Dataset on fat body proteome of Anopheles stephensi Liston
Manish Kumar,
Ajeet Kumar Mohanty,
Gourav Dey,
Sreelakshmi K. Sreenivasamurthy,
Ashwani Kumar,
Keshava Prasad
Affiliations
Manish Kumar
Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India; Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
Ajeet Kumar Mohanty
ICMR-National Institute of Malaria Research, Field Station, Goa 403001, India
Gourav Dey
Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India; Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
Sreelakshmi K. Sreenivasamurthy
Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India; Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
Ashwani Kumar
ICMR-National Institute of Malaria Research, Field Station, Goa 403001, India; Corresponding author.
Keshava Prasad
Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India; Center for Systems Biology and Molecular Medicine, Yenepoya Research Center, Yenepoya (Deemed to be University), University Road, Mangalore 575018, India; Corresponding author at: Center for Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), University Road, Mangalore 575018, India.
Fat body from Anopheles stephensi female mosquitoes were dissected and processed for proteomic analysis. Both SDS-PAGE and basic Reverse Phase Liquid Chromatography-based fractionation strategies were used to achieve a broad coverage of protein identification. The fractionated peptides were then analyzed on a high-resolution mass spectrometer. Searching the raw data against the protein database of An. stephensi resulted in identification of 4535 proteins, which is, to our knowledge, the largest catalog of fat body proteome in any mosquito vector species reported so far. Bioinformatics analysis on these fat body proteins suggested the enrichment of biological processes including carbon and lipid metabolism, amino acid metabolism, signal peptide processing and oxidation-reduction. In addition, using proteogenomic approaches, 43 novel proteins were identified, which were not listed in the annotated gene annotations of An. stephensi. The data used in the analysis are related to the article ‘Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes’ (Prasad et al., 2017).
