Inference of the Oxidative Stress Network in Anopheles stephensi upon Plasmodium Infection

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.

The cell biology of malaria infection of mosquito: advances and opportunities

“…allosamidin) were shown to decrease oocyst numbers (Shahabuddin et al, 1993;Shahabuddin and Kaslow, 1994a,b;Zieler et al, 1999;. Humoral insect defence mechanisms however remain potent inhibitors when the ookinete crosses the epithelial layer (James, 1928;Dimopoulos, 2003;Osta et al, 2004;Christophides, 2005;Povelones et al, 2011;Shrinet et al, 2014).…”

“…Recognizing the female gametocyte provides the majority of the biomass of the zygote/ookinete, this expansion of energy provision is understandable, but the male gametocyte forms eight microgametes devoid of both plastids, suggesting in the male cell that it performs a vital function during gametocytogenesis or during the dramatic events of gamete formation in the mosquito? It has been shown that the apicoplast provides isoprenoids essential for gametocytogenesis (Wiley et al ., ), and up‐regulation of glyoxalase provides defence against oxidative stress (Okamoto et al ., ) that has recently been shown to be a component of the mosquitoes' responses to malarial infection (Shrinet et al ., ). Up‐regulation of genes encoding type II fatty acid, and 15 of 16 TCA enzymes in the mature gametocyte are also consistent with the cytological observations.…”

miRNA–mRNA Conflux Regulating Immunity and Oxidative Stress Pathways in the Midgut of Blood-Fed Anopheles stephensi

“…This data was correlated with expression levels of 9255 mRNA transcripts of An. stephensi generated using same midgut samples [ 34 ]. Transcripts showing both negative and positive correlation with miRNAs and threshold cut off of 0.9 were selected for further analysis ( Table S2 ).…”

“…Fold change in miRNA expression profile deduced by real time PCR was used for co-expression analysis. For mRNA expression data, we obtained RNA-Seq data generated using midgut tissues of sugar fed naive female mosquitoes 6–8 days old (SF), female mosquitoes at 5 days post blood-feeding (BF 5d), 5 days post Plasmodium vinckei petteri infected blood feeding (iBF 5d) and 5 days post human blood-fed (HBF 5d) mosquitoes [ 34 ]. Data were normalized (FPKM values calculated) and fold change of transcripts were calculated using SF as a calibrator.…”

Machine learning approaches classify clinical malaria outcomes based on haematological parameters

“…The sample size (n=376) was very small to deduce meaningful classifications and the author concluded that more work would be needed [27]. Despite this, there have been far reaching studies on application of ML in other areas of malaria research [28][29][30][31][32]. The diagnosis of malaria using ML on clinical datasets has been impaired by lack of large data, as well as difficulty in data curation.…”