Identification of Neuronal RNA Targets of TDP-43-containing Ribonucleoprotein Complexes
Bibliographic record
Abstract
TAR DNA-binding protein 43 (TDP-43) is associated with a spectrum of neurodegenerative diseases. Although TDP-43 resembles heterogeneous nuclear ribonucleoproteins, its RNA targets and physiological protein partners remain unknown. Here we identify RNA targets of TDP-43 from cortical neurons by RNA immunoprecipitation followed by deep sequencing (RIP-seq). The canonical TDP-43 binding site (TG)(n) is 55.1-fold enriched, and moreover, a variant with adenine in the middle, (TG)(n)TA(TG)(m), is highly abundant among reads in our TDP-43 RIP-seq library. TDP-43 RNA targets can be divided into three different groups: those primarily binding in introns, in exons, and across both introns and exons. TDP-43 RNA targets are particularly enriched for Gene Ontology terms related to synaptic function, RNA metabolism, and neuronal development. Furthermore, TDP-43 binds to a number of RNAs encoding for proteins implicated in neurodegeneration, including TDP-43 itself, FUS/TLS, progranulin, Tau, and ataxin 1 and -2. We also identify 25 proteins that co-purify with TDP-43 from rodent brain nuclear extracts. Prominent among them are nuclear proteins involved in pre-mRNA splicing and RNA stability and transport. Also notable are two neuron-enriched proteins, methyl CpG-binding protein 2 and polypyrimidine tract-binding protein 2 (PTBP2). A PTBP2 consensus RNA binding motif is enriched in the TDP-43 RIP-seq library, suggesting that PTBP2 may co-regulate TDP-43 RNA targets. This work thus reveals the protein and RNA components of the TDP-43-containing ribonucleoprotein complexes and provides a framework for understanding how dysregulation of TDP-43 in RNA metabolism contributes to neurodegeneration.
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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) | 0.001 | 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".