Structural insights and milestones in TDP-43 research: A comprehensive review of its pathological and therapeutic advances
Bibliographic record
Abstract
Transactive response (TAR) DNA-binding protein 43 (TDP-43) is a critical RNA/DNA-binding protein involved in various cellular processes, including RNA splicing, transcription regulation, and RNA stability. Mislocalization and aggregation of TDP-43 in the cytoplasm are key features of the pathogenesis of several neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and Alzheimer's disease (AD). This review provides a comprehensive retrospective and prospective analysis of TDP-43 research, highlighting structural insights, significant milestones, and the evolving understanding of its physiological and pathological functions. We delineate five major stages in TDP-43 research, from its initial discovery as a pathological hallmark in neurodegeneration to the recent advances in understanding its liquid-liquid phase separation (LLPS) behavior and interactions with cellular processes. Furthermore, we assess therapeutic strategies targeting TDP-43 pathology, categorizing approaches into direct and indirect interventions, alongside modulating aberrant TDP-43 LLPS. We propose that future research will focus on three critical areas: targeting TDP-43 structural polymorphisms for disease-specific therapeutics, exploring dual temporal-spatial modulation of TDP-43, and advancing nano-therapy. More importantly, we emphasize the importance of understanding TDP-43's functional repertoire at the mesoscale, which bridges its molecular functions with broader cellular processes. This review offers a foundational framework for advancing TDP-43 research and therapeutic development.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".