RNA-binding proteins with mixed charge domains self-assemble and aggregate in Alzheimer’s Disease
Bibliographic record
Abstract
ABSTRACT U1 small nuclear ribonucleoprotein 70 kDa (U1-70K) and other RNA binding proteins (RBPs) are mislocalized to cytoplasmic neurofibrillary Tau aggregates in Alzheimer’s disease (AD), yet understanding of the mechanisms that cause their aggregation is limited. Many RBPs that aggregate in neurodegenerative diseases self-assemble into RNA granules through intrinsically disordered low complexity (LC) domains. We report here that a LC domain within U1-70K of mixed charge, containing highly repetitive complementary repeats of basic (R/K) and acidic (D/E) residues, shares many of the same properties of the Q/N-rich LC domains found in the RBPs TDP-43 and FUS. These properties include the ability to self-assemble into oligomers, and to form nuclear granules. To analyze the functional roles of the U1-70K LC domains, we performed co-immunoprecipitation and quantitative mass spectrometry analysis of recombinant U1-70K and deletions lacking the C-terminal LC domain(s). A network-driven approach resolved functional classes of U1-70K interacting proteins that showed dependency on the U1-70K LC domain(s) for their interaction. This included structurally similar RBPs, such as LUC7L3 and RBM25, which require their respective mixed charge domains for reciprocal interactions with U1-70K and for participation in nuclear RNA granules. Strikingly, a significant proportion of RBPs with mixed charge domains have elevated insolubility in AD brain proteome compared to controls. Furthermore, we show that the mixed charge LC domain of U1-70K can interact with Tau from AD brain. These findings highlight mechanisms for mixed charge domains in stabilizing RBP interactions and in potentially mediating co-aggregation with pathological Tau isoforms in AD.
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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 | 0.001 | 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".