In Silico Analysis of Post-COVID-19 Condition (PCC) Associated SNP rs9367106 Predicts the Molecular Basis of Abnormalities in the Lungs and Brain Functions
Bibliographic record
Abstract
Long- or post-COVID-19 syndrome, which is also designated by WHO as Post COVID-19 Condition (PCC), is characterized by the persistent symptoms that remain after recovery from SARS-CoV-2 infection. A worldwide consortium of Long COVID-19 Host Genetics Initiative (Long COVID-19 HGI) identified an SNP rs9367106 (G>C; chr6:41,515,652, GRCh38, p = 1.76 × 10−10, OR = 1.63, 95% CI: 1.40–1.89) that is associated with PCC. Unraveling the functional significance of this SNP is of prime importance to understanding the development of the PCC phenotypes and their therapy. Here, in Silico, I explored how the risk allele of this SNP alters the functional mechanisms and molecular pathways leading to the development of PCC phenotypes. Bioinformatic methods include physical interactions using HI-C and Chia-PET analysis, Transcription Factors (TFs) binding ability, RNA structure modeling, epigenetic, and pathway analysis. This SNP resides within two long RNA genes, LINC01276 and FOXP4-AS1, and is located at ~31 kb upstream of a transcription factor FOXP4. This DNA region, including this SNP, physically interacts with FOXP4-AS1 and FOXP4, implying that this regulatory SNP could alter the normal cellular function of FOXP4-AS1 and FOXP4. Furthermore, rs9367106 is in eQTL with the FOXP4 gene in lung tissue. rs9367106 carrying DNA sequences act as distant enhancers and bind with several transcription factors (TFs) including YY1, PPAR-α, IK-1, GR-α, and AP2αA. The G>C transition extensively modifies the RNA structure that may affect the TF bindings and enhancer functions to alter the interactions and functions of these RNA molecules. This SNP also includes an ALU/SINE sequence and alteration of which by the G>C transition may prevent IFIH1/MDA5 activation, leading to suppression of host innate immune responses. LINC01276 targets the MED20 gene that expresses mostly in brain tissues, associated with sleep disorders and basal ganglia abnormalities similar to some of the symptoms of PCC phenotypes. Taken together, G>C transition of rs9367601 may likely alter the function of all three genes to explain the molecular basis of developing the long-term symptomatic abnormalities in the lungs and brain observed after COVID-19 recovery.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".