Preprint Persistent immune dysregulation and metabolic alterations following SARS-CoV-2 infection, 2025, Lage, Deeks, Henrich, Peluso+

SNT Gatchaman

SNT Gatchaman

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Persistent immune dysregulation and metabolic alterations following SARS-CoV-2 infection
Silvia Lucena Lage; Katherine Bricker-Holt; Joseph M. Rocco; Adam Rupert; Frank X. Donovan; Yevgeniya A. Abramzon; Settara C. Chandrasekharappa; Colton McNinch; Logan Cook; Eduardo Pinheiro Amaral; Gabriel Rosenfeld; Thomas Dalhuisen; Avery Eun; Rebecca Hoh; Emily Fehrman; Jeffrey N. Martin; Steven G Deeks; Timothy J. Henrich; Michael J Peluso; Irini Sereti

SARS-CoV-2 can cause a variety of post-acute sequelae including Long COVID19 (LC), a complex, multisystem disease characterized by a broad range of symptoms including fatigue, cognitive impairment, and post-exertional malaise. The pathogenesis of LC is incompletely understood.

In this study, we performed comprehensive cellular and transcriptional immunometabolic profiling within a cohort that included SARS-CoV-2-naive controls (NC, N=30) and individuals with prior COVID-19 (~4-months) who fully recovered (RC, N=38) or went on to experience Long COVID symptoms (N=58).

Compared to the naive controls, those with prior COVID-19 demonstrated profound metabolic and immune alterations at the proteomic, cellular, and epigenetic level. Specifically, there was an enrichment in immature monocytes with sustained inflammasome activation and oxidative stress, elevated arachidonic acid levels, decreased tryptophan, and variation in the frequency and phenotype of peripheral T-cells. Those with LC had increased CD8 T-cell senescence and a distinct transcriptional profile within CD4 and CD8 T-cells and monocytes by single cell RNA sequencing.

Our findings support a profound and persistent immunometabolic dysfunction that follows SARS-CoV-2 which may form the pathophysiologic substrate for LC. Our findings suggest that trials of therapeutics that help restore immune and metabolic homeostasis may be warranted to prevent, reduce, or resolve LC symptoms.


Link | PDF (Preprint: MedRxiv) [Open Access]
 
There is a brief mention of ME/CFS:
Notably, low levels of ω-3 FAs have also been associated with pathophysiology of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) (66), a related infection-associated chronic condition with immunopathology involving mitochondrial, metabolic and immune dysfunction (67, 68). Tryptophan metabolism has also been implicated in ME/CFS pathology (69). Reduced circulating Try leads to depletion of serotonin levels, potentially contributing to neurologic and cognitive symptoms observed in ME/CFS and LC (37). Since LC can comprise a greater variety of symptoms than ME/CFS, future studies should evaluate the contribution of altered metabolic profiles in additional LC cohorts presenting specifically with ME/CFS phenotype.
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I haven’t had a chance to read the full paper but it’s interesting that the DHEA finding was replicated with my team’s LC paper, which also has come up in ME.

Also, I need to keep saying it because it continually annoys me in scRNA-seq analysis: I simply do not trust analyses that identify fine-grained immune cell subtypes on scRNA-seq alone. You simply will not get the necessary read depth to accurately differentiate half of those subsets, and I don’t see anything in the supplemental material that would make me more confident in their labeling. [edit: if you want to do that, do CITE-seq]

And I do not trust proportional differences in immune subtypes based off of that fine-grained scRNA-seq labeling whatsoever without concurrent flow cytometry data. I don’t know why papers keep doing it.

That being said, I think there are some things this paper did well, I’ll try to comment that when I have more time.
 
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Okay, having had a chance to revisit the paper:
1) It looks like the actual clustering (Fig 7A) was in fact done on flow cytometry markers and not simply on RNA-seq. So I do trust it more. Though as always, it is hard to speculate what a difference in the proportions of some specific immune subsets actually means.

2) For their actual single-cell analysis I don't see any supplementary information showing markers for their clustering. However, the subsets that they define (CD4+ T cell, CD8+ T cell, and monocytes) are generally possible to differentiate on the basis of scRNA-seq alone. I just wished they would have shown evidence that the subsets are actually the subsets they claim--hopefully a reviewer will ask for this.

3) They used the NASEM LC definition, which is so broad it basically covers every possible symptom that emerges after COVID infection. I understand the justification for making an intentionally broad clinical definition, but I don't think it should be used as a research definition.

4) Potentially because of their broad LC definition, it seems like a lot of the findings in monocytes are just general post-COVID findings. Meaning that we don't know if they actually correlate to any clinically relevant outcome since it was uniform across "recovered" and "Long COVID" participants

5) Same goes with the tryptophan findings. It's interesting that it's different post-COVID and that the finding is consistent with ME/CFS findings, but this study has no indication of whether it's actually correlated with any specific post-COVID outcome.
 
The biggest worry I have is that anyone feeling dodgy for any reason in the months after Covid gets called Long Covid. So they may just be reporting the changes that occur when people feel dodgy - either because they were already a bit dodgy before they got Covid or they are dodgy for some other reason and happened to have Covid.

The 'results' in the abstract are too vague for me to want to look further.
 
Jonathan Edwards said:
The biggest worry I have is that anyone feeling dodgy for any reason in the months after Covid gets called Long Covid. So they may just be reporting the changes that occur when people feel dodgy - either because they were already a bit dodgy before they got Covid or they are dodgy for some other reason and happened to have Covid.
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That's my biggest complaint with the NASEM definition. It basically boils down Long COVID to "anything that could ever happen to you other than being perfectly healthy, accompanied by a positive COVID test at some point."

I think this paper does a decent job of showing differences correlated with prior COVID exposure. But without any stringent LC definition, or even specific outcome measures to correlate with their findings, that's all it shows. If the majority of your findings were consistent between "recovered" and "Long COVID" participants, why even mention Long COVID at all?
 
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