Activation of immune pathways in common bed bugs, Cimex lectularius, in response to bacterial immune challenges - a transcriptomics analysis
Bibliographic record
Abstract
The common bed bug, Cimex lectularius, is an urban pest of global health significance, severely affecting the physical and mental health of humans. In contrast to most other blood-feeding arthropods, bed bugs are not major vectors of pathogens, but the underlying mechanisms for this phenomenon are largely unexplored. Here, we present the first transcriptomics study of bed bugs in response to immune challenges. To study transcriptional variations in bed bugs following ingestion of bacteria, we extracted and processed mRNA from body tissues of adult male bed bugs after ingestion of sterile blood or blood containing the Gram-positive (Gr+) bacterium Bacillus subtilis or the Gram-negative (Gr–) bacterium Escherichia coli. We analyzed mRNA from the bed bugs’ midgut (the primary tissue involved in blood ingestion) and from the rest of their bodies (RoB; body minus head and midgut tissues). We show that the midgut exhibits a stronger immune response to ingestion of bacteria than the RoB, as indicated by the expression of genes encoding antimicrobial peptides (AMPs). Both the Toll and Imd signaling pathways, associated with immune responses, were highly activated by the ingestion of bacteria. Bacterial infection in bed bugs further provides evidence for metabolic reconfiguration and resource allocation in the bed bugs’ midgut and RoB to promote production of AMPs. Our data suggest that infection with particular pathogens in bed bugs may be associated with altered metabolic pathways within the midgut and RoB that favors immune responses. We further show that multiple established cellular immune responses are preserved and are activated by the presence of specific pathogens. Our study provides a greater understanding of nuances in the immune responses of bed bugs towards pathogens that ultimately might contribute to novel bed bug control tactics.
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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 | 0.000 |
| 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) | 0.001 | 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".