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Qualitative and Quantitative Proteomics Methods for the Analysis of the Anopheles gambiae Mosquito Proteome

  • Matthew M. Champion
  • Aaron D. Sheppard
  • Samuel S. C. Rund
  • Stephanie A. Freed
  • Joseph E. O’Tousa
  • Giles E. Duffield
Chapter
Part of the Entomology in Focus book series (ENFO, volume 4)

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.

Keywords

Multiple Reaction Monitoring Multiple Reaction Monitoring Transition Isotope Dilution Mass Spectrometry Maxillary Palp Target List 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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

Notes

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.

References

  1. 1.
    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.CrossRefGoogle Scholar
  2. 2.
    Lee, J., Lei, Z., Watson, B. S., & Sumner, L. W. (2013). Sub-cellular proteomics of Medicago truncatula. Frontiers in Plant Science, 4, 112.PubMedPubMedCentralGoogle Scholar
  3. 3.
    Nirmalan, N., Banks, R., & Van Eyk, J. E. (2013). Proteomic analysis of formalin fixed tissue. Proteomics Clinical Applications, 7, 215–216.CrossRefPubMedGoogle Scholar
  4. 4.
    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.Google Scholar
  5. 5.
    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.CrossRefPubMedGoogle Scholar
  6. 6.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Wiśniewski, J. R., Zougman, A., Nagaraj, N., & Mann, M. (2009). Universal sample preparation method for proteome analysis. Nature Methods, 6, 359–362.CrossRefPubMedGoogle Scholar
  8. 8.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    malERA Consultative Group on Vector Control. (2011). A research agenda for malaria eradication: Vector control. Plos Medicine, 8, e1000401.CrossRefGoogle Scholar
  11. 11.
    Enayati, A., & Hemingway, J. (2010). Malaria management: Past, present, and future. Annual Review of Entomology, 55, 569–591.CrossRefPubMedGoogle Scholar
  12. 12.
    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.Google Scholar
  13. 13.
    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.CrossRefPubMedGoogle Scholar
  14. 14.
    Clements, A. N. (1999). The biology of mosquitoes. Oxon: CABI Publ.Google Scholar
  15. 15.
    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.CrossRefPubMedGoogle Scholar
  16. 16.
    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.CrossRefGoogle Scholar
  17. 17.
    Dunlap, J. C., Loros, J. J., & Decoursey, P. J. (2004). Chronobiology: Biological timekeeping. Sunderland: Sinauer Associates.Google Scholar
  18. 18.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Bock, G. R., & Cardew, G. (1996). Olfaction in mosquito–host interactions (p. 342). New York: Wiley.Google Scholar
  21. 21.
    Takken, W., & Knols, B. G. (1999). Odor-mediated behavior of Afrotropical malaria mosquitoes. Annual Review of Entomology, 44, 131–157.CrossRefPubMedGoogle Scholar
  22. 22.
    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.CrossRefPubMedGoogle Scholar
  23. 23.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    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.CrossRefPubMedGoogle Scholar
  25. 25.
    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.CrossRefPubMedGoogle Scholar
  26. 26.
    Brunner, E., Ahrens, C. H., Mohanty, S., et al. (2007). A high-quality catalog of the Drosophila melanogaster proteome. Nature Biotechnology, 25, 576–583.CrossRefPubMedGoogle Scholar
  27. 27.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    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.Google Scholar
  29. 29.
    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.CrossRefPubMedGoogle Scholar
  30. 30.
    Johnson, J. R., Florens, L., Carucci, D. J., & Yates, J. R. (2004). Proteomics in malaria. Journal of Proteome Research, 3(2), 296–306.CrossRefPubMedGoogle Scholar
  31. 31.
    Mastrobuoni, G., Qiao, H., Iovinella, I., et al. (2013). A proteomic investigation of soluble olfactory proteins in Anopheles gambiae. Plos One, 8, e75162.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    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.CrossRefPubMedGoogle Scholar
  33. 33.
    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.CrossRefPubMedGoogle Scholar
  34. 34.
    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.CrossRefPubMedGoogle Scholar
  35. 35.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  36. 36.
    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.CrossRefPubMedGoogle Scholar
  37. 37.
    Desiere, F., Deutsch, E. W., King, N. L., et al. (2006). The peptide atlas project. Nucleic Acids Research, 34, D655–D658.CrossRefPubMedGoogle Scholar
  38. 38.
    Anderson, L., & Hunter, C. L. (2006). Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins. Molecular and Cellular Proteomics, 5, 573–588.CrossRefPubMedGoogle Scholar
  39. 39.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    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.CrossRefPubMedGoogle Scholar
  41. 41.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  42. 42.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  44. 44.
    Aebersold, R. (2013). Method of the year 2012. Nature Methods, 10, 1–1.Google Scholar
  45. 45.
    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.CrossRefPubMedGoogle Scholar
  46. 46.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  47. 47.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  50. 50.
    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.CrossRefPubMedGoogle Scholar
  51. 51.
    Stejskal, K., Potěšil, D., & Zdráhal, Z. (2013). Suppression of peptide sample losses in autosampler vials. Journal of Proteome Research, 12, 3057–3062.CrossRefPubMedGoogle Scholar
  52. 52.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  53. 53.
    Matsumoto, H., O’Tousa, J. E., & Pak, W. L. (1982). Light-induced modification of Drosophila retinal polypeptides in vivo. Science, 217, 839–841.CrossRefPubMedGoogle Scholar
  54. 54.
    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.CrossRefPubMedGoogle Scholar
  55. 55.
    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.CrossRefPubMedGoogle Scholar
  56. 56.
    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.CrossRefPubMedGoogle Scholar
  57. 57.
    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.CrossRefPubMedGoogle Scholar
  58. 58.
    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.CrossRefPubMedGoogle Scholar
  59. 59.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  60. 60.
    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.CrossRefPubMedGoogle Scholar
  61. 61.
    Gillette, M. A., & Carr, S. A. (2013). Quantitative analysis of peptides and proteins in biomedicine by targeted mass spectrometry. Nature Methods, 10, 28–34.CrossRefPubMedPubMedCentralGoogle Scholar
  62. 62.
    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.Google Scholar
  63. 63.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  64. 64.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  65. 65.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  66. 66.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  67. 67.
    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.CrossRefPubMedGoogle Scholar
  68. 68.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  69. 69.
    Merlin, C., Gegear, R. J., & Reppert, S. M. (2009). Antennal circadian clocks coordinate sun compass orientation in migratory monarch butterflies. Science, 325, 1700–1704.CrossRefPubMedPubMedCentralGoogle Scholar
  70. 70.
    Domon, B. (2012). Considerations on selected reaction monitoring experiments: Implications for the selectivity and accuracy of measurements. Proteomics Clinical Applications, 6, 609–614.CrossRefPubMedGoogle Scholar
  71. 71.
    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.CrossRefPubMedGoogle Scholar
  72. 72.
    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.CrossRefPubMedGoogle Scholar
  73. 73.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  74. 74.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  75. 75.
    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.CrossRefPubMedGoogle Scholar
  76. 76.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  77. 77.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  78. 78.
    Kalume, D. E., Peri, S., Reddy, R., et al. (2005). Genome annotation of Anopheles gambiae using mass spectrometry-derived data. BMC Genomics, 6, 128.CrossRefPubMedPubMedCentralGoogle Scholar
  79. 79.
    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.CrossRefPubMedGoogle Scholar
  80. 80.
    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.CrossRefPubMedPubMedCentralGoogle Scholar
  81. 81.
    Leal, W. S. (2013). Odorant reception in insects: Roles of receptors, binding proteins, and degrading enzymes. Annual Review of Entomology, 58, 373–391.CrossRefPubMedGoogle Scholar
  82. 82.
    Das, S., & Dimopoulos, G. (2008). Molecular analysis of photic inhibition of blood-feeding in Anopheles gambiae. BMC Physiology, 8, 23.CrossRefPubMedPubMedCentralGoogle Scholar
  83. 83.
    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.CrossRefPubMedGoogle Scholar
  84. 84.
    Robles, M. S., & Mann, M. (2013). Proteomic approaches in circadian biology. Handbook of Experimental Pharmacology, 217, 389–407.Google Scholar
  85. 85.
    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.CrossRefPubMedGoogle Scholar
  86. 86.
    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.CrossRefPubMedGoogle Scholar
  87. 87.
    Dwivedi, S. B., Muthusamy, B., Kumar, P., et al. (2014). Brain proteomics of Anopheles gambiae. OMICS, 18(7), 421–37.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Matthew M. Champion
    • 1
  • Aaron D. Sheppard
    • 2
  • Samuel S. C. Rund
    • 2
  • Stephanie A. Freed
    • 2
  • Joseph E. O’Tousa
    • 2
  • Giles E. Duffield
    • 2
  1. 1.Department of Chemistry and Biochemistry, Nieuwland Science HallUniversity of Notre DameNotre DameUSA
  2. 2.Department of Biological Sciences, Galvin Life Science Center, Eck Institute for Global HealthUniversity of Notre DameNotre DameUSA

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