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Record W3111940417 · doi:10.1016/j.cris.2020.100006

Rhodnius prolixus uses the peptidoglycan recognition receptor rpPGRP-LC/LA to detect Gram-negative bacteria and activate the IMD pathway

2020· article· en· W3111940417 on OpenAlexafffund
Nicolás Salcedo-Porras, Shireen Noor, Charley Cai, Pedro L. Oliveira, Carl Lowenberger

Bibliographic record

VenueCurrent Research in Insect Science · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicInvertebrate Immune Response Mechanisms
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaMitacsSimon Fraser University
KeywordsBiologyPattern recognition receptorRhodnius prolixusPeptidoglycanInnate immune systemDrosophila melanogasterTrypanosoma cruziMicrobiologyAntimicrobial peptidesGeneticsCell biologyGeneImmune systemInsectBacteriaParasite hostingBotany

Abstract

fetched live from OpenAlex

Insects rely on an innate immune system to recognize and eliminate pathogens. Key components of this system are highly conserved across all invertebrates. To detect pathogens, insects use Pattern recognition receptors (PRRs) that bind to signature motifs on the surface of pathogens called Pathogen Associated Molecular Patterns (PAMPs). In general, insects use peptidoglycan recognition proteins (PGRPs) in the Immune Deficiency (IMD) pathway to detect Gram-negative bacteria, and other PGRPs and Gram-negative binding proteins (GNBPs) in the Toll pathway to detect Gram-positive bacteria and fungi, although there is crosstalk and cooperation between these and other pathways. Once pathogens are recognized, these pathways activate the production of potent antimicrobial peptides (AMPs). Most PRRs in insects have been reported from genome sequencing initiatives but few have been characterized functionally. The initial studies on insect PRRs were done using established dipteran model organisms such as Drosophila melanogaster, but there are differences in the numbers and functional role of PRRs in different insects. Here we describe the genomic repertoire of PGRPs in Rhodnius prolixus, a hemimetabolous hemipteran vector of the parasite Trypanosoma cruzi that causes Chagas disease in humans. Using a de novo transcriptome from the fat body of immune activated insects, we found 5 genes encoding PGRPs. Phylogenetic analysis groups R. prolixus PGRPs with D. melanogaster PGRP-LA, which is involved in the IMD pathway in the respiratory tract. A single R. prolixus PGRP gene encodes isoforms that contain an intracellular region or motif (cryptic RIP Homotypic Interaction Motif-cRHIM) that is involved in the IMD signaling pathway in D. melanogaster. We characterized and silenced this gene using RNAi and show that the PGRPs that contain cRHIMs are involved in the recognition of Gram-negative bacteria, and activation of the IMD pathway in the fat body of R. prolixus, similar to the PGRP-LC of D. melanogaster. This is the first functional characterization of a PGRP containing a cRHIM motif that serves to activate the IMD pathway in a hemimetabolous insect.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.169
GPT teacher head0.361
Teacher spread0.191 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2020
Admission routes2
Has abstractyes

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