Gut microbiome & serum metabolome analyses identify molecular biomarkers & altered glutamate metabolism in FM, 2019, Falcon-Perez et al

[Andy]

Abstract
Background
Fibromyalgia is a complex, relatively unknown disease characterised by chronic, widespread musculoskeletal pain. The gut-brain axis connects the gut microbiome with the brain through the enteric nervous system (ENS); its disruption has been associated with psychiatric and gastrointestinal disorders. To gain an insight into the pathogenesis of fibromyalgia and identify diagnostic biomarkers, we combined different omics techniques to analyse microbiome and serum composition.

Methods
We collected faeces and blood samples to study the microbiome, the serum metabolome and circulating cytokines and miRNAs from a cohort of 105 fibromyalgia patients and 54 age- and environment-matched healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from faeces samples. UPLC-MS metabolomics and custom multiplex cytokine and miRNA analysis (FirePlex™ technology) were used to examine sera samples. Finally, we combined the different data types to search for potential biomarkers.

Results
We found that the diversity of bacteria is reduced in fibromyalgia patients. The abundance of the Bifidobacterium and Eubacterium genera (bacteria participating in the metabolism of neurotransmitters in the host) in these patients was significantly reduced. The serum metabolome analysis revealed altered levels of glutamate and serine, suggesting changes in neurotransmitter metabolism. The combined serum metabolomics and gut microbiome datasets showed a certain degree of correlation, reflecting the effect of the microbiome on metabolic activity. We also examined the microbiome and serum metabolites, cytokines and miRNAs as potential sources of molecular biomarkers of fibromyalgia.

Conclusions
Our results show that the microbiome analysis provides more significant biomarkers than the other techniques employed in the work. Gut microbiome analysis combined with serum metabolomics can shed new light onto the pathogenesis of fibromyalgia. We provide a list of bacteria whose abundance changes in this disease and propose several molecules as potential biomarkers that can be used to evaluate the current diagnostic criteria.

Open access at https://www.ebiomedicine.com/article/S2352-3964(19)30473-6/fulltext

 

[arewenearlythereyet]

 

[Hutan]

Care to elaborate @arewenearlythereyet?

The study looks to have been written up nicely - I mean they seem to have explained what they did quite well, which is a good start.

This was interesting:

The metabolomics analysis yielded 8543 different metabolic features defined by retention time and mass/charge. ... The PCA analysis revealed that the metabolomics profiles differed between hospitals (Supplementary Fig. 3). This was expected because of the autoclaving performed in one of the hospitals. Thus, to avoid the bias caused by the chemicals released during the autoclaving procedure, the discriminating hospital features (p = 661), were removed from the study,

So, they chucked out all the results produced from one hospital because they were affected by autoclaving. It makes me wonder how often metabolomics studies are affected by autoclaving and the results get used anyway.

This was nice I thought:


So, there were over 1000 metabolic features identified that were different in more than 30% of the data and that were consistent between the different hospital cohorts (that's each of the dots in the left hand graph). Of those, 228 features were different between patients and controls (i.e. statistically different and a big fold change - shown as green dots.). I'd like to see other metabolomic researchers showing their data in graphs like that one above left.

But they didn't seem to be able to identify many of these features (which may mean that they are missing lots of clues?).

Of these 228, only 88 had tentative IDs in the METLIN database.

I don't understand this next statement - how did they get down from 88 to 7?

Using MS/MS data and chemical standards, we found that the levels of 7 of these metabolites were significantly altered in the fibromyalgia samples (Supplementary Table S3): ornithine, L-arginine, Nε-Methyl-l-lysine, l-glutamate, l-glutamine, asymmetric dimethylarginine (ADMA) and platelet activating factor (PAF-16) (Fig. 4B). Another metabolic feature among the 228 altered in fibromyalgia was tentatively identified as l-threonine or DL-homoserine (Fig. 4B). We could not discriminate between these two metabolites as they are structurally similar and have the same molecular mass and fragmentation pattern in LC-MS.

The seven metabolites that were identified as different between fibromyalgia patients and controls are shown in the right hand graph above.

Damn, I need a break, so tapping out.

Here's a link to another recent fibromyalgia microbiome study.
https://www.s4me.info/threads/alter...s-with-fibromyalgia-2019-minerbi-et-al.10094/

 

[Midnattsol]

Care to elaborate @arewenearlythereyet?

The study looks to have been written up nicely - I mean they seem to have explained what they did quite well, which is a good start.

This was interesting:


So, they chucked out all the results produced from one hospital because they were affected by autoclaving. It makes me wonder how often metabolomics studies are affected by autoclaving and the results get used anyway.

This was nice I thought:
View attachment 7833

So, there were over 1000 metabolic features identified in >30% of the data and that were consistent between the different hospital cohorts (that's each of the dots in the left hand graph). Of those, 228 features were different between patients and controls (i.e. statistically different and a big fold change - shown as green dots.). I'd like to see other metabolomic researchers showing their data in graphs like that one above left.

But they didn't seem to be able to identify many of these features (which may mean that they are missing lots of clues?).


I don't understand this next statement - how did they get down from 88 to 7?


The seven metabolites that were identified as different between fibromyalgia patients and controls are shown in the right hand graph above.

Damn, I need a break, so tapping out.

Here's a link to another recent fibromyalgia microbiome study.
https://www.s4me.info/threads/alter...s-with-fibromyalgia-2019-minerbi-et-al.10094/

There's more than one procedure done at the hospital that can alter metabolomics-data, not just autoclaving. And I wonder too how often it is not properly handled when analysing the data, although it is not always easy to see, so I guess you can only do your best. I'm also a fan of volcano plots.

To me it seems like the 7 they end up with were the significantly different features whereas the 228 was all different features (after the filtrering steps).

Edit: A bit unsure if it's the seven most significant features or seven they identified beyond just "tentatively" using MS/MS (basicly just sending a sample through two mass-spectrometers)? In the graph it says "identified" and in the text they talk about significance, but the graph are calling all 228 significant.

 

[arewenearlythereyet]

Care to elaborate @arewenearlythereyet?

I would say the use of the phrase gut brain axis was the first red flag.

Then followed by poor methodology (faeces sample linked to serum metabolites) using a small sample
With no established reference for what things should be and low precision by only looking at genus

Then a rather bullish and misleadingly overstated conclusion on many many fronts.

I find this sloppy and extremely poor science I don’t have a more extreme tripe picture and I think a pair of bollocks would probably be moderated as offensive.

I am sure many others will find this interesting...leaky gut and gluten free brigade probably. They seem to love correlation studies with low power and overstated conclusions.

 

[Kitty]

I haven't had the energy to read the paper properly yet...but realising I'm perhaps one of the few who'd recognise tripe when they see it made me feel very old!

 

[wigglethemouse]

Amy Proal on Twitter has some a thread with some comments on this study