SynthesisMitochondrion2025
Peripheral immune progression to long COVID is associated with mitochondrial gene transcription: A meta-analysis.
Synthesis in Mitochondrion, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- VSL#3British journal of biomedical science · 2026Trial
- Post-COVID impairment of T cell responses to community-acquired pathogens can be modified by activating cellular metabolism.PLoS pathogens · 2026Article
- Plasma Extracellular Vesicle Surface Marker Profiling Reveals Immune Cell-Associated Mitochondrial Membrane Potential Alterations in Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome.Open forum infectious diseases · 2026Article
- Disruption of neutrophil homeostasis is associated with functional alterations in mitochondria of critically ill COVID-19 patients.Scientific reports · 2026Article
- Post-COVID impairment of memory T cell responses to community-acquired pathogens can be rectified by activating cellular metabolism.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
SARS-CoV-2, the virus responsible for the COVID-19 pandemic, has led to millions of cases of Long COVID worldwide. Long COVID is a phenomenon characterized by persistent and debilitating mental and physical symptoms following acute infection. Despite ongoing research, trials, and considerable progress in understanding Long COVID, its exact causes remain only partially understood, with current hypotheses addressing specific aspects of the condition. We conducted one of the most comprehensive meta-analyses to date of all quality bulk RNA-seq studies worldwide from the COVID-19 pandemic and show significant mitochondrial transcript changes in the peripheral immune system of people with Long COVID, with unexpectedly low levels of intracellular viral RNA in Long COVID. This extensive analysis, which includes 26 studies and 1,272 individuals, shows that mononuclear cells, PBMC, and granulocytes from Long COVID patients exhibit significant alterations in mitochondrial genes and related processes. These findings likely represent the true transcriptomic landscape of Long COVID across diverse datasets, highlighting the long-lasting impacts of SARS-CoV-2 on peripheral immune function. In combination with other ex vivo and proteomics studies showing mitochondrial dysfunction, our results suggest critical new directions, such as the potential role of clonal hematopoiesis and infected seed cells. This work highlights the need for further investigation into the mechanisms underlying these immune changes and persistent symptoms in people with Long COVID. These findings will serve as a foundation for defining the paradigm underlying the biological mechanisms of Long COVID, driving research into the peripheral immune system, bone marrow, and mitochondria.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.