The neuropeptidome of Rhodnius prolixus brain - Wiley Online Library

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de la Provincia de Buenos Aires, Pergamino, Argentina. We show a sensitive and straightforward off-line nano-LC-MALDI-MS/MS workflow that allowed.
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DOI 10.1002/pmic.200800499

Proteomics 2009, 9, 788–792

DATASET BRIEF

The neuropeptidome of Rhodnius prolixus brain Sheila Ons1, Florian Richter2, Henning Urlaub2 and Rolando Rivera Pomar1, 3 1

Laboratorio de Genética y Genómica Funcional, Centro Regional de Estudios Genómicos, Universidad Nacional de La Plata, Buenos Aires, Argentina 2 Bioanalytical Mass Spectrometry Laboratory. Department of Cellular Biochemistry. Max Planck Institute for Biophysical Chemistry, Göttingen, Germany. 3 Departamento de Ciencias Básicas y Experimentales, Universidad Nacional del Noroeste de la Provincia de Buenos Aires, Pergamino, Argentina

We show a sensitive and straightforward off-line nano-LC-MALDI-MS/MS workflow that allowed the first comprehensive neuropeptidomic analysis of an insect disease vector. This approach was applied to identify neuropeptides in the brain of Rhodnius prolixus, a vector of Chagas disease. This work will contribute to the annotation of genes in the ongoing R. prolixus genome sequence project. Peptides were identified by de novo sequencing and comparisons to known neuropeptides from different organisms by database search. By these means, we were able to identify 42 novel neuropeptides from R. prolixus. The peptides were classified as extended FMRF-amiderelated peptides, sulfakinins, myosuppressins, short neuropeptide F, long neuropeptide F, SIFamide-related peptides, tachykinins, orcokinins, allatostatins, allatotropins, calcitonin-like diuretic hormones, corazonin, and pyrokinin. Some of them were detected in multiple isoforms and/or truncated fragments. Interestingly, some of the R. prolixus peptides, as myosuppressin and sulfakinins, are unique in their characteristic C-terminal domain among insect neuropeptides identified so far.

Received: June 12, 2008 Revised: August 5, 2008 Accepted: September 2, 2008

Keywords: Disease vectors / Insect neuropeptides / Off-line nano-LC-MALDI / Peptidomic / Rhodnius prolixus

Chagas’ disease affects 16–18 million people in the Central and South America, with another 120 million (onefourth of the population of the region) at risk. The protozoan Trypanosoma cruzi, infectious agent of the disease, is vectored by hematophagous insects of the family Reduviidae, commonly called “kissing bugs.” Among the many species, two of them are the main vectors: Triatoma infestans and Rhodnius prolixus. The control of vector populations in endemic areas has become the preferred disease management strategy.

Correspondence: Dr. Rolando Rivera Pomar, Av. Calchaquí 5900 4to. Piso, 1888, Florencio Varela, Buenos Aires, Argentina E-mail: [email protected] Fax: 154-11-4275-8100 Abbreviations: Ox-M, oxidated methionine; pQ, pyro-glutamic acid from glutamine

© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Disruption of the activity of vector physiology through manipulation of regulatory peptides is an attractive direction toward a novel generation of insecticides, the “smart sprays” [1]. Knowing which are the neuropeptides and understanding their active conformation can provide tools for the design of peptidomimetics, pseudopeptides, or small molecules capable of disrupting normal physiological processes. Surprisingly, however, a systematic screen for regulatory peptides in hematophagous Hemiptera, such as Triatomines, or even any disease vector insect, has not yet been carried out with proteomic tools. In this study we performed a peptidomic analysis of the brain of R. prolixus. This is the first comprehensive high-throughput neuropeptidomic study of a disease vector to date. R. prolixus individuals were reared at 28oC and fed on blood during the whole life cycle in regular intervals as described [2]. Nymphs were fed once before molting and the adults were fed ad libitum 7 days before dissection. Brains www.proteomics-journal.com

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from male and female adult R. prolixus were manually dissected, without the corpora cardiaca, and immediately placed in ice-chilled R. prolixus saline (NaCl, 129 mM; KCl, 8.6 mM; CaCl2 2.0 mM; MgCl2 8.5 mM; NaHCO3 10.2 mM; NaH2PO4 4.3 mM; HEPES 8.6 mM, pH 7). For tissue extraction, 20 brains were placed in 100 mL methanol/water/acetic acid (90:9:1), sonicated for 5 min and centrifuged for 10 min at 75006g. The supernatant was collected and the pellet was reextracted twice. The collected supernatant was placed under vacuum to remove organic solvents, rediluted in 20 mL 0.1% TFA and desalted using a C18 extraction disc (Varian, Darmstadt, Germany) activated with 80% ACN/0.1% TFA. C18 extraction material was washed with 0.1% TFA, sample was loaded, and peptides were eluted with 70% ACN/0.1% TFA. Organic solvent was removed under vacuum and sample rediluted in 20 mL 0.1% TFA. Nano-RP LC separation was performed on a Dionex nano-LC system (LC Packings) equipped with autosampler, loading pump, nano pump, Helium degasser, precolumn (2560.15 mm2) working in backflush, analytical column (15060.075 mm2), and a multichannel detector. Precolumn were self-packed using Dr. Maisch Reprosil-Pur 120 ODS-3 (5 mm particle size and 120 Å pore size, Dr. Maisch, Ammerbuch-Entringen, Germany) with increased hydrophobicity due to higher carbon loading onto the material. Analytical columns were self-packed with C18 RP material, (Vydac MS218, 5 mm particle size and 300 Å pore size, Vydac, Hesperia, CA). Brain extracts were loaded onto the precolumn at a flow rate of 10 mL/min in loading buffer (0.1% TFA) for 10 min. After valve switching peptides were eluted by a linear gradient of 10–60% solvent B (80% ACN, 0.1% TFA) in 180 min. The flow rate was 0.3 mL/min. The gradient was then raised from 60 to 100% of solvent B in 10 min. The eluate was mixed with CHCA (10 mg/mL in 70% ACN/0.1% TFA) containing Glu-fibrinogen peptide (10 fmol/mL; Sigma–Aldrich, USA) as internal standard, and directly spotted on a stainless steel LC-MALDI target plate (Applied Biosystems/MDS Sciex) using a Probot microfraction collector (LC Packing). Matrix was delivered with a flow rate of 0.9 mL/min and fractions were spotted onto the plates at intervals of 15 s. Samples were prepared in triplicate. MALDI-TOF-MS/MS was performed on an ABI 4800 analyzer (Applied Biosystems/MDS Sciex). Each spot was first analyzed by MS. Spectra in MS were recorded between 600 and 4000 m/z. Job-wide interpretation of the MS data allowed the 15 highest peptides in intensity (with S/N 30) for sequencing in MS/MS mode. 1000 (3800 J) and 5000 (4500 J) laser shots were applied for MS and MS/MS, respectively. Collision energy was set to 161026 Torr, with potential difference between the source acceleration voltage and the collision cell set at 1 kV. An eight-point plate model calibration was performed with the Calibration Mixture 5 kit for Proteomics Analyzer (Applied Biosystems/MDX Sciex). For peptide identification, MS/MS spectra were processed and peak lists were used for de novo sequencing. Peak lists from the identified peptides are provided as Supporting © 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

789 Information online. De novo sequencing was first performed automatically by Peaks Studio 3.1 software (Bioinformatic Solutions) with the following settings – enzyme: none; variable modification; Pyro-glutamic of Glutamine (Q), C-terminal amidation; parent ion mass error tolerance: 0.1 Da; fragment ion mass error tolerance: 0.1 Da. Manual analysis has been performed for every spectrum to confirm or modify auto de novo results and to detect PTMs as oxidation of Met, N-terminal pyro-glutamic from Gln, C-terminal amidation, and N-terminal acetylation from Ala. All results reported here are from careful manual analysis of each spectrum, identifying accurate series of b-and y- ions, and a-, z-ions, and immonium ions confirming sequences. All obtained sequences were submitted for BLAST search at www.ncbi. nlm.nih.gov/BLAST/in no redundant Swiss-Prot database without taxonomy specified. Peptides presenting highly conserved characteristic regions with other insect neuropeptides were identified and classified as neuropeptides from R. prolixus. By these means we were able to identify a total of 43 peptides from R. prolixus brains that are orthologs to known neuropeptides from other insects. Assigned names, sequences, accession numbers, retention times, and m/z values are presented in Table 1. From all of them, only a calcitonin-like diuretic hormone (DH31) has been previously reported [2]. Each individually detected and sequenced peptide was counted; some of them were sequenced with and without particular PTMs like oxidation from Met, N-terminal acetylation from Ala and N-terminal pyro-glutamic from Gln. In some cases multiple truncated peptides were detected from a precursor. Since fragments are consistently found in different chromatographic fractions, this cannot be a consequence of fragmentation during the ionization process. The same truncated forms are observed in different experimental replicas and the short peptide forms are observed in several specific neuropeptides and not in others. We cannot rule out this as a consequence of the extraction process, but it seems to be a common observation in other peptidomic studies performed with different methods [3, 4]. The same considerations can be regarded in the case of oxidated and nonoxidated forms of some peptides. It would appear that the truncated and the oxidated forms normally occur in the brain. Ambiguity between Leu and Ile aminoacids could not be resolved with this MS technique, owing to the exact equivalence in their molecular weight mass. In these cases, we report the new peptides according to the most conserved form in known insects, except for orcokinin for which we have identified the gene sequence in the R. prolixus genome (Ons and Rivera-Pomar, unpublished). Proteomic technology has revolutionized insect neuroendocrinology since the first peptidomic study in D. melanogaster larval CNS [5]. Nevertheless, and surprisingly, these studies lack in insects of medical interest. One reason may be the difficulties in the analysis of low concentration peptides out of a complex mixture derived from a whole organism, which in most of the cases are too small to be dissected www.proteomics-journal.com

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Table 1. Peptides identified by nano-HPLC and MALDI TOf/TOf MS/MS in R. prolixus brains

Name and accession number

Sequence

Ret. time (min)

m/z

AKDNFLRF–NH2 Acetyl-AKDNFLRF–NH2

98.25 107.25

1009.55 1051.60

pQFNEYGHMRF–NH2 pQFNEYGH(M-OH)RF–NH2 NSDEQFDDYGYMRF–NH2 NSDEQFDDYGY(M-OH)RF–NH2

104 109.25 108.75 108.75

1310.58 1326.59 1785.75 1801.76

QDLDHVFMRF–NH2 pQDLDHVFMRF–NH2 pQDLDHVF(M-OH)RF–NH2

119 117.25 120.75

1306.64 1289.60 1305.60

Extended FMRF-amide-related peptides Rhopr-FLRFa (P85815) Acetyl-Rhopr-FLRFa Sulfakinins Rhopr-SK-1 (P85810) ox-Rhopr-SK-1 Rhopr-SK-2 (P85811) ox-Rhopr-SK-2 Myosuppressins Rhopr-MS (P85816) pyro-Rhopr-MS pyro-ox-Rhopr-MS Long neuropeptide F Rhopr-LNF (P85813)

VAAGRPRF–NH2

67

872.53

Short neuropeptide F Rhopr-SNF (P85817)

NNRSPQLRLRF–NH2

103.25

1399.79

TYKKPPFNGSIF–NH2 YKKPPFNGSIF–NH2 KKPPFNGSIF–NH2 KPPFNGSIF–NH2

110.5 110.5 105 108.75

1397.74 1296.70 1133.66 1005.56

SGPGFMGVR–NH2 TSMGFQGVR–NH2 APASGFFGMR–NH2 ASGFFGMR–NH2 ASGFFG(M-OH)R–NH2 SGFFGMR–NH2 TPSDGFMGMR–NH2 APACVGFQGMR–NH2 GPSSSAFFGMR–NH2 SPATMGFAGVR–NH2 pQERRAMGFVGMR–NH2 FVGMR–NH2

81.75 76.75 101.5 97.25 80.5 98 103.25 87.75 99.5 110.25 110.5 68.5

906.45 981.49 1039.52 871.43 887.42 800.38 1097.48 1135.51 1142.53 1149.53 1419.72 608.33

NFDEIDRVGFGSFI NFDEIDRVGF NFDEIDRSGFDGFV NFDEIDRSGFDG NFDEIDRSGFNSFI NFDEIDRSGFN

141.5 114 125.25 95.75 129.5 93.75

1615.74 1211.57 1617.73 1371.59 1660.77 1313.58

LPVYNFGL–NH2 MRNYSFGL–NH2 pQVSLKYPEGKMYSFGL–NH2

116 103.25 128

921.51 986.52 1828.95

GFKNVQLSTARGF–NH2

100.75

1423.72

SIF-amide peptides Rhopr-SIFa (P85818) Rhopr-SIFa(2–12) Rhopr-SIFa(3–12) Rhopr-SIFa(4–12) Tachykinins-related peptides Rhopr-TRP-1 (P85802) Rhopr-TRP-2 (P85803) Rhopr-TRP-3 (P85804) Rhopr-TRP-3(3–10) ox-Rhopr-TRP-3(3–10) Rhopr-TRP-3(4–10) Rhopr-TRP-4 (P85805) Rhopr-TRP-5 (P85806) Rhopr-TRP-6 (P85807) Rhopr-TRP-7 (P85808) Rhopr-TRP-8 (P85809) Rhopr-TRP-8(8–12) Orcokinins Rhopr-OK-1 (P85819) Rhopr-OK-1(1–10) Rhopr-OK-2 (P85820) Rhopr-OK-2(1–12) Rhopr-OK-3 (P85821) Rhopr-OK-3(1–11) Allatostatins Rhopr-AST-1 (P85822) Rhopr-AST-2 (P85823) Rhopr-AST-3 (P85824) Allatotropins Rhopr-AT (P85825)

© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

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Proteomics 2009, 9, 788–792 Table 1. Continued

Name and accession number

Sequence

Rhopr-AT(2–13) Rhopr-AT(4–13)

FKNVQLSTARGF–NH2 NVQLSTARGF–NH2

Ret. time (min)

m/z

97.5 85.25

1366.75 1091.57

Calcitonin-like diuresis hormone (DH31) Rhopr-DH31 (described in [2]) Rhopr-DH31(1–16)

GLDLGLSRGFSGSQAAKHLM GLAAANYAGGP–NH2 GLDLGLSRGFSGSQAA

142.25

2986.5

109.75

1535.79

pQTFQYSRGWTN–NH2

104

1369.64

Corazonin Rhopr-Crz (P85812) Pyrokinin Rhopr-PK (P85827)

SPPFAPRL–NH2

90.75

883.51

Leu/Ile ambiguities (underlined) could not be solved by MS. Numbers within brackets indicates accessions numbers in the UniProt Knowdlegebase. Modified or truncated forms of the same peptide are annotated within the same entry.

to obtain enough starting material. Triatomines are large insects easy to dissect and separate tissues, such as the brain, which is the source of key neuropeptides. Thus, here we developed a sensitive and straightforward off-line nano-LCMALDI MS-MS workflow that allowed a comprehensive neuropeptidomic analysis of a fundamental tissue in the regulation of feeding and disease transmission. We identified many specific peptides, unique among insect neuropeptides described so far at the sequence level of their highly conserved characteristic domains, as is the case of myosuppressin and sulfakinin. The identification of these unique sequences is interesting from the evolutionary point of view, and provides a solid base for forthcoming pharmacological studies for the design of novel generation insecticides, more species-specific, and environmental-friendly. In addition, this work will contribute to the identification of neuropeptide-encoding genes in the genome of R. prolixus, which is currently being sequenced [6]. The annotation of these small peptides in a genome is difficult without the experimental evidence of their occurrence and de novo sequencing. In hematophagous Hemiptera, the neurosecretory system is promptly activated by feeding. Blood intake promotes physiological changes such as water balance, moulting, or egg laying, mediated by the neuropeptides present in the brain. The protocol for neuropeptide analysis in very small amount of R. prolixus tissues, as we showed here, provides the opportunity for physiological investigations at the individual level and enabled a qualitative analysis of the peptide repertoire. To confirm the hormonal function of neuropeptides the method described here can be applied to the hemolymph. This is not a common approach for most insect hormones, owing to the difficulties associated with the identification of molecules present in very low concentrations in such a complex fluid as hemolymph, in contrast to, for © 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

example, antimicrobial peptides. Several brain neuropeptides such as the orcokinins, corazonin, pyro-myosuppressin, and short neuropeptide F identified in this study have also been detected in R. prolixus hemolymph suggesting their release from brain (Ons, S., Rivera-Pomar, R., unpublished results). Quantitative and comparative proteomic analysis will complement our findings in R. prolixus, an ideal model to study the regulatory events triggered by feeding, the key issue in reproduction and parasite and disease transmission.

We thank to Uwe Plessmann for technical assistance and to Christina McCarthy for critical review of the manuscript. This work was supported by the Argentine National Research Council (CONICET), Agencia Nacional de Ciencia y Tecnología, and Fundación Bunge y Born (Argentine), Max Planck Gesellschaft (MPG), and the Deutsches Bundesministerium für Bildung und Forschung (Germany). S. O. and R. R. P. are investigators of the CONICET. R. R. P. is recipient of an MPG International Partner Laboratory Program. The authors have declared no conflict of interest.

References [1] Christophides, G. K., Vlachou, D., Kafatos, F. C., Comparative and functional genomics of the innate immune system in the malaria vector Anopheles gambiae. Immunol. Rev. 2004, 198, 127–148. [2] Brugge, V. A., Schooley, D. A., Orchard, I., Amino acid sequence and biological activity of a calcitonin-like diuretic

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S. Ons et al. hormone (DH31) from Rhodnius prolixus. J. Exp. Biol. 2008, 211, 382–390.

[3] Hummon, A. B., Richmond, T. A., Verleyen, P., Baggerman, G. et al., From the genome to the proteome: Uncovering peptides in the Apis brain. Science 2006, 314, 647–649. [4] Predel, R., Russell, W. K., Russell, D. H., Lopez, J. et al., Comparative peptidomics of four related hemipteran species: Pyrokinins, myosuppressin, corazonin, adipokinetic

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Proteomics 2009, 9, 788–792 hormone, sNPF, and periviscerokinins. Peptides 2008, 29, 162–167. [5] Baggerman, G., Cerstiaens, A., De Loof, A., Schoofs, L., Peptidomics of the larval Drosophila melanogaster central nervous system. J. Biol. Chem. 2002, 277, 40368–40374. [6] Huebner, E., The Rhodnius genome project: The promises and challenges it affords in our understanding of reduviid biology and their role in Chagas’ transmission. Comp. Biochem. Physiol. Part A: Mol. Integ. Physiol. 2007, 148, S130.

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