Abstract
Anopheles gambiae is the major African malaria vector. Insecticide and drug resistance highlight the need for novel malaria control strategies. A. gambiae exhibits daily (diel and/or circadian) rhythms in physiology and behavior that include flight, mating, sugar and blood-meal feeding, and oviposition. Olfaction is important for detecting blood-feeding hosts, sugar feeding sources, and oviposition sites. We previously reported on mRNA array-based changes in gene expression under light–dark cycle (diel) and constant dark (circadian) conditions. We were able to characterize 25 known or putative olfactory genes in female heads. We sought to follow up on these reported changes in gene expression and correlate them with expected changes in protein response. Here, we describe our recently developed methods and meta-level results for both qualitative and differential proteomics analyses of A. gambiae mosquitoes collected in a time-of-day-specific framework to assess temporal changes in protein abundance over the 24-h day. The traditional challenges associated with proteomics are amplified in an insect such as the mosquito, which contains a large amount of non-proteinaceous material associated with the cuticle and trachea and a high dynamic background of proteins associated with flight muscles and oxidative metabolism (e.g., myosin, glutathione S-transferases). We thus sought to use targeted, quantitative proteomics to directly measure differences in protein abundance in a time-of-day-dependent manner. We used multiple reaction monitoring (MRM), which has the advantage of being able to probe selected target lists with high sensitivity, wide dynamic range, and good/excellent reproducibility. We first characterized proteins in a qualitative format and subsequently examined subsets of specific proteins of interest in a high-fidelity quantifiable approach. Targeted quantitative multiple/single reaction monitoring (MRM/SRM) proteomics allowed for the measurement of changes in protein abundance in a time-of-day-specific manner over the 24-h diel cycle. Utilizing this accurate technique requires robust and reproducible protein/peptide preparation techniques in order to obtain consistent data. Here, we describe a technique using liquid N2 homogenization-based protein extraction and proteolytic digestion applied to multiple discrete tissues (whole heads, antennae, total head appendages, compound eyes, and bodies), with subsequent liquid chromatography/tandem mass spectrometry (LC/MS/MS)-based analysis of the resulting tryptic peptides. This technique is largely portable and should function well in any arthropod system with little modification. The results of our analyses are the generation of tissue-discrete determination of peptides, targeted quantitative analysis of peptides, and the deposition of datasets in VectorBase.org for use by the vector biology, arthropod, and proteomics research communities.
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Abbreviations
- ABC:
-
Ammonium bicarbonate, Ambic
- BEH:
-
Ethylene-bridged hybrid
- CID/CAD:
-
Collision-induced dissociation/collisionally activated dissociation
- CV:
-
Coefficient of variation
- DTT:
-
Dithiothreitol
- EDTA:
-
Ethylenediaminetetraacetic acid
- FASP:
-
Filter-aided sample prep
- FDR:
-
False discovery rate
- IAA:
-
Iodoacetamide
- LC/MS/MS:
-
Liquid chromatography tandem mass spectrometry
- MS-MS/MS:
-
Mass spectrometry tandem mass spectrometry
- MRM/SRM:
-
Multiple reaction monitoring/selected reaction monitoring
- NaDOC:
-
Sodium deoxycholate
- OBP:
-
Odorant-binding protein
- ORF:
-
Open reading frame
- PCR:
-
Polymerase chain reaction
- PMSF:
-
Phenylmethylsulfonyl fluoride
- Q1, Q2, etc.:
-
First and second quadrupoles
- QqQ:
-
Triple quadrupole
- RT:
-
Retention time
- SDS-PAGE:
-
Sodium dodecyl sulfate-polyacrylamide gel electrophoresis
- TCEP:
-
Tris(2-carboxyethyl)phosphine hydrochloride
- TFE:
-
2,2,2-Trifluoroethanol
- THAs:
-
Total head appendages
- TIC:
-
Total ion current
- TOP8,10:
-
Top 8,10 precursor selection method
- UHPLC:
-
Ultrahigh-performance liquid chromatography
- XIC/EIC:
-
Extracted ion chromatogram
- ZT:
-
Zeitgeber time
References
Hood, B. L., Conrads, T. P., & Veenstra, T. D. (2006). Unravelling the proteome of formalin-fixed paraffin-embedded tissue. Briefings in Functional Genomics & Proteomics, 5, 169–175.
Lee, J., Lei, Z., Watson, B. S., & Sumner, L. W. (2013). Sub-cellular proteomics of Medicago truncatula. Frontiers in Plant Science, 4, 112.
Nirmalan, N., Banks, R., & Van Eyk, J. E. (2013). Proteomic analysis of formalin fixed tissue. Proteomics Clinical Applications, 7, 215–216.
Paulo, J. A., Kadiyala, V., Brizard, S., et al. (2013). A proteomic comparison of formalin-fixed paraffin-embedded pancreatic tissue from autoimmune pancreatitis, chronic pancreatitis, and pancreatic cancer. Journal of Pancreas, 14, 405–414.
Shevchenko, A., Tomas, H., Havlis, J., et al. (2006). In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nature Protocols, 1, 2856–2860.
Pirmoradian, M., Budamgunta, H., Chingin, K., et al. (2013). Rapid and deep human proteome analysis by single-dimension shotgun proteomics. Molecular and Cellular Proteomics, 12, 3330–3338.
Wiśniewski, J. R., Zougman, A., Nagaraj, N., & Mann, M. (2009). Universal sample preparation method for proteome analysis. Nature Methods, 6, 359–362.
Chaerkady, R., Kelkar, D. S., Muthusamy, B., et al. (2011). A proteogenomic analysis of Anopheles gambiae using high-resolution Fourier transform mass spectrometry. Genome Research, 21, 1872–1881.
Rund, S. S. C., Bonar, N. A., Champion, M. M., et al. (2013). Daily rhythms in antennal protein and olfactory sensitivity in the malaria mosquito Anopheles gambiae. Scientific Reports, 3, 2494.
malERA Consultative Group on Vector Control. (2011). A research agenda for malaria eradication: Vector control. Plos Medicine, 8, e1000401.
Enayati, A., & Hemingway, J. (2010). Malaria management: Past, present, and future. Annual Review of Entomology, 55, 569–591.
Rund, S. S. C., Lee, S. J., Bush, B. R., & Duffield, G. E. (2012). Strain- and sex-specific differences in daily flight activity and the circadian clock of Anopheles gambiae mosquitoes. Journal of Insect Physiology, 58, 1609–1619.
Balmert, N. J., Rund, S. S. C., Ghazi, J. P., et al. (2014). Time-of-day specific changes in metabolic detoxification and insecticide resistance in the malaria mosquito Anopheles gambiae. Journal of Insect Physiology, 64, 30–39.
Clements, A. N. (1999). The biology of mosquitoes. Oxon: CABI Publ.
Gary, R. E., & Foster, W. A. (2006). Diel timing and frequency of sugar feeding in the mosquito Anopheles gambiae, depending on sex, gonotrophic state and resource availability. Medical and Veterinary Entomology, 20, 308–316.
Jones, M. D. R., & Gubbins, S. J. (1978). Changes in the circadian flight activity of the mosquito Anopheles gambiae in relation to insemination, feeding and oviposition. Physiological Entomology, 3, 213–220.
Dunlap, J. C., Loros, J. J., & Decoursey, P. J. (2004). Chronobiology: Biological timekeeping. Sunderland: Sinauer Associates.
Rund, S. S., Gentile, J. E., & Duffield, G. E. (2013). Extensive circadian and light regulation of the transcriptome in the malaria mosquito Anopheles gambiae. BMC Genomics, 14, 218.
Rund, S. S. C., Hou, T. Y., Ward, S. M., et al. (2011). Genome-wide profiling of diel and circadian gene expression in the malaria vector Anopheles gambiae. Proceedings of the National Academy of Sciences of the United States of America, 108, E421–E430.
Bock, G. R., & Cardew, G. (1996). Olfaction in mosquito–host interactions (p. 342). New York: Wiley.
Takken, W., & Knols, B. G. (1999). Odor-mediated behavior of Afrotropical malaria mosquitoes. Annual Review of Entomology, 44, 131–157.
Shilov, I. V., Seymour, S. L., Patel, A. A., et al. (2007). The Paragon algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Molecular and Cellular Proteomics, 6, 1638–1655.
Li, Y., Champion, M. M., Sun, L., et al. (2012). Capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry as an alternative proteomics platform to ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry for samples of intermediate complexity. Analytical Chemistry, 84, 1617–1622.
Washburn, M. P., Wolters, D., & Yates, J. R., 3rd. (2001). Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nature Biotechnology, 19, 242–247.
Wolters, D. A., Washburn, M. P., & Yates, J. R., 3rd. (2001). An automated multidimensional protein identification technology for shotgun proteomics. Analytical Chemistry, 73, 5683–5690.
Brunner, E., Ahrens, C. H., Mohanty, S., et al. (2007). A high-quality catalog of the Drosophila melanogaster proteome. Nature Biotechnology, 25, 576–583.
Djegbe, I., Cornelie, S., Rossignol, M., et al. (2011). Differential expression of salivary proteins between susceptible and insecticide-resistant mosquitoes of Culex quinquefasciatus. Plos One, 6, e17496.
Fang, Y., Feng, M., Han, B., et al. (2014). In-depth proteomics characterization of embryogenesis of the honey bee worker (Apis mellifera L.). Molecular and Cellular Proteomics, M114, 037846.
Hummon, A. B., Richmond, T. A., Verleyen, P., et al. (2006). From the genome to the proteome: Uncovering peptides in the Apis brain. Science, 314, 647–649.
Johnson, J. R., Florens, L., Carucci, D. J., & Yates, J. R. (2004). Proteomics in malaria. Journal of Proteome Research, 3(2), 296–306.
Mastrobuoni, G., Qiao, H., Iovinella, I., et al. (2013). A proteomic investigation of soluble olfactory proteins in Anopheles gambiae. Plos One, 8, e75162.
Ribeiro, J. M. C., Charlab, R., Pham, V. M., et al. (2004). An insight into the salivary transcriptome and proteome of the adult female mosquito Culex pipiens quinquefasciatus. Insect Biochemistry and Molecular Biology, 34, 543–563.
Dinglasan, R. R., Devenport, M., Florens, L., et al. (2009). The Anopheles gambiae adult midgut peritrophic matrix proteome. Insect Biochemistry and Molecular Biology, 39, 125–134.
Ubaida Mohien, C., Colquhoun, D. R., Mathias, D. K., et al. (2013). A bioinformatics approach for integrated transcriptomic and proteomic comparative analyses of model and non-sequenced anopheline vectors of human malaria parasites. Molecular and Cellular Proteomics, 12, 120–131.
Andrews, G. L., Dean, R. A., Hawkridge, A. M., & Muddiman, D. C. (2011). Improving proteome coverage on a LTQ-Orbitrap using design of experiments. Journal of the American Society for Mass Spectrometry, 22, 773–783.
Nagaraj, N., Kulak, N. A., Cox, J., et al. (2012). System-wide perturbation analysis with nearly complete coverage of the yeast proteome by single-shot ultra HPLC runs on a bench top Orbitrap. Molecular and Cellular Proteomics, 11(3), M111.013722.
Desiere, F., Deutsch, E. W., King, N. L., et al. (2006). The peptide atlas project. Nucleic Acids Research, 34, D655–D658.
Anderson, L., & Hunter, C. L. (2006). Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Molecular and Cellular Proteomics, 5, 573–588.
Kuzyk, M. A., Smith, D., Yang, J., et al. (2009). Multiple reaction monitoring-based, multiplexed, absolute quantitation of 45 proteins in human plasma. Molecular and Cellular Proteomics, 8, 1860–1877.
Ludwig, C., Claassen, M., Schmidt, A., & Aebersold, R. (2012). Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry. Molecular and Cellular Proteomics, 11(3), M111.013987.
Aebersold, R., Burlingame, A. L., & Bradshaw, R. A. (2013). Western blots versus selected reaction monitoring assays: Time to turn the tables? Molecular and Cellular Proteomics, 12, 2381–2382.
Chang, C.-Y., Picotti, P., Huettenhain, R., et al. (2011). Protein significance analysis in selected reaction monitoring (SRM) measurements. Molecular and Cellular Proteomics, 11(4), M111.014662.
Picotti, P., Clément-Ziza, M., Lam, H., et al. (2013). A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis. Nature, 494, 266–270.
Aebersold, R. (2013). Method of the year 2012. Nature Methods, 10, 1–1.
Champion, M. M., Campbell, C. S., Siegele, D. A., et al. (2003). Proteome analysis of Escherichia coli K-12 by two-dimensional native-state chromatography and MALDI-MS. Molecular Microbiology, 47, 383–396.
Llarrull, L. I., Toth, M., Champion, M. M., & Mobashery, S. (2011). Activation of BlaR1 protein of methicillin-resistant Staphylococcus aureus, its proteolytic processing, and recovery from induction of resistance. Journal of Biological Chemistry, 286, 38148–38158.
Picotti, P., Bodenmiller, B., Mueller, L. N., et al. (2009). Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell, 138, 795–806.
Sun, L., Li, Y., Champion, M. M., et al. (2013). Capillary zone electrophoresis-multiple reaction monitoring from 100 pg of RAW 264.7 cell lysate digest. Analyst, 138, 3181–3188.
Schubert, O. T., Mouritsen, J., Ludwig, C., et al. (2013). The Mtb proteome library: A resource of assays to quantify the complete proteome of Mycobacterium tuberculosis. Cell Host and Microbe, 13, 602–612.
Deshusses, J. M. P., Burgess, J. A., Scherl, A., et al. (2003). Exploitation of specific properties of trifluoroethanol for extraction and separation of membrane proteins. Proteomics, 3, 1418–1424.
Stejskal, K., Potěšil, D., & Zdráhal, Z. (2013). Suppression of peptide sample losses in autosampler vials. Journal of Proteome Research, 12, 3057–3062.
Fujita, S. C., Inoue, H., Yoshioka, T., & Hotta, Y. (1987). Quantitative tissue isolation from Drosophila freeze-dried in acetone. The Biochemical Journal, 243, 97–104.
Matsumoto, H., O’Tousa, J. E., & Pak, W. L. (1982). Light-induced modification of Drosophila retinal polypeptides in vivo. Science, 217, 839–841.
Weigel, K. J., Jakimenko, A., Conti, B. A., et al. (2014). CAF-Secreted IGFBPs regulate breast cancer cell Anoikis. Molecular Cancer Research, 12, 855–866.
Olsen, J. V., de Godoy, L. M. F., Li, G., et al. (2005). Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap. Molecular and Cellular Proteomics, 4, 2010–2021.
Elias, J. E., & Gygi, S. P. (2007). Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nature Methods, 4, 207–214.
Elias, J. E., Haas, W., Faherty, B. K., & Gygi, S. P. (2005). Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations. Nature Methods, 2, 667–675.
Tang, W. H., Shilov, I. V., & Seymour, S. L. (2008). Nonlinear fitting method for determining local false discovery rates from decoy database searches. Journal of Proteome Research, 7, 3661–3667.
Addona, T. A., Abbatiello, S. E., Schilling, B., et al. (2009). Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nature Biotechnology, 27, 633–641.
Bertsch, A., Jung, S., Zerck, A., et al. (2010). Optimal de novo design of MRM experiments for rapid assay development in targeted proteomics. Journal of Proteome Research, 9, 2696–2704.
Gillette, M. A., & Carr, S. A. (2013). Quantitative analysis of peptides and proteins in biomedicine by targeted mass spectrometry. Nature Methods, 10, 28–34.
Mani, D. R., Abbatiello, S. E., & Carr, S. A. (2012) Statistical characterization of multiple-reaction monitoring mass spectrometry (MRM-MS) assays for quantitative proteomics. BMC Bioinformatics, 13(Suppl 16), S9.
Carr, S. A., Abbatiello, S. E., Ackermann, B. L., et al. (2014). Targeted peptide measurements in biology and medicine: Best practices for mass spectrometry-based assay development using a fit-for-purpose approach. Molecular and Cellular Proteomics, 13, 907–917.
MacLean, B., Tomazela, D. M., Shulman, N., et al. (2010). Skyline: An open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics, 26, 966–968.
Kennedy, G. M., Hooley, G. C., Champion, M. M., et al. (2014). A novel ESX-1 locus reveals that surface-associated ESX-1 substrates mediate virulence in Mycobacterium marinum. Journal of Bacteriology, 196, 1877–1888.
Li, Y., Wojcik, R., Dovichi, N. J., & Champion, M. M. (2012). Quantitative multiple reaction monitoring of peptide abundance introduced via a capillary zone electrophoresis-electrospray interface. Analytical Chemistry, 84, 6116–6121.
Gooyit, M., Peng, Z., Wolter, W. R., et al. (2014). A chemical biological strategy to facilitate diabetic wound healing. ACS Chemical Biology, 9, 105–110.
Fan, J.-Y., Preuss, F., Muskus, M. J., et al. (2009). Drosophila and vertebrate casein kinase Idelta exhibits evolutionary conservation of circadian function. Genetics, 181, 139–152.
Merlin, C., Gegear, R. J., & Reppert, S. M. (2009). Antennal circadian clocks coordinate sun compass orientation in migratory monarch butterflies. Science, 325, 1700–1704.
Domon, B. (2012). Considerations on selected reaction monitoring experiments: Implications for the selectivity and accuracy of measurements. Proteomics Clinical Applications, 6, 609–614.
Gallien, S., Bourmaud, A., Kim, S. Y., & Domon, B. (2014). Technical considerations for large-scale parallel reaction monitoring analysis. Journal of Proteomics, 100, 147–159.
Loziuk, P. L., Wang, J., Li, Q., et al. (2013). Understanding the role of proteolytic digestion on discovery and targeted proteomic measurements using liquid chromatography tandem mass spectrometry and design of experiments. Journal of Proteome Research, 12, 5820–5829.
Burgess, M. W., Keshishian, H., Mani, D. R., et al. (2014). Simplified and efficient quantification of low-abundance proteins at very high multiplex via targeted mass spectrometry. Molecular and Cellular Proteomics, 13, 1137–1149.
Thakur, S. S., Geiger, T., Chatterjee, B., et al. (2011). Deep and highly sensitive proteome coverage by LC-MS/MS without prefractionation. Molecular and Cellular Proteomics, 10(8), M110.003699.
Albertin, W., Langella, O., Joets, J., et al. (2009). Comparative proteomics of leaf, stem, and root tissues of synthetic Brassica napus. Proteomics, 9, 793–799.
Rhee, H.-W., Zou, P., Udeshi, N. D., et al. (2013). Proteomic mapping of mitochondria in living cells via spatially restricted enzymatic tagging. Science, 339, 1328–1331.
Zhou, C., Simpson, K. L., Lancashire, L. J., et al. (2012). Statistical considerations of optimal study design for human plasma proteomics and biomarker discovery. Journal of Proteome Research, 11, 2103–2113.
Kalume, D. E., Peri, S., Reddy, R., et al. (2005). Genome annotation of Anopheles gambiae using mass spectrometry-derived data. BMC Genomics, 6, 128.
Megy, K., Emrich, S. J., Lawson, D., et al. (2012). VectorBase: Improvements to a bioinformatics resource for invertebrate vector genomics. Nucleic Acids Research, 40, D729–734.
Pitts, R. J., Rinker, D. C., Jones, P. L., et al. (2011). Transcriptome profiling of chemosensory appendages in the malaria vector Anopheles gambiae reveals tissue- and sex-specific signatures of odor coding. BMC Genomics, 12, 271.
Leal, W. S. (2013). Odorant reception in insects: Roles of receptors, binding proteins, and degrading enzymes. Annual Review of Entomology, 58, 373–391.
Das, S., & Dimopoulos, G. (2008). Molecular analysis of photic inhibition of blood-feeding in Anopheles gambiae. BMC Physiology, 8, 23.
Mauvoisin, D., Wang, J., Jouffe, C., et al. (2014). Circadian clock-dependent and -independent rhythmic proteomes implement distinct diurnal functions in mouse liver. Proceedings of the National Academy of Sciences of the United States of America, 111, 167–172.
Robles, M. S., & Mann, M. (2013). Proteomic approaches in circadian biology. Handbook of Experimental Pharmacology, 217, 389–407.
Dresen, S., Ferreirós, N., Gnann, H., et al. (2010). Detection and identification of 700 drugs by multi-target screening with a 3200 Q TRAP LC-MS/MS system and library searching. Analytical and Bioanalytical Chemistry, 396, 2425–2434.
Cázares-Raga, F. E., Chávez-Munguía, B., González-Calixto, C., et al. (2014). Morphological and proteomic characterization of midgut of the malaria vector Anopheles albimanus at early time after a blood feeding. Journal of Proteomics, 111, 100–12.87.
Dwivedi, S. B., Muthusamy, B., Kumar, P., et al. (2014). Brain proteomics of Anopheles gambiae. OMICS, 18(7), 421–37.
Acknowledgments
This research was supported by grants (to GED) from Eck Institute for Global Health and the Center for Rare and Neglected Diseases, University of Notre Dame (UND), and the Indiana Clinical Translational Sciences Institute, funded in part by a grant (UL1TR001108) from the National Institutes of Health, National Center for Advancing Translational Sciences, Clinical and Translational Sciences Award, and NIGMS (R01-GM087508). We thank B. Boggess and M. Joyce and the UND Mass Spectrometry & Proteomics Facility (MSPF) for their ongoing support and assistance with proteomics research, J. Ghazi for his assistance with sample preparation, and the editors for their careful reading and critique of this chapter.
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Champion, M.M., Sheppard, A.D., Rund, S.S.C., Freed, S.A., O’Tousa, J.E., Duffield, G.E. (2016). Qualitative and Quantitative Proteomics Methods for the Analysis of the Anopheles gambiae Mosquito Proteome. In: Raman, C., Goldsmith, M., Agunbiade, T. (eds) Short Views on Insect Genomics and Proteomics. Entomology in Focus, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-319-24244-6_2
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