How can P450 distinguish between foreign and native compounds?

How can P450 distinguish between foreign and native compounds?

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It is my understanding that P450 enzymes are capable of selectively degrading compounds that enter the cell from the outside (e.g. synthetic drugs) without damaging compounds that are metabolic intermediates normally found in cells. What is the mechanism that provides these enzymes with the ability to distinguish the foreign compounds that are substrates from the normal metabolic intermediates that are not substrates, if they are structually similiar?

False Premise

The false premise in this question lies in the end of the last sentence:

… the ability to distinguish the foreign compounds that are substrates from the normal metabolic intermediates that are not substrates, if they are structurally similar

The assumption here would seem to be that the similarity between normal metabolic intermediates and foreign compounds is too great for discrimination to occur. There is no reason to assume this.


The substrate specificity of those P450 enzymes that metabolise xenobiotics is such that they will not metabolize normal cellular constituents.

There is a wealth of evidence that enzymes can have substrate specificity that is either extremely narrow or relatively wide. This obviously depends on the structure of the substrate binding site - its shape and size and the chemical nature of the amino acid residues there. Thinking in these terms one can envisage an enzyme that will discriminate against substrates with a single substituent at a particular position, but metabolize a variety of similar compounds that lack this. The evolution of P450 enzymes can be imagined as a process whereby mutations that allowed metabolism of toxic environmental compounds would convey an advantage and be selected for, but mutations that disrupted normal metabolism would be lethal (or disadvantageous) and be selected against.

The Broader context of P450 Enzymes

The question is posed in terms that might suggest that the only function of P450 enzymes is in removal of xenobiotics, and perhaps that they are only a feature higher organisms. In fact neither of these are true. Details of their occurrence in bacteria can be found in the Wikipedia article. However, a paper which I, as a non-expert, found interesting in this respect was published by Kawashima and Satta in PLOS ONE in 2014. This makes the point that there are two classes of P450 genes - those involved in normal cellular biosynthesis (e.g. steroids, cholesterols, vitamin D3 and bile acids), and those active against xenobiotics (e.g. plant alkaloids, aromatic compounds and fatty acids). Comparison of the genes for these in a variety of (mainly) vertebrates implies those involved in the metabolism of xenobiotics evolved from those involved in normal cellular metabolism, and such detoxification genes have arisen not just once, but on several occasions.

Critical Thinking Questions

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    Chen, J. K., Falck, J. R., Reddy, K. M., Capdevila, J., & Harris, R. C. (1998). Epoxyeicosatrienoic acids and their sulfonimide derivatives stimulate tyrosine phosphorylation and induce mitogenesis in renal epithelial cells. Journal of Biological Chemistry, 273, 29254–29261.

    Chen, J. K., Wang, D.-W., Falck, J. R., Capdevila, J., & Harris, R. C. (1999). Transfection of an active cytochrome P450 arachidonic acid epoxygenase indicates that 14,15-epoxyeicosatrienoic acid functions as an intracellular messenger in response to epidermal growth factor. Journal of Biological Chemistry, 274, 4764–4769.

    Munzenmaier, D. H., & Harder, D. R. (2000). Cerebral microvascular endothelial cell tube formation: Role of astrocytic epoxyeicosatrienoic acid release. American Journal of Physiology—Heart and Circulatory Physiology, 278, H1163–H1167.

    Wang, Y., Wei, X., Xiao, X., Hui, R., Card, J. W., Carey, M. A., et al. (2005). Arachidonic acid epoxygenase metabolites stimulate endothelial cell growth and angiogenesis via mitogen-activated protein kinase and phosphatidylinositol 3-kinase/Akt signaling pathways. Journal of Pharmacology and Experimental Therapeutics, 314, 522–532.

    Chen, J.-K., Capdevila, J., & Harris, R. C. (2002). Heparin-binding EGF-like growth factor mediates the biological effects of P450 arachidonate epoxygenase metabolites in epithelial cells. Proceedings of the National Academy of Sciences of the United States of America, 99, 6029–6034.

    Michaelis, U. R., Fisslthaler, B., Medhora, M., Harder, D., Fleming, I., & Busse, R. (2003). Cytochrome P450 2C9-derived epoxyeicosatrienoic acids induce angiogenesis via cross-talk with the epidermal growth factor receptor (EGFR). The FASEB Journal, 17, 770–772.

    Pozzi, A., Macias-Perez, I., Abair, T., Wey, S., Su, Y., Zent, R., et al. (2005). Charaterization of 5,6-and 8,9-epoxyeicosatrienoic acids (5,6- and 8,9-EET) as potent in vivo angiogenic lipids. Journal of Biological Chemistry, 280, 27138–27146.

    Park, H. S., Kim, M. S., Huh, S. H., Park, J., Chung, J., Kang, S. S., et al. (2002). Akt (protein kinase B) negatively regulates SEK1 by means of protein phosphorylation. Journal of Biological Chemistry, 277, 2573–2578.

    Levy, R. H. (1995). Cytochrome P450 isozymes and antiepileptic drug interactions. Epilepsia, 36(Suppl 5), S8–S13.

    Michaelis, U. R., Fisslthaler, B., Barbosa-Sicard, E., Falck, J. R., Fleming, I., & Busse, R. (2005). Cytochrome P450 epoxygenases 2C8 and 2C9 are implicated in hypoxia-induced endothelial cell migration and angiogenesis. Journal of Cell Science, 118, 5489–5498.

    Webler, A. C., Michaelis, U. R., Popp, R., Barbosa-Sicard, E., Murugan, A., Falck, J. R., et al. (2008). Epoxyeicosatrienoic acids are part of the VEGF-activated signaling cascade leading to angiogenesis. American Journal of Physiology. Cell Physiology, 295, C1292–C1301.

    Yang, S., Wei, S., Pozzi, A., & Capdevila, J. H. (2009). The arachidonic acid epoxygenase is a component of the signaling mechanisms responsible for VEGF-stimulated angiogenesis. Archives of Biochemistry and Biophysics, 489, 82–91.

    Cheranov, S. Y., Karpurapu, M., Wang, D., Zhang, B., Venema, R. C., & Rao, G. N. (2008). An essential role for SRC-activated STAT-3 in 14,15-EET-induced VEGF expression and angiogenesis. Blood, 111, 5581–5591.

    Oguro, A., Sakamoto, K., Suzuki, S., & Imaoka, S. (2009). Contribution of hydrolase and phosphatase domains in soluble epoxide hydrolase to vascular endothelial growth factor expression and cell growth. Biological and Pharmaceutical Bulletin, 32, 1962–1967.

    Gerety, S. S., Wang, H. U., Chen, Z. F., & Anderson, D. J. (1999). Symmetrical mutant phenotypes of the receptor EphB4 and its specific transmembrane ligand ephrin-B2 in cardiovascular development. Molecular Cell, 4, 403–414.

    Shin, D., Garcia-Cardena, G., Hayashi, S., Gerety, S., Asahara, T., Stavrakis, G., et al. (2001). Expression of ephrinB2 identifies a stable genetic difference between arterial and venous vascular smooth muscle as well as endothelial cells, and marks subsets of microvessels at sites of adult neovascularization. Developmental Biology, 230, 139–150.

    Webler, A. C., Popp, R., Korff, T., Michaelis, U. R., Urbich, C., Busse, R., et al. (2008). Cytochrome P450 2C9-induced angiogenesis is dependent on EphB4. Arteriosclerosis, Thrombosis, and Vascular Biology, 28, 1123–1129.

    Amaral, S. L., Maier, K. G., Schippers, D. N., Roman, R. J., & Greene, A. S. (2003). CYP4A metabolites of arachidonic acid and VEGF are mediators of skeletal muscle angiogenesis. American Journal of Physiology—Heart and Circulatory Physiology, 284, H1528–H1535.

    Jiang, M., Mezentsev, A., Kemp, R., Byun, K., Falck, J. R., Miano, J. M., et al. (2004). Smooth muscle-specific expression of CYP4A1 induces endothelial sprouting in renal arterial microvessels. Circulation Research, 94, 167–174.

    Chen, P., Guo, M., Wygle, D., Edwards, P. A., Falck, J. R., Roman, R. J., et al. (2005). Inhibitors of cytochrome P450 4A suppress angiogenic responses. American Journal of Pathology, 166, 615–624.

    Guo, A. M., Arbab, A. S., Falck, J. R., Chen, P., Edwards, P. A., Roman, R. J., et al. (2007). Activation of vascular endothelial growth factor through reactive oxygen species mediates 20-hydroxyeicosatetraenoic acid-induced endothelial cell proliferation. Journal of Pharmacology and Experimental Therapeutics, 321, 18–27.

    Sun, J., Sui, X. X., Bradbury, A., Zeldin, D. C., Conte, M. S., & Liao, J. K. (2002). Inhibition of vascular smooth muscle cell migration by cytochrome P450 epoxygenase-derived eicosanoids. Circulation Research, 90, 1020–1027.

    Ng, V. Y., Huang, Y., Reddy, L. M., Falck, J. R., Lin, E. T., & Kroetz, D. L. (2007). Cytochrome P450 eicosanoids are activators of peroxisome proliferator-activated receptor α. Drug Metabolism and Disposition, 35, 1126–1134.

    Davis, B. B., Thompson, D. A., Howard, L. L., Morisseau, C., Hammock, B. D., & Weiss, R. H. (2002). Inhibitors of soluble epoxide hydrolase attenuate vascular smooth muscle cell proliferation. Proceedings of the National Academy of Sciences of the United States of America, 99, 2222–2227.

    Davis, B. B., Morisseau, C., Newman, J. W., Pedersen, T. L., Hammock, B. D., & Weiss, R. H. (2006). Attenuation of vascular smooth muscle cell proliferation by 1-cyclohexyl-3-dodecyl urea is independent of soluble epoxide hydrolase inhibition. Journal of Pharmacology and Experimental Therapeutics, 316, 815–821.

    Revermann, M., Schloss, M., Barbosa-Sicard, E., Mieth, A., Liebner, S., Morisseau, C., et al. (2010). Soluble epoxide hydrolase deficiency attenuates neointima formation in the femoral cuff model of hyperlipidemic mice. Arteriosclerosis, Thrombosis, and Vascular Biology, 30, 909–914.

    Simpkins, A. N., Rudic, R. D., Roy, S., Tsai, H. J., Hammock, B. D., & Imig, J. D. (2009). Soluble epoxide hydrolase inhibition modulates vascular remodeling. American Journal of Physiology—Heart and Circulatory Physiology, 298, H795–H806.

    Jung, O., Brandes, R. P., Kim, I. H., Schweda, F., Schmidt, R., Hammock, B. D., et al. (2005). Soluble epoxide hydrolase is a main effector of angiotensin II-induced hypertension. Hypertension, 45, 759–765.

    Vafeas, C., Mieyal, P. A., Urbano, F., Falck, J. R., Chauhan, K., Berman, M., et al. (1998). Hypoxia stimulates the synthesis of cytochrome P450-derived inflammatory eicosanoids in rabbit corneal epithelium. Journal of Pharmacology and Experimental Therapeutics, 287, 903–910.

    Yamaura, K., Gebremedhin, D., Zhang, C., Narayanan, J., Hoefert, K., Jacobs, E. R., et al. (2006). Contribution of epoxyeicosatrienoic acids to the hypoxia-induced activation of Ca 2+ -activated K + channel current in cultured rat hippocampal astrocytes. Neuroscience, 143, 703–716.

    Suzuki, H., Kimura, K., Shirai, H., Eguchi, K., Higuchi, S., Hinoki, A., et al. (2009). Endothelial nitric oxide synthase inhibits G12/13 and Rho-kinase activated by the angiotensin II type-1 receptor: Implication in vascular migration. Arteriosclerosis, Thrombosis, and Vascular Biology, 29, 217–224.

    Schultze, A. E., & Roth, R. A. (1998). Chronic pulmonary hypertension—The monocrotaline model and involvement of the hemostatic system. Journal of Toxicology and Environmental Health B, 1, 271–346.

    Revermann, M., Barbosa-Sicard, E., Dony, E., Schermuly, R. T., Morisseau, C., Geisslinger, G., et al. (2009). Inhibition of the soluble epoxide hydrolase attenuates monocrotaline-induced pulmonary hypertension in rats. Journal of Hypertension, 27, 322–331.

    Zheng, C., Wang, L., Li, R., Ma, B., Tu, L., Xu, X., et al. (2010). Gene delivery of cytochrome P450 epoxygenase ameliorates monocrotaline-induced pulmonary artery hypertension in rats. American Journal of Respiratory Cell and Molecular Biology, 43, 740–749.

    Yokose, T., Doy, M., Taniguchi, Y., Shimada, T., Kakiki, M., Horie, T., et al. (1999). Immunohistochemical study of cytochrome P450 2C and 3A in human non-neoplastic and neoplastic tissues. Virchows Archiv, 434, 401–411.

    Jiang, J. G., Chen, C. L., Card, J. W., Yang, S., Chen, J. X., Fu, X. N., et al. (2005). Cytochrome P450 2J2 promotes the neoplastic phenotype of carcinoma cells and is up-regulated in human tumors. Cancer Research, 65, 4707–4715.

    Xu, X., Zhang, X. A., & Wang, D. W. (2011). The roles of CYP450 epoxygenases and metabolites, epoxyeicosatrienoic acids, in cardiovascular and malignant diseases. Advanced Drug Delivery Reviews, 63, 597–609.

    Chen, C., Li, G., Liao, W., Wu, J., Liu, L., Ma, D., et al. (2009). Selective inhibitors of CYP2J2 related to terfenadine exhibit strong activity against human cancers in vitro and in vivo. Journal of Pharmacology and Experimental Therapeutics, 329, 908–918.

    Enayetallah, A. E., French, R. A., & Grant, D. F. (2006). Distribution of soluble epoxide hydrolase, cytochrome P450 2C8, 2C9 and 2J2 in human malignant neoplasms. Journal of Molecular Histology, 37, 133–141.

    Schmelzle, M., Dizdar, L., Matthaei, H., Baldus, S. E., Wolters, J., Lindenlauf, N., et al. (2011). Esophageal cancer proliferation is mediated by cytochrome P450 2C9 (CYP2C9). Prostaglandins & Other Lipid Mediators, 94, 25–33.

    Jiang, J. G., Ning, Y. G., Chen, C., Ma, D., Liu, Z. J., Yang, S., et al. (2007). Cytochrome P450 epoxygenase promotes human cancer metastasis. Cancer Research, 67, 6665–6674.

    Chen, C., Wei, X., Rao, X., Wu, J., Yang, S., Chen, F., et al. (2011). Cytochrome P450 2J2 is highly expressed in hematologic malignant diseases and promotes tumor cell growth. Journal of Pharmacology and Experimental Therapeutics, 336, 344–355.

    Mitra, R., Guo, Z., Milani, M., Mesaros, C., Rodriguez, M., Nguyen, J., et al. (2011). CYP3A4 mediates growth of estrogen receptor-positive breast cancer cells in part by inducing nuclear translocation of phospho-Stat3 through biosynthesis of (±)-14,15-epoxyeicosatrienoic acid (EET). Journal of Biological Chemistry, 286, 17543–17559.

    Jung, O., Jansen, F., Mieth, A., Barbosa-Sicard, E., Pliquett, R. U., Babelova, A., et al. (2010). Inhibition of the soluble epoxide hydrolase promotes albuminuria in mice with progressive renal disease. PLoS One, 5, e11979.

    Liu, J. Y., Yang, J., Inceoglu, B., Qiu, H., Ulu, A., Hwang, S. H., et al. (2010). Inhibition of soluble epoxide hydrolase enhances the anti-inflammatory effects of aspirin and 5-lipoxygenase activation protein inhibitor in a murine model. Biochemical Pharmacology, 79, 880–887.

    Certikova Chabova, V., Walkowska, A., Kompanowska-Jezierska, E., Sadowski, J., Kujal, P., Vernerova, Z., et al. (2010). Combined inhibition of 20-hydroxyeicosatetraenoic acid formation and of epoxyeicosatrienoic acids degradation attenuates hypertension and hypertensioninduced end-organ damage in Ren-2 transgenic rats. Clinical Science, 118, 617–632.

    Chun, Y. J., & Kim, S. (2003). Discovery of cytochrome P450 1B1 inhibitors as new promising anti-cancer agents. Medicinal Research Reviews, 23, 657–668.

    Stearns, V., Johnson, M. D., Rae, J. M., Morocho, A., Novielli, A., Bhargava, P., et al. (2003). Active tamoxifen metabolite plasma concentrations after coadministration of tamoxifen and the selective serotonin reuptake inhibitor paroxetine. Journal of the National Cancer Institute, 95, 1758–1764.

    Coller, J. K. (2003). Oxidative metabolism of tamoxifen to Z-4-hydroxy-tamoxifen by cytochrome P450 isoforms: An appraisal of in vitro studies. Clinical and Experimental Pharmacology and Physiology, 30, 845–848.

    Panigrahy, D., Kaipainen, A., Greene, E., & Huang, S. (2010). Cytochrome P450-derived eicosanoids: The neglected pathway in cancer. Cancer and Metastasis Reviews, 29, 723–735.

    Park, S. W., Heo, D. S. & Sung, M. W. (2011). The shunting of arachidonic acid metabolism to 5-lipoxygenase and cytochrome p450 epoxygenase antagonizes the anti-cancer effect of cyclooxygenase-2 inhibition in head and neck cancer cells. Cellular Oncology 1–8. doi:10.1007/s13402-011-0051-7.

    Serhan, C. N. (2011). The resolution of inflammation: The devil in the flask and in the details. The FASEB Journal, 25, 1441–1448.

    Diaz Encarnacion, M. M., Warner, G. M., Cheng, J., Gray, C. E., Nath, K. A., & Grande, J. P. (2011). n-3 Fatty acids block TNF-a-stimulated MCP-1 expression in rat mesangial cells. American Journal of Physiology. Renal Physiology, 300, F1142–F1151.

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    Finzi, A. A., Latini, R., Barlera, S., Rossi, M. G., Ruggeri, A., Mezzani, A., et al. (2011). Effects of n-3 polyunsaturated fatty acids on malignant ventricular arrhythmias in patients with chronic heart failure and implantable cardioverter-defibrillators: A substudy of the Gruppo Italiano per lo Studio della Sopravvivenza nell’Insufficienza Cardiaca (GISSI-HF) trial. American Heart Journal, 161, 338–343.

    Arnold, C., Markovic, M., Blossey, K., Wallukat, G., Fischer, R., Dechend, R., et al. (2010). Arachidonic acid-metabolizing cytochrome P450 enzymes are targets of w-3 fatty acids. Journal of Biological Chemistry, 285, 32723–32733.

    Egert, S., & Stehle, P. (2011). Impact of n-3 fatty acids on endothelial function: results from human interventions studies. Current Opinion in Clinical Nutrition and Metabolic Care, 14, 121–131.

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    Heinze, V. M., & Actis, A. B. (2011). Dietary conjugated linoleic acid and long-chain n-3 fatty acids in mammary and prostate cancer protection: A review. International Journal of Food Sciences and Nutrition. doi:10.3109/09637486.2011.598849.

    Recommended articles (6)

    Molecular and functional characterization of CYP6BQ23, a cytochrome P450 conferring resistance to pyrethroids in European populations of pollen beetle, Meligethes aeneus

    The pollen beetle (Meligethes aeneus F.) is widespread throughout much of Europe where it is a major coleopteran pest of oilseed rape (Brassica napus). The reliance on synthetic insecticides for control, particularly the pyrethroid class, has led to the development of populations with high levels of resistance. Resistance to pyrethroids is now widespread throughout Europe and is thought to be mediated by enhanced detoxification by cytochrome P450ś and/or mutation of the pyrethroid target-site, the voltage-gated sodium channel. However, in the case of cytochrome P450 mediated detoxification, the specific enzyme(s) involved has (have) not yet been identified. In this study a degenerate PCR approach was used to identify ten partial P450 gene sequences from pollen beetle. Quantitative PCR was then used to examine the level of expression of these genes in a range of pollen beetle populations that showed differing levels of resistance to pyrethroids in bioassays. The study revealed a single P450 gene, CYP6BQ23, which is significantly and highly overexpressed (up to ∼900-fold) in adults and larvae of pyrethroid resistant strains compared to susceptible strains. CYP6BQ23 overexpression is significantly correlated with both the level of resistance and with the rate of deltamethrin metabolism in microsomal preparations of these populations. Functional recombinant expression of full length CYP6BQ23 along with cytochrome P450 reductase in an insect (Sf9) cell line showed that it is able to efficiently metabolise deltamethrin to 4-hydroxy deltamethrin. Furthermore we demonstrated by detection of 4-hydroxy tau-fluvalinate using ESI-TOF MS/MS that functionally expressed CYP6BQ23 also metabolizes tau-fluvalinate. A protein model was generated and subsequent docking simulations revealed the predicted substrate-binding mode of both deltamethrin and tau-fluvalinate to CYP6BQ23. Taken together these results strongly suggest that the overexpression of CYP6BQ23 is the primary mechanism conferring pyrethroid resistance in pollen beetle populations throughout much of Europe.

    A cis-regulatory sequence driving metabolic insecticide resistance in mosquitoes: Functional characterisation and signatures of selection

    Although cytochrome P450 (CYP450) enzymes are frequently up-regulated in mosquitoes resistant to insecticides, no regulatory motifs driving these expression differences with relevance to wild populations have been identified. Transposable elements (TEs) are often enriched upstream of those CYP450s involved in insecticide resistance, leading to the assumption that they contribute regulatory motifs that directly underlie the resistance phenotype. A partial CuRE1 (Culex Repetitive Element 1) transposable element is found directly upstream of CYP9M10, a cytochrome P450 implicated previously in larval resistance to permethrin in the ISOP450 strain of Culex quinquefasciatus, but is absent from the equivalent genomic region of a susceptible strain. Via expression of CYP9M10 in Escherichia coli we have now demonstrated time- and NADPH-dependant permethrin metabolism, prerequisites for confirmation of a role in metabolic resistance, and through qPCR shown that CYP9M10 is >20-fold over-expressed in ISOP450 compared to a susceptible strain. In a fluorescent reporter assay the region upstream of CYP9M10 from ISOP450 drove 10× expression compared to the equivalent region (lacking CuRE1) from the susceptible strain. Close correspondence with the gene expression fold-change implicates the upstream region including CuRE1 as a cis-regulatory element involved in resistance. Only a single CuRE1 bearing allele, identical to the CuRE1 bearing allele in the resistant strain, is found throughout Sub-Saharan Africa, in contrast to the diversity encountered in non-CuRE1 alleles. This suggests a single origin and subsequent spread due to selective advantage. CuRE1 is detectable using a simple diagnostic. When applied to C. quinquefasciatus larvae from Ghana we have demonstrated a significant association with permethrin resistance in multiple field sites (mean Odds Ratio = 3.86) suggesting this marker has relevance to natural populations of vector mosquitoes. However, when CuRE1 was excised from the allele used in the reporter assay through fusion PCR, expression was unaffected, indicating that the TE has no direct role in resistance and hence that CuRE1 is acting only as a marker of an as yet unidentified regulatory motif in the association analysis. This suggests that a re-evaluation of the assumption that TEs contribute regulatory motifs involved in gene expression may be necessary.


    Although most biological oxidations occur according to the predictions of known chemical principles, those that do not are often found to involve particularly interesting cofactors, such as previously unsuspected metals or organic coenzymes. In other instances, novel functions of amino acid residues in the enzymes are discovered, thus altering our concepts of biological catalysis. I have long been intrigued by difficult oxidations of unfunctionalized alkyl groups, as in the conversion of the side chain of leucine to acetoacetate (as described above) the anabolism and catabolism of poorly soluble lipids the degradation of natural products such as terpenoids and the transformations of some chemically unreactive “foreign” substances such as drugs, solvents, and pesticides to products that may be more or less toxic than their precursors. Even the highly inert alkanes in petroleum have been known for many years to undergo microbial oxidation.

    In the late 1950s, I picked fatty acid ω-oxidation, in which the attack occurs at the least reactive position, the terminal methyl group, as a model for such difficult oxidations. In 1932, Verkade et al. (9) in the Netherlands had discovered this unexpected conversion when they fed fatty acids of intermediate chain length to dogs and to human volunteers and isolated the resulting urinary dicarboxylic acids. Halina Den, a graduate student in my laboratory, was able to show that a 14 C-labeled α,α-dimethyl-substituted fatty acid underwent terminal oxidation in liver tissue (10), but the instability and insolubility of the enzyme system prevented further progress.

    We then turned to the microbial oxidation of hydrocarbons as even more inert substrates. A postdoctoral associate from Illinois, James Baptist, isolated from soil samples a strain of Pseudomonas oleovorans, called the “gasoline bug” by our colleagues, which grew well on alkanes such as hexane. Cell-free extracts were soon obtained that required NADH for the aerobic conversion of octane to octanol (11, 12) and, of particular interest, the ω-oxidation of fatty acids as demonstrated by Masamichi Kusunose and his wife Emi, visitors from Japan (13). After the successful resolution of the enzyme system into three enzyme fractions by Bill Peterson (14), the components were eventually purified and characterized as rubredoxin, a red nonheme iron protein (15) previously only known to occur in anaerobic bacteria a flavoprotein containing FAD and functioning as NADH-rubredoxin reductase that was characterized by Tetsufumi (Ted) Ueda (16, 17) and the ω-hydroxylase, an almost colorless protein that aggregated extensively and was activated by the addition of ferrous ions (18, 19). The properties of the bacterial hydroxylase made it a difficult candidate for further mechanistic studies, but it has continued to be investigated by others, who have established that it contains a nonheme diiron cluster (20).

    In the hope that our findings with bacteria would be applicable to mammalian metabolism, we returned to the liver system we had abandoned approximately ten years previously. Fortunately, Anthony Lu joined my research group in 1966 as a postdoctoral associate upon completion of his graduate studies in biochemistry at the University of North Carolina. My immediate impression was that this extremely capable young scientist deserved to be given a suitably challenging problem. I doubt that I made it clear just how challenging the hepatic microsomal system would be, but I advised him to begin with the methods that had succeeded with the pseudomonad. The lack of success with this approach did not discourage either of us, nor did the dearth of knowledge at that time about membrane-bound enzymes. After more than two years, Anthony's dedicated efforts eventually resulted in solubilization of the catalytically active rabbit liver microsomal ω-hydroxylation system by the use of various detergents with glycerol and other agents to prevent enzyme denaturation. As is now well known, ion exchange column chromatography resolved the system into three components, which upon recombination under controlled conditions, catalyzed the ω-hydroxylation of 14 C-labeled lauric acid (21, 22). As shown in Figure 1, these included a reddish-brown fraction that we soon identified, to our surprise and considerable delight, as cytochrome P450 by the spectral change upon reaction with carbon monoxide after dithionite reduction, and a yellow fraction containing NADPH-cytochrome P450 reductase that was fully active in electron transfer to P450, unlike preparations isolated by others after solubilization by protease treatment, with loss of the hydrophobic peptide at the NH2-terminus. The third fraction contained an active component that was colorless, heat-stable, and extractable by organic solvents. This was later found by Henry Strobel, another talented postdoctoral associate from North Carolina, to contain microsomal phospholipids, of which phosphatidylcholine was the most effective (23). Thus, we had in our hands the solubilized, reconstituted enzyme system that would allow us to purify and characterize the enzymes involved. A variety of drugs, including aminopyrine, benzphetamine, hexobarbital, ethylmorphine, norcodeine, and p-nitroanisole, were also found to be oxidized by the reconstituted system (24), and, of much interest, Robert Kaschnitz (25) and Wilfried Duppel & Jean-Michel Lebeault (26) found that the same methods used with rabbit liver were successful with human liver and with Candida tropicalis, respectively. Our findings were greatly aided by previous knowledge that the microsomal CO-binding pigment of unknown function (27–29) had been characterized as a b type cytochrome by Omura & Sato (30). In addition, it was known that this catalyst in hepatic microsomes is involved in the hydroxylation of several steroids and drugs, as established in pioneering photochemical action spectroscopic experiments by Omura et al. in 1965 (31).

    In his Bernard Brodie Award Lecture, Anthony Lu (32) has also commented on our limited knowledge of membrane-bound enzymes in the early days and the challenge of working on mammalian cytochrome P450. To indicate the many important questions remaining at that time, a brief summary of the proceedings of the first Symposium on Microsomes and Drug Oxidations held in Bethesda, Maryland, in 1968 (33) is in order. The idea came from the Committee on Drug Safety, Drug Research Board of the National Academy of Sciences. Organized by James Gillette, an expert on biochemical pharmacology, and other distinguished scientists, including Allan Conney, George Cosmides, Ronald Estabrook, James Fouts, and Gilbert Mannering, the program included 27 lectures by experts from around the world on microsomal morphology and what was known about drug metabolism. (Posters had not yet been invented.) The properties of the endoplasmic reticulum were described, and evidence was presented that approximately 20 compounds, encompassing several drugs, steroids, and hydrocarbons, as well as fatty acids (34), undergo oxidation in liver microsomes from experimental animals. Hydroxylation, including drug N-demethylation, was the only reaction considered. Carbon monoxide and SKF-525A were the inhibitors mentioned, and phenobarbital and 3-methylcholanthrene the two chief inducers. Debate ensued on how many “forms” of P450 exist, with one camp believing in only a single enzyme. The interesting proposal was also made on the basis of the effects of inducers on the activities and spectra of liver microsomes isolated from the treated animals for two types of CO-binding pigments, or possibly two interconvertible forms of a single cytochrome. With respect to the active oxidant produced by P450, oxene, analogous to compound I of peroxidases, was proposed. All in attendance agreed that the very intriguing field of drug metabolism was on the threshold of major progress.

    This prefatory chapter is concerned with my research interests that led our laboratory to study the biochemical aspects of drug metabolism, and no attempt is made to provide a general review of what has become a huge field of endeavor. However, mention should be made of the outstanding contributions of the Gunsalus laboratory with bacterial P450cam, a nonmembranous cytochrome that is specific for camphor oxidation (35, 36) and has served as a model for the versatile but less tractable mammalian P450s. Readers interested in developments in this field over the years are referred to the proceedings of several series of international meetings, all with an emphasis on basic science: Symposia on Microsomes and Drug Oxidations, as already mentioned Conferences on the Biochemistry and Biophysics of Cytochrome P450 (37), originated in 1976 by Klaus Ruckpaul, who was working at Berlin-Buch to overcome the barriers that had divided eastern and western Europe since the end of World War II, and whose valiant efforts in this endeavor attracted worldwide support as acknowledged by Sinisi Maricic, the organizer of the first conference (38) meetings on Cytochrome P450 Diversity (37), with an emphasis on microbial and plant systems, initiated by Hans-Georg Mueller, a colleague of Ruckpaul's at Berlin-Buch and meetings of the International Society for the Study of Xenobiotics, started by Bruce Migdaloff in discussions with Fred DiCarlo, John Baer, and Ina Snow at the 1980 Gordon Conference on Drug Metabolism and launched the following year. Perhaps surprisingly, sufficient new results are obtained from laboratories around the world to justify all of these and other related meetings on a regular basis. I had the pleasure of chairing the committees that provided oversight for the Microsomes and Drug Oxidations symposia and P450 conferences for many years. Without a doubt, the collaborations and friendships that grew out of such international meetings were a major stimulus to the rapid development of this broad field, including its application to drug design and development.


    The SuperCYP website was developed as a user-friendly platform for researchers and health professionals. The navigation bar on the left side offers ‘FAQs’ or Frequently Asked Questions, for first-time users.

    ‘Drug search’ enables the user to search for a drug and find information on its metabolism. ‘Get Information’ leads to a table listing CYPs involved in the metabolism of the drug. Here there is also a description of possible consequences and after clicking on the drug name on the results page ‘Drug search’ enables the user to get information for compounds by means of the CAS-number or name.

    The ‘ATC tree’ is the WHO classification system that classifies drugs into different groups according to anatomic site of action, their therapeutical effect and chemical structure. It is the basis for drug alternative drug recommendations. In a Java applet, the user finds a drop-down tree with major and minor branches of classification. All drugs affiliated with a minor branch are listed in a table and information on the CYP metabolism is provided.

    ‘Drug–drug interaction’ is the main feature of the database. It allows users to enter names of several different drugs and to check interactions between these drugs, but they also receive alternative drug options.

    As an example, Omeprazol, a proton pump inhibitor, and Nebivolol, a beta-blocker, interact on the CYP level. After selecting the drugs, the database provides detailed information on drug structures and ATC group plus CAS numbers ( Figure 2).

    Queries and results of the SuperCyp web-interface explaining the various possibilities of the ‘Drug–drug-interaction’ option with the help of two example drugs: Omeprazole and Nebivolol.

    Queries and results of the SuperCyp web-interface explaining the various possibilities of the ‘Drug–drug-interaction’ option with the help of two example drugs: Omeprazole and Nebivolol.

    The successive ‘results’ page warns that Omeprazol has an inhibitory effect on CYP 2D6, whereas Nebivolol is a substrate. The colored background of the table illustrates this dual use of the CYP metabolism pathway. To avoid this and to optimize the drug composition, Omeprazol can be substituted with other drugs from the same ATC-group, for example Pantoprazol, achieving a comparable effect, but using another pathway.

    The proposal of Pantoprazol is derived from the assumption that it does not interact with CYP 2D6. All data on the proposed drugs are provided, and the reference to related publications is given.

    The ‘CYP–Drug-interaction’ allows users to browse substrates, inducers and inhibitors of a certain CYP ( Figure 3).

    Results of the SuperCyp web-interface for CYP 2D6 explaining the functionality of the ‘CYP–drug interaction’ table.

    Results of the SuperCyp web-interface for CYP 2D6 explaining the functionality of the ‘CYP–drug interaction’ table.

    After the user has selected a CYP from the task menu, all known relations with drugs are listed in a table. Then users can specify the relation and focus on substrates, inducers or inhibitors. Respective drugs are given in a table and combined with further information on the particular drug and all CYP interactions. References are linked to PubMed and other scientific websites or articles. The ‘Drug Info’ button is linked to the SuperDrug Database ( 27), which provides a large number of more specific information on the particular drug.

    ‘Polymorphism’ shows single nucleotide polymorphisms for a particular CYP. All known alleles ( 15, 28) are shown and if there is a decrease or increase in activity or expression, this information is provided. Nucleotide changes and their effects, as well as enzyme activity and assay type are given with corresponding references. Some mutation entries address the protein level directly, in which cases information on SNPs may be missing. However, it is desirable to include protein data, as they provide valuable insights into structure-function relationships.

    Example: For CYP11B2, which encodes the enzyme aldosterone synthase (P450aldo), no SNPs were retrieved through keyword searches. However, our mutation/gene association text mining system found 54 protein mutations in 41 PubMed abstracts, which were then added to the database. Among those, the substitution of the highly conserved arginine at position 384 by proline reportedly led to a complete loss of function of this enzyme as part of the autosomal recessively inherited disorder CMO-I deficiency in male Caucasians.

    ‘Alignments’ uses a structure-based alignment program to match the amino acid sequence of all CYPs. It is possible to create a multiple sequence alignment from any number of sequences or to align them with external sequences by uploading a file or entering a sequence in FASTA format. Users may also draft a convenient output with Jalview.

    ‘Three-dimensional structures’ displays protein structures of human CYPs. Existing structures were extracted from the PDB. Theoretical models were generated with Swiss-Model ( 29) or built manually. All structures are downloadable as PDB-files and more information on the CYP is given in the box on the right side.

    Clicking the ‘Browse’-button leads to a Java applet, where all CYPs are listed in a drop-down tree, ordered by main families and subfamilies. Each CYP is viewable as a model and further information on its interactions is provided.

    Figure 4

    Figure 4. Binding mode of aromatic ligands in WT and variant GcoA in crystal structures and molecular simulations. (A) Crystal structure of guaiacol bound at the active site of WT GcoA (5NCB).(25) (B) WT GcoA structure in complex with p-vanillin (5OMR).(25) (C) GcoA T296S variant in complex with p-vanillin (6CYM). Also shown is the dual occupancy of the serine residue in position 296. Binding mode from MD for WT GcoA bound with guaiacol (D), WT bound with p-vanillin (E), and T296S bound with p-vanillin (F). In D–F, the position of the side chain of T296 or S296 (as well as R298) is shown every 2 ns over the course of a 240 ns MD simulation. The probability distributions of the hydrogen bond distance between T296 and the heme propionate group in WT/guaiacol system (G), between T296 and heme or vanillin in WT/vanillin system (H), and between S296 and heme or vanillin in T296S/vanillin system (I) reveal that T296 in WT stabilizes either heme when guaiacol is bound (A, D, G) or ligand when p-vanillin is bound (B, E, H), whereas the T296S residue stabilizes both heme and p-vanillin (C, F, I).

    In Vivo Measurements in P. putida Demonstrate Improved Turnover of p-Vanillin by GcoA-T296S

    Connective Tissue Lab

    In contrast to epithelia, connective tissue is sparsely populated by cells and contains an extensive extracellular matrix consisting of protein fibers, glycoproteins, and proteoglycans. The function of this type of tissue is to provide structural and mechanical support for other tissues, and to mediate the exchange of nutrients and waste between the circulation and other tissues. These tissues have two principal components, an extracellular matrix and a variety of support cells. These two components will be the focus of this lab.

    Most frequently, the different types of connective tissues are specified by their content of three distinguishing types of extracellular fibers: collagenous fibers, elastic fibers, and reticular fibers.

    Collagen Fibers

    Collagenous fibers consist of types I, II, or III collagen and are present in all types of connective tissue. Collagenous connective tissue is divided into two types, based upon the ratio of collagen fibers to ground substance. Ground substance is an aqueous gel of glycoproteins and proteoglycans that occupies the space between cellular and fibrillar elements of the connective tissue.

    • Loose (areolar connective tissue) is the most abundant form of collagenous connective tissue. It occurs in small, elongated bundles separated by regions that contain ground substance.
    • Dense connective tissue is enriched in collagen fibers with little ground substance. If the closely packed bundles of fibers are located in one direction, it is called regular if oriented in multiple directions, it is referred to as irregular. An example of regular dense connective tissue is that of tendons an example of irregular dense connective tissue is that of the dermis.

    Reticular Fibers

    Reticular fibers are composed of type III collagen. Unlike the thick and coarse collagenous fibers, reticular fibers form a thin reticular network. Such networks are widespread among different tissues and form supporting frameworks in the liver, lymphoid organs, capillary endothelia, and muscle fibers.

    Elastic Fibers

    Elastic fibers contain the protein elastin, which co-polymerizes with the protein fibrillin. These fibers are often organized into lamellar sheets, as in the walls of arteries. Dense, regular, elastic tissue characterizes ligaments. Elastic fibers are stretchable because they are normally disorganized – stretching these fibers makes them take on an organized structure.

    Cells of Connective Tissue


    Fibroblasts are by far the most common native cell type of connective tissue. The fibroblast synthesizes the collagen and ground substance of the extracellular matrix. These cells make a large amount of protein that they secrete to build the connective tissue layer. Some fibroblasts have a contractile function these are called myofibroblasts.


    The macrophage is the connective tissue representative of the reticuloendothelial, or mononuclear phagocyte, system. This system consists of a number of tissue-specific, mobile, phagocytic cells that descend from monocytes - these include the Kupffer cells of the liver, the alveolar macrophages of the lung, the microglia of the central nervous system, and the reticular cells of the spleen. You will encounter each of these later in the course for now, make sure you recognize that they all descend from monocytes, and that the macrophage is the connective tissue version. Macrophages are indistinguishable from fibroblasts, but can be recognized when they internalize large amounts of visible tracer substances like dyes or carbon particles. Macrophages phagocytose foreign material in the connective tissue layer and also play an important role as antigen presenting cells, a function that you will learn more about in Immunobiology.

    Mast Cell

    Mast cells are granulated cells typically found in connective tissue. These cells mediate immune responses to foreign particles. In particular, they release large amounts of histamine and enzymes in response to antigen recognition. This degranulation process is protective when foreign organisms invade the body, but is also the cause of many allergic reactions.

    White Fat Cell

    White fat cells or adipocytes are specialized for the storage of triglyceride, and occur singly or in small groups scattered throughout the loose connective tissue. They are especially common along smaller blood vessels. When fat cells have accumulated in such abundance that they crowd out or replace cellular and fibrous elements, the accumulation is termed adipose tissue. These cells can grow up to 100 microns and usually contain once centrally located vacuole of lipid - the cytoplasm forms a circular ring around this vacuole, and the nucleus is compressed and displaced to the side. The function of white fat is to serve as an energy source and thermal insulator.

    Brown Fat Cell

    Brown fat cells are highly specialized for temperature regulation. These cells are abundant in newborns and hibernating mammals, but are rare in adults. They have numerous, smaller lipid droplets and a large number of mitochondria, whose cytochromes impart the brown color of the tissue. The electron transport chain of these mitochondria is disrupted by an uncoupling protein, which causes the dissipation of the mitochondrial hydrogen ion gradient without ATP production. This generates heat.


    Cartilage is a specialized form of connective tissue produced by differentiated fibroblast-like cells called chondrocytes. It is characterized by a prominent extracellular matrix consisting of various proportions of connective tissue fibers embedded in a gel-like matrix. Chondrocytes are located within lacunae in the matrix that they have built around themselves. Individual lacunae may contain multiple cells deriving from a common progenitor. Lacunae are separated from one another as a result of the secretory activity of the chondrocytes. Three kinds of cartilage are classified according to the abundance of certain fibers and the characteristics of their matrix.

    Hyaline Cartilage

    Hyaline cartilage has a matrix composed of type II collagen and chondromucoprotein, a copolymer of chondroitin sulfates A and C with protein. Its high concentration of negatively-charged sulfate groups makes it appear intensely basophilic under H&E. This cartilage is found in the nose, tracheal rings, and where the ribs join the sternum.


    Fibrocartilage is distinguished by its high content and orderly arrangement of type I collagen fibers. It is typically located in regions where tendons attach to bones, the intervertebral discs, and the pubic symphysis.

    Elastic Cartilage

    Elastic cartilage is characterized by the presence of abundant elastic fibers and is quite cellular. It is made up of type II collagen and is located in the auricle of the ear and the epiglottis.


    The mountain pine beetle (MPB Dendroctonus ponderosae) is a forest pest that attacks pine forests across western North America [1]. As part of its lifecycle, which is spent mostly in the phloem of its various pine (Pinus) hosts, the MPB is exposed to the tree’s oleoresin defenses. Oleoresin is mostly comprised of monoterpenes and diterpene resin acids (DRAs) [2]. The relationship between MPB and host terpenoids has been studied extensively, as these compounds have chemo-ecological roles not only as defense chemicals, but also as volatile signal molecules by which MPB identify suitable hosts and as MPB pheromone precursors [3]. While some monoterpenes are toxic to MPB [4], MPB and its microbial associates can detoxify some of the oleoresin metabolites [5–10]. MPB-associated fungi can utilize some of the oleoresin terpenes, specifically (+)-(4R)-limonene, as a carbon source [7]. Female MPB produce the aggregation pheromone trans-verbenol from the host monoterpene α-pinene, which is abundant in pine oleoresin [11–13].

    The MPB genome contains 86 different cytochrome P450 genes [14], and three of these P450s have previously been shown to function in terpenoid metabolism or terpenoid pheromone biosynthesis in the MPB [13,15,16]. At least seven different MPB P450s are differentially expressed in organs or tissues where metabolism of monoterpenes and DRAs is likely to occur, specifically in antennae for olfaction, as well as in the alimentary canal and fat body for detoxification and pheromone formation [13,17,18]. Of these seven P450s, CYP345E2 metabolizes the monoterpenes (+)-3-carene, (–)-camphene and both enantiomers of α-pinene, β-pinene and limonene [15], and CYP6DE1 oxidizes (+)-3-carene, and both enantiomers of α-pinene, β-pinene and converted (–)-α-pinene to (–)-trans-verbenol, an aggregation pheromone released by female MPB [13]. Among this same set of seven genes, transcripts of CYP6DJ1 and CYP6BW3 were highly abundant in antennae, while CYP6BW1 was highly abundant in the midgut [17]. CYP6BW1 and CYP6BW3 share 96% amino acid identity, suggesting that they originated from a gene duplication. Their expression in different tissues indicates divergent biological functions. Analysis of transcript abundance in male and female MPB at different life stages, specifically 3 rd instar larvae, pupae, and teneral, emerging and colonizing adults also revealed sex-specific differences in the expression of these P450s [17]. Transcript abundance of CYP6DJ1 was significantly higher in colonizing females compared to colonizing males, while transcript abundance of CYP6BW3 was higher in colonizing males. CYP6BW1 did not show sex-specific differences in expression.

    Here, we investigated biochemical functions of CYP6DJ1, CYP6BW1 and CYP6BW3 by testing each of the three heterologously expressed P450s in a series of in vitro assays with ten different monoterpenes and six DRAs as substrates. The substrates were selected to include major oleoresin compounds of common MPB hosts. We compared the monoterpene oxidation products of the P450 in vitro assays with products formed in vivo by female MPB that were exposed to monoterpenes.


    Reference H. armigera genome data and assemblies

    DNA was extracted from the offspring of a single pair of the GR laboratory colony of H. armigera maintained in Canberra. The colony derives from collections in the 1980s from cotton fields in the Namoi Valley in New South Wales, Australia, and has been maintained on a suitable laboratory diet since then. DNA extraction was performed from whole, late stage pupae using a standard phenol chloroform protocol.

    Library construction and sequencing was performed at the Baylor College of Medicine, Human Genome Sequencing Center (BCM HGSC), Houston, TX, USA. Several different types of sequencing libraries were generated — a few for the 454 sequencing platform but most for the Illumina platform. Raw data were pre-processed to remove low-quality reads and bases.

    An AllpathsLG [91] assembly of the Illumina data (from a 180-bp paired-end (PE) and 3-kb, 6-kb and 8-kb mate pair (MP) libraries) and a 20-kb MP 454 library produced a scaffold N50 of 1 Mb. This assembly, termed csiro4b, formed the basis for the final genome freeze, as described in Additional file 4: Section 13. Further AllpathsLG assemblies used different combinations and subsets of the available data as input (Additional file 4: Table S26). A Celera Assembler with the Best Overlap Graph (CABOG) [92] assembly of contigs was also made using selected 454 and Illumina data. These other assemblies were used in confirmation or repair of gene models during the annotation process described below. The csiro4b assembly was then corrected at 100 locations with sequences identified as giving correct gene models from the other assemblies or transcriptome data, to generate the patched genome freeze csiro4bp. Further details of the GR colony, sequencing data and assembly methods are provided in Additional file 4: Section 13.

    H. armigera transcriptomics

    Material from the GR colony was also used in the two major transcriptomics experiments, either whole organisms or dissected tissues for the tissue/developmental transcriptome atlas (see Additional file 4: Table S8) and whole fourth instar larvae for the experiment investigating the effects of diet (see below). Total RNA from all samples was extracted by grinding the material in ‘RLT’ solution, and RNA from the equivalent of 30 mg of tissue from each sample was then purified using an RNeasy mini kit (Qiagen, Victoria, Australia). RNA was eluted in water, with a minimum yield of 40 μg. RNA quality and quantity in an aliquot of each sample were determined by electrophoresis on an Agilent 2100 Bioanalyser (Agilent Technologies, Santa Clara, CA, USA) chip system and by UV absorption on a NanoDrop spectrophotometer ND-1000 (ThermoFisher Scientific, Waltham, MA, USA). The remaining RNA from each sample was precipitated with ethanol and sodium acetate and stored at –80 °C. Library construction and RNA sequencing were done at BCM HGSC.

    An initial comprehensive transcriptome assembly using all the RNA-seq reads from both these transcriptomics experiments was generated using TopHat and Cufflinks [93, 94]. A second assembly, following trimming of PE reads (100 b) to 80 b using the FASTX-Toolkit (, was then generated using Trinity [95], as described in detail in Kanost et al. [40].

    MicroRNAs were sequenced from total RNA harvested from first instar larvae, the midguts of fourth instar larvae and from pupae, again all from the GR colony. After phenol/chloroform extraction and ethanol precipitation, the total RNA was resuspended in diethyl pyrocarbonate (DEPC)-treated MQ water, quantified with a NanoDrop Spectrophotometer ND-1000 and quality checked in an Agilent 2100 Bioanalyser. About 100 ng of total RNA was denatured at 70 °C for 1 min, followed by chilling on ice and Illumina sequencing (Geneworks, Adelaide, Australia).

    Annotation of the H. armigera genome

    This step involved automated annotation with MAKER and Program to Assemble Spliced Alignments (PASA2). The first step in our automated annotation of csiro4b involved the MAKER pipeline [96]. The Augustus [97], Semi-HMM-based Nucleic Acid Parser (SNAP) [98] and GeneMark [99] ab initio gene prediction tools incorporated in MAKER were trained using a set of manually curated genes (see below). As detailed in Additional file 4: Section 13, the process was then repeated several times with inclusion of the RNA-seq assemblies and additional evidence databases consisting of gene sets predicted from other insect genomes. A customised method using the OrthoMCL [100] and CD-HIT [101] pipelines was then used to assess the quality of the predicted genes from each of the nine MAKER runs and to consolidate the genes from the various MAKER runs into a consensus set (Additional file 4: Section 13). The nine MAKER runs and OrthoMCL + CD-HIT approach together produced 18,636 distinct proteins.

    Many protein models produced by MAKER resulted from fusions of adjacent duplicated genes. However, these problems were resolved in a comprehensive re-annotation using JAMg ( as per Papanicolaou et al. [102]. Briefly, the MAKER, protein domain evidence, Kassiopeia [103], GeneMark, RNA-seq coverage, intron-spanning cDNA reads and previously manually curated genes were provided as evidence with respectively increasing weight to the Augustus de novo gene predictor. This multi-layered output was then reconciled using EVidenceModeler [104] and annotated for untranslated regions (UTRs) and alternative transcription using the RNA-seq data and PASA2 [104, 105], yielding 22,818 transcript models. A reference unigene set (i.e. containing a single protein model for each locus), termed the official gene set 1 (OGS1 Additional file 4: Section 13), was derived from this. Finally, 1088 manually annotated gene models for specific gene families (see below) replaced the corresponding automated gene models, giving OGS2. Scipio [106] was used to derive genome location coordinates for the manually annotated gene models.

    Functional annotation of gene models in key families

    The automatically generated gene models for the key detoxification, digestion and chemosensory gene families were cross-checked and manually curated using all available sequences, cDNAs and gene models. For the detoxification and digestion families this included the use of a specially developed gene finding and alignment pipeline (Additional file 4: Section 13) where the models generated differed from those in the final assemblies, the latter were then patched appropriately. Other families listed in the comprehensive family annotation table (Additional file 2: Table S2) were annotated based on either the use of custom perl scripts to identify proteins with specific motifs (e.g. the cuticular proteins) or by the semi-automated screening of Basic Local Alignment Search Tool (BLAST)-derived annotations.

    Whole genome functional annotations

    The OGS2 protein sequences were analysed using a custom version of the InterProScan pipeline [107], including the GO [108], Pfam [109], PROSITE [110] and Simple Modular Architecture Research Tool (SMART) [111] annotations. Proteins carrying relevant domains identified by these analyses were flagged for confirmation as members of specific gene families. GO term assignments were extensively used in custom pipelines built on the GO database and in the Biological Networks Gene Ontology tool (BiNGO) plugin [112] for Cytoscape [113]. To analyse functional enrichment in specific gene sets, GO terms were summarised through semantic similarity filtering and visualised using REVIGO [114].

    Repeats and microRNAs

    Repeat sequences in the genome were identified using RepeatModeler [115]. All previously identified lepidopteran repeats were first obtained from RepBase and used to query the H. armigera genome. These repeats were then used as known repeat libraries for 10 iterations of RepeatModeler runs using RepeatScout and rmblast. The repeats recovered were then masked in the H. armigera genome using RepeatMasker. RNA sequence data for miRNA analysis were first processed using custom perl scripts, and then miRNAs were predicted using miRDeep2 [116]. Further analysis against known miRNAs from other insects was undertaken using miRBase19 [117].

    Reference H. zea genome and transcriptome assemblies and annotation

    Genome sequencing for H. zea used DNA extracted from pupae of a laboratory colony established prior to introduction of transgenic Bt crops and maintained without infusing feral insects for at least 25 years [118]. This laboratory colony was highly susceptible to all Bt toxins compared to feral H. zea [118,119,120]. Virgin males and females were used to inbreed the insects through three generations of single-pair matings. Male pupae of the final generation were used to obtain high molecular weight genomic DNA for preparing Illumina sequencing libraries. Libraries were constructed and sequenced as for H. armigera above.

    An AllpathsLG assembly of the Illumina data produced an N50 of 196 kb (Hz-csiro5 in Additional file 4: Table S27). Again, a series of further AllpathsLG assemblies used different combinations and subsets of the input data as listed in Additional file 4: Table S27. Correction and patching of Hz-csiro5 to produce the final H. zea genome freeze (hz5p5) is described in Additional file 4: Section 13, together with further details of the H. zea colony and the sequencing data and assembly methods used.

    Transcriptome data used in annotation of the H. zea genome included a preliminary assembly of 454 and Illumina RNA-seq data. All 454 data were obtained from a pool of RNA starting with 24–48 h embryos, all larval stages, pupae and adult males and females. The Illumina RNA-seq data were from 24–48 h embryos and third instar larvae. The larvae were treated with sublethal doses of Cry1Ac, novaluron, cypermethrin and Orthene to induce genes involved in xenobiotic degradation that may not normally be expressed. The 454 libraries were normalised. RNA sequence data were assembled with Trinity (version trinityrnaseq_r20140413p1) using genome-guided and de novo assembly methods as above for H. armigera.

    The H. zea genomes were screened using the H. armigera OGS2 gene model protein sequences and Scipio [106] to identify the best possible gene models for H. zea. See Additional file 4: Section 13 for details.

    Orthology and evolutionary analyses of target gene families

    Gene models for the detoxification- and digestion-related gene families in H. armigera and H. zea were obtained as described above. For other species analysed in Table 2, the automatically generated gene models and official gene sets were cross-checked and manually curated by domain specialists using available sequences, cDNAs and gene models generated by the EXONERATE-based dedicated pipeline. Current annotations of B. mori and M. sexta members of these families were cross-checked and in some cases revised by a similar procedure, albeit in this case the few models that differed from those in the genome assembly were not patched into that assembly. All our final gene models for these families for the three species are summarised in Additional file 6: Table S5. Other families of interest whose gene models are listed in this table were identified and annotated either using custom perl scripts to screen for proteins with specific motifs (e.g. the cuticular proteins) or by semi-automated screening of BLAST-derived annotations.

    The phylogenetic methods used to analyse the evolutionary processes operating in most gene families were as described in the Methods for Supplementary Figures 19–21 of Kanost et al. [40]. Briefly, we used multiple sequence alignment software (MAFFT) [121] with the linsi option to make a multiple sequence alignment, which we then masked for sites with more than 50% gaps or ambiguous characters. Phylogenetic analyses were then carried out using IQ-TREE [122], which implements an ultrafast bootstrap method [123] and ModelFinder, a new model-selection method that greatly improves the accuracy of phylogenetic estimates [124]. Having found the optimal model for each family, we then inferred the most likely tree for it using IQ-TREE, with bootstrap scores inferred using the ultrafast bootstrap method. Two other phylogenetic methods were used for a few data sets. PhyML [125] was used for some smaller data sets, and for the lower quality GR data set Randomised Axelerated Maximum Likelihood (RAxML) [126] was used. Trees were illustrated using the R package ggtree [127].

    Divergence dating analyses among subsets of gene families within or across different species or lines used the Bayesian MCMC method in BEAST v2.4.3 [55]. Protein sequences aligned using MAFFT as described above for the phylogenetic analyses were used to inform coalignment of nucleotide sequences using a custom perl script. Where necessary, the site models were unlinked to enable different evolutionary rates at each locus (as determined in IQ-TREE above), but clock and tree models were linked so that they would not vary among locus partitions. An XML input file was then generated for BEAST v2.4.3 using BEAUti v2.4.3. The prior for t MRCA (time to the Most Recent Common Ancestor) and root height were set at a lognormal distribution, with a mean of ln (1.5) and a standard deviation of 0.01. A strict molecular clock with a uniform distribution was applied using the mutation rate determined for H. melpomene of 2.9 × 10 –9 (95% confidence interval, 1.3 × 10 −9 through 5.5 × 10 −9 ) substitutions per site per generation [128]. A generation time of 0.25 year corresponding to the midrange defined by Fitt [67] for subtropical and temperate regions was used for some analyses. Trees were annotated in TreeAnnotator v2.4.3 [129] and visualised in FigTree v1.4.2 [130].

    Relative rate tests of H. armigera genes used the nearest paralogues shown in the phylogenetic trees for each family in Additional file 4: Sections 1–8. Protein sequences aligned using MAFFT as described above for the phylogenetic analyses were used to inform coalignment of nucleotide sequences using a custom perl script. Tajima’s relative rate tests [131] were done in Molecular Evolutionary Genetics Analysis (MEGA) software [132].

    Tissue/developmental transcriptomic atlas

    Thirty-one GR samples reared on standard diet were collected for this analysis, four from whole organisms of specific life stages and 27 from tissues or body parts of feeding fifth instar larvae or adults. Details of the samples are given in Additional file 4: Table S8. RNA and library preparation and sequencing were as described above.

    Diet transcriptomics experiment

    Patterns of gene expression were compared between larvae raised on different host plants. The plants were selected to maximise the diversity of responses that might be observed [64]. The set comprised one monocot, maize, Zea mays (larval RNA libraries M-3, GenBank BioSamples 6608687-9), and plants from four dicotyledonous plant families: Malvaceae, cotton, Gossypium hirsutum (larval RNA libraries Ct1-3, GenBank BioSamples 6608702-4) Brassicaceae, thale cress, Arabidopsis thaliana (larval RNA libraries AR1-3, GenBank BioSamples 6608666-8) Fabaceae, green bean, Phaseolus vulgaris (larval RNA libraries GB1-3, GenBank BioSamples 6608675-7) and Solanaceae, tobacco, Nicotiana tabacum (larval RNA libraries Tb1-3, GenBank BioSamples 6608696-8), tomato, Lycopersicon esculentum (larval RNA libraries TM1-3, GenBank BioSamples 6608699-701) and hot pepper, Capsicum frutescens (larval RNA libraries Hp1-3, GenBank BioSamples 6608678-80). For reference, larvae were also raised on a standard laboratory diet [133, 134] (larval RNA libraries Sd1-3, GenBank BioSamples 6608693-5).

    About 10 larvae from the GR colony were transferred to plants or the laboratory diet in triplicate within 24 h of hatching and without exposure to any previous diet. Each replicate consisted of one pot containing either a single plant for the larger species or several plants for the smaller species. Larvae were transferred to plants when flowers had started to form but before any fruit was present. The plants were grown under the same glasshouse conditions, and each of the three replicates used larvae from a different cohort of the laboratory culture. As pointed out by others [64, 135], larvae raised on an artificial diet prior to such a host-response experiment are seen as offering the advantage of not being primed for any particular plant host.

    In order to harvest all larvae at a comparable developmental stage irrespective of the host plant, six larvae from each replicate were collected from the plants when they had returned to feeding one day after moulting to the fourth instar. The time taken to reach this stage was noted, and the larvae were weighed they were then immediately cut with dissecting scissors into three or four pieces. Their RNA was preserved by immediately dropping the pieces into RNAlater solution (Ambion, Austin, TX, USA), which was held initially on ice to allow the solution to diffuse into the tissue and then frozen at –80 °C.

    Total RNA was prepared from the six larvae comprising each replicate as per the methods described above, except that the libraries for sequencing were made at the United States Department of Agriculture-Agricultural Research Service (USDA-ARS, Stoneville, MS, USA). RNA sequencing was done at BCM HGSC as above.

    It was not possible to undertake parallel diet transcriptomic experiments on H. zea in this study, since it is not found in Australia and therefore subject to stringent biosecurity quarantine prohibitions. Such a follow-up study would therefore need to be undertaken in a country known to harbour both species.

    Transcriptome analyses

    Sequencing reads were cleaned using Trimmomatic [136] to remove adapter sequence and low-quality reads. Passing reads were aligned to the H. armigera csiro4bp assembly with the subread aligner implemented in the Rsubread package [137]. A maximum of three mismatches were allowed in the alignment, and the best scoring alignment for each read was reported. The numbers of reads per library that overlapped with the predicted transcripts described above were summarised at the gene level with featureCounts [138]. To be considered for further analysis, a minimum level of five reads per million across three libraries was required. In the case of the developmental/tissue atlas, an alternative inclusion criterion of at least 20 reads per million in at least one library was allowed to capture genes that may have been expressed in only a single life stage or tissue sampled. These criteria resulted in 13,099 and 11,213 genes being considered expressed in the developmental/tissue atlas and host use analysis, respectively, with a total of 13,689 unique genes across the two data sets.

    Read counts were normalised between samples using the trimmed mean of M-values method [139] and converted to log2 counts per million values (log2cpm) with associated quality weights using the voom-limma pipeline [140]. For the host use experiment, gene expression was modelled simply as a factor of the diet the larvae were raised on. To remove the effects of unwanted variation due to latent variables not correlated with larval diet, three surrogate variables [141, 142] were estimated from the data and included in the expression model. Genes with a significant difference in expression relative to the control diet (false discovery rate adjusted p value less than 0.05) and a log2 fold change in expression greater than 1.5 were considered to be diet-responsive.

    For a broader analysis of gene expression, we constructed gene co-expression networks from our expression data to identify sets of genes that show correlated expression profiles. Additional filtering criteria were used to ensure that only genes that displayed some level of expression variation were considered in the network construction. The criteria for inclusion were that the mean log2cpm expression value had to be greater than 1 and the standard deviation of the value had to be greater than 0.5. Similar to the previous filtering step, an additional acceptance criterion was included for the tissue data set to allow for genes expressed in only a small number of libraries to be included. The extra criterion for this data set was that any gene with a standard deviation greater than 2 was included. Unsigned, weighted correlation networks were produced from both the diet and tissue/developmental data sets with the R package weighted correlation network analysis (WGCNA) [143]. The power parameter used for each network was 11 and 8, respectively, chosen as the lowest value with a scale-free topology fit R squared greater than 0.85. Gene expression modules were determined from a topological overlap matrix, and modules with highly correlated eigengene expression patterns (>0.85) were merged.

    Resequencing experiments and analyses

    Three additional H. armigera lines, one from Africa and two from China, and four additional H. zea individuals, all from the USA, were sequenced as a database for various population genomic analyses. The African H. armigera strain, SCD, originated from the Ivory Coast in the 1970s and was maintained in the laboratory without exposure to insecticides or Bt toxins for more than 130 generations of mass mating before DNA preparation. One Chinese line, SW, was founded in 2012 from 150 moths collected in cotton fields from Shawan in the Xinjiang Uygur Autonomous Region. SW was reared for 17 mass-mating generations in the laboratory without exposure to insecticides or Bt toxins before DNA preparation. The other Chinese line, AY, was started from a single pair of moths collected in 2011 from Anyang in Henan Province [79]. AY, which survived the diagnostic Cry1Ac concentration of 1 μg/cm 2 , was reared for more than 30 generations before DNA preparation. For these SCD, SW and AY lines of H. armigera, DNA was prepared from individual male pupae. The DNA was then used in construction of 500b PE libraries which were quantified and sequenced on an Illumina HiSeq2000 platform at the Beijing Genomics Institute (BGI, Shenzhen, China) using standard in-house protocols.

    The four H. zea individuals had been collected as larvae from wild host plants in Bolivar County, Mississippi. DNA was prepared from their thoraces when they emerged as adults and used for constructing sequencing libraries using an Illumina Nextera library construction kit. Genomic DNA libraries were size fractionated on a Pippin Prep instrument (Sage Science Inc., Beverly, MA, USA) to obtain 550 ± 20 b fragments (inset size 400–450 b) and quantified using a KAPA library quantification kit (KAPA Biosystems, Wilmington, MA, USA). An equimolar pool of the four libraries was sequenced on an Illumina HiSeq2500 instrument at the USDA-ARS Genomics and Bioinformatics Research Unit, Stoneville, MS, USA.

    Sequence reads from each line or individual were error corrected using Blue [144] and aligned to the H. armigera reference genome with the Genomic Short-read Nucleotide Alignment Program (GSNAP) [145]. To ensure that the choice of reference genome did not influence our results, reciprocal alignments of all lines or individuals against the H. zea reference genome were also performed. Using the Genome Analysis Toolkit (GATK) [146] we applied duplicate removal and local realignment around indels followed by SNP genotyping using standard hard filtering parameters as per the GATK Best Practices recommendations [147, 148]. As an extra step to allow us to better compare sequences from the two species, we imposed the additional filtering criterion that a variant must be genotyped across all sequenced lines or individuals to be included in our analysis.

    Genetic relationships between H. armigera and H. zea were examined using MDS on SNP data files generated for all sequences in our data set, including both the H. armigera and H. zea reference sequences.

    Coalescence analysis was performed on 16 loci (see Additional file 3: Figure S5 Additional files 11 and 12), representing genes present across all of the H. armigera and H. zea samples, including both reference sequences, as well as in the outgroup H. punctigera (i.e. n = 10 for each locus). The set of loci selected for this analysis were one-to-one orthologues across all samples, with only up to 1% of sites in a given locus being soft-masked (i.e. for sequencing coverage <10×) or heterozygous. These criteria resulted in a set of well-conserved loci across these 10 samples being used subsequently in the coalescence analysis in BEAST v2.4.3 [149]. All loci were first aligned independently using the linsi option in MAFFT v7.182 [121]. IQ-TREE v1.4.1 [122] was then used with the -m TESTNEWONLY option to determine the best-fit evolutionary rate model for each locus. BEAUti v2.4.3 (StarBeast template) was used to generate a BEAST XML input file, setting individual rate models for each locus as identified in IQ-TREE, and unlinking tree models. A Yule process for the multi-species coalescent, and a ‘linear with constant root’ population size prior were the parameters selected to generate the BEAST input file. The analysis was run for >100 × 10 6 MCMC chains to reach convergence of tree likelihoods and to get effective sample size (ESS) values >200 (assessed in Tracer v1.6.0 [150]). The BEAST analysis produced an overall species tree for H. armigera, H. zea and H. punctigera, as well as individual gene trees for each locus. The latter were fed to DensiTree v2.2.2 [55] to check whether the topology is consistent with the overall species tree. In instances of conflict between the gene and species trees, we investigated the loci in question to assess whether we could find evidence for incomplete lineage sorting between H. armigera and H. zea.

    The historical effective population sizes and their changes over time were estimated for H. armigera and H. zea using the Bayesian skyline plot method as implemented in BEAST v1.8.2 [151]. The data sets used were genome-wide SNPs called separately for each of the following samples: for H. armigera, sequences from the AY, SW and SCD lines against the H. armigera reference genome and for H. zea, the four individuals described above against the H. zea reference genome. The two sets of samples were also called against the other species’ genome as a control. MCMC samples were based on 10 8 generations, logging every 1000 steps, with the first 10 7 generations discarded as burn-in. We used a piecewise linear skyline model, an HKY substitution model and a strict clock with the mean substitution rate as determined for H. melpomene of 2.9 × 10 –9 (95% confidence interval, 1.3 × 10 –9 through 5.5 × 10 –9 ) substitutions per site per generation [128].

    To examine synonymous and non-synonymous diversity between the two species, we analysed nucleotide diversity (pi) in our resequenced H. armigera and H. zea samples (i.e. excluding the reference strains). We explored mean genomic diversity further by examining all polymorphic sites (i.e.

    8.2 M SNPs called across the genome). Diversity measurements only counted windows where there were a minimum of 10 SNPs per 10-kb genome window.

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