The mosquito Aedes aegypti is distributed worldwide and is recognized as the primary vector for dengue in numerous countries. To investigate whether the fitness cost of a single DENV-1 isolate varies among populations, we selected four Ae. aegypti populations from distinct localities: Australia (AUS), Brazil (BRA), Pakistan (PAK), and Peru (PER). Utilizing simple methodologies, we concurrently assessed survival rates and fecundity. Overall, DENV-1 infection led to a significant decrease in mosquito survival rates, with the exception of the PER population. Furthermore, infected Ae. aegypti from PAK, the population with the lowest infection rate among those tested, exhibited a noteworthy reduction in egg laying. These findings collectively suggest that local mosquito-virus adaptations may influence dengue transmission in endemic settings.

Several countries have been using Wolbachia deployments to replace highly competent native Aedes aegypti populations with Wolbachia-carrying mosquitoes with lower susceptibility to arboviruses such as dengue, Zika, and chikungunya. In Rio de Janeiro, Wolbachia deployments started in 2015 and still present a moderate introgression with a modest reduction in dengue cases in humans (38%). Here, we evaluated the vector competence of wild-type and wMel-infected Ae. aegypti with a Brazilian genetic background to investigate whether virus leakage could contribute to the observed outcomes in Brazil. We collected the specimens in three areas of Rio de Janeiro with distinct frequencies of mosquitoes with wMel strain and two areas with wild Ae. aegypti. The mosquitoes were orally exposed to two titers of DENV-1 and the saliva of DENV-1-infected Ae. aegypti was microinjected into wMel-free mosquitoes to check their infectivity. When infected with the high DENV-1 titer, the presence of wMel did not avoid viral infection in mosquitoes’ bodies and saliva but DENV-1-infected wMel mosquitoes produced lower viral loads than wMel-free mosquitoes. On the other hand, wMel mosquitoes infected with the low DENV-1 titer were less susceptible to virus infection than wMel-free mosquitoes, although once infected, wMel and wMel-free mosquitoes exhibited similar viral loads in the body and the saliva. Our results showed viral leakage in 60% of the saliva of wMel mosquitoes with Brazilian background; thus, sustained surveillance is imperative to monitor the presence of other circulating DENV-1 strains capable of overcoming the Wolbachia blocking phenotype, enabling timely implementation of action plans.

Proteomics workflows are designed to answer predefined questions, leaving analytical problems that emerge after first results difficult to investigate without additional bioinformatics work. We introduce data set interrogation, a conversational framework in which a researcher uses a large language model (LLM) agent to translate emerging questions into executable tests, check the answers against controls, and keep the code, inputs, and decisions in a dated record. Applied retrospectively to four LC–MS/MS data sets, the framework exposed problems that changed the interpretation of three in-house studies: a search database lacking one of two symbionts, a swapped pair of tandem mass tag (TMT) channels, statistics inflated by non-host proteins, and false deamidation assignments caused by selection of the ¹³ C isotope peak as monoisotopic; the same question asked of every data set revealed failed or partial cysteine alkylation in all three. A public TMT data set was reanalyzed from raw files in 24 min, a feasibility measure without a human comparison; among spectra identified by both workflows, 99.2% received the same peptide assignment as in Proteome Discoverer, and protein fold changes correlated (r = 0.93). In seeded tests, a purpose-built channel-assignment check detected all 216 between-strain channel swaps and none of 90 within-strain swaps. The framework couples natural-language interaction with executable analyses, explicit controls, and traceable decisions, enabling researchers to investigate questions not anticipated by the original pipeline. These results establish feasibility in a limited retrospective setting with one researcher; broader evaluation across users, models, and data sets is required to determine general performance.