ArticlePloS one2014
Inference of the oxidative stress network in Anopheles stephensi upon Plasmodium infection.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- CSDE1 promotes miR-451 biogenesis.Nucleic acids research · 2023Article
- Machine learning approaches classify clinical malaria outcomes based on haematological parameters.BMC medicine · 2020Article
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- Leveraging User-Friendly Network Approaches to Extract Knowledge From High-ThroughputFrontiers in genetics · 2019Review
- Redox state affects fecundity and insecticide susceptibility in Anopheles gambiae.Scientific reports · 2018Article
- Systems Biology-Based Investigation of Host-Plasmodium Interactions.Trends in parasitology · 2018Review
- miRNA⁻mRNA Conflux Regulating Immunity and Oxidative Stress Pathways in the Midgut of Blood-FedNon-coding RNA · 2015Article
- The cell biology of malaria infection of mosquito: advances and opportunities.Cellular microbiology · 2015Review
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ookinete invasion of Anopheles midgut is a critical step for malaria transmission; the parasite numbers drop drastically and practically reach a minimum during the parasite's whole life cycle. At this stage, the parasite as well as the vector undergoes immense oxidative stress. Thereafter, the vector undergoes oxidative stress at different time points as the parasite invades its tissues during the parasite development. The present study was undertaken to reconstruct the network of differentially expressed genes involved in oxidative stress in Anopheles stephensi during Plasmodium development and maturation in the midgut. Using high throughput next generation sequencing methods, we generated the transcriptome of the An. stephensi midgut during Plasmodium vinckei petteri oocyst invasion of the midgut epithelium. Further, we utilized large datasets available on public domain on Anopheles during Plasmodium ookinete invasion and Drosophila datasets and arrived upon clusters of genes that may play a role in oxidative stress. Finally, we used support vector machines for the functional prediction of the un-annotated genes of An. stephensi. Integrating the results from all the different data analyses, we identified a total of 516 genes that were involved in oxidative stress in An. stephensi during Plasmodium development. The significantly regulated genes were further extracted from this gene cluster and used to infer an oxidative stress network of An. stephensi. Using system biology approaches, we have been able to ascertain the role of several putative genes in An. stephensi with respect to oxidative stress. Further experimental validations of these genes are underway.
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