Generation and characterisation of seven induced pluripotent stem cell lines from two patients with Parkinson’s disease carrying the pathological variant c.1087G>T of the LGR4 gene
Bibliographic record
Abstract
Parkinson's disease is a neurodegenerative disorder affecting dopaminergic neurons of the substantia nigra pars compacta. The known pathological genetic variants may explain the cause of only 5 % of cases of the disease. In our study, we found two patients with a clinical diagnosis of Parkinson's disease with the genetic variant c.1087G>T (p.Gly363Cys) of the LGR4 gene. The LGR4 gene encodes the membrane receptor LGR4 (leucine rich repeat containing G protein-coupled receptor 4) associated with the G protein. We hypothesize that the LGR4 gene may be either a direct cause or a risk factor for this disease, since it is one of the main participants of the WNT/β-catenin signalling pathway. This signalling pathway is necessary for the proliferation of neurons during their differentiation, which may lead to Parkinson's disease. To study the relationship between this genetic variant and Parkinson's disease, an ideal tool is a cellular model based on induced pluripotent stem cells (iPSCs) and their differentiated derivatives, dopaminergic neurons. We reprogrammed the peripheral blood mononuclear cells of the two patients with the c.1087G>T variant of the LGR4 gene with non-integrating episomal vectors expressing OCT4, SOX2, KLF4, LIN28, L-MYC and mp53DD proteins. The obtained seven lines of induced pluripotent stem cells were characterised in detail. The iPSCs lines obtained meet all the requirements of pluripotent cells, namely, they stably proliferate, form colonies with a morphology characteristic of human pluripotent cells, have a normal diploid karyotype, express endogenous alkaline phosphatase and pluripotency markers (OCT4, NANOG, SSEA-4 and SOX2) and are capable to differentiate into derivatives of the three germ layers. The iPSC lines obtained in this work can be used as a tool to generate a relevant model to study the effect of the pathological variant c.1087G>T of the LGR4 gene on dopaminergic neuron differentiation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".