Identification and Characterisation of Simiate, a Novel Protein Linked to the Fragile X Syndrome
Bibliographic record
Abstract
A strict regulation of protein expression during developmental stages and in response to environmental signals is essential to every cell and organism. Recent research has shown that the mammalian brain is particularly sensitive to alterations in expression patterns of specific proteins and cognitive deficits as well as autistic behaviours have been linked to dysregulated protein expression. An intellectual disability characterised by changes in the expression of a variety of proteins is the fragile X syndrome. Due to the loss of a single mRNA binding protein, the Fragile X Mental Retardation Protein FMRP, vast misregulation of the mRNA metabolism is taking place in the disease. Here, we present the identification and characterisation of a novel protein named Simiate, whose mRNA contains several FMRP recognition motifs and associates with FMRP upon co-precipitation. Sequence analysis revealed that the protein evolved app. 1.7 billion years ago when eukaryotes developed. Applying antibodies generated against Simiate, the protein is detected in a variety of tissues, including the mammalian brain. On the subcellular level, Simiate localises to somata and nuclear speckles. We show that Simiate and nuclear speckles experience specific alterations in FMR1(-/-) mice. An antibody-based block of endogenous Simiate revealed that the protein is essential for cell survival. These findings suggest not only an important role for Simiate in gene transcription and/or RNA splicing, but also provide evidence for a function of nuclear speckles in the fragile X syndrome. Indeed, transcription and splicing are two fundamental mechanisms to control protein expression, that underlie not only synaptic plasticity and memory formation, but are also affected in several diseases associated with mental disabilities.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".