Decoding Transcriptomic Landscape and Molecular Pathways in DNAJC21 Deficiency
Bibliographic record
Abstract
Background: DNAJ homolog subfamily C member 21 (DNAJC21) is a member of the DNAJ heat shock protein 40 family. Biallelic DNAJC21 mutations lead to DNAJC21 deficiency, disrupt ribosome biogenesis and cause a Schwachman Diamond like syndrome. DNAJC21 deficiency causes bone marrow (BM) failure, a risk of evolution to myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), and extra-hematopoietic symptoms. Due to the limited published data, risk stratification for clonal evolution is currently not possible, and treatments beyond hematopoietic stem cell transplantation (HSCT) are unavailable. Understanding the molecular alterations in hematopoietic stem and progenitor cells (HSPCs) of DNAJC21 deficiency is critical for developing targeted therapies and stratifying the risk for disease progression. Objective: To collect clinical and laboratory data of DNAJC21-deficient patients. To elucidate the transcriptomic landscape and molecular pathways of DNAJC21 deficiency we performed scRNA seq in DNAJC21-deficient BM samples and performed shRNA-mediated DNAJC21 knock down in K562 cell model. Methods: Clinical and laboratory data were collected from 13 patients (9 unpublished and 4 published individuals, respectively). BM samples were collected from 6 DNAJC21-deficient patients and a targeted panel of genes involved in MDS/AML was performed. scRNA-seq was performed on CD34+ cells freshly isolated from the BM of 2 DNAJC21-deficient patients and compared with 2 healthy donors. The role of DNAJC21 was further investigated by reducing its expression in K562 cells using lentiviral shRNA and performing untargeted proteomic analysis. Bioinformatics analysis identified differentially expressed genes, pathways, and cell populations. Results: Data were collected from 13 patients (male:female = 8:5, median age 11 years; age range 1.5-15 years). All patients showed abnormal pancreatic imaging or function. Short stature, developmental delay, skeletal and eye abnormalities were frequently observed (11/13, 10/12, 8/13 and 7/9, respectively). Hypocellular BM was reported in 9/10 patients. Cytogenetic abnormalities were detected in 1 case (46,XY, der(15)t(1;15)(q12;p11)). Five out of 6 patients carried 1 or more somatic TP53 mutations (VAF range 5.3-33%) in the BM samples. P1 (n = 4) and P2 (n = 3) BM biopsy samples revealed progressive decrease in CD3+ T cells with a relative increase in cytotoxic CD8+ cells over time. HSCT was performed in 6 patients due to BM failure. Eleven patients were alive at the time of last follow up. Two patients died due to EBV-associated lymphoproliferative disorder (2.5 months post HSCT) or S. Aureus sepsis, respectively. scRNA-seq analysis revealed distinct transcriptional profiles in HSPCs from DNAJC21-deficient patients. DNAJC21 mutations affect translation and protein synthesis, immune response and metabolism. Notably, enrichment in gene sets involved in ribosome biogenesis emerged with a marked increase in the expression of ribosomal protein genes RPS17, RPL41, and RPL39, as well as the translation elongation factor EEF1G. These dysregulated genes are crucial components of the protein synthesis machinery, suggesting a compensatory response to impaired ribosome biogenesis. A marked decrease in genes involved in proliferation (MKI67, H2AFX, H3F3B, DBF4, UBB, UBC, UBE2S, FOSB, CCNL1) was also observed. Preliminary mass spectrometry analysis of DNAJC21-knockdown K562 cell lines identified several dysregulated pathways, including downregulation of DNA replication and galactose metabolism pathways and upregulation of hematopoietic cell lineage, purine metabolism, and SNARE interactions in vesicular transport pathways. Conclusion: Biallelic DNAJC21 mutations are associated with pancreatic abnormalities and multisystem disorders along with BM failure. The majority of patients harbors TP53 mutated clones with high VAF, whose role in disease evolution and malignant myeloid transformation remains to be elucidated. Biallelic DNAJC21 mutations profoundly impact the transcriptomic landscape of HSPCs, leading to dysregulated ribosome biogenesis and impaired proliferation. Our findings unveil the molecular pathways of BM failure in DNAJC21-deficient patients, highlighting critical pathways and gene sets that could be used as potential biomarkers for disease progression or targeted for therapeutic intervention.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".