Drug targets and lipid biomarkers of hyperlipidemia associated diseases
Bibliographic record
Abstract
Hyperlipidemia (HL) is characterized by elevated level of plasma lipids affecting regular functioning of lipid-protein interactions leading to disease. The traditional lipid biomarkers utilized for detection of individuals with HL and Cardiovascular Diseases (CVD) risk, sometimes fail to predict future CVD events therefore, establishing need of novel lipid biomarkers for CVD detection. It was attempted to predict the lipid biomarkers using systems biology and by a meta-analysis of lipidomics studies. A lipid-protein-protein interaction network (LPPIN) was built by incorporating differentially expressed genes in fatty liver of obese subjects obtained from Gene Expression Omnibus, with the interacting lipids obtained from STITCH database. The protein interactions were derived using PathwayLinker and lipid interactions were acquired from STITCH 4.0. For identification of novel lipid biomarkers comparative analysis of normolipidemic, hyperlipidemic and CVD lipid profile was conducted. Cholesterol, diacylglycerol, phosphatidylinositol-bis-phosphate and inositol-triphosphate were central to LPPIN, therefore had the largest systemic effect. The choke point analysis identified proteins having maximum interactions with network lipids, malfunctioning of which could lead to HL associated diseases. Cluster analysis recognized CVD, cancer, Alzheimer's disease and type-2-diabetes to be linked with HL. Lipids associated with the disease clusters consisted of triacylglycerol, cholesterol, oleic acid, linoleic acid, arachidonic acid, palmitate, inositol triphosphate, inositol-1,4-bisphosphate and phosphatidinositol-4-phosphate. Approved HL drug targets like Proprotein Convertase Subtilisin Kexin Type 9 and Niemann-Pick C1-Like 1 may be repurposed for treatment of hyperlipidemia associated diseases. Using a combination of gene prioritization and bridging centrality, Coactosin-like binding protein 1, Vasodilator stimulated phosphoprotein and Hedgehog acyltransferase were identified as a potential drug target for CVD, cancer and Alzheimer's, respectively. A comparative lipidomics analysis revealed that palmitoyl-lysophosphatidylcholine level was decreased while plasma level of free fatty acids and ceramides were elevated in HL and CVD. Both HL and CVD were associated with changes in lipid composition. HL was associated with increased level of saturated diacylglycerol, triacylglycerol and phospholipids while CVD was associated with increase in small chain fatty acids with low double bond content in triacylglycerol, cholesteryl ester and sphingomyelin. Our work highlights new drug targets and biomarkers for hyperlipidemia.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".