Characterization of the prohormone complement in cattle using genomic libraries and cleavage prediction approaches

1471-2164-10-228 1471-2164 Research article Characterization of the prohormone complement in cattle using genomic libraries and cleavage prediction approaches Southey R Bruce southey@illinois.edu Rodriguez-Zas L Sandra rodrgzzs@illinois.edu Sweedler V Jonathan jsweedle@illinois.edu

Department of Chemistry, University of Illinois, Urbana, IL, USA

Department of Animal Sciences, University of Illinois, Urbana IL, USA

BMC Genomics 1471-2164 2009 10 1 228 http://www.biomedcentral.com/1471-2164/10/228 19445702 10.1186/1471-2164-10-228 10 12 2008 16 5 2009 16 5 2009 2009 Southey et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Neuropeptides are cell to cell signalling molecules that regulate many critical biological processes including development, growth and reproduction. These peptides result from the complex processing of prohormone proteins, making their characterization both challenging and resource demanding. In fact, only 42 neuropeptide genes have been empirically confirmed in cattle. Neuropeptide research using high-throughput technologies such as microarray and mass spectrometry require accurate annotation of prohormone genes and products. However, the annotation and associated prediction efforts, when based solely on sequence homology to species with known neuropeptides, can be problematic.

Results

Complementary bioinformatic resources were integrated in the first survey of the cattle neuropeptide complement. Functional neuropeptide characterization was based on gene expression profiles from microarray experiments. Once a gene is identified, knowledge of the enzymatic processing allows determination of the final products. Prohormone cleavage sites were predicted using several complementary cleavage prediction models and validated against known cleavage sites in cattle and other species. Our bioinformatics approach identified 92 cattle prohormone genes, with 84 of these supported by expressed sequence tags. Notable findings included an absence of evidence for a cattle relaxin 1 gene and evidence for a cattle galanin-like peptide pseudogene. The prohormone processing predictions are likely accurate as the mammalian proprotein convertase enzymes, except for proprotein convertase subtilisin/kexin type 9, were also identified. Microarray analysis revealed the differential expression of 21 prohormone genes in the liver associated with nutritional status and 8 prohormone genes in the placentome of embryos generated using different reproductive techniques. The neuropeptide cleavage prediction models had an exceptional performance, correctly predicting cleavage in more than 86% of the prohormone sequence positions.

Conclusion

A substantial increase in the number of cattle prohormone genes identified and insights into the expression profiles of neuropeptide genes were obtained from the integration of bioinformatics tools and database resources and gene expression information. Approximately 20 prohormones with no empirical evidence were detected and the prohormone cleavage sites were predicted with high accuracy. Most prohormones were supported by expressed sequence tag data and many were differentially expressed across nutritional and reproductive conditions. The complete set of cattle prohormone sequences identified and the cleavage prediction approaches are available at http://neuroproteomics.scs.uiuc.edu/neuropred.html.

Bovine: the companion papers for the publication of the bovine genome sequence

Background

Neuropeptides are a diverse class of signalling peptides that include neurotransmitters and peptide hormones that have various paracrine, endocrine, and autocrine effects 12. Neuropeptides support cell to cell communication and regulate diverse biological processes such as blood flow, synaptogenesis, memory, learning, reproduction, lactation, development, growth, feeding, behavior and cell morphology 12. Only 42 neuropeptide-containing genes, appreciably fewer than the expected number, have been empirically confirmed in cattle tissues. Annotating the cattle neuropeptide complement is important as these molecules play a critical role in cattle production, health and well-being. For example, cattle neuropeptide Y (NPY) stimulates food intake, oxytocin stimulates smooth muscle contraction, vasopressin stimulates water re-absorption in the kidney, and ghrelin stimulates appetite and feeding activity through interactions with NPY and other peptides 3. Genetic variation in cattle neuropeptide genes has been associated with variation in traits of economical importance including birth weight, average daily gain, body weight, feed conversion ratio, rib-eye area, marbling score and subcutaneous fat depth 4567.

The annotation of neuropeptides will aid functional studies that use high-throughput transcriptomic (e.g., microarray) and proteomic (e.g., 2D gels, mass spectrometry) techniques. Several microarray platforms derived from the cattle genome and expressed sequence tag (EST) databases (e.g., NCBI Gene Expression Omnibus or GEO 8) are available such as GEO platforms GPL2853, GPL2864, GPL3301, GPL3810, GPL6497, GPL2112, and GPL1854. These platforms include a variable number of probes that map to neuropeptide-containing genes. However, the incomplete annotation of the cattle neuropeptide complement has hindered the ability to characterize the expression profiles of neuropeptide-containing genes. Experimental confirmation of neuropeptides and experimental peptidome studies are resource intensive 291011. Although neuropeptides can be biochemically characterized using mass spectrometry, such efforts are considerably enhanced by the addition of neuropeptide-containing gene annotations that allows the association of mass spectral peaks with specific peptides 2.

Annotating the neuropeptide complement is complicated because neuropeptides are derived from larger proteins by a complex series of post-translational modifications. Translation of the neuropeptide-containing gene generates a large protein known as a preproneuroptide or preprohormone, which conceptually consists of a signal peptide region and a region that contains one or more peptides. The signal peptide is removed by signal peptidases to form the proneuropeptide or prohormone (hereinafter referred to as prohormone) 1212. The prohormone may undergo further cleavage by other proteases, notably proprotein or prohormone proteases, at basic amino acids (Arg or Lys) 1212. After cleavage, the terminal basic amino acids are typically removed by carboxylases or Arg/Lys aminopeptidases and various additional post-translational modifications (e.g., amidation, glycosylation) can occur before the final bioactive neuropeptides are produced 1212. The resulting bioactive neuropeptides are often small, typically between 3 to 40 residues long 12.

Prohormone processing is also highly dependent on the environment (e.g., pH), prohormone structural properties, alternative splicing, presence of specific proteases or proteases with different affinity for cleavage 121213. Furthermore, the presence of neuropeptides varies across species, tissues, developmental stages, and with other conditions 1212. Accordingly, experimental detection of neuropeptides in mammals has been limited to only a few species (notably human, mouse and rat) and neuropeptide families such as insulin.

There are two distinct phases in the process of annotating the neuropeptide complement of a species. The first phase requires the identification of the prohomone genes. Prohormones and neuropeptides frequently have different lengths across species and a very short conserved region that interacts with its cognate receptor 12. As a consequence, the prohormone may contain large and highly variable sections that show limited homology to well-studied species. Therefore, while the prohormone gene in one species may be identified by sequence homology to another better studied species, homology alone is insufficient to accurately annotate the actual neuropeptides within the prohormone sequence. The second phase of the annotation process involves the identification of neuropeptides within the prohormone and this requires the prediction of the prohormone cleavage sites. Accurate prediction of prohormone cleavage sites and resulting peptides has been achieved using machine learning techniques such as logistic regression, artificial neural networks, support vector machines and memory-based reasoning 1415161718.

The availability of the cattle genome sequence provides an unprecedented opportunity to conduct a comprehensive identification of cattle neuropeptide genes using complementary bioinformatic tools, databases and information resources. The goal of this study was to compile and characterize the cattle prohormone genes and neuropeptide complement. First, prohormone genes were located on the recently published cattle genome sequence 19 using a strategy that integrates information from complementary genomic databases 18. This strategy addressed the situation where the initial automated annotation failed to detect genes or detected only partial sequences. Second, biological evidence for predicted prohormone genes was obtained from public genomic and EST databases. Third, the prohormone genomic census was used to accurately annotate cattle microarray platforms and subsequently to assess the presence and differential expression of neuropeptide genes in microarray experiments. Lastly, machine and statistical learning tools were applied to our database of prohormone gene sequences to predict prohormone cleavage sites and resulting peptides. The resulting catalogue also benefits neuropeptide annotation in other ungulate and mammalian livestock species that do not have sequenced genomes. Our integrative bioinformatics strategy can be applied to annotate the neuropeptide complements in other species that have comparable genomic and transcriptomic information.

Methods

Neuropeptide prohormone identification and characterization

To detect prohormone genes in cattle, a list of candidate prohormone genes was generated from multiple sources including public databases and the literature. First, the mammalian (primarily human, mouse, rat, and cattle) prohormone genes used by Amare et al. 15 and Tegge et al. 17 were combined. Additional prohormone genes were identified from the UniProt database release 14.0 20 using the protein family field and a search using neuropeptide-like characteristics such as hormone or neuropeptide molecular functions. The SwePep database 21 was also used to supplement the list of candidate prohormone genes because it focuses on small peptides detected by mass spectrometry.

Candidate genes were searched for in the cattle genome Btau_3.1 assembly using the sequence alignment tool BLAST 22 following the approach described by Southey et al. 18. The search was conducted using the NCBI BLAST standalone version 2.18 with default parameters (E-value of 10 and BLOSUM62 scoring matrix) and disabled filtering. The BLAST results from each prohormone were screened based on the alignment score and E-value to identify the most likely matches and location of the corresponding cattle prohormone gene in the genome. In addition, results were examined for multiple homologous prohormone genes that could indicate gene duplication events in the cattle genome. The protein prohormone sequences were identified within the detected genome regions using the gene parsing tool Wise2 23. Wise2 predicts the gene structure using a gene prediction model that includes introns and frameshift errors based on a target protein sequence and a genomic DNA sequence. The target protein sequences were selected from the candidate list with a preference for cattle or sheep genes. The genomic region encompassing the BLAST match was extended approximately 500 base pairs to the 5' and 3' ends of the match. Each predicted prohormone gene was compared to the UniProt and Entrez Gene 24 databases to assess the accuracy of the prediction based on previously reported prohormone genes. The predicted protein sequence was then compared to the corresponding published sequences using the multiple sequence alignment tool, Clustalw 25. This step also served to confirm the suitability of the Wise2 prediction. If a suitable prediction was not obtained from the extended genomic region, protein sequences from other species were also used. Raw genomic data (including unassigned genomic regions, whole genome shotgun sequencing and trace archives) were also searched when there was no suitable BLAST match to a candidate or when the alignment to the genome assembly indicated a missing genomic region. This strategy allowed the annotation of genomic regions that were partly or not included in the assembly. The more recent Btau_4.0 assembly 26 and University of Maryland assembly 1.5 (UMD_1.5; ftp://ftp.cbcb.umd.edu/pub/data/assembly/Bos_taurus/UMD_Freeze1.5/)27, which became available during the annotation process, were used to identify remaining prohormone genes not found in the Btau_3.1 assembly.

Expressed sequence tag information

The comprehensive identification of prohormone genes in the cattle genome constitutes the first step toward a comprehensive characterization of the neuropeptide gene set. However, prediction of prohormone gene sequences is insufficient evidence of the actual presence and expression of these genes. Reported cattle ESTs provide independent support for prohormone genes, especially for the unpublished cattle prohormone genes. The candidate genes were searched for on the UniGene database (build #92) and information on the ESTs (number of ESTs, sequence, overlap) and tissue of expression were extracted from the database for each cattle prohormone gene. This search was complemented with searches for candidate genes on the NCBI EST (dbEST release 080107) and NCBI Nucleic databases to encompass any cattle ESTs and nucleic acid sequences that were not included in the available UniGene release.

Transcriptomic analysis

A comprehensive characterization of neuropeptide gene expression profiles was attained by querying and analyzing two complementary resources. First, a survey of gene expression records across tissues and developmental stages available in the UniGene and EST databases was performed. This survey offered an introductory glimpse at the expression patterns of prohormone genes. However, the nature of the UniGene data-spanning experiments, most with no connecting samples, prevents the profiling and relative quantification of prohormone gene expression. To address this, a second resource, the NCBI GEO database, was inspected for informative microarray studies. Consideration was given to experiments that included at least five biological replicates per condition and two technical replicates per sample and that used a platform with at least 50% of the identified cattle prohormone genes. These requirements ensured a minimum accuracy on the detection of prohormone gene expression and precision on the profile estimates.

Two large microarray investigations met these criteria and were selected for examination of the presence of neuropeptides and for evidence of differential expression between conditions. The first, reported by Loor et al. 28, consisted of liver samples from healthy cows and those exposed to a nutritional plane conducive to ketosis. The second, reported by Everts et al. 29, consisted of placentome samples of pregnancies from calves obtained using three reproductive techniques: in vitro fertilization (IVF), somatic cell nuclear transfer (NT) and artificial insemination (AI). Both experiments used the same cattle microarray platform, GEO platform GPL2853, which has 13,257 70-oligomer elements printed in duplicate. The microarray platform contained 45 known cattle prohormone genes with the complete gene sequence available, two prohormone genes with only a partial sequence previously reported, and nine previously unreported prohormone genes. The platform also contained the sequence of a probe (OLIGO_09208) that spans a splice site of the torsin family 2 member A (TOR2A) gene. Due to the location of this splice site, this oligomer represents the TOR2A isoform 1 ([Swiss-Prot:A4FUH1]) that is not a prohormone and not the TOR2A isoform 4 (Swiss-Prot:P0C7W1), which is the prohormone that produces salusin neuropeptides in other mammals 30. However, this microarray element was considered a probe for the prohormone gene due to possible cross-hybridization of the TOR2A isoform 4 to the region of the probe prior to the splice site.

The microarray data filtering, normalization and analyses used in this study were the same as described in Loor et al. 28 and Everts et al. 29, respectively. Briefly, fluorescence data processing encompassed the filtering of spots marked as unreliable by the scanning software or weak (when compared to control elements) and loess normalization before fitting a two-stage, mixed-model analysis. In the first stage, gene expression values were adjusted for global dye and microarray effects and in the second stage, the expression of each microarray element was described with a model including the effects of dye-, sample-, microarray- and experimental-specific factors. Only the patterns of prohormone gene expression across health status 28 and embryo type 29 are reported here. The statistical significance of the differential expression was adjusted for multiple testing across neuropeptide genes using the false discovery rate approach 31.

Prediction of cleavage sites

The cleavage sites of all prohormone genes were predicted using logistic regression and artificial neural network models developed using 42 cattle prohormone sequences 17 in NeuroPred 32. Prior to prediction, the signal peptide and known cleavage sites were identified based on experimental evidence from the UniProt record when available. When no experimental evidence was available, the signal peptide length was predicted using SignalP 33 and cleavage sites were assigned based on homology to known cleavage sites from other species.

Results and Discussion

Prohormone identification

There were 92 candidates for cattle prohormone genes identified from the literature and protein databases and these included 42 cattle prohormone genes with empirical evidence. The bioinformatics search identified 92 cattle prohomone genes that included a novel calcitonin gene but failed to identify one candidate. Table 1 presents the distribution of the cattle prohormone genes with complete and partial sequences that were identified in the cattle genome across the nucleic and protein resources used to detect the genes. A detailed description of the 92 prohormone genes with supporting evidence from the Entez Gene, Unigene and UniProt databases is provided [see Additional file 1]. The protein sequences of the discovered prohormone genes with cleavage sites identification is provided in the format used by NeuroPred [see Additional file 2].

Additional file 1

Cattle prohormone sequences with cleavage data. Predicted sequences of cattle prohormone genes with cleavage data in NeuroPred format.

Click here for file

Additional file 2

Inventory of the cattle prohormones across multiple repositories and microarray analysis. Inventory of cattle prohormone genes with accession numbers of major sequence repositories and results of the microarray analysis.

Click here for file

Table 1

Distribution of the candidate prohormone genes identified in the cattle genome among public databases

UniProt¹

Genome²

Nucleic³

Complete

Fragment

TrEMBL

Unreported⁴

Complete

UniGene

DNA

Unreported

Fragment

UniGene

Pseudogene

Not found

Unreported

¹Derived from information provided in the corresponding UniProt release; Complete indicates the complete prohormone is present in the SwissProt database, Fragment indicates a partial sequence in the SwissProt database, and TrEMBl indicates a complete sequence without experimental data is reported in the TrEMBL database.

²Complete, Fragment and Not found denote if the prohormone gene was completely recovered, partially recovered or there were no matches from the cattle genome, respectively.

³Denotes the resource supporting the presence of the prohormone sequence in the cattle genome: UniGene, DNA (nucleotide and EST sequences not present in UniGene), pseudogenes and unreported.

⁴Not previously reported (predicted or empirically confirmed) in cattle.

The initial BLAST query to the Btau_3.1 assembly indicated that 88 prohormone gene candidates were likely to be present (E-value < 10^-6). The complete sequences of 80 cattle prohormone genes were subsequently obtained by using Wise2 with the Btau_3.1 assembly. The remaining eight candidates with strong BLAST evidence were located in the genome but had incomplete sequences in the Btau_3.1 assembly. Complete sequences for six candidates, including five that have been previously reported with complete sequences, were recovered using the recent Btau_4.0 and UMD_1.5 assembly. Of the remaining two candidates with strong BLAST evidence, the secretin gene (SECR) including the reported cattle secretin peptide ([Swiss-Prot:P63296]) was not recovered due to incomplete coverage of the genomic region based on the sequence available (Dr. Steven Salzberg, Dr. Liliana Florea and Finn Hanrahan, personal communication), and sequence characteristics (discussed below) suggested that galanin-like peptide gene (GALP) is a pseudogene (discussed below). In addition, three candidate genes, cocaine and amphetamine responsive transcript (CART), peptide YY (PYY) and seminalplasmin or peptide YY2 (PYY2), have published cattle sequences and were recovered using the UMB_1.5 assembly because there were no significant matches (E-value > 1) to the Btau_3.1 and Btau_4.0 assemblies.

Of the detected genes available in UniProt, 56 prohormone genes have complete and annotated sequences, 16 prohormone genes have complete sequences without annotation, four prohormone genes have complete sequences but have only been reported as fragments (three in SwissProt and one in TrEMBL), and nine new prohormone genes have complete sequences previously unreported (not reported in UniGene) in cattle (Table 1). A comparison of genomic and reported sequences showed that 14 prohormone genes had different sequences due to single nucleotide polymorphisms, two prohormone genes had undetermined amino acids, and adenylate cyclase activating polypeptide 1 (ADCYAP1) includes an apparently incorrect sequence. The predicted amino acid sequence of the ADCYAP1 prohormone gene was more consistent (higher percentage of identity and similarity) with other species than the cattle SwissProt sequence ([Swiss-Prot:Q29W19]). The available UniProt protein sequence for cattle TOR2A does not include the isoform corresponding to the alpha-salusin and beta-salusin neuropeptides that was found in this study.

The use of complementary nucleotide databases was critical to validate the complete set of predicted prohormone genes. There were 81 prohormone sequences with cattle ESTs in the UniGene database and two prohormone genes, relaxin 3 (RLN3, [GenBank:BI682322] and neuropeptide W, (NPW; [GenBank:DY084317]), with cattle ESTs not included in UniGene. In addition, the full DNA sequence of cattle islet amyloid polypeptide gene (IAPP; [GenBank:AJ675853]) was not present in UniGene. Although a cattle gonadotropin-releasing hormone 2 (GON2) was detected, the lack of EST data may support the finding that this gene is functionally inactive 34. Our approach predicted adrenomedullin 2 (ADM2), neuromedin U (NMU), tuberoinfundibular 39 residue protein (TIP39) and tachykinin 4 (TAC4) prohormone genes with complete sequences but without cattle EST data. This constitutes important findings because molecular techniques that rely on EST information (e.g., the design of microarray platforms or primers when there is no genome information available) will not be able to detect these genes.

Multiple genomic matches to the candidate query sequences can uncover gene duplication events. Species-specific neuropeptide prohormone variants resulting from duplication have been reported in other mammalian species. Examples include insulin-like 4 gene (INSL4) in humans and chimpanzees 35, hepcidin antimicrobial peptide 2 gene (HEPC2) in mouse 36, and two variants of insulin gene (INS) found in various rodents including rat and mouse 37. In all previous cases, the searches resulted in a single match to a single cattle prohormone, indicating that there was no support for duplicated genes or cattle-specific prohormone genes. With the exception of the calcitonin family, the examination of additional BLAST matches provided no evidence for duplicated prohormone genes in the cattle genome that were not previously expected based on homology to protein families (e.g., the insulin family). Our approach uncovered a potential duplication in the calcitonin family because it had four matches; interestingly, there are two members of the calcitonin family in human, mouse and rat. Further findings about the calcitonin family are discussed in the forthcoming calcitonin family section.

Galanin-like peptide (GALP) pseudogene

The evidence for a cattle GALP pseudogene is due to a lack of matches to cattle EST data and predicted gene structure. The matching region of cattle genome BLAST match on cattle chromosome 18 included the sequence GWTLNSAGYLLGP, which is completely conserved across mammalian GALP and the related galanin (GALA) genes. The cattle GALA [SwissProt: P11242] has been previously reported on cattle chromosome 29 and the match also included a larger region that is only conserved across the GALP sequences. Additional GALP matches to unassigned contigs in the recent cattle genome assemblies were detected using as query the human GALP genomic sequence (including exons and introns), but there was insufficient coverage to recover a complete sequence. This discovery is likely to correspond to a pseudogene because no initiation codon was found (Dr. Steven Salzberg, Dr. Liliana Florea and Finn Hanrahan, personal communication). A fragment of a sheep GALP prohormone sequence that also matched the same genomic location (E-value < 0.15) has been previously reported ([Swiss-Prot:A2TEF1]. However, the reliability of the sheep sequence information is questionable because the sheep GALP nucleic and protein sequences were identical to the rat GALP nucleic ([GenBank:AF188491] and protein ([Swiss-Prot:Q9QXQ6]) sequences, respectively.

Relaxin family

The relaxin family is a subfamily of the insulin family 35 although the family member notation varies across species. Human, mouse and rat have relaxin 1 (RLN1), relaxin 3 (RLN3), insulin-like 3 (INSL3), insulin-like 5 (INSL5) and insulin-like 6 (INSL6), of which rat INSL5 has been reported as a pseudogene 35. Cattle INSL3 and INSL6 prohormone genes have been previously reported and the complete sequences were also recovered from the genome. The complete sequences for the genes INSL5 (supported by [Unigene:Bt.101509]) and RLN3 (supported by EST [GenBank:BI682322]) were recovered in this study, although these genes have not previously been reported in cattle.

A remarkable result is that there were no matches on the cattle genome or on the cattle ESTs to any available mammalian RLN1 sequence that was not attributable to another member of the relaxin family. No matches to cattle genome sequences or ESTs were identified using the sheep relaxin-like pseudo-gene ([GenBank:S60580]; 38) and the RLN1 protein and mRNA sequences from camel ([GenBank:AF254739]), dog ([SwissProt:Q9TRM8]), horse ([SwissProt:P22969] and pig ([SwissProt: P01348]). A search for RLN1 was conducted using the trace archives from other species belonging to Cetartiodactyla clade. There was a strong match (E-value < 10^-100) to a relaxin-like gene in the Vicugna vicugna (vicuña) that was very similar to the camel sequence, as expected, since both species are members of the Tylopoda subclade. In the Cetacea subclade there was a strong match (E-value < 10^-100) to the Tursiops truncatus (bottlenosed dolphin). Except for a match (E-value < 10^-100) in the sheep trace archives for the known pseudo-gene, there were no matches in any of the Ruminantia species including various deer species (such as red and fallow deer) and Antilocapra americana (pronghorn).

To further elucidate the possible location of a cattle relaxin 1 gene or genes, the human chromosome that contains RLN1 and the corresponding cattle chromosome were compared. Human RLN1 is located on chromosome 9 between the INSL6 and chromosome 9 open reading frame 46 (C9orf46) and the distance between these loci is approximately 170K bp. Both INSL6 and C9orf46 are located on cattle chromosome 8 but are only approximately 35 K bp apart. These results strongly suggest that the relaxin 1 gene has been lost from the cattle genome.

Calcitonin family

The composition of the calcitonin family is complex due to gene duplication, alternative splicing, different nomenclature, pseudogenes and partial protein sequences. The human, mouse and rat calcitonin family includes calcitonin or the calcitonin gene-related peptide 1 (CGRP-I or alpha-type CGRP or CALCA) and calcitonin gene-related peptide 2 (CGRP-II or beta-type CGRP or CALCB) genes. Alternative splicing of CALCA, a human calcitonin pseudogene (CALCP) 39 and three calcitonin-related proteins in pig 40 have been reported.

A query of the calcitonin prohormone family resulted in four matches on cattle chromosome 15 that spanned a region of 500 Kbp. These matches were supported by ESTs in four UniGene clusters; UniGene cluster Bt.29881 (including four ESTs), UniGene cluster Bt.73268 (including 1 EST), UniGene cluster Bt.14302 (including 11 ESTs), UniGene cluster Bt.60861 (including 6 ESTs) and UniGene cluster Bt.28622 (including 5 ESTs). Two of the matches were also supported by three UniProt TrEMBL calcitonin-related records with complete gene sequences in cattle: CALCB ([Swiss-Prot:Q17Q98]), calcitonin receptor-stimulating peptide-1 ([Swiss-Prot:Q75V95]), and calcitonin-related polypeptide 3 or CALC3 ([Swiss-Prot:Q0VBW3]). There is strong evidence suggesting that [Swiss-Prot:Q17Q98] and [Swiss-Prot:Q75V95] are the result of alternative splicing. The first 75 amino acids of these two sequences are identical, there is substantial overlap of the corresponding genomic sequences, and both prohormone genes were predicted from the same genomic region using Wise2.

A third calcitonin family match in the cattle genome corresponded to a complete cattle calcitonin gene ([SwissProt:B5UBG1]) that contains the previously reported calcitonin peptide ([Swiss-Prot:P01260]), a complete sheep calcitonin gene ([Swiss-Prot:P01261]), and the UniGene cluster Bt.14302. A prohormone gene was recovered by Wise2 using the sheep sequence and the genomic region of the UMD_1.5 assembly. The same protein sequence was also obtained using two human calcitonin variants ([GenBank:NP_001029124] and [GenBank:NP_001732]). A different sequence was obtained from the same region using another human calcitonin variant ([GenBank:NP_001029125]), suggesting that this gene could undergo alternative splicing. This region also had a strong match to the human calcitonin pseudogene, implying that this calcitonin gene was present before the evolutionary split that ultimately originated the human and cattle species.

The last cattle genome match of the calcitonin family corresponded to UniGene cluster Bt.60861, that was associated with a predicted gene ([GenBank:XP_001253111]). The predicted gene contained the domain associated with the Calc_CGRP_IAPP gene superfamily. The goat calcitonin receptor-stimulating peptide-2 ([Swiss-Prot: B3IWF8]) provided the best BLAST match (E-value < 10^-28) followed by the cattle CALC3 (E-value < 10^-27) to this predicted cattle gene. Although the genomic region is homologous to the other matches, there is no candidate prohormone gene that shows sufficient homology to the predicted gene.

Prohormone gene expression across tissues and developmental stages

A census of the expression of 62 prohormone genes available in UniGene offered a first glimpse of the cattle transcript profiles. The most frequently expressed prohormone genes (and percentage of reports) were adrenomedullin (ADM; 7%), insulin-like growth factor 2 (IGF2; 7%), apoptosis-inducing, TAF9-like domain 1 (APITD1; 5%), esophageal cancer-related gene 4 protein (ECRG4; 5%), secretogranin II (SCG2; 5%), platelet-derived growth factor alpha polypeptide (PDGFA; 4%), proenkephalin (PENK; 4%), proopiomelanocortin (POMC; 4%), chromogranin B (CHGB; 3%), endothelin 1 (EDN1; 3%), TOR2A (3%), and somatostatin (SST; 3%). The UniGene expression reports were almost equally distributed across the three developmental stages (fetus, calf and adult) with the fetus and adult having the highest and lowest number of reports, respectively. There were 26 tissues or body sites that exhibited expression of at least one prohormone gene. Most reports of tissues or body sites (and percentage of reports across tissues) were in the brain (12%), kidney (12%) intestine (8%), extraembryonic tissue (7%), ovary (7%) and liver (6%). The distribution across tissues and developmental stages reflects the limited experiments and microarray platforms used. The lack of Unigene expression reports for other prohormone genes are most likely due to incomplete study of cattle neuropeptide genes across tissues and stages.

Microarray analysis

The analysis of the expression levels of prohormone gene reporters from two cattle microarray experiments indicated that all prohormone genes present in the platform were detected [see Additional file 2]. Two prohormone genes, platelet-derived growth factor beta polypeptide (PDGFB) and cortistatin (CORT) were significantly (False Discovery Rate adjusted P-value < 0.05) differentially expressed in both studies. In the liver study, cows with ketosis had a 27% higher fold change in PDGFB levels and a 50% lower fold change in CORT than healthy cows. In the placentome study, IVF embryos had a 45% higher expression of PDGFB compared to AI embryos, but there were no significant differences between NT and either AI or IVF embryos. For CORT, NT embryos had at least a 50% fold decrease in expression compared to both AI and IVF, but there was no significant difference between AI and IVF. The findings on PDGFB confirm reports of high expression levels in the placenta and the important role of this growth factor in stimulating adjacent cells to grow 41. The expression of CORT in a subset of GABAergic cells in the cortex and hippocampus has been associated with synaptic transmission, and furthermore, CORT binds to somatostatin receptor subtypes and inhibits cAMP 42.

Six prohormone genes, apelin (APEL), chromogranin A (CMGA), EDN1, insulin (INS), neuromedin B (NMB), and tachykinin 3 (TAC3), were significantly differentially expressed only in the placentome study. Nineteen prohormone genes, ADM, natriuretic peptide precursor type C (ANFC), ECRG4, gastrin (GAST), ghrelin/obestatin prepropeptide (GHRL), motilin (MOTI), arginine vasopressin (AVP), neuropeptide FF-amide peptide (NPFF), NPY, pyroglutamylated RFamide peptide (QRFP), PPY, proprotein convertase subtilisin/kexin type 1 inhibitor (PCSK1N), PDGFA, prodynorphin (PDYN), prolactin releasing hormone (PRRP), PYY, CHGB, SCG2, and SST were differentially expressed only in the liver study. Of the remaining 25 prohormone genes, nine had differential expression for other non-embryo type factors in the placentome study. The expression of the CALCA, glucagon (GCG), and osteocrin (OSTN) prohormone genes in the placentome study and natriuretic peptide precursor type A (NPPA) in the liver study failed to surpass the expression level of the background, indicating that these prohormone genes were either not expressed or were present in quantities too low to be reliably detected.

Proprotein convertases

It is critical to assess the presence of proprotein or prohormone convertase enzymes that cleave the prohormone proteins in the cattle genome because a change in these proteases could affect the presence or abundance of a neuropeptide. The mammalian proprotein convertase complement includes furin (FURIN), proprotein convertase subtilisin/kexin type 1 (PCSK1), proprotein convertase subtilisin/kexin type 2 (PCSK2), proprotein convertase subtilisin/kexin type 4 (PCSK4), proprotein convertase subtilisin/kexin type 5 (PCSK5), proprotein convertase subtilisin/kexin type 6 (PCSK6), proprotein convertase subtilisin/kexin type 7 (PCSK7), proprotein convertase subtilisin/kexin type 9 (PCSK9) and membrane-bound transcription factor peptidase site 1 (MBTPS1) 112. Only the cattle PCSK1 ([SwissProt:Q9GLR1]) and PCSK2 ([SwissProt:Q9GLR0]) sequences are available. In this study, the complete sequences of FURIN, PCSK1, PCSK4, PCSK5, PCSK7, MBTPS1 and the 7B2 or secretogranin V gene (SCG5), which is essential for PCSK2 function 434445, were recovered using the same approach as the prohormone discovery. The complete PCSK2 and PCSK6 sequences could not be recovered in the Btau_3.1 assembly but were recovered in the Btau_4.0 assembly. Table 2 provides supporting evidence for the presence of the proprotein convertases based on records in the Entez Gene, Unigene and UniProt databases. A partial match of 70 residues to the human PCSK9 protein sequence of 690 amino acids was detected but subsequent searches in the cattle EST and trace archives did not support the presence of cattle PCSK9. Utilizing the information of the introns and exons from human PCSK9, the chromosomal region containing that contained the partial match was found to contain multiple stop codons in different reading frames, suggesting that this gene has been lost from the cattle genome (Dr. Steven Salzberg, Dr. Liliana Florea and Finn Hanrahan, personal communication).

Table 2

Inventory of the cattle prohormone convertases across multiple repositories

Entrez Gene Symbol

Entrez Gene Full Name

Entrez Gene ID

Unigene Cluster

Accession Number¹

FURIN

furin (paired basic amino acid cleaving enzyme)

281374

Bt.21338

Q28193

PCSK1

proprotein convertase subtilisin/kexin type 1

281967

Bt.5562

Q9GLR1

PCSK2

proprotein convertase subtilisin/kexin type 2

281968

Bt.5563

Q9GLR0

PCSK4

proprotein convertase subtilisin/kexin type 4

508744

Bt.77029

XP_585571

PCSK5

proprotein convertase subtilisin/kexin type 5

528098

Bt.91475

XP_606509

PCSK6

proprotein convertase subtilisin/kexin type 6

524684

Bt.62498

XP_603014

PCSK7

proprotein convertase subtilisin/kexin type 7

515398

Bt.62043

XP_869877

SCG5

secretogranin V (7B2 protein)

508224

Bt.65187

Q2HJG0

MBTPS1

membrane-bound transcription factor peptidase, site 1

511682

Bt.18986

Q08E55

¹UniProt or GenBank accession number.

Prohormone cleavage prediction

The prohormone cleavage prediction models developed by Tegge et al. 17 were applied to 44 newly identified prohormone genes with complete sequences. These sequences excluded the 41 cattle prohormone genes used to develop the cleavage prediction models 17. Although there were 34% more sites in the new prohormone genes than in the prohormone genes used to develop the predictive models (831 sites compared to 621 sites), the correct classification rate of sequence positions into cleaved and non-cleaved sites was over 86% and the area under the receiver operating characteristic curves was over 76% (Table 3). The amino-acids-plus-properties models provided slightly more true-positive predictions (predicted cleavage sites that were confirmed by empirical data) but slightly more false-positive predictions (predicted cleavages sites that have not yet been reported) than the amino-acids-only models. The artificial neural networks provided slightly higher correct classification rates than the logistic regression models.

Table 3

Accuracy of different cleavage prediction models developed on cattle neuropeptides

Cleavage Prediction Model

AA¹

AA-Prop²

Model Accuracy Statistic

LR³

ANT⁴

ANT

True Negative

669

691

667

690

True Positive

False Negative

False Positive

Correct Classification Rate (%)

Sensitivity (%)

Specificity (%)

Area under receiver operating characteristic curve (%)

¹AA = Models developed using only amino acids

²AA-Prop = Models developed using amino acids plus amino acid physiochemical properties

³LR = Models developed using logistic regression

⁴ANT = Models developed using artificial neural networks

The cleavage prediction models were useful in evaluating the differences between prohormone sequences predicted from the genome information and those reported in the literature or databases like UniProt. The sequences of 14 prohormone genes detected in the genome differed from previously reported sequences. While these were not used in comparing model performance, these sequence differences resulted in 35 locations with a basic amino acid that had a different probability of prediction of cleavage between the published and predicted sequences. However, the differences in probability of cleavage were typically 0.1 or lower and none of these differences resulted in a different prediction of cleavage.

Conclusion

Neuropeptides are essential regulators of biological processes including development and growth. The release of the cattle genome sequence has provided a unique opportunity to improve our knowledge of cattle neuropeptides so influential in many biological processes. This first genomic survey of the prohormone gene complement in cattle was conducted using an integrated bioinformatics approach that combines empirical and inferred genomic, transcriptomic and proteomic information to achieve a comprehensive characterization of the cattle neuropeptidome. This approach was able to address and resolve complications that arise from alternative splicing, differential processing of the prohormone, and non-neuropeptide genes that hinder the experimental confirmation and functional characterization of neuropeptides. The cattle neuropeptide census was complemented with a genome-wide characterization of prohormone gene expression profiles and prediction of prohormone cleavage sites that could result in neuropeptides.

Complementary bioinformatic searches of genome and EST resources identified 92 cattle prohormone genes and one prohormone gene not found in the cattle genome. The understanding of the neuropeptide gene complement was substantially advanced because 28 out of the 92 prohormone genes either had no prior experimental evidence at the protein level or only the nucleotide sequence was available, and 9 prohormone genes lacked EST data.

Several findings at the genomic and transcriptomic levels are notable. The bioinformatics approach uncovered a putative TAC4 gene that has not been reported in cattle and the isoform of the cattle TOR2A gene that contained the putative cattle alpha- and beta-salusin neuropeptides. The integrated strategy also uncovered a potential novel duplication in the calcitonin family and galanin-like pseudogene. There were no matches on the cattle genome for the known mammalian relaxin 1 gene, indicating that this gene has been lost in the cattle genome. The complete sequences for genes INSL5 and RLN3 recovered in this study have not previously been reported in cattle. With the exception of PCSK9, all of the proprotein convertase enzymes that cleave the proprotein sequences were located in the cattle genome, suggesting that PCSK9 has been lost from the cattle genome.

The available EST expression information offered an introductory view to the expression patterns of prohormone genes. Of the 62 prohormone genes that have expression reports, ADM and IGF2 were the most frequently reported. This information was complemented with investigation of the expression profile of prohormone genes in two different microarray studies. The analysis of these studies confirmed the association between prohormone gene expression patterns and reproductive and nutritional processes. There were 8 prohormone genes differentially expressed among placentomes from different embryo types and 21 prohormone genes were differentially expressed in the liver of cows under different nutritional levels.

Available logistic and artificial neural network models had high accuracy (86% correct classification rate) in predicting the cleavage sites of prohormone sequences that result in peptides. The performance was particularly outstanding considering that these models were developed on independent data not used to evaluate the prediction accuracy. Models that include all prohormone data available are being developed. These models will constitute a powerful tool to annotate prohormones on other species related to cattle but without genome sequence information or extensive empirical data to support the development of models. Cattle prohormone gene sequences and neuropeptide prediction approaches are available at http://neuroproteomics.scs.uiuc.edu/neuropred.html. This resource will facilitate the functional characterization of the neuropeptides in cattle and related species with no genome sequence and/or limited neuropeptide studies.

Authors' contributions

BRS implemented the integrated bioinformatics approach to search for prohormone genes across multiple databases, identified prohormone gene probes in the microarray platform, predicted the cleavage sites on the prohormone sequences that result in putative neuropeptides, interpreted the results, and drafted the manuscript. SRZ analyzed the microarray experiments, helped interpret the results and write the manuscript. JVS obtained funding for the study, participated in its conception, coordination, interpretation of results, and reviewed the manuscript. All authors have read and approved the final version of this manuscript.

Acknowledgements

We would like to thank Dr. Steven Salzberg, Dr. Liliana Florea and Finn Hanrahan at the Center for Bioinformatics and Computational Biology, University of Maryland for the use of the UMD_1.5 assembly and for insightful comments on the identification of particular prohormone sequences. This material is based upon work supported by the NIH National Institute on Drug Abuse under Award No. P30 DA 018310 to the UIUC Neuroproteomics Center, USDA CSREES under Award No. ILLU-538-311 and by NIH National Institute of General Medical Science under Award No. 1R01GM068946.

Proteases for processing proneuropeptides into peptide neurotransmitters and hormones Hook V Funkelstein L Lu D Bark S Wegrzyn J Hwang SR Annu Rev Pharmacol Toxicol 2008 48 393 423 18184105 Peptides in the Brain: Mass Spectrometry-Based Measurement Approaches and Challenges Li L Sweedler JV Annual Review of Analytical Chemistry 2008 1 1 451 483 http://arjournals.annualreviews.org/doi/abs/10.1146/annurev.anchem.1.031207.113053 Endocrine and metabolic activities of a recently isolated peptide hormone ghrelin, an endogenous ligand of the growth hormone secretagogue receptor Jarkovska Z Krsek M Rosicka M Marek J Endocr Regul 2004 38 2 80 86 15497931 Linkage mapping of IGF2 on cattle chromosome 29 Goodall JJ Schmutz SM Anim Genet 2003 34 4 313 12873229 Genetic variation in the pro-melanin-concentrating hormone gene affects carcass traits in Bos taurus cattle Helgeson SC Schmutz SM Anim Genet 2008 39 3 310 315 18410474 Fine mapping quantitative trait loci for feed intake and feed efficiency in beef cattle Sherman EL Nkrumah JD Li C Bartusiak R Murdoch B Moore SS J Anim Sci 2009 87 1 37 45 18791150 Corticotropin releasing hormone is a promising candidate gene for marbling and subcutaneous fat depth in beef cattle Wibowo TA Michal JJ Jiang Z Genome 2007 50 10 939 945 18059556 NCBI GEO: archive for high-throughput functional genomic data Barrett T Troup DB Wilhite SE Ledoux P Rudnev D Evangelista C Kim IF Soboleva A Tomashevsky M Marshall KA Phillippy KH Sherman PM Muertter RN Edgar R Nucleic Acids Res 2009 37 Database D885 90 2686538 18940857 From the genome to the proteome: uncovering peptides in the Apis brain Hummon AB Richmond TA Verleyen P Baggerman G Huybrechts J Ewing MA Vierstraete E Rodriguez-Zas SL Schoofs L Robinson GE Sweedler JV Science 2006 314 5799 647 649 17068263 Peptidomics: identification and quantification of endogenous peptides in neuroendocrine tissues Fricker LD Lim J Pan H Che FY Mass Spectrom Rev 2006 25 2 327 344 16404746 Neuropeptidomics: expanding proteomics downwards Svensson M Skold K Nilsson A Falth M Svenningsson P Andren PE Biochem Soc Trans 2007 35 Pt 3 588 593 17511658 Neuropeptide-processing enzymes: applications for drug discovery Fricker LD AAPS J 2005 7 2 E449 55 16353923 Differential processing of pro-neurotensin/neuromedin N and relationship to pro-hormone convertases Kitabgi P Peptides 2006 27 10 2508 2514 16904237 From precursor to final peptides: a statistical sequence-based approach to predicting prohormone processing Hummon AB Hummon NP Corbin RW Li L Vilim FS Weiss KR Sweedler JV J Proteome Res 2003 2 6 650 656 14692459 Bridging neuropeptidomics and genomics with bioinformatics: Prediction of mammalian neuropeptide prohormone processing Amare A Hummon AB Southey BR Zimmerman TA Rodriguez-Zas SL Sweedler JV J Proteome Res 2006 5 5 1162 1167 2548284 16674105 Prediction of neuropeptide cleavage sites in insects Southey BR Sweedler JV Rodriguez-Zas SL Bioinformatics 2008 24 6 815 825 18252740 Comparative analysis of neuropeptide cleavage sites in human, mouse, rat, and cattle Tegge AN Southey BR Sweedler JV Rodriguez-Zas SL Mamm Genome 2008 19 2 106 120 18213482 A Python analytical pipeline to identify prohormone precursors and predict prohormone cleavage sites Southey BR Sweedler JV Rodriguez-Zas SL Front Neuroinform 2008 2 7 The genome sequence of taurine cattle: a window to ruminant biology and evolution Bovine Genome Sequencing and Analysis Consortium Elsik CG Tellam RL Worley KC Gibbs RA Muzny DM Weinstock GM Adelson DL Eichler EE Elnitski L Guigo R Hamernik DL Kappes SM Lewin HA Lynn DJ Nicholas FW Reymond A Rijnkels M Skow LC Zdobnov EM Schook L Womack J Alioto T Antonarakis SE Astashyn A Chapple CE Chen HC Chrast J Camara F Ermolaeva O Henrichsen CN Hlavina W Kapustin Y Kiryutin B Kitts P Kokocinski F Landrum M Maglott D Pruitt K Sapojnikov V Searle SM Solovyev V Souvorov A Ucla C Wyss C Anzola JM Gerlach D Elhaik E Graur D Reese JT Edgar RC McEwan JC Payne GM Raison JM Junier T Kriventseva EV Eyras E Plass M Donthu R Larkin DM Reecy J Yang MQ Chen L Cheng Z Chitko-McKown CG Liu GE Matukumalli LK Song J Zhu B Bradley DG Brinkman FS Lau LP Whiteside MD Walker A Wheeler TT Casey T German JB Lemay DG Maqbool NJ Molenaar AJ Seo S Stothard P Baldwin CL Baxter R Brinkmeyer-Langford CL Brown WC Childers CP Connelley T Ellis SA Fritz K Glass EJ Herzig CT Iivanainen A Lahmers KK Bennett AK Dickens CM Gilbert JG Hagen DE Salih H Aerts J Caetano AR Dalrymple B Garcia JF Gill CA Hiendleder SG Memili E Spurlock D Williams JL Alexander L Brownstein MJ Guan L Holt RA Jones SJ Marra MA Moore R Moore SS Roberts A Taniguchi M Waterman RC Chacko J Chandrabose MM Cree A Dao MD Dinh HH Gabisi RA Hines S Hume J Jhangiani SN Joshi V Kovar CL Lewis LR Liu YS Lopez J Morgan MB Nguyen NB Okwuonu GO Ruiz SJ Santibanez J Wright RA Buhay C Ding Y Dugan-Rocha S Herdandez J Holder M Sabo A Egan A Goodell J Wilczek-Boney K Fowler GR Hitchens ME Lozado RJ Moen C Steffen D Warren JT Zhang J Chiu R Schein JE Durbin KJ Havlak P Jiang H Liu Y Qin X Ren Y Shen Y Song H Bell SN Davis C Johnson AJ Lee S Nazareth LV Patel BM Pu LL Vattathil S Williams RL Jr Curry S Hamilton C Sodergren E Wheeler DA Barris W Bennett GL Eggen A Green RD Harhay GP Hobbs M Jann O Keele JW Kent MP Lien S McKay SD McWilliam S Ratnakumar A Schnabel RD Smith T Snelling WM Sonstegard TS Stone RT Sugimoto Y Takasuga A Taylor JF Van Tassell CP Macneil MD Abatepaulo AR Abbey CA Ahola V Almeida IG Amadio AF Anatriello E Bahadue SM Biase FH Boldt CR Carroll JA Carvalho WA Cervelatti EP Chacko E Chapin JE Cheng Y Choi J Colley AJ de Campos TA De Donato M Santos IK de Oliveira CJ Deobald H Devinoy E Donohue KE Dovc P Eberlein A Fitzsimmons CJ Franzin AM Garcia GR Genini S Gladney CJ Grant JR Greaser ML Green JA Hadsell DL Hakimov HA Halgren R Harrow JL Hart EA Hastings N Hernandez M Hu ZL Ingham A Iso-Touru T Jamis C Jensen K Kapetis D Kerr T Khalil SS Khatib H Kolbehdari D Kumar CG Kumar D Leach R Lee JC Li C Logan KM Malinverni R Marques E Martin WF Martins NF Maruyama SR Mazza R McLean KL Medrano JF Moreno BT More DD Muntean CT Nandakumar HP Nogueira MF Olsaker I Pant SD Panzitta F Pastor RC Poli MA Poslusny N Rachagani S Ranganathan S Razpet A Riggs PK Rincon G Rodriguez-Osorio N Rodriguez-Zas SL Romero NE Rosenwald A Sando L Schmutz SM Shen L Sherman L Southey BR Lutzow YS Sweedler JV Tammen I Telugu BP Urbanski JM Utsunomiya YT Verschoor CP Waardenberg AJ Wang Z Ward R Weikard R Welsh TH Jr White SN Wilming LG Wunderlich KR Yang J Zhao FQ Science 2009 324 5926 522 528 19390049 The universal protein resource (UniProt) UniProt Consortium Nucleic Acids Res 2008 36 Database D190 5 2238893 18045787 SwePep, a database designed for endogenous peptides and mass spectrometry Falth M Skold K Norrman M Svensson M Fenyo D Andren PE Mol Cell Proteomics 2006 5 6 998 1005 16501280 Gapped BLAST and PSI-BLAST: a new generation of protein database search programs Altschul SF Madden TL Schaffer AA Zhang J Zhang Z Miller W Lipman DJ Nucleic Acids Res 1997 25 17 3389 3402 146917 9254694 GeneWise and Genomewise Birney E Clamp M Durbin R Genome Res 2004 14 5 988 995 479130 15123596 Entrez Gene: gene-centered information at NCBI Maglott D Ostell J Pruitt KD Tatusova T Nucleic Acids Res 2007 35 Database D26 31 1761442 17148475 CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice Thompson JD Higgins DG Gibson TJ Nucleic Acids Res 1994 22 22 4673 4680 308517 7984417 Bos taurus genome assembly Liu Y Qin X Song XZ Jiang H Shen Y Durbin KJ Lien S Kent MP Sodeland M Ren Y Zhang L Sodergren E Havlak P Worley KC Weinstock GM Gibbs RA BMC Genomics 2009 10 1 180 2686734 19393050 A whole-genome assembly of the domestic cow, Bos taurus Zimin AV Delcher AL Florea L Kelley DR Schatz MC Puiu D Hanrahan F Pertea G Van Tassell CP Sonstegard TS Marcais G Roberts M Subramanian P Yorke JA Salzberg SL Genome Biol 2009 10 4 R42 2688933 19393038 Nutrition-induced ketosis alters metabolic and signaling gene networks in liver of periparturient dairy cows Loor JJ Everts RE Bionaz M Dann HM Morin DE Oliveira R Rodriguez-Zas SL Drackley JK Lewin HA Physiol Genomics 2007 32 1 105 116 17925483 Aberrant gene expression patterns in placentomes are associated with phenotypically normal and abnormal cattle cloned by somatic cell nuclear transfer Everts RE Chavatte-Palmer P Razzak A Hue I Green CA Oliveira R Vignon X Rodriguez-Zas SL Tian XC Yang X Renard JP Lewin HA Physiol Genomics 2008 33 1 65 77 18089771 Salusins: newly identified bioactive peptides with hemodynamic and mitogenic activities Shichiri M Ishimaru S Ota T Nishikawa T Isogai T Hirata Y Nat Med 2003 9 9 1166 1172 12910263 Controlling the false discovery rate: A practical and powerful approach to multiple testing Benjamini Y Hochberg Y J R Statist Soc Series B 1995 57 289 300 http://www.jstor.org/stable/2346101 NeuroPred: a tool to predict cleavage sites in neuropeptide precursors and provide the masses of the resulting peptides Southey BR Amare A Zimmerman TA Rodriguez-Zas SL Sweedler JV Nucleic Acids Res 2006 34 Web Server W267 72 1538825 16845008 Improved prediction of signal peptides: SignalP 3.0 Bendtsen JD Nielsen H von Heijne G Brunak S J Mol Biol 2004 340 4 783 795 15223320 Bovine and ovine gonadotropin-releasing hormone (GnRH)-II ligand precursors and type II GnRH receptor genes are functionally inactivated Morgan K Sellar R Pawson AJ Lu ZL Millar RP Endocrinology 2006 147 11 5041 5051 16916952 The evolution of the relaxin peptide family and their receptors Wilkinson TN Bathgate RA Adv Exp Med Biol 2007 612 1 13 18161477 Comparative analysis of mouse hepcidin 1 and 2 genes: evidence for different patterns of expression and co-inducibility during iron overload Ilyin G Courselaud B Troadec MB Pigeon C Alizadeh M Leroyer P Brissot P Loreal O FEBS Lett 2003 542 1–3 22 26 12729891 Phylogenetic analysis of the insulin-like growth factor binding protein (IGFBP) and IGFBP-related protein gene families Rodgers BD Roalson EH Thompson C Gen Comp Endocrinol 2008 155 1 201 207 2241922 17537438 A single-copy relaxin-like gene sequence is present in sheep Roche PJ Crawford RJ Tregear GW Mol Cell Endocrinol 1993 91 1–2 21 28 7682520 A third human CALC (pseudo)gene on chromosome 11 Hoppener JW Steenbergh PH Zandberg J Adema GJ Geurts van Kessel AH Lips CJ Jansz HS FEBS Lett 1988 233 1 57 63 2898385 Calcitonin receptor-stimulating peptide, a new member of the calcitonin gene-related peptide family. Its isolation from porcine brain, structure, tissue distribution, and biological activity Katafuchi T Kikumoto K Hamano K Kangawa K Matsuo H Minamino N J Biol Chem 2003 278 14 12046 12054 12556539 Mechanism of action and in vivo role of platelet-derived growth factor Heldin CH Westermark B Physiol Rev 1999 79 4 1283 1316 10508235 The role of cortistatin in the human immune system van Hagen PM Dalm VA Staal F Hofland LJ Mol Cell Endocrinol 2008 286 1–2 141 147 18450367 The neuroendocrine polypeptide 7B2 is an endogenous inhibitor of prohormone convertase PC2 Martens GJ Braks JA Eib DW Zhou Y Lindberg I Proc Natl Acad Sci USA 1994 91 13 5784 5787 44081 8016065 7B2 facilitates the maturation of proPC2 in neuroendocrine cells and is required for the expression of enzymatic activity Zhu X Lindberg I J Cell Biol 1995 129 6 1641 1650 2291188 7790360 7B2 prevents unfolding and aggregation of prohormone convertase 2 Lee SN Lindberg I Endocrinology 2008 149 8 4116 4127 18467442