The SARS-CoV-2 receptor angiotensin-converting enzyme 2 (ACE2) in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: analysis of high-throughput epigenetic and gene expression studies
Bibliographic record
Abstract
Abstract Patients affected by Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) show specific epigenetic and gene expression signatures of the disease. However, it is unknown whether these signatures include abnormal levels of the human angiotensin-converting enzymes, ACE and ACE2, the latter being the main receptor described for the host-cell invasion by SARS-CoV-2. To investigate that, we first re-analyzed available case-control epigenome-wide association studies based on DNA methylation data, and case-control gene expression studies based on microarray data. From these published studies, we found an association between ME/CFS and 4 potentially hypomethylated probes located in the ACE locus. We also found another disease association with one hypomethylated probe located in the transcription start site of ACE2. The same disease associations were obtained for women but not for men after performing sex-specific analyses. In contrast, a meta-analysis of gene expression levels could not provide evidence for a differentially expression of ACE and ACE2 in affected patients when compared to healthy controls. In line with this negative finding, the analysis of a new data set on the gene expression of ACE and ACE2 in peripheral blood mononuclear cells did not find any differences between a female cohort of 37 patients and 34 age-matched healthy controls. Future studies should be conducted to extend this investigation to other potential receptors used by SARS-CoV-2. These studies will help researchers and clinicians to improve the understanding of the health risk imposed by this virus when infecting patients affected by this debilitating disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".