues in ME/CFS?

I skipped ahead and skimmed the Discussion to see what was said about MS not being identified as different, even from healthy controls. There is something there about the TCR clonotype diversity not necessarily being manifest in the blood, but instead in tissues as is the case in MS.

Finally, this study’s results could also reflect an absence of clonotype diversity differences between groups. If so, then the TCR clonotype sequences themselves, rather than their diversity, could be predictive of disease status, or else TCR repertoire differences are manifest not in blood, but in other more disease-relevant tissues as in MS [36]. Our negative results could also reflect causal mechanisms of ME/CFS that do not result in T-cell repertoire change.

When I first heard about the findings of this study, I assumed that was the end of the idea of T-cell clonal expansion in ME/CFS. But, reading this, I don't think it is. I think there is still a need to look, as is suggested in the Discussion, at TCR clonotype sequences themselves, and to look at T-cells in tissues rather than in blood.

 

[Hutan]

Amoriello R, Chernigovskaya M, Greiff V, Carnasciali A, Massacesi L, Barilaro A, Repice AM, Biagioli T, Aldinucci A, Muraro PA, et al. TCR repertoire diversity in Multiple Sclerosis: High-dimensional bioinformatics analysis of sequences from brain, cerebrospinal fluid and peripheral blood. EBioMedicine. 2021;68:103429.

This is the reference for the suggestion that TCR clonal diversity in MS is in tissues, not blood

Findings: CSF repertoires showed a significantly higher public clones percentage and sequence similarity compared to peripheral blood (PB). On the other hand, we failed to reject the null hypothesis that the repertoire polarization is the same between CSF and PB. One Primary-Progressive MS (PPMS) CSF repertoire differed from the others in terms of TCR similarity architecture. Cluster analysis splits MS from HD.

Interpretation: In MS patients, the presence of a physiological barrier, the blood-brain barrier, does not impact clone prevalence and distribution, but impacts public clones, indicating CSF as a more private site. We reported a high Vβ sequence similarity in the CSF-TCR architecture in one PPMS. If confirmed it may be an interesting insight into MS progressive inflammatory mechanisms. The clustering of MS repertoires from HD suggests that disease shapes the TCR Vβ clonal profile.

This is making my head explode a bit. Public and private clones?
There's this paper that might shed some light on that:
Public and private human T-cell clones respond differentially to HCMV antigen when boosted by CD3 copotentiation

But, I'm going to have a bit of a lie down. 'More T-cells, less psychology' still seems like a good plan though.

 

[chillier]

It's important to note that this study looked at the diversity of T- cell receptors. There are different ways of measuring diversity. Some of us will have heard of Shannon diversity, from ecology. So, the method chosen could result in something important being missed, (and it's something to be watched for in analyses like this) but the chosen metric here was consciously chosen to be robust.

Deciding exactly what part of the T-cell to evaluate and then sorting the receptors into clonal groups doesn't sound to be as straightforward as might have expected. The Methods section is after the Discussion, so I haven't got to it yet, and I still might not understand.

They are using Renyi entropy which is sort of a generalised measure of diversity which includes many different measures of diversity within it. You get a curve like one of these (taken from this website):

Where the y axis Ha(X) is the entropy a.k.a a measure of diversity, and the x axis is a parameter alpha. Different values of alpha represent different diversity metrics. At alpha=1 you are looking at shannon diversity for example.

From the methods:

Choice of alpha yields different TCR diversity profiles: higher values of alpha increase the weight assigned to the most frequently occurring clonotypes, whereas when alpha=0 all clonotypes are weighted equally, irrespective of how many instances of each are found.

So I think what they're doing is for a given cell type (CD4 or 8) and chain, every patient gets a curve like this and they compare every possible pair of patients and calculate how 'different' their curves are by calculating euclidean distances between them, generating a single number for each pair. Then they use machine learning to sort them into the groups and see how accurately they can do it. They get their p values by randomly shuffling the data labels around and trying to sort into groups based on these random labels many times, and basically seeing how their real data compares against this randomised null distribution.

Here is their logic of why they do it this way, rather than just picking one diversity measure:

To avoid biasing results by choosing a specific diversity metric prior to analysis, we decided to use the more general, and thus more appropriate, Rényi entropy

 

[chillier]

The logic kind of seems fair enough, although I wonder if comparing the whole curve might in someway dilute the signal from whichever source of diversity is actually most relevant - but I suppose in the absence of knowing exactly what kind of diversity you are looking for this makes sense. They test their method out on a simulated dataset to validate their approach, but in doing so you are making all sorts of assumptions about what you think a dataset with clonal expansions will look like.

I think it might have been nice to validate their method on real datasets where there is a known difference in clonal expansion. I'm wondering also if such other datasets actually exist and whether changes in diversity have been seen from this kind of analysis in different diseases. I guess there would be T cell lymphomas where you would see a huge expansion of a singular clone? What about other disease? Clearly they don't see a signal in MS in this dataset for example, possibly because any relevant cells are not in the blood but in the CSF or other tissues as @Hutan alluded to. I seem to recall from various sources that measuring things like this in the blood can be tricky since the action is usually happening elsewhere, and where it is relevant it might be because you are capturing cells at the right timepoint in the process of trafficking from one place to another. I have to defer to people who know much more than I do about this sort of thing.

 

[Trish]

Trying to get my head around this from a quick skim and Hutan and chillier's helpful comments:

Groups included were ME mild/moderate, ME severe, MS, healthy.
The samples are from the UK ME/CFS biobank, so blood, not CSF, and MS as the disease control.

From the background:

When T-cells are sampled from blood, most TCR sequences are observed only once, although some are found multiply in part due to identical recombination events occurring in the thymus [3] and in part due to clonal expansion of T-cells whose TCR binds to an antigen-bound major histocompatibility complex protein. Clonal expansion of T-cells occurs in disease states, such as Alzheimer’s disease [4], amyotrophic lateral sclerosis-4 [5], IgG4-related disease [6], Kawasaki Disease [7], multiple sclerosis [8], and Ras-associated lymphoproliferative disease [9].

From the discussion:

Finally, this study’s results could also reflect an absence of clonotype diversity differences between groups. If so, then the TCR clonotype sequences themselves, rather than their diversity, could be predictive of disease status, or else TCR repertoire differences are manifest not in blood, but in other more disease-relevant tissues as in MS [36]. Our negative results could also reflect causal mechanisms of ME/CFS that do not result in T-cell repertoire change.

Future work could consider refining the analytical pipeline using samples from a disease with a more clearly established incidence of clonal expansion. A good candidate may be T-cell leukaemia/lymphoma, where research has shown that more than 50% of sample repertoires can consist of a single clonotype [37].

So my simplistic understanding was that they only expected to find the method could sort the samples into disease groups accurately would be if there was clonal expansion in T cells in the blood of each patient group that was both different between disease groups and different from healthy controls.

But they have said that they already know this isn't the case between MS and healthy controls, because the clonally expanded T cells are in tissues, not in blood. So logically they should not have expected the analysis method to be able to separate the healthy controls from the MS patients.

It seems to me that they really needed to have a disease control group of blood samples from a disease known to demonstrate t-cell clonal expansion in blood. Limitations of samples only coming from the UK ME/CFS biobank presumably made this difficult within the cost constraints of a PhD project.

But without the blinded inclusion of such disease samples, how can they know whether the blood samples used were, for example, changed by the storage process, or their lab work was measuring what it was intended to measure? I'm not intending to cast aspersions on the scientists involved, more to ask a general question of whether it's possible to be sure in any lab experiment whether data is meaningful if there isn't inclusion of blinded samples known to exhibit a difference on the thing being measured?

 

[RedFox]

Where the y axis Ha(X) is the entropy a.k.a a measure of diversity, and the x axis is a parameter alpha. Different values of alpha represent different diversity metrics. At alpha=1 you are looking at shannon diversity for example.

Wow, never expected Claude Shannon to come up here.

 

[Jonathan Edwards]

That makes me wonder about the utility of this diversity measure. If even the MS group (which is known to have T-cell clonal expansion)

I realise you have written further on this. But my initial thought was 'is this so'?
I don't think we have good evidence for MS involving a specific T cell response. B cells yes, but the data on T cells has always looked iffy to me and for the autoimmune diseases I studied there is really no evidence for specific T cell responses (the clear exception is coeliac disease for sa special reason).

The problem with all this is that T cells in blood may well not show up restricted clonality because the relevant T cells are busy in tissue. But restricted clonality in target tissue isn't any good because there is no control situation - normal people do not have many T cells in these tissues. Lymphoid tissue might be a good guide but getting lymphoid samples has always been very tricky in practice. In any inflamed tissue there will be restricted T cell clonality because the T cells are recruited selectively and may expand there. In ME there aren't any tissues with T cells in of note. We all have a few everywhere but not enough to get any data from I suspect.

My gut feeling is that this approach is like trying to roll a boulder up a mountain. It is good that they tried to repeat some preliminary data from the USA but I wouldn't want to pour money into going further.

 

[Hutan]

In ME there aren't any tissues with T cells in of note.

Do we know this? e.g. in tender lymph glands during PEM? in painful muscles during PEM?

It sounds then as though finding t-cells in tissue might be the thing to look for, rather than t-cell diversity in blood?

 

[Jonathan Edwards]

Do we know this? e.g. in tender lymph glands during PEM? in painful muscles during PEM?

It sounds then as though finding t-cells in tissue might be the thing to look for, rather than t-cell diversity in blood?

Lymph nodes will be packed with T cells, as they are in normal people. But biopsying lymph nodes has proved impractical and raises huge sampling problems.

T cells infiltrate normal muscle after exercise and again sampling and access are really tricky. If ME muscles were significantly more inflamed than normal people's it would have shown upon magnetic resonance spectroscopy or imaging.

In barn door pathological states like RA and lupus we tried to get useful information from lymphocyte populations in the 1980s and people have again since, with new techniques, but I don't think it has told us anything fundamental.

 

[Hutan]

Thanks Jonathan.

If ME muscles were significantly more inflamed than normal people's it would have shown upon magnetic resonance spectroscopy or imaging.

Perhaps not if it was a transient phenomenon, as you say? I doubt much magnetic resonance spectroscopy or imaging has been done during PEM. (?)

Maybe it's worth doing magnetic resonance spectroscopy on muscles before and during PEM? There has to be some reason for the dreadful crushing muscle pain that I experience in PEM.

 

[Jonathan Edwards]

Perhaps not if it was a transient phenomenon, as you say? I doubt much magnetic resonance spectroscopy or imaging has been done during PEM. (?)

Well, PEM is said to let for months in some cases and also to be an exacerbation of symptoms already present. I find it hard to see why people who are persistently severely ill with ME would not have pathology if there is some?

 

[Trish]

I find it hard to see why people who are persistently severely ill with ME would not have pathology if there is some?

Would it have been found, given that few people with very severe long lasting ME have been throughly investigated as they are too sick for things like scans, muscle biopsies and CSF sampling.

 

[RedFox]

I love seeing a study that admits it found nothing. We need more of these! It will prevent researchers from turning over the same stone repeatedly.

 

[chillier]

Well, PEM is said to let for months in some cases and also to be an exacerbation of symptoms already present. I find it hard to see why people who are persistently severely ill with ME would not have pathology if there is some?

this is getting a bit off topic but I'm curious if you think there is a sensible tissue to look at/biopsy in general for ME. Or with our current level of knowledge would it be too much of a blind guess?

 

[Hoopoe]

Well, PEM is said to let for months in some cases and also to be an exacerbation of symptoms already present. I find it hard to see why people who are persistently severely ill with ME would not have pathology if there is some?

It would be difficult to get a severely ill patient in PEM to collaborate with anything that requires leaving the house, or even the stress of strangers coming into their home to take blood and do an examination.

 

[Jonathan Edwards]

Would it have been found, given that few people with very severe long lasting ME have been throughly investigated as they are too sick for things like scans, muscle biopsies and CSF sampling.

I think there was enough inflammation to significantly alterT cell populations in muscle it ought to have been picked upon some cases by now. In polymyalgia we thought there was nothing much to find in muscles, just a raised ESR, but when MRI became available it showed up like a light bulb perimuscular tissues in the shoulders.

And if the pathology only occurs in people who cannot tolerate investigations we are up against it yet again!

 

[Jonathan Edwards]

this is getting a bit off topic but I'm curious if you think there is a sensible tissue to look at/biopsy in general for ME. Or with our current level of knowledge would it be too much of a blind guess?

No, I do not think biopsy is going to help us.