Simultaneous host and parasite expression profiling identifies tissue-specific transcriptional programs associated with susceptibility or resistance to experimental cerebral malaria
Bibliographic record
Abstract
BACKGROUND: The development and outcome of cerebral malaria (CM) reflects a complex interplay between parasite-expressed virulence factors and host response to infection. The murine CM model, Plasmodium berghei ANKA (PbA), which simulates many of the features of human CM, provides an excellent system to study this host/parasite interface. We designed "combination" microarrays that concurrently detect genome-wide transcripts of both PbA and mouse, and examined parasite and host transcriptional programs during infection of CM-susceptible (C57BL/6) and CM-resistant (BALB/c) mice. RESULTS: Analysis of expression data from brain, lung, liver, and spleen of PbA infected mice showed that both host and parasite gene expression can be examined using a single microarray, and parasite transcripts can be detected within whole organs at a time when peripheral blood parasitemia is low. Parasites display a unique transcriptional signature in each tissue, and lung appears to be a large reservoir for metabolically active parasites. In comparisons of susceptible versus resistant animals, both host and parasite display distinct, organ-specific transcriptional profiles. Differentially expressed mouse genes were related to humoral immune response, complement activation, or cell-cell interactions. PbA displayed differential expression of genes related to biosynthetic activities. CONCLUSION: These data show that host and parasite gene expression profiles can be simultaneously analysed using a single "combination" microarray, and that both the mouse and malaria parasite display distinct tissue- and strain-specific responses during infection. This technology facilitates the dissection of host-pathogen interactions in experimental cerebral malaria and could be extended to other disease models.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".