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Record W3133625359 · doi:10.3389/fvets.2020.564791

An Integrated Molecular Approach to Untangling Host–Vector–Pathogen Interactions in Mosquitoes (Diptera: Culicidae) From Sylvan Communities in Mexico

2021· article· en· W3133625359 on OpenAlexafffund
Luis M. Hernández‐Triana, Javier A. Garza‐Hernández, Aldo Iván Ortega Morales, Sean W. J. Prosser, Paul D. N. Hebert, Nadya Nikolova, Elsa Barrero, Erick de Jesús de Luna-Santillana, Vicente Homero González-Álvarez, Ramón Méndez‐López, Rahuel J. Chan-Chablé, Anthony R. Fooks, Mario A. Rodríguez‐Pérez

Bibliographic record

VenueFrontiers in Veterinary Science · 2021
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsUniversity of Guelph
FundersCanada First Research Excellence FundDepartment for Environment, Food and Rural Affairs, UK GovernmentScottish GovernmentConsejo Nacional de Ciencia y TecnologíaSecretaría de Educación PúblicaHorizon 2020 Framework ProgrammeClemson University
KeywordsBiologyDNA barcodingAedes albopictusVector (molecular biology)ZoologyAedesArbovirusBlood mealChikungunyaHost (biology)OutbreakDengue virusVirologyVirusDengue feverAedes aegyptiAnophelesDNA sequencingEcologyGeneGeneticsLarvaMalaria

Abstract

fetched live from OpenAlex

There are ~240 species of Culicidae in Mexico, of which some are vectors of arthropod-borne viruses such as Zika virus, dengue virus, chikungunya virus, and West Nile virus. Thus, the identification of mosquito feeding preferences is paramount to understanding of vector–host–pathogen interactions that, in turn, can aid the control of disease outbreaks. Typically, DNA and RNA are extracted separately for animal (insects and blood meal hosts) and viral identification, but this study demonstrates that multiple organisms can be analyzed from a single RNA extract. For the first time, residual DNA present in standard RNA extracts was analyzed by DNA barcoding in concert with Sanger and next-generation sequencing (NGS) to identify both the mosquito species and the source of their meals in blood-fed females caught in seven sylvan communities in Chiapas State, Mexico. While mosquito molecular identification involved standard barcoding methods, the sensitivity of blood meal identification was maximized by employing short primers with NGS. In total, we collected 1,634 specimens belonging to 14 genera, 25 subgenera, and 61 morphospecies of mosquitoes. Of these, four species were new records for Mexico ( Aedes guatemala, Ae. insolitus, Limatus asulleptus, Trichoprosopon pallidiventer ), and nine were new records for Chiapas State. DNA barcode sequences for >300 bp of the COI gene were obtained from 291 specimens, whereas 130 bp sequences were recovered from another 179 specimens. High intraspecific divergence values (>2%) suggesting cryptic species complexes were observed in nine taxa: Anopheles eiseni (5.39%), An. pseudopunctipennis (2.79%), Ae. podographicus (4.05%), Culex eastor (4.88%), Cx. erraticus (2.28%), Toxorhynchites haemorrhoidalis (4.30%), Tr. pallidiventer (4.95%), Wyeomyia adelpha/Wy. guatemala (7.30%), and Wy. pseudopecten (4.04%). The study increased the number of mosquito species known from 128 species to 138 species for Chiapas State, and 239 for Mexico as a whole. Blood meal analysis showed that Aedes angustivittatus fed on ducks and chicken, whereas Psorophora albipes fed on humans. Culex quinquefasciatus fed on diverse hosts including chicken, human, turkey, and Mexican grackle. No arbovirus RNA was detected by reverse transcriptase–polymerase chain reaction in the surveyed specimens. This study demonstrated, for the first time, that residual DNA present in RNA blood meal extracts can be used to identify host vectors, highlighting the important role of molecular approaches in both vector identification and revealing host–vector–pathogen interactions.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.595
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.297
Teacher spread0.276 · 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 teacher head, not a consensus.

Study designObservational
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

Citations22
Published2021
Admission routes2
Has abstractyes

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