Target Knowledge Gap: FTD Gap #1: "What determines whether TDP-43 vs tau pathology develops in GRN vs MAPT mutation carriers?" (Score: 33/40) — Both GRN and MAPT mutations cause FTD but produce different pathologies. Understanding the molecular switch could enable pathology-specific therapies.
Disease: Frontotemporal Dementia (FTD)
Priority Rank: 1 (Tier 1: Critical)
Approximately 10-15% of FTD cases are caused by GRN (progranulin) or MAPT (tau) gene mutations. Despite both causing FTD:
- GRN mutations → TDP-43 pathology (FTLD-TDP)
- MAPT mutations → Tau pathology (FTLD-tau)
The fundamental question is: what molecular mechanisms determine which pathology develops? Understanding this "fate switch" could:
- Enable targeted therapy development for each pathology type
- Reveal whether pathology conversion is possible
- Identify protective mechanisms applicable to both
The pathology type is determined by a combination of:
- Loss-of-function vs gain-of-function: GRN haploinsufficiency causes TDP-43 pathology through loss of nuclear TDP-43 function; MAPT mutations cause tau pathology through toxic gain-of-function
- Cellular stress responses: Different cellular stress pathways trigger aggregation of either TDP-43 or tau
- Post-translational modification patterns: PTM signatures that favor one aggregation pathway over another
Multi-center, multi-omics comparative study of GRN and MAPT mutation carriers
- Primary: Induced pluripotent stem cells (iPSCs) from GRN and MAPT mutation carriers → differentiated cortical neurons
- Secondary: Patient-derived brain tissue from FTD autopsy cases (GRN vs MAPT)
- Validation: CRISPR-corrected iPSC lines to confirm mutation-specific effects
- iPSC lines: 10 GRN carriers, 10 MAPT carriers, 10 non-carrier controls
- Brain tissue: 30 FTLD-TDP (GRN), 30 FTLD-tau (MAPT), 30 controls
| Layer |
Technique |
Readout |
| Transcriptomics |
Single-nucleus RNA-seq |
Cell-type specific gene expression changes |
| Proteomics |
Quantitative MS (TMT-labelled) |
Protein abundance and PTM patterns |
| Phosphoproteomics |
Phospho-enrichment MS |
Kinase activation signatures |
| Interactomics |
Proximity-dependent biotinylation (BioID) |
Protein-protein interaction networks |
-
Comparative analysis of GRN vs MAPT neurons:
- Identify differentially expressed genes and proteins
- Map pathway activation differences
- Compare stress response signatures
-
TDP-43 phosphorylation analysis:
- pSer409/410 TDP-43 levels in GRN vs MAPT neurons
- Kinases/phosphatases regulating TDP-43 phosphorylation
-
Tau isoform analysis:
- 3R vs 4R tau ratio in MAPT vs GRN neurons
- Post-translational modifications (phosphorylation, acetylation)
-
Nuclear vs cytoplasmic localization:
- TDP-43 mislocalization in GRN neurons
- Nuclear import/export alterations
-
Molecular signature distinguishing GRN vs MAPT pathology:
- Identified signaling pathways specific to each pathology type
- Validated biomarker panel for pathology prediction
-
Fate-switch mechanism identified:
- Single factor or combination that determines pathology type
- Potential intervention points for pathology conversion
- iPSC disease model validation (reproduces human pathology)
- Drug target identification for each pathology type
- Biomarker panel for patient stratification in clinical trials
| Dimension |
Score |
Rationale |
| Technical Feasibility |
8/10 |
iPSC technology mature; single-nucleus seq standard |
| Timeline |
24 months |
6 mo iPSC derivation, 12 mo profiling, 6 mo analysis |
| Cost |
$2.5M |
iPSC lines ($500K), omics ($1.5M), personnel ($500K) |
| Data Availability |
7/10 |
Existing iPSC banks; autopsy tissue from many centers |
| Risk |
Likelihood |
Mitigation |
| Insufficient differentiation |
Medium |
Optimize cortical neuron protocol |
| Batch effects in omics |
High |
Use standardized pipelines, include controls in each batch |
| Limited brain tissue |
Medium |
Multi-center collaboration (UCSF, Mayo, Cambridge) |
- DIAN-TU: Focuses on AD; FTD-specific studies needed
- ARTFL/LEFFTDS: Clinical network exists; could enable tissue sharing
- Previous iPSC studies: Limited to single-gene comparisons; this is first systematic multi-omics
| Role |
Institution |
Expertise |
| Lead PI |
UCSF (Adam Boxer) |
FTD clinical trials, GRN biology |
| iPSC differentiation |
Stanford (Mali) |
Neural differentiation |
| Proteomics |
UCLA (Cotta) |
MS-based proteomics |
| Bioinformatics |
UCSF (Yokomori) |
Single-cell analysis |
- Target identification: Pathways specific to each pathology could yield targeted drugs
- Patient stratification: Biomarkers to select patients for pathology-specific trials
- Mechanistic validation: Confirm mechanisms before expensive clinical programs
- Rascovsky K, et al, Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia (2011)
- Boxer AL, et al, Advancing research and treatment for frontotemporal lobar degeneration (ARTFL) (2019)
- Rohrer JD, et al, The heritability and genetics of frontotemporal lobar degeneration (2009)