CRISPR/Cas9 knockouts reveal genetic interaction between strain-transcendent erythrocyte determinants of Plasmodium falciparum invasion.
Bibliographic record
Abstract
During malaria blood-stage infections, Plasmodium parasites interact with the RBC surface to enable invasion followed by intracellular proliferation. Critical factors involved in invasion have been identified using biochemical and genetic approaches including specific knockdowns of genes of interest from primary CD34+ hematopoietic stem cells (cRBCs). Here we report the development of a robust in vitro culture system to produce RBCs that allow the generation of gene knockouts via CRISPR/Cas9 using the immortal JK-1 erythroleukemia line. JK-1 cells spontaneously differentiate, generating cells at different stages of erythropoiesis, including terminally differentiated nucleated RBCs that we term "jkRBCs." A screen of small-molecule epigenetic regulators identified several bromodomain-specific inhibitors that promote differentiation and enable production of synchronous populations of jkRBCs. Global surface proteomic profiling revealed that jkRBCs express all known Pfalciparum host receptors in a similar fashion to cRBCs and that multiple Pfalciparum strains invade jkRBCs at comparable levels to cRBCs and RBCs. Using CRISPR/Cas9, we deleted two host factors, basigin (BSG) and CD44, for which no natural nulls exist. BSG interacts with the parasite ligand Rh5, a prominent vaccine candidate. A BSG knockout was completely refractory to parasite invasion in a strain-transcendent manner, confirming the essential role for BSG during invasion. CD44 was recently identified in an RNAi screen of blood group genes as a host factor for invasion, and we show that CD44 knockout results in strain-transcendent reduction in invasion. Furthermore, we demonstrate a functional interaction between these two determinants in mediating Pfalciparum erythrocyte invasion.
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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 | 0.000 | 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 | 0.001 |
| 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".