A large-scale collection of phenotypic data describing an insertional mutant population to facilitate functional analysis of rice genes

In order to facilitate the functional analysis of rice genes, we produced about 50,000 insertion lines with the endogenous retrotransposon Tos17. Phenotypes of these lines in the M2 generation were observed in the field and characterized based on 53 phenotype descriptors. Nearly half of the lines showed more than one mutant phenotype. The most frequently observed phenotype was low fertility, followed by dwarfism. Phenotype data with photographs of each line are stored in the Tos17 mutant panel web-based database with a dataset of sequences flanking Tos17 insertion points in the rice genome (http://tos.nias.affrc.go.jp/). This combination of phenotypic and flanking sequence data will stimulate the functional analysis of rice genes.
Introduction
Rice is the most important staple crop for half the world’s population. Improvements in rice yield and quality beyond the benefits of the green revolution of 30 years ago are required to meet the demands of an increasing global population. At the beginning of the 21st century, with the hope of finding creative solutions to the problems of food production, nutrition and transportation, nearly the entire nucleotide sequence of the rice genome, Oryza sativa L. cv. Nipponbare, was determined through a world-wide collaborative effort (International Rice Genome Sequencing Project 2005). The sequence data provide an important resource for promoting the discovery of important genes for crop improvement. Currently, a Rice Annotation Project (RAP) using nucleotide sequences of full-length cDNAs (The Rice Full-Length cDNA Consortium 2003) is in progress to position functional genes on the rice genome map (Ohyanagi et al. 2006); however, many genes remain “unknown” due to lack of experimental evidence or sufficient similarity with characterized genes from other organisms. The next great challenge after completion of genome sequencing is the functional characterization of these genes and discovery of genes that affect vital developmental, agronomic or biochemical plant functions (Hirochika et al. 2004).
Gene annotation is mostly based on the sequence similarity to known genes from other species. The limitations of this method are that every organism may have unique genes that do not have homologues even in closely related species, and assignment of a protein’s function based on similarity may only give a partial description. For example, a gene may contain a domain that is conserved among protein kinases, but the actual substrate of the enzyme would be difficult or impossible to determine without experimental evidence. Categorization using cladistic associations (i.e. a phylogenetic tree) is more sensitive and is able to detect BLAST mis-hits or false positives (Sjolander 2004). However, characteristics supported by experimental data, e.g., correlation between the phenotype and the function of the protein kinase, are indispensable to the exact annotation, because all of the associative algorithms ultimately depend on experimental data.
Mutational analysis through gene disruption is one of the most efficient methods for identifying gene function. The related approaches of interrupting gene expression with RNAi (Miki and Shimamoto 2004), and overexpression are also effective because the functions of genes can be determined by the correlation of disrupted genes and their associated phenotypes. Currently, more than 130,000 T-DNA insertion lines of Arabidopsis thaliana have been created and are publicly available (Alonso et al. 2003). Phenotyping of Ds insertion lines in Arabidopsis is also in progress (Kuromori et al. 2006). For characterizing monocot genomes, we have produced more than 50,000 disruption lines of Oryza sativa cv. Nipponbare (Japonica), using the endogenous retrotransposon Tos17, which has a “copy and paste” type of transposition activity (Miyao et al. 2003). There are two native copies of Tos17 in the Nipponbare genome that are activated specifically in cultured cells (Hirochika et al. 1996). On average, ten new copies of Tos17 are transposed in each cell during 5 months in culture. When plants have been regenerated from the cultured cells, Tos17 retrotransposition is immediately inactivated, and Tos17 copies become fixed and segregate in a Mendelian fashion in the next generation.
There are many advantages to the Tos17 disruption system for mutational analyses. Because the flanking regions of Tos17 insertion points are easily amplified by TAIL-PCR or a suppression PCR method using a 3′-end primer of Tos17, insertion sites in the rice genome can be easily determined. Furthermore, mutants can be screened by PCR using the Tos17 end primer and a primer from any desired genomic sequence. The distribution of transposed Tos17s is not random, and a large number of transpositional “hot spots” are detected throughout the rice genome. The insertion frequency of Tos17 into genic regions is three-fold higher than that into non-coding regions. Owing to this polarization, Tos17 insertion lines have great advantages for the functional analysis of rice genes (Miyao et al. 2003). Because Tos17 is an endogenous retrotransposon, regenerated lines can be grown in the field, and seeds can be exported without regulations associated with genetically modified organisms such as rice lines that have undergone “transgenic-” insertion of T-DNA (Jeon et al. 2000; Sallaud et al. 2003; Wu et al. 2003), Ds (Greco et al. 2003; Kim et al. 2004), or En/Spm (Kumar et al. 2005).
Mutant lines generated by Tos17 retrotransposition have already been used for the functional analysis of rice genes. For example, Tos17 insertion mutants with a dwarf or viviparous phenotype were used to identify and analyze genes for gibberellin and abscisic acid metabolism (Sakamoto et al. 2004; Agrawal et al. 2001). Phenotypic characteristics of most of the Tos17 insertion lines, however, remain to be described. A large number of plant scientists working with rice or other monocotyledonous species could benefit from a systematic phenotypic analysis of many Tos17 insertion lines and the creation of a public database. To promote the functional analysis of rice genes, the phenotypes of all of our mutant lines have been observed in rice fields through the collaboration of seven laboratories. Collected phenotypic data are useful for predicting the function of a disrupted gene. In this paper, we report the phenotypic statistics of a Tos17 insertion mutant population for the discovery of agronomically important genes.
Materials and methods
Plant materials
Nipponbare calli derived from embryos were grown in N6 liquid medium (Otsuki 1990) containing 1 mg/l 2,4-D for 5 months. In total, about 50,000 plants were regenerated. Seeds of the M2 generation were harvested from each M1 plant. To check the activity of Tos17 under various hormone conditions, N6 liquid media containing 1, 2, 5, 10 or 20 mg/l 2,4-D with or without 0.1 mg/l BA were used.
Phenotyping
Ten to twenty-five seeds were planted per line. Germination rates and seedling phenotypes were observed in the nursery. After 1 month from seeding, the seedlings were transferred to the paddy field. Phenotypes in the field were observed at the vegetative stage, near the heading stage, at the seed maturation stage, and at harvest. Lines segregating abnormal plants at about 25% frequency were digitally photographed and assigned a phenotype ID.
Database
All phenotype data were stored into a relational database on the PostgreSQL relational database managing system (http://www.postgresql.org/) with a FreeBSD 5.5 operating system (http://www.freebsd.org/). FreeBSD 6.1 was used for the statistical analysis. Tabular structure for phenotypic description consists of the line name, plant serial number, observation date, observing person, a link to the photograph file, phenotype ID, and detailed descriptions. Additional data for each line are inserted as a new row. Data can be modified only by the observing person. With this table structure, observation logs for each line by many persons over the course of many years can be stored without conflict. For counting lines of each mutant phenotype, Perl script, which can connect with the PostgreSQL server with a Pg.pm module, reads the phenotype table, stores line names and phenotype IDs into an associative array (“hash”), to convert from a redundant number of phenotype IDs to a unique number for each line. The number of lines showing each phenotype ID in the hash was counted. Flanking sequence data of Tos17 insertion sites are also stored in the same database. Loci of Tos17 insertions are determined by BLASTN searches against rice genome sequences of the International Rice Genome Sequencing Project (IRGSP) Build3. Loci of annotated genes are also stored in the relational database.
SOM analysis
Software for SOM analysis was downloaded from the Neural Networks Research Center of Helsinki University of Technology (http://www.cis.hut.fi/research/som_pak/). For the initialization program, randinit, parameters: 24 for x dimension, 16 for y dimension, hexa for topology, bubble for neighborhood function, and 123 for seeds were used. For the map training program, vsom, parameters for first learning: 1000 for learning length in training, 0.05 for initial learning rate, 10 for initial radius of the training area in som-algorithm, parameters for second learning: 10000 for learning length, 0.02 for initial learning rate, 3 for initial radius. The analyzed data were visualized using the umat program.
Results
Flow of phenotypic analysis
Nipponbare calli from 92 seeds were independently cultured for 5 months, and about 50,000 plants were regenerated. Seeds (M2 generation) from the regenerated plants were independently harvested and labeled with a line name. Ten to twenty-five M2 plants of each line were observed over a full developmental cycle in the nursery and paddy.
Each line was designated by “N” for “Nipponbare” followed by a letter A-G that indicates the yearly lot, and four figures, e.g. NA1234. Lines named with “NA” and “NB” were used in a small-scale pilot study and “NC” to “NG” were used in large-scale studies.
To evaluate the effect of hormone (auxin and cytokinin) concentration in the medium on Tos17 retrotransposition activity, the NC line was subcultured in media containing 1, 2, 5, 10, or 20 mg/l of 2,4-dichlorophenoxyacetic acid (2,4-D) with or without 0.1 mg/l benzyl adenine (BA). There was no significant difference in observed transposition events among lines derived from the subcultures. In lots, ND, NE, NF, and NG, calli were induced in a medium containing 2 mg/l 2,4-D, and maintained in a medium supplemented with 1 mg/l 2,4-D for 5 months. The cultural conditions for each line are available with the phenotypic description list from the mutant panel database.
Co-segregation of mutant phenotypes with Tos17 insertion can be detected by DNA blot hybridization, which will help the further analysis of gene function. If co-segregation is detected, the flanking region of co-segregated Tos17 can be isolated by TAIL-PCR or suppression PCR (Miyao et al. 1998).
Classification of phenotypes
To provide a classification system and a database useful for most rice scientists, phenotype scoring was limited to 53 phenotype descriptors belonging to 12 classes. Because environmental conditions such as day length, temperature and soil conditions differ significantly among the seven fields in which this project was conducted, some variability in traits such as heading date or leaf color may have occurred between different fields. Thus, some of the present data may vary with environmental conditions, although most of mutant phenotypes are stably expressed. Phenotype classifications and a summary of observations are shown in Table 1. Each subclass has a phenotype ID code to enable data entry as a barcode with a portable recording device and to enable data compilation from all seven laboratories.Germination. This trait was evaluated by measuring germination rate under defined conditions. Since wild type (cv. Nipponbare) showed a germination rate higher than 95%, lines that showed germination rates less than 75% were recorded in the present project. Since several laboratories measured germination rates of all lines, all primary data are also stored in the database. In total, 3489 lines showed a low germination rate, and 525 lines showed germination rates less than 50%. Some of the lines with low germination rates may be embryo mutants.Growth. Growth was observed at the seedling stage in the rice nursery or at an early stage after transfer to the field. “Weak” refers to mutants that formed slim seedlings with retarded growth, probably caused by a deficiency in some housekeeping gene product (Fig. 1, NG0352). Some of the “Weak” lines were reclassified eventually to “Lethal”. An example of “Abnormal shoot” is shown in Fig. 1 (NG0356). Another example is represented by NE3024. This seedling is smaller than wild type and its leaves are short and wide, resembling mutants defective in genes associated with gibberellin biosynthesis or signaling pathways (Sakamoto et al. 2004; Uegchi-Tanaka et al. 2005).Leaf color. Frequently appearing pigmentation phenotypes are “Albino” (Fig. 1, NG1048) and “Virescent” (Fig. 1, NE1517). Completely white or yellow (Fig. 1, NG1469) seedlings died within 3 weeks after seeding. If green and white segments coexisted on leaves, a condition called virescent, the seedlings survived in the field. Zebra mutants that show repetition of a white or a pale green band and a green band in the longitudinal direction were also observed (Fig. 1, NF6044). However, in most lines, the “Zebra” phenotype was limited to young stages. The “Stripe” phenotype (Fig. 1, NE4001) often showed extremely biased segregation, e.g., only one plant in 25, and often was not stably inherited.Leaf shape. Many different kinds of abnormally shaped leaves were observed. “Short Leaf” and some “Wide leaf” phenotypes were eventually reclassified as “Dwarf” or “Severely dwarf” phenotypes (Fig. 1, NE8114). The “Short leaf” phenotype is similar to mutants defective in gibberellin biosynthesis or signal transduction. The line NE5022 could not develop a normal, flat leaf blade and eventually died (Fig. 1). Several lines showed pleiotropic phenotypes of the shoot. The line NE8329 did not develop tillers, showed dwarfism and formed rolled or twisted leaves (Fig. 1). Since these phenotypes are difficult to characterize exactly by only phenotype codes, additional remarks were presented in the comment column of the database. Frequency distribution of the leaf width mutants was biased toward the narrow type (Fig. 1, NG0754), rather than the wide type.Culm shape. Dwarfism is the most abundant mutant phenotype, along with sterility. The “Semi-dwarf” condition is characterized by plant heights that are 70–80% of wild type values. “Dwarf” describes plants with heights smaller than “Semi-dwarf” but larger than “Severely dwarf”. Plants are classified “Severely dwarf” when the plant height is smaller than 30 cm at maturity. Some dwarf phenotypes often co-segregated with other abnormal phenotypes such as “Fine leaf”, “Wide leaf”, “Spiral leaf”, or “Abnormal panicle shape”. The dwarf mutants accompanying other shoot/panicle abnormalities are expected to be involved in hormonal signaling or synthesis pathways. Other culm phenotypes such as “Thick culm” (Fig. 1, NG9874) appeared infrequently.Spotted leaf/lesion mimic. Various types of “Spotted leaf/lesion mimic” phenotypes, e.g., small and scattered spots, large and dispersed spots, were observed. Heavy lesions caused the early death of leaves (Fig. 1, NG0752). Mutants with large white lesions were also observed. Brown spots were more frequently observed than white ones.Tillering. In the tillering class, “Low tillering” mutants were abundant. The “High tillering” phenotype tends to co-segregate with the “Fine leaf” phenotype. The “Lazy” (Fig. 1, NG0667) mutants have open and recumbent tillers. A quite unusual phenotype among “High tillering” lines is NG2552 that produced tillers reiteratively from upper internodes and failed to produce panicles.Heading date. In this category, lines whose heading date deviated more than 7 days from normal were classified as heading date mutants. This category includes “Early heading”, “Late heading”, and “Non-heading?” mutants. A small number of lines segregated as “Non-heading?” mutants that did not form panicles even at harvest (five to 6 months after sowing). The number of “Early heading” lines (1797) is a little larger than that of “Late heading” lines (1244).Spikelet. Each spikelet of rice is composed of two rudimentary glumes, two empty glumes and one floret comprised of one lemma, one palea and three kinds of floral organs (two lodicules, six stamens and one pistil). “Abnormal hull” refers to any glume abnormality and “Abnormal floral organ” refers to any abnormality in floral organs (e.g. an abnormal pistil). In the “Abnormal hull” class, there is a mutant that failed to close hulls after flower opening. On the other hand, mutants that could not open flowers due to underdeveloped stamens were often re-classified as “Completely sterile” mutants. Another mutant produced an extra glume. Abnormal flower phenotypes showing shoots growing from floral organs as in Fig. 1 (NC7672), were also detected at relatively low frequency.Panicle. The most frequently observed abnormal panicle phenotype was the precocious germination of seeds while still attached to the maturing panicle, or “Viviparous”. As for panicle shape, “Dense panicle” and “Short panicle” mutants (Fig. 1, NE6778) were also frequently observed, and “Long panicle” and “Lax panicle” were somewhat rare. Incomplete emergence of the panicle from the flag leaf sheath was categorized as “Neck leaf”.Sterility. “Sterile” is the third most frequent mutant in this Tos17 insertion mutant population after low seed fertility and dwarf mutants. The “Sterile” and “Low fertility” conditions correspond to lines with less than 2% and ca. 50% seed fertility, respectively.Seed. Only “Large grain”, “Small grain”, and “Slender grain” were distinguished in this category. The frequency of “Small grain” was two and four times higher than the frequency of “Large grain” and “Slender grain”, respectively. Among the “Others”, white or dull kernel phenotypes were often observed.Table 1Summary of phenotype dataClassPhenotypeID codeNCNDNENFNGTotal1GerminationLow germination rate13275311326100530034892GrowthLethal21353006892712341629Abnormal shoot31891722198233841787Weak411420434155639016053Leaf colorAlbino112643842302752541407Yellow129613918631771809Dark green 1329528535458691061Pale green 142704293722653951731Virescent1535278206131184834Stripe16464064132102384Zebra171215133929108Others18915232216854Leaf shapeWide leaf21382851421142Narrow leaf221653244322042511376Long leaf2333144529Short leaf2424258140Drooping leaf258140351231226Rolled leaf2631521532383342Spiral leaf27102355318109Brittle leaf/culm28514405310122Abnormal lamina joint angle29142713142896Withering3011227025188147868Others31375446113933435Culm shapeSemi-dwarf416458226616339003661Dwarf428031550135188211235709Severely dwarf432484113551432171374Long culm44258217506883676Fine culm45116118Thick culm46312421462Others47114286Spotted leaf/lesion mimicSpotted leaf/lesion mimic5111521119726930210947TilleringHigh tillering611127252127111Low tillering624186786966094212822Lazy63701155380563748Heading dateEarly heading6535297699297651789Late heading661115612472151101244Non-heading?6722423218969SpikeletAbnormal hull71412683652111508Abnormal floral organ721212843111721110PanicleLong panicle8113193329Short panicle82517942692104752Lax panicle831816152332104Dense panicle844118976543264Viviparous85854732671421381105Shattering861326Neck leaf873851481024171Abnormal panicle shape882143265792543311SterilitySterile9144110268186588823825Low fertility9228773127935299126121254212SeedLarge grain101557833425195Small grain10247959614544427Slender grain1032028182114101Others10497911178831433473469Lots NC through NG were harvested in 1997 through 2001, respectively. Numbers of mutant lines with the corresponding phenotypes are listedFig. 1Representative phenotypes of Tos17 insertional mutants. Phenotype and line designation are indicated on each photograph
Correlation between phenotypes
To understand how the 53 phenotypic descriptors are related, lines showing two or more abnormal phenotypes were selected, and a matrix with 53 rows and 53 columns of phenotypes with a number of lines showing each pair of phenotypes was devised. Values for the number of respective phenotypes were changed to “x” in the matrix, (“x” is ignored by the SOM program), and data were subjected to self-organizing map (SOM) analysis (Kohonen 1995). The SOM algorithm is used for visualization of multidimensional complex data using an unsupervised learning method based on a grid of artificial neurons. The 53-dimensional correlation data of phenotypes was reflected in a two-dimensional map (Fig. 2).
Fig. 2Self-organizing map for correlation of phenotypes. Lines that showed two or more abnormal phenotypes were analyzed by SOM. Distances between phenotypes indicate the magnitude of correlation of phenotypes. Col_Oth., Color Others; Lg_lf, Long leaf; L_joint, Abnormal lamina joint angle; Lg_pa, Long panicle; Ab_floral_organ, Abnormal floral organ; Sl_grain, Slender grain; Thick_cm, Thick culm. Grayscale levels of each node represents the distance between references
Topologies (not distances) of phenotypes on the SOM coordinates coincide with the correlation between phenotypes. Distance between two phenotypes is indicated by grayscale color on the SOM. For example, assume that phenotype A, B, and C appeared in 5000, 50, and 10 lines, respectively. Seven lines showed phenotype A and C. Three-lines showed phenotype B and C. The color between phenotype A and C should be darker than the color between phenotype B and C, because the line number of phenotype A lines is much greater than that of phenotype B lines, although the phenotype correlation of C with A is stronger than that with B. It is difficult to explain multidimensional data completely on a two dimensional map, but a SOM map indicates generally that neighboring phenotypes have relatively tight correlations. The SOM analysis in Fig. 2 shows that there are many pairs of phenotypes that are apparently correlated. That is, among 53 phenotypes, several phenotypes have a high probability to emerge simultaneously with other specific phenotypes. This phenotypic correlation could be caused by pleitropic expression of a single gene, or could reflect developmental causality of the two abnormalities. For example, “Long leaf” and “Long panicle” (lg_lf, lg_pa) located in the upper left corner on the SOM map in Fig. 2 have a very strong correlation. This result suggests that the gene involved in leaf length also affects panicle length. On the contrary, long leaf and dwarfism, at the opposite corner of the map, do not have any correlation. Both dwarf and semi-dwarf phenotypes have a relatively strong correlation with sterile and low fertile phenotypes. Since this correlation is not so strong, a part of “Dwarf” and “Semi-dwarf” mutants do not affect seed fertility, but other “Dwarf” and “Semi-dwarf” mutants may be regulated by genes associated with some housekeeping functions.
Correlations between mutant phenotype and Tos17 insertion
If allelic insertion lines show the same phenotype, it is highly probable that the mutant phenotype is caused by disruption of the target gene by a Tos17 insertion. We searched for genes that were disrupted by Tos17 in at least two lines. A total of 391 genes were detected based on the Rice Annotation Project (Ohyanagi et al. 2006). Table 2 is a partial list of such loci and their mutant phenotypes. For example, five lines have insertions of Tos17 in the Os06g027500 gene (Fig. 3A), of which two lines have the Tos17 insertions in exons and show an early heading phenotype. On the other hand, the other three lines have Tos17 insertions in introns and are not early heading. (see http://tos.nias.affrc.go.jp/). The Os06g027500 gene is Hd1, a key gene for determining heading date (Yano et al. 2000). Another example is the magnesium chelatase subunit gene, Os07g0656500 in Fig. 3B (Jung et al. 2003). Tos17 insertion into this gene was correlated with an albino phenotype, because mutants defective in this gene lack active chlorophyll and eventually become albino. All correlations between phenotypes and disrupted genes by Tos17 insertion are displayed on the mutant panel database website (http://tos.nias.affrc.go.jp/).
Table 2List of loci that have Tos17 insertions in exons in at least two linesLocus nameDescriptionPhenotypesOs01g0113200LRK14Pale green leaf, low fertilityOs01g0113300Receptor-like kinase ARK1ASDwarf, spotted leaf/lesion mimicOs01g0147800Protein of unknown function DUF547 domain containing proteinPale green leaf, semi-dwarf, long culm, short panicle, sterile, low fertileOs01g068590065 kD Microtubule associated proteinNarrow leaf, semi-dwarfOs02g05526008-Oxoguanine DNA glycosylaseLethalOs04g0464200Betaine-aldehyde dehydrogenase (EC 1.2.1.8) (BADH)Low fertilityOs04g0680400Allantoinase (EC 3.5.2.5)Dwarf, low fertilityOs05g0318600Protein kinase domain containing proteinNarrow leafOs05g0548900Phosphoethanolamine methyltransferaseEarly headingOs05g0552400Zn-finger, RING domain containing proteinEarly headingOs06g01768002OG-Fe(II) oxygenase domain containing proteinDark green leaf, dwarf, severely dwarfOs06g0275000Hd1Dwarf, early headingOs06g0680500Glutamate receptor 3.1 precursor (Ligand-gated ion channel 3.1) (AtGLR2)Low fertilityOs07g0197100HexokinaseDwarf, sterileOs07g0646500SWIM Zn-finger domain containing proteinLate headingOs07g0656500Protoporphyrin IX Mg-chelatase subunit precursorLethal, albino, dwarfOs09g0278300Phosphatidylinositol-4-phosphate 5-kinase family proteinSemi-dwarf, dwarf, low tillering, sterile, low fertilityOs10g0567100Chlorophyll b synthase (Fragment)Dark green leaf, pale green leaf, withering, semi-dwarf, severely dwarf, early heading, late heading, low fertilityOs12g0127600WRKY transcription factor 57Sterile, low fertilityOs12g0566000HCO3-Transporter domain containing proteinSeverely dwarfOs12g0572500Protein of unknown function XH domain containing proteinEarly headingLocus name and description are from RAP dataFig. 3Examples of genes that have insertions of Tos17. (A) Os06g0275000, Hd1 gene. (B) Os07g0656500, Magnesium chelatase subunit gene. The structure and insertion points of these genes are drawn on demand from our web site (http://tos.nias.affrc.go.jp/). Coding regions are indicated with broad lines. Positions of Tos17 insertions are indicated by flags. The direction of the flag is the direction of the Tos17 insertion. Detailed descriptions of insertion points, sequence names, accession numbers, and line names will appear on the web page. When a sequence name is clicked, the figure will be re-drawn with a flag labeling the sequence name to distinguish closely inserted Tos17s
A large-scale phenotypic characterization of an M2 population generated by Tos17 retrotransposition has revealed that this population contains a large number of mutants covering many easily scored phenotypes. In addition, linkage between the mutant phenotype and a specific Tos17 insertion facilitates greatly the isolation of the causal genes and the elucidation of the gene functions.
Discussion
Three-years of collaboration among seven laboratories has led to a large-scale phenotypic characterization of about 50,000 M2 plants generated by Tos17 retrotransposition. We have examined 53 kinds of abnormal phenotypes in rice from the seedling to harvest stages that are easily evaluated in the field. This project has revealed that this population contains a large number of mutants covering a wide range of phenotypes. Although several mutant phenotypes may be environment-sensitive and their expression may be unstable, most of the mutant phenotypes were stable. We also deposited additional information about certain phenotypes in a comment field within the phenotype description table, if this information was provided. A text search box on the phenotype list web page is envisioned to facilitate use of the resource.
Because M2 plants were observed, inserted Tos17s segregate among individuals. An average of 10 new copies of Tos17 are inserted into each regenerated plant. If the phenotype co-segregates with an insertion of Tos17, the phenotype is likely to be caused by disruption of the target gene. Of course, there are lines whose mutant phenotypes are not correlated with Tos17 insertion. These mutants might be caused by insertions of other native transposons, by chromosomal aberrations, or by other mutations during tissue culture. In this case, their causal genes could be isolated by usual positional cloning methods.
There were some difficulties in clustering phenotypes using standard relational algorithms, because each of the 53 phenotypes has as many as 52 kinds of frequencies against other phenotypes, respectively. SOM analysis is a kind of clustering method suitable for such multi-dimensional non-linear data (Kohonen 1995). SOM analysis confirmed some associations due to hormonal or developmental constraint, but some additional intriguing correlations were also detected, e.g., between leaf color/shape and heading date, between dwarfism and sterility. In Fig. 2, the “Long leaf” phenotype is strongly correlated with the “Long panicle” phenotype. The “Weak” phenotype at the seedling stage has a relatively strong correlation with “Lethal”, “Abnormal shoot”, “Pale green” Leaf and “Spotted leaf/lesion mimic” phenotypes, indicating that the “Weak” plants at the seedling stage likely show “Lethal”, “Abnormal shoot”, “Pale green” and/or “Spotted leaf/lesion mimic” phenotypes at later stages. “Dwarf”, “Semi-dwarf”, “Sterile”, “Low fertile” and “Low tillering” phenotypes showed relatively high correlations. The SOM map is quite useful for the overview of phenotype correlations. This method of analysis will be useful not only for considering the major and side-effects of gene disruption but also for re-categorization of phenotypes. The advantage of SOM analysis is the ability to analyze many different kinds of data simultaneously. Currently, metabolome data have been obtained from Arabidopsis (Hirai et al. 2004), tomato (Schauer et al. 2006) and other plants. It is reasonable to predict that the combinatorial analysis of phenotype and metabolome data of rice will reveal correlations between metabolic pathways and phenotypes without additional genetic information.
Phenotype databases of rice have been developed in many countries, e.g., Oryzabase (Kurata and Yamazaki 2006), RMD (Zhang et al. 2006), and IRRS (Wu et al. 2005). Our system includes both phenotypic and insertion data on the rice genome in a relational database. The number of photographs on file is more than 58,000, and the number of phenotype description records is more than 158,000. Our database structure enables direct access of Perl script to the database and the extraction of many kinds of correlations. Flanking sequences of insertion points and phenotypes of the insertion lines provide a provisional assignment of the function of a disrupted gene. We have already sequenced more than 25,000 flanking sequences from 20% of the insertion lines, and the number of flanking sequences is still increasing. Of the 27,448 total annotated loci based on RAP1/IRGSP Build3, 391 loci have more than two insertions in exons. When two or more lines have Tos17 insertions in a common gene, they usually exhibited similar phenotypes. These data are a strong indication of the function of a disrupted gene. However, several phenotypes such as “Dwarf” and “Low fertile” were observed in many lines. It might be difficult to assign a gene function based solely on correlation for these frequently observed phenotypes, and a complementation test would be required to confirm the correlation. If flanking sequence data from all insertion lines can be obtained, this correlation will be more useful for the annotation of genes. We are continuing to sequence the flanking regions of all our mutant lines.
In this study, we collected a large amount of phenotypic data in seven fields under natural conditions. If phenotypes are observed under other conditions such as drought or temperature stress conditions or under pathogen pressure, phenotypic description of this population would be much more enriched. In addition, the present study evaluated only a limited number of traits easily scored on above-ground organs. Thus, other traits such as roots and seed storage composition remain to be investigated in the future. Expansion of this study to include new traits would enable investigators to find new correlations with disrupted genes. Furthermore, integration of phenotype data with those of microarray experiments, metabolic profiling, and other approaches will be a powerful tool for revealing new aspects of plant physiology.
All phenotype and flanking sequence data can be obtained via http://tos.nias.affrc.go.jp/. At this site, all annotated rice genes and locations of Tos17 insertions are shown on a clickable chromosome map. Details containing illustrated gene structures with insertion points, phenotypes of corresponding lines, nucleotide sequences flanking the disrupted region, candidate primer sequences for segregation analysis, and annotation from RAP data corresponding to the selected position are displayed. Mutant seeds are available for scientific use from the Genome Resource Center at NIAS (http://www.rgrc.dna.affrc.go.jp/).

phenotyping
retrotransposon
database
oryza sativa
insertion mutagenesis


Acromegaly caused by growth hormone-releasing hormone-producing tumors: long-term observational studies in three patients

We report on three newly diagnosed patients with extracranial ectopic GHRH-associated acromegaly with long-term follow-up after surgery of the primary tumor. One patient with a pancreatic tumor and two parathyroid adenomas was the index case of a large kindred of MEN-I syndrome. The other two patients had a large bronchial carcinoid. The first patient is still in remission now almost 22 years after surgery. In the two other patients GHRH did not normalize completely after surgery and they are now treated with slow-release octreotide. IGF-I normalized in all patients. During medical treatment basal GH secretion remained (slightly) elevated and secretory regularity was decreased in 24 h blood sampling studies. We did not observe development of tachyphylaxis towards the drug or radiological evidence of (growing) metastases. We propose life-long suppressive therapy with somatostatin analogs in cases with persisting elevated serum GHRH concentrations after removal of the primary tumor. Independent parameters of residual disease are elevated basal (nonpulsatile) GH secretion and decreased GH secretory regularity.
Introduction
Acromegaly caused by ectopic extracranial growth hormone-releasing hormone (GHRH) secretion is a very rare disorder occurring probably in less than 1 % of the acromegalic patients [1]. The majority of the GHRH-secreting tumors are bronchial carcinoids. Other GHRH-secreting tumors in decreasing order of occurrence are pancreatic adenomas, gastro-intestinal tumors, thymic tumors, and tumors associated with the MEN-I syndrome [2].
Here we report our clinical experience in three patients with the ectopic GHRH-syndrome derived from a population of over 200 acromegalic patients, diagnosed and treated at the Leiden University Medical Center from 1976 till 2002. Long-term follow-up studies in these patients and results of medical therapy with somatostatin analogs are scarce. The purpose of this report is to expand our knowledge of this rare clinical entity. In addition, we report detailed results of diurnal GH secretion, before and after removal of the GHRH-producing source in order to investigate whether pulsatile and basal GH secretion due to GHRH overproduction differs from that of a primary pituitary somatotropinoma.
Methods
Basal concentrations of hormones, including prolactin, free thyroxin, triiodothyronine, cortisol, testosterone, estradiol, progesterone, IGF-I, and IGFBP3 were measured. In addition the following tests were performed: Oral glucose loading test (75 g), TRH test (200 μg i.v. ), and a GHRH test (50 μg i.v. ) and the following hormones were measured: glucose tolerance test: GH, insulin and glucose at 0, 30, 60, 90 and 120 min, TRH test: TSH, prolactin and GH at −15, 0, 15, 20, 30 45, 60, 90 and 120 min; GHRH test: GH and prolactin at 0, 20, 30, 45, 60, and 90 min. For the 24-h GH secretion profile the patients were hospitalized, and an indwelling i.v. cannula was inserted in a forearm vein, and blood samples were withdrawn at 10-min intervals. The patients were free to move around, but not to sleep during daytime. Meals were served at 0800, 1230 and 1730 h. Lights were turned off between 2200 and 2400 h.
Assays
Plasma GH was measured with a sensitive time-resolved fluoro-immunoassay (Wallac Oy, Turku, Finland). The assay is specific for the 22 kDa GH. The standard was biosynthetic recombinant human GH (Genotropin, Pharmacia & Upjohn, Uppsala, Sweden), and was calibrated against the WHO First International Reference Preparation 80/505 (to convert μg/l to mU/l multiply by 2.6). The limit of detection of this assay (defined as the value 2 SD above the mean value of the zero standard) was 0.01 mU/l (0.0038 ng/ml). The intraassay coefficient of variation varied between 1.6% and 8.4% in the range from 0.01 μg/l to 18 μg/l and interassay coefficient of variation was 2.0–9.0% in the same range.
Total IGF-I was determined by RIA (Incstar, Stillwater, MN) after extraction and purification on ODS-silica columns. The intraassay coefficient of variation was less than 11%. The detection limit was 1.5 nmol/l. Age-related normal data were determined in the same laboratory. The measurement of IGFBP3 was performed by RIA (Nichols Institute Diagnostics, San Juan Capistrano, CA). The limit of detection of this assay was 0.08 mg/l, and the interassay coefficient variation was below 6.8%.
Deconvolution analysis
A multiparameter deconvolution technique was used to estimate relevant measures of GH secretion from the 24-h serum GH concentration profiles, as described previously [3]. Initial estimates of basal GH secretion rate were calculated to approximate the lowest 5% of all plasma GH concentrations in the time series. Peak detection entailed application of 95% statistical confidence intervals to two thirds of all GH secretory peaks considered jointly and individual 95% statistical confidence intervals to the remaining one third smaller pulses, as validated in simulations [4]. The following four secretory and clearance measures of interest were estimated: (1) the number and locations of secretory events; (2) the amplitudes of secretory bursts; (3) the durations of randomly dispersed GH secretory bursts; and (4) the endogenous single component subject specific plasma half-life of GH. It was assumed the GH distribution volume and half-life were time and concentration invariant. The following parameters were calculated: Half-duration of secretory bursts (duration of the secretory burst at half-maximal amplitude), hormone half-life, burst frequency, amplitude of the secretory burst (maximal secretory rate attained within a burst), mass secreted per burst, basal secretion rate, pulsatile secretion rate (product of burst frequency and mean burst mass) and total secretion (sum of basal and pulsatile).
Approximate entropy
The univariate approximate entropy (ApEn) statistic was developed to quantify the degree of irregularity, or disorderliness, of a time series [5]. Technically, ApEn quantifies the summed logarithmic likelihood that templates (of length m) of patterns in the data that are similar (within r), remain similar (within the same tolerance r) on next incremental comparison and has been formally defined elsewhere [6]. The ApEn calculation provides a single non-negative number, which is an ensemble estimate of relative process randomness, wherein larger ApEn values denote greater irregularity, as observed for ACTH in Cushing’s disease, GH in acromegaly, and PRL in prolactinomas [7–9]. In the present analysis, we calculated ApEn with r = 20% of the SD of the individual time-series and m = 1. This choice of parameters affords sensitive, valid and statistically well-replicated ApEn metrics for assessing hormone time-series of this length. ApEn results are reported as absolute values or as the ratio of the absolute value to that of the mean of 1,000 randomly shuffled data series. Ratio values that approach 1.0 thus denote mean empirical randomness.
Copulsatility
Copulsatility between the GHRH and GH time-series was quantified by the hypergeometric (joint binomial) distribution [10]. This program calculates the probability that hormone pulses in time-series occur randomly. We used a time-window of 20 min, with GHRH as leading hormone series. Because details of the secretion characteristics of GHRH are not well established, we estimated significant pulses in both time-series with Cluster, which is largely model-free [11].
Clinical findings at diagnosis and initial treatment
Case 1. A 50-year-old male was referred in 1982 because of recurrent kidney stones, hypercalciuria (24 h urinary calcium excretion between 14.5 mmol and 16.3 mmol, normal upper value 6 mmol/24 h) and hypercalcaemia (serum Ca between 2.86 mmol/l and 2.95 mmol/l, normal values between 2.25 mmol/l and 2.60 mmol/l). The referring internist suspected the patient of having mild acromegaly, because of the coarse facial features. The diagnosis primary hyperparathyroidism was confirmed and the patient underwent parathyroid surgery, and two large adenomas were removed, after which the patient became normocalcaemic until now. The patient had noted increase in size of his feet and hands since several years, but otherwise he had no complaints. Glucose loading decreased GH from 12 mU/l to below 0.5 mU/l (normal value during glucose suppression is below 2.5 mU/l with RIA). Intravenous administration of 200 μg TRH, however, increased GH from 3.5 mU/l to 58.0 mU/l and PRL increased from 7.0 μg/l to 13 μg/l. CT scanning of the pituitary gland with the first generation CT-scanner (in 1983) did not show abnormalities, and CT scanning of the thorax and abdomen also failed to show the presence of a tumor. Because of progressive complaints of fatigue after cure for hyperparathyroidism and strong clinical suspicion of acromegaly, the patient underwent transsphenoidal pituitary exploration. At surgery a small suspect lesion was removed with part of the surrounding pituitary gland tissue. Histology of the lesion was compatible with somatotrope hyperplasia. After the patient had recovered from surgery, CT, MRI and arteriographic studies were repeated, and serum samples were sent to St. Bartholomew’s Hospital, London, UK (Dr L.H. Rees) for GHRH measurement. The abdominal CT-scan showed a 4 cm mass in the middle section of the pancreas (Fig. 1) and the mass was also visible with selective arteriography of the superior mesenterial artery (Fig. 1). The fasting GHRH concentration amounted to 3,810 pg/ml (normal range 10–60 pg/ml). GHRH concentration in the arterial supply to the tumor was 12,470 pg/ml, and in the venous tumor outflow 31,120 pg/ml, while in the systemic venous system the concentration was 8,900 pg/ml. After removal of the pancreatic tumor, peripheral GHRH concentration decreased to normal values of 16–33 pg/ml. In retrospect, the first abdominal CT-scan, 2 years before, already showed the pancreatic tumor with a similar size.
Fig. 1Upper panel shows the abdominal CT at the level of the pancreas and the lower panel the selective arteriography of the superior mesenterial artery
The patient was the index case of a large kindred affected by MEN-I syndrome. Later, a gene mutation was revealed in exon 2 of chromosome 11q13. During long-term follow-up GH and IGF-I concentrations remained normal.
Six years later, in 1988, the patient developed diabetes mellitus, initially treated with oral hypoglycemic drugs and with insulin from 1992 onwards. The patient also had mild bilateral nodular adrenal hyperplasia since 1988, with no evidence of growth during the last recent 10 years. No excess of adrenal (cortex and medulla) hormones or precursors was demonstrable during follow-up. During the last 3 years the plasma level of pancreatic polypeptide (PP) increased to 350 nmol/l (normal < 100 nmol/l). Repeat CT and MRI scanning of the upper abdomen failed to reveal the presence of a pancreatic tumor thus far.
Random serum GH during follow-up ranged from 0.16 mU/l to 1.81 mU/l and IGF-I ranged from 12 nmol/l to 17 nmol/l from 1994 till now (see Fig. 2). All these values are perfectly normal for his age and gender. The last serum GHRH measurement in 2006 was normal with 32 pg/ml.
Fig. 2Follow-up of GH and IGF-I concentration in patient 1 after removal of the pancreatic source of GHRH. Fasting serum GH concentrations are depicted as circles, and IGF-I as triangles
Case 2. A 27-year-old acromegalic female patient was referred in 1993 to our center for octreotide treatment. She had a 5-year history of hyperhydrosis, fatigue, paraesthesias, and acral enlargement. After delivery of a healthy daughter in 1992 she had persisting amenorrhea and galactorrhea. Physical examination revealed mild, although characteristic features of acromegaly. Serum GH concentration was elevated at 99 mU/l and decreased insufficiently to 55 mU/l following oral glucose administration. IGF-I concentration amounted to 86 nmol/l (normal upper level for her age 32 nmol/l) Thyroid and adrenal functions were normal, but prolactin concentration was elevated to 20 μg/l (normal upper limit for females 12 μg/l). Other investigations performed (TRH test, GHRH test, and the i.v. octreotide test) are summarized in Fig. 3. TRH bolus injection (200 μg iv) caused a ∼8-fold increase of GH and a 3.5-fold increase of PRL. Intravenous injection of 50 μg GHRH (1-40) caused a decrease of GH while PRL concentrations remained unchanged. Octreotide (50 μg i.v.) caused a 98% inhibition of serum GH but no effect on PRL. MRI scanning of the pituitary gland showed global enlargement without signs of an adenoma (Fig. 4). Further investigations revealed a large tumor in the right lower lobe of the lung (Fig. 5). The tumor and the pituitary gland were positive on 111In-labeled-octreotide scintigraphy (Fig. 6). The plasma GHRH concentration was increased 50-fold by 2,519 pg/ml (normal values <50 pg/ml).
Fig. 3Dynamic GH (circles) and PRL (triangles) tests in two patients with a GHRH-secreting lung carcinoid before (closed symbols) and after surgery (open symbols). Note the GH increase after TRH (200 μg) administration in both patients, the decrease in serum GH concentration in patient 2 after GHRH (50 μg), and the moderate GH decrease after i.v. octreotide (50 μg)Fig. 4MRI scans of the pituitary gland of patient 2 (left panel) and of patient 3 (right panel) before treatmentFig. 5Chest X-rays of patient 2 (left panel) and patient 3 (right panel), showing the large bronchial carcinoidFig. 6Octreoscans of the patients 2 and 3. In the female patient (left panel) the tumor is seen in the right lower lobe of the lung and also the positive staining of the pituitary gland. The male patient (right panel) had a large tumor in the left lung, without pituitary staining
Under the diagnosis of ectopic GHRH-producing lung tumor (carcinoid) the patient underwent thoracotomy and the tumor was resected completely. The plasma GHRH disappearance profile after removal of the tumor is shown in Fig. 7, but the GHRH concentration did not normalize. GHRH concentrations in simultaneously withdrawn blood samples from the arterial supply to the tumor, venous tumor outflow and the systemic venous system were 1,890, 2,180, and 1,680 pg/ml, respectively.
Fig. 7Decrease in serum GHRH concentration in two patients before and immediately following removal of the lung tumor. Patient 2 is shown by triangles and patient 3 by circles. Note that the GHRH concentration is shown on a logarithmic scale. The GHRH concentration did not normalize in the patients after 2 and 3 days, respectively. Upper normal GHRH is 50 pg/ml
Two weeks after surgery, endocrine investigations revealed a persisting paradoxical increase (9-fold) of GH to TRH, insufficient GH suppression after oral glucose loading (52–16 mU/l) and a GH increase after GHRH injection. Octreotide caused a 98% decrease of GH from 41 mU/l to 0.9 mU/l (see Fig. 3). Treatment with octreotide was started in 1994, about 8 months after surgery, because of persisting elevated GHRH concentration (between 360 pg/ml and 488 pg/ml), increased GH( 31–42 mU/l), insufficient suppression by glucose loading (minimum GH concentration 9.13 mU/l), an increased IGF-I concentration ( 40–43 nmol/l), and lesions in the liver on repeat CT scans, suspect for metastases (one lesion in segments 2 and 8, and two lesions in segment 7).
Case 3. A 27-year-old male was referred in 1997 to our center. He had a 9-year history of hyperhydrosis, acral enlargement and headaches. Examination revealed moderately advanced features of acromegaly. The circulating GH concentration was elevated at 110 mU/l and decreased insufficiently to 52 mU/l following glucose administration, and IGF-I was elevated to 63 nmol/l (normal upper level at this age 32 nmol/l). Thyroid and adrenal functions were normal, but prolactin concentration was elevated to 21 μg/l (normal upper limit for males 6 μg/l). Other investigations performed (TRH test and the i.v. octreotide test) are summarized in Fig. 3. TRH bolus injection (200 μg i.v.) caused a ∼8-fold increase of GH and a 2-fold increase of PRL. Octreotide (50 μg i.v) caused a 94% inhibition of GH (Fig. 3). MRI scanning of the pituitary gland showed a macroadenoma II AE (Hardy classification, modified by Wilson [12, 13] see Fig. 4). He was treated for 6 months with sc octreotide (100 μg tid). The size of the adenoma decreased slightly, but because of the moderate clinical response to medical treatment and failure of normalization of GH and IGF-I, pituitary surgery was advised. One day before surgery a chest X-ray was taken, showing a large parahilar lesion (Fig. 5). CT scanning of this lesion was classified by the consultant pulmonologist and radiologist as a bronchial cyst and apparently unrelated to acromegaly. During pituitary surgery the adenoma was removed completely, but the neurosurgeon remarked that the consistency of the adenoma was firmer than normal. Since we suspected that the lung tumor was potentially a GHRH-producing source, postoperative investigations were focused on this possibility. After surgery, MRI scanning of the pituitary region did not show residual tumor, GH concentrations decreased considerably and PRL concentrations became normal. GH dynamic tests did not completely normalize: GH increased after TRH injection from 4.63 mU/l to 22.1 mU/l and after glucose loading GH decreased from 4.33 mU/l to 2.56 mU/l (normal value below 1 mU/l). During an i.v. octreotide test GH decreased from 6.21 mU/l to 0.85 mU/l, but treatment with this drug was not reinstituted. On 111In-labelled-octreotide scanning the tumor was positive (see Fig. 6).
Under the diagnosis of ectopic GHRH-producing lung tumor (carcinoid) the patient underwent thoracotomy and the tumor was removed completely. GHRH concentrations in the arterial supply to the tumor were 48,290 pg/ml, in the venous outflow 94,000 pg/ml and in the systemic venous system 49,000 pg/ml. After removal of the tumor GHRH concentrations remained slightly elevated, as shown in Fig. 7.
Histopathological studies
Pituitary gland
The removed part of the anterior pituitary gland of patient 1 consisted of hyperplastic cells, immunostaining positively for GH. The removed tissue of the third patient consisted of a mixture of hyperplasia and adenoma formation. The cells stained positively for both GH and PRL.
GHRH-producing tumors
Patient 1. The pancreatic tumor had a diameter of 5 cm. Amorphous material was present between the cells, staining as amyloid. On electronmicroscopy, the cells contained neurosecretory granules with a diameter between 100 nm and 200 nm. The tumor stained positively, but sparsely for somatostatin, insulin and glucagon and negatively for cytokeratine, vimentine, neurofilaments, desmine and GH. In the removed part of the pancreas three additional small adenomas with identical staining characteristics were present.
Patient 2. The diameter of the removed lung tumor was 5 cm, and contained centrally calcified material. The cells were layered in nests, slightly polymorphic, but without mitotic figures. The tumor cells stained positively for keratine, vimentin, synaptophysin, SCCL (N-CAM), leu 7, and chromogranin and negatively for calcitonin, GH, pancreatic polypeptide, insulin, prolactin, somatostatin, gastrin, ACTH, CEA, and neurofilaments.
Patient 3. The dimensions of the tumor were 8 × 7×7 cm3. The tumor showed clear proliferation of neuroendocrine cells with three mitotic figures per high power field, staining positively for NSE, CD56, and synaptophysin and negatively for keratine, chromogranin, serotonin, somatostatin, prolactin, insulin, glucagons, gastrin, ACTH, GH, and insulin.
Somatostatin analog therapy
After removal of the lung carcinoid, patients 2 and 3 received long-term treatment with octreotide, because GHRH was not normalized. Patient 2 received the medication via chronic sc infusion (300 μg/24 h) till the end of 1998. Her complaints quickly disappeared and the menstrual cycle was restored. GH and IGF-I concentrations normalized and are detailed in the left panel of Fig. 9. The size of the pituitary gland decreased markedly (Fig. 8). PRL concentration also normalized from 15.2 ± 0.2 μg/l to 6.8 ± 0.2 μg/l. From 1999 onwards the medication was changed to octreotide long-acting repeatable (Sandostatin LAR), 20 mg in 4-weekly i.m. injections. Growth hormone and IGF-I concentrations remained unchanged (see Fig. 9). During chronic treatment with the short-acting octreotide formulation treatment was withheld several times for GHRH measurement. Invariably, the concentration was (slightly) elevated (range 116–363 ng/ml) so that in combination with the radiological suspicion for liver metastases, treatment is continued until now. During treatment with octreotide long-acting repeatable (Sandostatin LAR) GHRH concentration ranged from 63 ng/ml to 108 ng/ml. Repeat CT scanning of the liver during the successive years demonstrated the stabilization of size and number of lesions until now.
Fig. 8MRI of the pituitary gland of patient 2 during therapy with octreotide. These pictures were taken after 12 months treatment with 300 μg octreotide given as a continuous subcutaneous infusionFig. 9GH (circles) and IGF-I (triangles) concentrations during long-term treatment with octreotide. Patient 3 received only the long-acting repeatable form, but patient 2 was treated initially with chronic sc octreotide infusion. The time of change into the slow-release formulation is indicated by the arrow. Normal values for IGF-I for this age: <32 nmol/l. Normal value for random GH < 5 mU/l
After removal of the carcinoid tumor in patient 3, GHRH, GH, and IGF-I concentrations remained elevated, and therefore he was also treated with the long-acting repeatable octreotide (20 mg/4 weeks). GH and IGF-I concentrations are shown in Fig. 9, right panel, showing clinically normal values. Repeat investigations with CT, 111In-labeled octreotide and 131I-MIBG however, did not reveal suspect (liver) metastases. At the end of 2001, GHRH was elevated to 1,725 ng/ml, so that thereafter the dose of Sandostatin was increased to 30 mg at 4-weekly intervals, which normalized IGF-I concentrations. Last year the patient stopped medication. Subsequently, IGF-I levels increased, as shown in Fig. 9. GHRH concentration became larger than 2,000 pg/ml. Detailed localizing studies with octreotide and CT revealed a small metastasis in the superior anterior mediastinum for which surgery is scheduled.
Fig. 10Serum GH concentrations obtained by 10 min blood sampling for 24 h. Patient 2 was studied before therapy and after surgical removal of the lung tumor. Note that GH concentration decreased more than 10-fold and that the secretion pattern became more regular, but basal GH concentration remained slightly elevated. The left lower panel represent the profile of patient 3 after pituitary surgery, but before removal of the carcinoid tumor. Nadir values were clearly increased. After thoracic surgery and under octreotide treatment GH secretion pattern visually normalized
GH secretory profiles
Detailed GH secretory profiles were obtained before removal of the GHRH-producing bronchial carcinoids (Fig. 10). The secretory patterns were irregular, showing increased burst frequency and increased basal concentrations. The GH secretory parameters as estimated by multiparameter deconvolution are listed in Table 1 with normal values obtained in healthy adults of comparable age. The distinct and persisting abnormality in both patients after removal of the carcinoid and while on octreotide treatment was the increased basal (nonpulsatile) GH secretion.
Table 1Deconvolution of the 24 hour serum GH profiles in patients with ectopic GHRH syndrome and controlsPatient 2 before surg.Patient 2 after surg.Female controlsPatient 3 before surg. Patient 3 after surg. Male controlsJaffe’s patientVance’s patientPulse frequency (no/24 h)301617 (14–21)302412 (7–14)3021Half-life (min)15.515.912.9 (12.0–15.5)15.615.717.5 (15.2–19.7)24.616.2Pulse half-duration (min)20.932.927.7 (22.7–29.8)25.323.225.3 (19.5–34.4)30.541.3Pulse height (mU/l /min)4.920.4420.387 (0.198–0.881)0.2270.1600.141 (0.087–0.422)6.01.87Pulse mass (mU/l)10114.410.2 (7.3–18.5)5.663.684.44 (2.42–9.55)18176.5Basal secretion(mU/l /24 h)2,26130.29.1 (5.5–17.6)18.014.93.4(1.4–5.3)3,9004,60Pulsatile secretion (mU/l /24 h)3,047230173 (122–312)1708843.5 (17.8–103)5,4001,600Total secretion(mU/l /24 h)5,309260182 (132–325)18810347 (19.7–107)9,3002,060Blood samples were taken at 10-min intervals for 24-h and analyzed by multiparameter deconvolution. The female patient (no. 2) was studied before surgical removal of the GHRH-secreting bronchus carcinoid and repeat sampling study was done after thoracic surgery under octreotide LAR. The male patient (no. 3) was studied first after adenomectomy of the pituitary tumor, but before thoracic surgery. The second sampling study was performed after removal of the bronchial carcinoid during octreotide-LAR treatment. The serum profiles of the patients reported in literature were digitized and deconvoluted with the assay precision according to the authors. The GH data were subsequently transformed from μg-mass units into mU using the conversion factor 2.0. Reference values were obtained in nine males and 10 females healthy controls. Values shown are medians and 95% confidence intervals between brackets
In addition, the secretory regularity was quantified with the approximate entropy statistic, ApEn. In patient 2, ApEn was 1.256 before removal of the carcinoid, and after surgery and under somatostatin analog treatment ApEn was still increased to 0.686 (median normal for women 0.400, 95% confidence interval 0.300–0.440). In patient 3 ApEn also remained abnormal: preoperative 1.256 and after surgery 0.687, median normal for males 0.240, 95% confidence interval 0.160–0.350 (see Table 2). In addition, the serum GH profiles of two patients reported in literature were digitized and analyzed in a similar way [14, 15]. The results of these analyses are also displayed in Table 1. In these male patients basal GH secretion was much higher than in our healthy controls and pulsatile secretion was augmented via increased pulse frequency and pulse amplitude. ApEn of GH secretion was 1.533 in Jaffe’s patient and 1.248 in the patient reported by Vance (increased SD scores by 8- and 6-fold, respectively). ApEn for the serum GHRH-time series were 1.759 and 1.223, respectively. Copulsatility of the GH and GHRH hormone series was highly significant in both patients (P < 0.0001).Table 2Approximate entropy of GH secretion in ectopic GHRH syndromePatientBefore removal of the ectopic GHRH sourceAfter surgery and during octreotide treatmentReference values, Median and 95% CINo. 2 (female)1.2560.6860.400 (0.300–0.440)No. 3 (male)0.8420.5610.240 (0.166–0.350)Jaffe’s patient1.5330.240 (0.166–0.350)Vance’s patient1.2480.240 (0.166–0.350)Calculations were performed on GH data series consisting of 145 samples obtained at 10 min intervals during 24 h. Normal values were derived from nine males and 10 females healthy controls
Discussion
In this study we described in detail the clinical and biochemical characteristics of three patients with ectopic extracranial GHRH secretion diagnosed in a series of about 200 acromegalics investigated and treated in our center during the last 25 years. The incidence in the present series agrees with that mentioned in literature [1, 16]. Faglia summarized the clinical findings of 39 reported acromegalic patients with proven ectopic GHRH secretion [17]. Subsequently, van den Bruel and colleagues [18] mentioned 52 reported patients in 1999, including their own patient and since then 14 other patients have been reported, bringing the total number reported to 66 patients [19–32]. From a conservative estimation of the total number of newly diagnosed acromegalic patients in Europe, Japan and the USA, it is evident that most patients with ectopic GHRH syndrome are either not reported or remain undiagnosed.
The criteria for demonstration of ectopic extracranial GHRH-induced acromegaly are summarized by Losa and von Werder [2], and include the presence of high circulating concentration GHRH by specific radioimmunoassays, the presence of GHRH in the tumor, the presence of mRNA for GHRH by in situ hybridization and/or a significant arterio-venous gradient across the ectopic source. The second requirement to be fulfilled is the reversibility of acromegaly after complete removal of the ectopic-hormone producing tumor. All our patients met at least two criteria, although the tumors were not investigated for the presence of GHRH.
The clinical symptomatology in ectopic GHRH-induced acromegaly is not different from that of the primary pituitary adenomatous form. However, symptoms due to the underlying neoplasm or cosecretion of other substances by the tumor might suggest the ectopic origin of acromegaly [33]. Specific dynamical tests for GH excess do not allow classification with certainty in either category, although most patients with ectopic GHRH syndrome exhibit a paradoxical increase of GH after TRH and glucose (i.e. >50%) and a blunted GH rise (<100%) after exogenous GHRH injection [33]. In addition, most patients also exhibit a moderate increase in serum prolactin concentration, which regresses after removal of the ectopic GHRH source, supporting the notion that GHRH is directly responsible for the hyperprolactinemia, a finding also present in hGHRH-transgenic mice [34]. The patients reported here all exhibited GH increase after TRH, and two had mild hyperprolactinemia. After surgery, hyperprolactinemia normalized, but GH still increased after TRH administration, suggesting that GHRH was still being produced by tumor remnants or metastases in two patients (nos. 2 and 3).
The most frequent source of ectopic GHRH is the bronchial carcinoid, followed by pancreatic islet tumors [17], as we also found in the present evaluation of our patient series. The pancreatic tumor in the first patient was found by CT-scanning and MRI. The large bronchial carcinoids were easily seen by chest X-ray examination, and showed uptake of radio-labeled octreotide, thus demonstrating in vivo the presence of somatostatin receptors. Although positive somatostatin receptor-scintigraphy is usually found in carcinoids in general, its demonstration in ectopic GHRH tumors is limited. Nevertheless, this examination could be particularly useful in the diagnostic phase of patients with suspected, but not yet proven, ectopic GHRH secretion.
Histological investigation of surgically removed pituitary tissue revealed pituitary hyperplasia in most cases (i.e. with an intact reticulin fiber network), but in other patients adenomatous transformation can be found [35]. Indeed, the histological findings in hGHRH transgenic mice resemble those in the human: in young animals diffuse hyperplasia was found, while those of older mice showed adenomas [34, 36]. The pathological changes in the pituitary gland are the result of GHRH per se and not of GH, since hyperplasia and tumor formation still occur in the absence of GH signaling [37]. A hypothalamic GHRH-producing tumor (gangliocytoma) is generally associated with a pituitary adenoma rather than with pituitary hyperplasia and is attributed to a much higher (assumed) local (pituitary) concentration of GHRH or the presence of other growth stimulating factors [38]. In addition, the combination of an intrasellar GHRH-containing gangliocytoma and a somatotropinoma has been described in rare patients [39, 40]. In two of our patients pituitary histology was available: one patient (no. 1) showed hyperplasia in the absence of an enlarged gland, while histology in the other patient (no. 3) showed a mixture of hyperplasia and adenomatous transformation.
Neuroimaging studies of the pituitary gland are variable: in about half of the patients no tumor or a slight enlargement of the sella is detected, while in other patients intrasellar adenomas or macroadenomas with suprasellar extension are found [18]. The CT-scan of patient 1 was normal and MRI scanning in patient 2 showed diffuse enlargement of the pituitary gland without a clear adenoma and with diffuse uptake of gadolinium DTPA. In patient 3 asymmetric enlargement was present, suggestive of a macroadenoma, but uptake of gadolinium DTPA throughout the whole pituitary gland was diffuse, illustrating the variability of imaging results. Although the pituitary MRI studies in the ectopic GHRH syndrome are non-specific, in the absence of a clear adenoma, ectopic GHRH secretion should be considered and appropriate investigations installed to prevent unnecessary pituitary surgery. On the other hand, primary medical treatment for acromegaly in these patients might obscure the real cause.
Carcinoids, especially of the lung and gastrointestinal tract constitute about two-thirds of all cases reported so far, followed by pancreatic islet cell tumors [17, 33]. Metastases of carcinoid tumors were present in about 30%, a frequency similar to lung carcinoids in general [35]. Two of our patients had a large primary lung carcinoid, but had no histological proven metastases. Because serum GHRH concentration did not normalize after removal of the GHRH source we assumed (liver) metastases.
Immunochemistry of the GHRH-secreting tumors has regularly demonstrated cosecretion of other hormones, including gastrin, gastrin-releasing peptide, calcitonin, pancreatic polypeptide, VIP, glucagon, insulin, and somatostatin, pointing to the multihormonal nature of these tumors. Particularly, cosecretion of somatostatin may modify the clinical picture and severity [31–44].
Only few patients with a GHRH-producing pancreatic islet cell tumor and with MEN I syndrome have been described [41, 43, 44]. In agreement with our patient 1, they had multiple tumors; however, the pancreatic tumors in our patient showed an identical immunohistochemical profile in contrast with the other reported patients. The tumor stained positively for somatostatin, which might explain the only moderately increased GH concentration and possibly also the normal GH response to glucose loading, as alluded to above. In the follow-up of more than 20 years, no recurrence of acromegaly occurred, but the (modest) increase of pancreatic polypeptide could point to the development of a new pancreatic adenoma.
GH secretory characteristics of two patients were investigated before removal of the GHRH source. We established that pulsatile and non-pulsatile (basal) secretion, pulse frequency and secretory regularity (ApEn) were comparable to untreated acromegalic patients with primary pituitary adenomas [8, 45]. Because of the highly significant copulsatility of GHRH and GH in two other patients reported in literature, it is reasonable to postulate that pulsatile GH secretion in these patients was generated via episodic GHRH secretion of the primary tumor, while in patients with a pituitary GH-secreting adenoma pulsatile secretion is probably a tumorous feature [8].
Approximate entropy calculations of serum GHRH profiles disclosed high values, suggesting highly irregular secretion, a feature common with many other hormone-secreting tumors [7–9]. It is reasonable to assume that the irregular secretion of GH in the four patients who were investigated till now was mainly the result of the irregular GHRH-input signal, and analogous to the irregular cortisol secretion driven by ACTH in pituitary-dependent Cushing’s syndrome [7, 46]. Nevertheless, part of the irregularity of GH secretion might also be due to increased GHRH per se, since GHRH infusion in healthy subjects augments irregular GH secretion [47]. After surgery and on effective octreotide therapy aimed at normalizing IGF-I, GH secretion became almost normal. GH secretion remained however irregular (increased ApEn), notwithstanding the increased feedback signal of octreotide, and the diminished forward input signal of GHRH by octreotide [48, 49]. Similarly, octreotide treatment in (classical) acromegaly represses secretory-burst mass and non-pulsatile secretion but does not restore event frequency or orderly GH secretion [50]. Collectively, these results suggest that even a modestly increased GHRH concentration can maintain irregular GH secretion in ectopic acromegaly.
Therapy
Surgical removal of the GHRH source is obviously the treatment of choice and results in the resolution of acromegaly when no metastases are present, as we observed in the first patient with more than 20 year follow-up. In the presence of metastases octreotide treatment is often, but not invariable, successful in suppressing GH and GHRH, because of its dual action on the pituitary gland and the GHRH-secreting tumor [51]. Most published reports concerned subcutaneously administered octreotide mostly by three daily injections and in a minority by chronic subcutaneous infusion [48, 52] and summarized by van den Bruel et al. [18]. In the majority of patients GH decreased substantially, but IGF-I remained elevated in eight of 13 patients in whom IGF-I was measured. The serum concentration of GHRH generally remained elevated [2], as we found in patient 3. Publications of treatment results with the slow release formulation of octreotide and lanreotide are scarce and follow-up short-term. We could find only four cases in literature: three patients were treated with lanreotide and one with octreotide-long acting repeatable (octreotide LAR) [19, 21, 24, 25]. In the present study we reported the long-term effect of octreotide-LAR treatment in two patients, which resulted in suppressed GH secretion and normalized IGF-I concentration in both patients, but with still slightly elevated GHRH concentrations, as long as the patients continued suppressive therapy. The latter finding is indeed often mentioned in other reports as well, as discussed above. Other treatment modalities applied in single cases are the use of a GHRH-receptor antagonist and chemotherapy in a patient with severely metastasized disease [15, 53].
During octreotide treatment no visible growth of (eventual) metastases was noted for many years with high-sensitivity liver CT scanning and CT scanning of the thorax in our patients, which suggests that in selected cases octreotide can inhibit growth of metastases from a carcinoid tumor. In addition, pituitary hyperplasia, the likely cause of the preoperative enlargement in patient 2, slowly disappeared completely, as previously described in other patients under medical therapy or after complete surgical removal of the GHRH source [27, 54]. Nevertheless, in patient 3 GHRH levels increased slowly, suggesting growing metastasis under octreotide restraint. Recently, we could localize a suspect lesion in the anterior mediastinum.
GHRH and its receptor are normally present in several organs, including the ovary and testis, playing a role in the regulation of steroidogenesis, and the pancreas [55–57]. It has been found that some GHRH-producing carcinoids also express GHRH-receptors, which may stimulate tumor growth via an autocrine mechanism [22]. Particularly interesting was the description of a single acromegalic patient whose large pituitary adenoma co-expressed GHRH-receptor and GHRH together with a 100-fold increased plasma GHRH concentration. After pituitary adenomectomy plasma GHRH normalized. It is likely that the high local concentration of GHRH contributed to the growth of the adenoma [58].
GHRH and its receptor are also expressed in cancers of the breast, ovary, and endometrium and small-cell lung cancer, and may function as a paracrine/autocrine growth factor in regulating local IGF-I and/or IGF-II secretion. Other studies have shown that GHRH receptor-antagonists inhibit the growth of colorectal, prostatic, mammary, lung, and pancreatic cancers, partly by direct suppression of IGF-I and IGF-II production, or acting directly without influencing these growth factors [59–63]. When more potent GHRH-receptor antagonists will become available for clinical application patients with advanced disease may benefit from this new approach, because they respond at best to somatostatin analogue therapy by suppressing GH secretion and partly that of GHRH, but the tumor and metastases will usually grow with fatal outcome.

acromegaly
ectopic ghrh
octreotide
gh secretion
approximate entropy