Behavioral genomics of honeybee foraging and nest defense

The honeybee has been the most important insect species for study of social behavior. The recently released draft genomic sequence for the bee will accelerate honeybee behavioral genetics. Although we lack sufficient tools to manipulate this genome easily, quantitative trait loci (QTLs) that influence natural variation in behavior have been identified and tested for their effects on correlated behavioral traits. We review what is known about the genetics and physiology of two behavioral traits in honeybees, foraging specialization (pollen versus nectar), and defensive behavior, and present evidence that map-based cloning of genes is more feasible in the bee than in other metazoans. We also present bioinformatic analyses of candidate genes within QTL confidence intervals (CIs). The high recombination rate of the bee made it possible to narrow the search to regions containing only 17–61 predicted peptides for each QTL, although CIs covered large genetic distances. Knowledge of correlated behavioral traits, comparative bioinformatics, and expression assays facilitated evaluation of candidate genes. An overrepresentation of genes involved in ovarian development and insulin-like signaling components within pollen foraging QTL regions suggests that an ancestral reproductive gene network was co-opted during the evolution of foraging specialization. The major QTL influencing defensive/aggressive behavior contains orthologs of genes involved in central nervous system activity and neurogenesis. Candidates at the other two defensive-behavior QTLs include modulators of sensory signaling (Am5HT7 serotonin receptor, AmArr4 arrestin, and GABA-B-R1 receptor). These studies are the first step in linking natural variation in honeybee social behavior to the identification of underlying genes.
Introduction
Mapping genes for natural variation in behavior
The need to understand within- and between-population variation in behavior, especially social behavior and its evolution, remains a central theme in biology. Honeybee societies have facultatively sterile female workers and a specialized workforce. Honeybee social behavior is dramatically demonstrated by the efforts of worker bees collecting and hoarding pollen and nectar, and mass stinging responses in defense of the nest. Division of labor within a honeybee nest is a consequence of age-related changes in physiology plus genetic variation within the colony for tendency to perform specific tasks (Page and Robinson 1991). A number of studies have demonstrated genetic variation within and between honeybee populations in specialization for water, nectar, and pollen collecting (reviewed in Page et al. 2000) and variation in defensive responses (reviewed in Breed et al. 2004). The genetic architectures of these two task sets are more thoroughly characterized than other behavioral traits in the honeybee. For these reasons, we sought to determine whether it is possible to define, within a manageable number of candidate genes, regions influencing these behaviors.
Quantitative trait locus (QTL)-mapping studies provide information about the genetic architecture of a trait that cannot be determined by other methods. This information includes estimates for the number and location of loci influencing population variation in the trait, the mode of inheritance of these loci (dominance, epistasis, or imprinting effects), and amount of phenotypic variance each locus accounts for (Lander and Botstein 1989). But QTL studies do not have a good track record for isolation of causal genes. For example, genes for only 20 of 2,000-rodent QTLs have been cloned (Flint et al. 2005). Identifying genes responsible for naturally occurring phenotypic variation is especially challenging for behavioral traits where complex interactions of genes and the environment are expected (Flint 2003; Plomin and McGuffin 2003; Arnholdt and Mackay 2004; Goldman et al. 2005).
The endgame in determining nucleotide sequence variation responsible for natural variation in honeybee behavior will require identification of QTLs, confirmation in independent crosses, fine-scale mapping, expression assays, and finally, experimental modulation of gene expression or complementation tests (Mackay 2004). The biggest problem in taking a map-based approach to isolation of genes is that confidence intervals (CIs) for QTL location are usually quite large. The size of the CI primarily depends on the number of individuals in the mapping population, the number of genetic markers scored, the magnitude of effect that the QTL has on the phenotype, and the recombination rate (numbers of crossovers per unit of chromosomal distance). The first two factors, sample size and marker density, are important for reducing CIs, but they are subject to diminishing returns as more individuals or more markers are added to the experiment. The effect of the QTL is a major concern because QTLs often account for only a small proportion (often <5%) of phenotypic variance. The QTL effect can only be increased up to a certain level by improving the phenotypic assay and using the most appropriate cross (Darvasi 1998; Arnholdt and Mackay 2004). On the other hand, recombination rate can have a large effect on the size of chromosomal regions covered by CIs because the physical size scales inversely with recombination rate. For this reason, fine-scale mapping studies are often designed to effectively increase recombination by taking advantage of historical recombination events through linkage-association studies in populations or by developing multigeneration, recombinant inbred lines (Darvasi 1998). In the honeybee, map resolution is enhanced by a meiotic recombination rate that currently ranks highest among metazoans (Hunt and Page 1995; Solignac et al. 2004), so that large genetic distances correspond to relatively small physical regions containing few genes.
In this study, we review what is currently known about the genetics of honeybee foraging and defensive behaviors, and how a map-based approach leads us to a manageable number of candidate genes that seem to fit what is known of the behavioral patterns. Information from the draft honeybee genome sequence (Honeybee Genome Sequencing Consortium, HGSC 2006) was used to delimit sets of candidate genes flanked by marker sequences that were identified in prior QTL studies, and expression data from candidate genes provided additional information on likely candidates.
Behavioral genetics of foraging
Foraging specialization involves two components: the onset of foraging that establishes division of labor between foragers and bees that perform tasks inside the hive and the subsequent bias in foraging for either pollen or nectar. These components are related. Bees from lines selected for storing more surplus pollen (hoarding) are more likely to initiate foraging earlier in life and to specialize in pollen collection than bees from lines selected for low pollen hoarding. These associations are also linked to sensory response in that bees selected for higher levels of pollen hoarding are more responsive to low concentrations of sugar (less discriminatory) when tested with the proboscis extension assay (Fig. 1 and Page et al. 1998). This link between sucrose responsiveness and the task of pollen foraging is also present in “wild-type” bees that have not been selected for pollen hoarding (Scheiner et al. 2004).
Fig. 1Genetic and phenotypic associations involved in foraging division of labor. Arrows indicate significant correlations between phenotypic traits at the levels of behavior, hormonal signaling, and development and associations between traits and genotypes at specific QTLs (pln 1–4). Colored lines indicate relative titers of specific hormones. The picture illustrates a method for determining the threshold concentration of sugar that a bee will respond to by extending its proboscis or tongue
QTLs were mapped based on whole-colony behavioral traits, and QTL effects were subsequently confirmed based on the behavior of individual bees. Three “pollen” QTLs, designated as pln-1, pln-2, and pln-3, were detected (Hunt et al. 1995; Page et al. 2000) based on the quantity of pollen in colonies from a backcross population derived from high and low pollen-hoarding strains. Association of marker alleles near the QTLs with individual foraging traits within single backcross families of bees confirmed the effects of pln-1, pln-2, and pln-3 on behavior. Pln-1 and pln2 were associated with the size of the pollen loads collected by workers (Fig. 2a and b). Pln-2 and pln-3 were shown to influence the discrimination for the sugar concentration of the nectar collected (Hunt et al. 1995; Page et al. 2000; Fig. 2b,c). Subsequent association studies using a candidate gene AmFor as a marker have mapped and confirmed an additional QTL region designated as pln-4 (Rueppell et al. 2004). This QTL maps to about 50 cM from pln-1. Allelic variation and pleiotropic effects of these QTLs have been associated with sucrose responsiveness and age at onset of foraging (Fig. 1 and Rueppell et al. 2004, 2006).
Fig. 2QTLs were mapped based on the amount of pollen stored in combs of colonies and confirmed based on individual behavior. Solid bars represent linkage groups with markers. Markers used in confirmation studies are shown. Orthologs of fly genes are indicated with arrows. Dashed lines indicate 97% CIs. Sequenced markers are underlined. No figure is shown for pln-4 because it was mapped by association to one marker (AmFOR) rather than by interval mapping. aPln-1 Map on the left is the localization of the QTL based on colony pollen stores. Map on the right is based on response thresholds to sucrose of individual worker bees. bPln-2QTL map on the left is based on colony pollen stores in a cross between European strains and the map on the right is based on a European by African strain cross. cPln-3QTL map is based on colony pollen stores
Preferential foraging for either a nectar or protein source such as pollen are sequential parts of the gonotrophic cycle of many insect females. When nonreproductive, females tend to forage for nectar as a carbohydrate source for maintenance. When reproductively active, insects such as solitary bees and mosquitoes seek protein that is incorporated into eggs (Amdam et al. 2004). Although worker honeybees are facultatively sterile, they can produce eggs if their ovaries develop sufficiently as happens in the absence of a queen. Amdam et al. (2004, 2006) hypothesized that remnants of the ancestral gonotrophic cycle and the correlated foraging behavior remain and “drive” foraging behavior (Fig. 1). In support of this hypothesis, it was established that workers from the high pollen-hoarding strain are characterized by elevated titers of the conserved yolk precursor vitellogenin and have larger and more active ovaries than low strain bees. It was also found that bees that were unselected for pollen hoarding but had enlarged ovaries foraged earlier in life showed a preference for pollen foraging and collected nectar of lower concentration than bees with fewer ovarioles. Thereby, “wild type” bees show the same correlated phenotypes that differ between high and low pollen-hoarding strains (Amdam et al. 2006).
In honeybees, ovariole number is determined during the third larval instar through a nutrient-dependent endocrine signaling cascade. The endocrine factors, juvenile hormone and ecdysteroids, are involved in the initiation of vitellogenin expression at adult emergence, and vitellogenin and juvenile hormone interact during adult life to affect sensory responsiveness and onset of foraging behavior (Guidugli et al. 2005). In solitary insects, endocrine cascades involving juvenile hormone and ecdysteroids have pleiotropic effects on sensory tuning, yolk protein production, ovarian physiology, and life span (Amdam et al. 2004; Flatt et al. 2005; Guidugli et al. 2005). This hormonal pleiotropy appears to be regulated by upstream signaling through the insulin/insulin-like signaling (IIS) pathway (Claeys et al. 2001; Flatt et al. 2005; Tu et al. 2005). The association between traits in high and low pollen-hoarding bees, therefore, suggests that honeybee foraging division of labor has evolved from an ancestral reproductive regulatory network involving IIS. With knowledge of the association between components of physiology and foraging behavior, we expected an overrepresentation of genes involved in IIS and ovarian development within the CIs for “pollen” QTLs.
Behavioral genetics of defensive behavior
Honeybee defensive behavior is not as thoroughly characterized as foraging behavior in terms of correlated physiological and sensory traits. Honeybees exhibit defensive behavior near the nest, but highly defensive bees may pursue for considerable distances away from the nest. Defensive behavior involves at least two tasks: guarding behavior at the hive entrance and flying out and stinging. Guards specialize in exploratory behavior in the nest entrance. They learn to recognize the hydrocarbon blend in the cuticles of their nestmates by olfaction, and they reject non-nestmates by biting or stinging. Only 10–15% of workers have been observed to guard the entrance during their lifetime (Moore et al. 1987). Both the number of days that individuals in a colony persist at guarding and the number of bees guarding the nest entrance correlate with the intensity of the stinging response (Arechavaleta-Velasco and Hunt 2003; Breed et al. 2004).
Multiple sensory modalities influence stinging behavior. A moving visual stimulus usually is necessary to release stinging behavior (Free 1961). Substrate vibrations also increase the chance of a mass stinging response. Alarm pheromone also is an important component of colony defense. This pheromone blend is released from the sting apparatus during the act of stinging and as guards extrude their stings at the colony entrance in response to relevant stimuli. A transient increase in metabolic rate occurs after exposure of bees to alarm pheromone, and this increased rate genetically correlates with the defensiveness of colonies (Southwick and Moritz 1985; Moritz and Southwick 1987; Andere et al. 2002). Although the alarm pheromone components vary with strains of bees, QTLs influencing this variation were distinct from QTLs influencing stinging behavior (Hunt et al. 1999, 2003). Defensive strains of bees respond more quickly to all of these stimuli.
Crosses involving highly defensive African-derived honeybees and low-defensive European races were used to map putative “sting” QTLs based on colony-level stinging assays at hive entrances (Hunt et al. 1998). Subsequent crosses with stocks unrelated to the first studies confirmed that three “sting” QTLs affect individual guarding behavior because guards from a backcross family were more likely to have the allele from the defensive parent of the F1 queen mother than were sisters chosen at random (Fig. 3; Arechavaleta-Velasco et al. 2003). These sting QTLs also were associated with higher activity levels of colonies, which was assessed as their tendency to fly up or to sting when colonies were opened (Hunt et al. 1998). The QTL that had the largest effect on the phenotypic variance of colony stinging responses, sting-1, was shown to influence individual stinging behavior in two independent studies (Guzmán-Novoa et al. 2002; Arechavaleta-Velasco et al. 2003). Higher activity levels, faster stinging responses, and greater sensitivity to stimuli exhibited by high-defensive strains suggest that sensory signaling pathways and heightened neuronal activity in the central nervous system (CNS) are involved in the defensive response, so we searched the QTL CIs for conserved genes with neuronal functions.
Fig. 3Genetic and phenotypic correlations in defensive responses and specific QTLs. Arrows indicate significant associations between QTL genotypes and behavioral traits and between individual guarding behavior and colony stinging response
Materials and methods
QTL mapping and confirmation
QTLs’ influencing traits related to foraging behavior or defensive behavior were mapped and confirmed previously (Hunt et al. 1995, 1998; Page et al. 2000; Rueppell et al. 2004, 2006), but new analyses were performed on the data to include additional markers, and in addition, “sting” QTLs were reanalyzed by combining two traits as stated below. Maps were based on crosses that are appropriate for a haplodiploid, colonial species. For QTL detection, haploid drone progeny of an F1 queen were each backcrossed to sister queens (that were daughters of a single haploid drone). Colony phenotypes were correlated with inheritance of paternal marker alleles. Whole-colony phenotypes resulted from the behavior of many individuals, such as the number of stings in a leather patch in 1 min or the area of wax combs containing stored pollen. Confirmation studies all involved the use of F1 queens backcrossed to single drones and analyses of worker progeny for individual behavior and genotypes. Linkage maps that were used to identify candidate genes were constructed with JoinMap (3.0) and MapManager QTX software using the Kosambi mapping function (Van Ooijen and Voorrips 2001; Manly et al. 2001), and interval mapping was performed with MapQTL (v. 4.0; Van Ooijen et al. 2002). Interval mapping was performed as previously described, except that new analyses were used to map defensive-behavior QTL that combined two sets of phenotypic data. Individual z scores for the two correlated traits, the number of stings in 1 min, and ratings for the degree to which bees flew up at the beekeeper during colony manipulations were averaged to produce composite z scores used for interval mapping. The z scores were calculated using the formula z = (y−μ)/δ, in which y is the phenotypic value, μ is the mean, and δ is the standard deviation. This provides a new trait value with a mean of zero and standard deviation of one. The colonies were rated on a relative scale of one to five based on the researchers’ experience for the tendency of bees to fly up during colony manipulations. Ratings from two observation periods were averaged. This analysis resulted in somewhat reduced CIs and higher LOD scores for the three QTLs (3.84, 2.25, and 2.39, respectively for sting1, sting2, and sting3). A map with 1,154 markers was used to locate candidate genes for “sting” QTLs, and two maps, each with about 400 markers, were used to locate “pln” QTLs. Markers in common between the dense map and other maps were used to interpolate the location of pln QTL and align QTL CIs with the physical map because more cloned markers were available from the dense-map population.
Simple interval mapping was used rather than multiple factor interval mapping to determine CIs to have a more conservative (inclusive) search for candidate genes. The genomic region within 1.5 LOD value of the LOD-score peak was used to define each CI for QTL location. This corresponds to an approximate 97% CI for QTL location in a dense-marker map (Dupuis and Siegmund 1999). The LOD score is a log-likelihood estimator, for the probability that a QTL influencing the trait is present at a given map position (Lander and Botstein 1989). The honeybee genome assembly (v. 3.0) and positions of sequenced markers were used to identify predicted peptides that were evaluated for likely gene function.
Cloning and sequencing marker fragments
Sequences derived from DNA markers were obtained to integrate physical and genetic maps. More than 300 marker fragments not only from (primarily) amplified fragment length polymorphic markers (AFLPs), but also from random amplified polymorphic DNAs (RAPDs) and microsatellites linked to behavioral QTLs and throughout the genome, were cloned and sequenced. The first step in cloning was the excision of fragments from gels. For AFLPs from polyacrylamide gels, products were re-amplified and resolved on agarose gels to verify correct size before cloning with the TOPO-TA cloning kit and the pCR4-TOPO vector (Invitrogen, Carlsbad CA). RAPD marker fragments were excised from agarose gels and cloned into the same vector. Multiple sequences were obtained from each clone, and the consensus sequence was aligned with the genomic sequence scaffolds (HGSC 2006) using the nucleotide–nucleotide basic local alignment search tool (blastn) algorithm with Pymood BLAST software (Allometra, Davis CA).
RACE and cDNA cloning
Before expression analyses by quantitative real-time polymerase chain reaction (qRT-PCR), cDNA cloning was performed to confirm sequences and the gene prediction (location of introns and exons) and to provide information on sequence variation within some of the candidate genes. In the case of the serotonin receptor, this process provided complete sequence of the gene by making primers based on sequence of a putative G protein-coupled receptor (GPCR). Rapid amplification of cDNA ends (RACE) and cloning was performed using kits and manufacturers’ instructions. Total RNA was extracted from individual bees using the RNAqueous kit (Ambion, Austin TX). The cDNA was synthesized using the SMART PCR cDNA Synthesis kit (SMART, simple modular architecture research tool, BD Biosciences, Palo Alto CA). The cDNA clones were obtained using the TOPO-TA kit and the pCR4-TOPO vector (Invitrogen). Clones were sequenced from multiple worker bees. Several sequence reads were obtained from each clone.
Comparative bioinformatics
This study is based on predicted peptides from the draft sequence (HGSC 2006). However, gene ontology (GO) terms and functional annotation of many of the homologous genes are incomplete. For this reason, further analyses were performed. Predicted peptides from the HGSC “Glean3” dataset that were found within QTL CIs by first using blastn to determine which sequence scaffolds from the genome assembly contained sequences corresponding to markers within the CI. Then, scaffolds were searched for presence of predicted peptides using protein–nucleotide 6-frame translation (tblastn). Predicted peptides from the QTL CIs were used to search the nonredundant database (using protein–protein BLAST [blastp]) at the National Center for Biotechnology Information website (http://www.ncbi.nlm.nih.gov). Protein domain information, gene similarities, and GO terms of homologs or orthologs were recorded. Literature and website searches were performed to assign putative functions based on reports from homologous genes of various species.
Quantitative real-time PCR
Some candidate genes for defensive behavior QTLs were tested for differential expression to better evaluate their potential for influencing behavior. These were chosen from a list of genes we were initially interested in based on functional studies in other species. Two families of worker bees were used: a low-defensive source and high-defensive source, each having a queen naturally mated to about 12–17 drones. The source families differed in the number of stings per minute deposited in a leather target (0 for low line and more than 100 for the high-defensive source) using a standard assay (Hunt et al. 1998). Although it is not known at which life stage a gene might exert its influence on defensive behavior, bees that are 10- to 20-days old are much more likely to sting than younger bees. Workers were collected within 12 h of emergence from brood combs placed in an incubator or marked and co-fostered in an unrelated hive and then collected 20 days later. Co-fostering was performed to eliminate potential environmental effects between hives. All bees were frozen in liquid nitrogen and kept at −80°C before RNA extraction. Heads of eight to nine bees from each of the two families were removed, and RNA was extracted using the RNAqueous® kit (Ambion). RNA yield was quantified using Ribogreen™ (Molecular Probes) dye on a fluorometer (Turner Biosystems, Sunnyvale CA). An aliquot of RNA was treated with DNAse (DNAfree® kit from Ambion) to remove any genomic DNA contamination before cDNA synthesis.
The cDNA template for qRT-PCR was generated according to Puthoff et al. (2005). For first-strand cDNA synthesis, the SuperScript First Strand cDNA Synthesis kit (Invitrogen) was used as per manufacturers protocol. The cDNA synthesis was monitored in a parallel tracer reaction as follows. A 5-μl aliquot was removed from each sample and mixed with 1 μl of a 1:5 dilution of 32P–dCTP (dCTP, 2′-deoxycytidine 5′-triphosphate, Amersham, Piscataway, NJ) in water. The remaining 15 μl of each reverse transcriptase reaction and the corresponding 5-μl tracer reaction were incubated at 42°C for 2 h. Reactions were terminated at 70°C for 15 min then chilled on ice. The 32P-tracer reactions were used to quantify the amount of cDNA synthesized in the larger experimental samples. The 32P-tracer reactions were spotted onto DE-81 filters (Fisher, Fairlawn, NJ), dried for 10 min, and then washed four times for 4 min each in 0.5 M sodium phosphate buffer (1 M of monobasic and 1 M of dibasic in 4 l of water). After two 1-min rinses in water, filters were washed in 95% EtOH and allowed to dry. Each filter was placed in a scintillation vial containing 5 ml of ScintiVerse (Fisher), and radiation from the newly synthesized cDNA was quantified in a scintillation counter. Resulting counts were used to normalize the cDNA from the corresponding reverse transcriptase reactions to a final concentration of 10 ng per μl of sample.
qRT-PCR was conducted on an ABI 7000 using the following mixture: 2 μl of normalized cDNA, 10 μl of 2X SYBR Green Mix (ABI, Foster City CA), and 0.25 μM of each primer in a 20-μl reaction. Reactions were carried out using the following cycling parameters: 50°C for 2 min, 95°C for 10 min, and 40 cycles of 95°C for 10 s and 60°C for 1 min. At the end of each run, a melt curve analysis was conducted to ensure primer specificity and purity of the PCR product. Relative mRNA levels were calculated by the standard curve method (User Bulletin 2: ABI PRISM 7700 Sequence Detection System) as described here. An aliquot was taken from each cDNA to construct a pooled sample. This pooled sample was serially diluted and subjected to qRT-PCR. The threshold cycle (Ct) for each dilution was plotted against its cDNA concentration (with an arbitrary starting quantity for the undiluted pooled sample assigned the value of 1) and used as the standard curve regression equation to generate the arbitrary expression values (AEVs). A standard curve was generated for each target gene on the same PCR plate that held the experimental samples. Linear standard curves, with a slope between −3.5 and −3.2 and R2 value of at least 0.98, were required for all primers used in this study. The AEVs were then normalized to the expression values of the eukaryotic initiation factor EIF-S8 for each bee. Two technical replicates for each bee were used, and results averaged before obtaining the family average. Normalized, average AEVs for each bee were analyzed by two-factor analysis of variance (ANOVA) without replication to compare transcript levels of the high-defensive family to the low-defensive family.
Results and discussion
Bioinformatic analyses of putative gene functions and results of qRT-PCR allowed us to form hypotheses concerning gene networks involved in either foraging or defensive behaviors. Results of analyses and hypotheses concerning genes with potential to influence behavior are presented in the following two sections.
Candidate genes for honeybee foraging behavior
Inspection of the predicted peptides (HGSC 2006) in genome sequence surrounding the mapped QTLs lends support to the hypothesis of the involvement of the IIS pathway in pollen foraging (Table 1; Fig. 4). Within, the pln-1 CI is the bee ortholog of the Drosophila gene bazooka, a gene involved in oocyte fate determination and influencing IIS through modulation of PI3K activity, as discussed below. Closer to the center of the CI is the honeybee ortholog of the fly gene, midway, which is involved in lipid metabolism and oocyte development. This gene is particularly interesting because it encodes diacylglycerol acyltransferase (DGAT) and changes in DGAT activity have been shown to alter sensitivity to IIS (Yu and Ginsberg 2004). Near the center of the CI of pln-1 is a gene encoding a protein with homology to class W phosphoinositolglycan-peptide (PIG-P), involved in the production of glycosylphosphatidylinositol (GPI) anchors to attach receptors to plasma membrane in various species. Lipids/GPI anchors have insulin mimetic properties in some systems, where they modulate the IIS pathway by stimulating phosphoinositide-3-kinase (PI3K) activity (Müller and Frick 1999; Müller et al. 2002).
Table 1Annotation of peptides within 97% confidence intervals for foraging-behavior QTLsFig. 4Hypothetical regulatory network influencing honeybee pollen foraging behavior modulated by insulin-like signaling and its effects on ovarian development. Inhibitory blue arrows bridging IIS with the reproductive physiology and hormonal dynamics of the honeybee denote the unique and mutually suppressive feedback interaction between vitellogenin and JII. This interaction is mediated via the allatoregulatory system (Guidugli et al. 2005, and references therein), which includes the IIS pathway (Flatt et al. 2005). ILPs Insulin-like peptides; PI phosphoinositol; PIP phosphoinositol phosphate; IRS insulin receptor substrate gene; PI3K phosphoinositide-3 kinase (class I or II); PIP5K 1-phosphatydylinositol-4-phosphate 5-kinase; PIG-P phosphatidylinositolglycan-peptide; PDK1 3-phosphoinositide-dependent kinase 1; PKB protein kinase B; HR46 honeybee ortholog of Dmel/HR46; PTEN phosphatidylinositol-3,4,5-trisphosphate 3-phosphatase; JH juvenile hormone
The pln-2 region contains two LOD-score peaks that were resolved into two CIs, suggesting separate but linked QTLs (Fig. 2b). The CI with the higher LOD score contains a bee ortholog of a nuclear hormone receptor (Dmel/HR46). Nuclear hormone receptors bind ligands (such as ecdysteroids) and enter the nucleus as transcription factors (Simonet et al. 2004). The bee ortholog of HR46 was differentially regulated in microarray experiments that compared bees with and without application of queen mandibular pheromone treatment, which inhibits worker ovarian development and delays onset of foraging behavior (Grozinger et al. 2003). The pln-2 region also contains the tyramine receptor AmTyr1, which has a higher expression level in young bees selected for high levels of pollen collection and storage (M. H. Humphries, unpublished data). A Drosophila mutant for the tyramine receptor (hono) is deficient in its behavioral response to olfactory stimuli, an observation that is relevant because honeybee foragers respond to floral scents and brood pheromone. In addition, tyramine increases responsiveness to sucrose and is found at elevated levels in the brains of egg-laying worker bees, a pattern that fits the behavioral and reproductive state of pollen foragers (Scheiner et al. 2004). The second CI associated with pln-2 contains an ortholog of Dmel/skittles, encoding 1-phosphatydylinositol-4-phosphate 5-kinase (PIP5K), one of two phospholipid kinases known to produce phosphoinositol 4,5 phosphate (PI4,5P2), which is a key metabolite involved in IIS and the substrate of class I PI3K activity (Fig. 4; Carricaburu et al. 2003). The Drosophila skittles mutant is deficient in oocyte polarity and nurse cell development, and the gene is essential for germ line development (Hassan et al. 1998; Table 1).
At the most likely position for pln-3 is the honeybee ortholog of the fly gene for a class II PI3K (PI3K 68D). Class II PI3Ks have been shown to respond to insulin signals in mammals and use phosphoinositol to produce phosphoinositol phosphate, which influences glucose transport, a common effect of IIS (MacDougall et al. 2004; Shepherd 2005). In addition, three predicted peptides with glucose transport domains were found in the pln-3 CI. It is unknown whether class II PI3Ks can act directly on PI4,5P2 to produce PI3,4,5P3, thereby stimulating the primary downstream kinase in the IIS pathway 3-phosphoinositide-dependent kinase 1 (PDK1). However, the honeybee gene encoding PDK1 also lies within the pln-3 CI. PDK1 is a positive regulator of cell growth and size through its action on downstream protein kinase B (PKB; Fig. 4; Rintelen et al. 2001), which, in Drosophila, is required for egg chamber development and influences egg follicle cell size (Cavaleire et al. 2005). The presence of these genes at pln QTLs suggests a possible network of genes influencing ovarian development and foraging (Fig. 4). Linkage of a PI3K and PDK1 at pln-3 is intriguing, especially given the presence of PIG-P and the bazooka ortholog at pln-1 and the PIP5K ortholog near pln-2. PIG-P has the potential to activate PI3K, and thus PDK1, whereas bazooka has been shown to bind the protein tyrosine phosphatase PTEN. This phosphatase is a negative regulator of PI3K because it dephosphorylates PI3,4,5P3, and binding of dPTEN by the product of the bazooka modulates this process (Von Stein et al. 2005). The convergence of these pathways also suggests the potential for interaction between pln-3 and both the pln-1 and pln-2 regions, which has been observed experimentally (Rueppell et al. 2004, 2006).
A search of the 10 cM window surrounding AmFOR (pln-4) revealed only three predicted peptides, one of which was the insulin receptor substrate (IRS). Although AmFOR was chosen as a candidate because of its influence on foraging behavior in Drosophila and association with foraging-related behavioral states in bees (Ben-Shahar 2005), the IRS could be partly or wholly responsible for the behavioral effects of this QTL. In Drosophila, ovarian expression of this gene is necessary for vitellogenesis, independent of the action of juvenile hormone and ecdysteroids (Richard et al. 2006).
When the phenotypic architecture of foraging behavior is taken into consideration (Fig. 1), the identification of genes encoding class II PI3K and PDK1 at pln-3 and other key components of IIS at pln-1, pln-2, and pln-4, supports the hypothesis that IIS is the upstream mediator of foraging division of labor (Fig. 4). These IIS components do not constitute random distributions of genes. We obtained a rough estimate of the likelihood that a gene lies within the CIs for foraging behavior QTLs by comparing the genetic size of these four QTL regions relative to the genome (about 145:4600 cM, or 0.03). There are 12 Drosophila genes in Flybase with the GO term for insulin receptor signaling pathway (GO: 0008286), so the expected number of these genes within the CIs is 0.36. However, bee orthologs for two of these genes occur within foraging behavior QTL CIs, at least 5.5 times the expected number assuming independence of gene distributions. In addition, orthologs of four of these 12 genes were not in genome sequence assigned to chromosomes and so could not be sampled in our analyses. We also found at least four other genes known to interact with IIS, but not annotated as such with GO terms, and one nuclear hormone receptor within the QTL regions, which are characterized by epistatic interactions suggesting the presence of components of a common pathway. In contrast, we could not find any genes influencing IIS within CIs for QTLs that influence defensive behavior, although they represent a region of comparable size.
Candidate genes for defensive behavior
Sting-1 had the highest LOD score for colony stinging response and was also the only QTL associated with initiation of stinging at the individual-bee level (Guzmán-Novoa et al. 2002; Arechavaleta-Velasco et al. 2003; Fig. 5a). Among the 50 predicted peptides in this 1.2-Mb region, at least nine are orthologs or homologs of genes reportedly involved in neuronal development and CNS activity (Table 2). The interval includes the gene encoding 14-3-3 epsilon, a protein abundantly expressed in the CNS that modulates the activity of a number of kinases and ion channels (Berg et al. 2003). A Drosophila mutant for the ortholog (FBgn0020238) shows a failure to habituate to stimuli during nonassociative (unrewarded) learning trials (Skoulakis and Acevedo 2003), which is the type of learning that guard bees engage in when distinguishing nestmates from non-nestmates. The sting-1 CI contains six orthologs of Drosophila genes involved in CNS or antennal development. Of particular interest is the ortholog of the Dmel/tango gene, which is an aryl hydrocarbon receptor nuclear transporter (ARNT)-like transcription factor. Tango is a basic helix-loop-helix-PER-ARNT-SIM (bHLH-PAS) transcription factor that responds to hypoxia and is critical for the development of the fly neural midline and antennae. Other bHLH-PAS transcription factors act as heterodimers to sense light, temperature, oxygen, or endogenous hormones, and some have roles in regulating circadian rhythm (Roenneberg and Merrow 2003). The honeybee ortholog shares just 56% amino acid identity with tango and is diverged in the region important for activation of target genes, making it impossible to infer function (Sonnenfeld et al. 2005). The region also contains the gene for Huntingtin protein (htt), a large and unique protein with a complex structure that is conserved among metazoans. Expansions in htt cause Huntington’s chorea. Htt interacts with many proteins and has roles in modulating neuronal transcription, intracellular neuronal transport, synaptic transmission, and morphology of dendrites (Harjes and Wanker 2003; Li and Li 2004). Finally, mRNA for a carboxylesterase of unknown function was more abundant in high-defensive bees compared to low-defensive bees (see Table 3), and RACE sequences from seven workers revealed six amino acid substitutions and five alleles. Allelic variation for this gene was found in the population used to map sting-1, which is a necessary condition for a gene conferring variability in the behavior.
Fig. 5Defensive-behavior QTLs were mapped based on the stinging response of colonies derived from crosses involving haploid drones of a F1 queen (European×African) each backcrossed to a European queen. Markers used for confirmation studies are indicated. Letters and numbers next to vertical bar represent linked markers. Sequenced markers are underlined. Dashed lines indicate approximate 97% CIs. Approximate positions of honeybee orthologs to Drosophila genes are indicated. a Sting-1. b Sting-2. c Sting-3Table 2Annotation of peptides within 97% confidence intervals for defensive-behavior QTLsTable 3Expression of candidate genes in high-defensive bees relative to low-defensive worker bees
The primary stimuli that elicit stinging behavior are moving visual targets and alarm pheromone. The sting-2 region contains two obvious candidates for modulation of response to these stimuli. At the most-likely position of the QTL is the bee ortholog of Drosophila arr1 (AmArr4, Fig. 5b), an arrestin that binds metarhodopsin, the light-activated form of rhodopsin in the eye. Arrestins are involved in the desensitization of specific GPCRs and their recycling through clathrin-mediated endocytosis. But the so-called visual arrestins are also expressed in the antennae of Drosophila and are involved in olfaction. Fly arr1 mutants are insensitive to classes I and II odorants (Merrill et al. 2005). Near the edge of the CI is an ortholog of the fly gene encoding the metabotropic gamma-aminobutyric acid receptor (GABA-B-R1). GABA serves as the primary inhibitor of neuronal excitability in the CNS of both insects and mammals (Bettler et al. 2004).
The sting-3 CI contains only 17 predicted peptides in 0.96 Mb of DNA. Like sting-2, it has genes with the potential to modulate sensitivity to visual and olfactory stimuli. Results of cDNA sequencing of a putative GPCR revealed a 5-HT7 serotonin receptor (Am5HT7, Fig. 5c). Serotonin influences associative learning and circadian rhythm in mollusks and insects, and both serotonin and GABA influence mood disorders in mammals (Hayley et al. 2005). Application of serotonin to the optic lobe of the bee brain reduced behavioral and neural responses to moving visual stimuli (Erber and Kloppenburg 1995). However, pharmacological experiments likely target all types of serotonergic neurons. The 5-HT7 receptor is just one of four serotonin receptor types known in insects and is unique in that it activates adenylate cyclase, resulting in increased levels of cyclic adenosine monophosphate (AMP) and activation of PKA. Therefore, activation of 5-HT7 receptors would likely cause a stimulatory response. Total serotonin levels have sometimes been reported to influence mammalian, insect, and crustacean aggressive interactions (Nelson and Chiavegatto 2001; Panksepp et al. 2003). Recent evidence suggests that 5-HT7 receptors modulate exploratory behavior and anxiety in mice (Takeda et al. 2005). The sting-3 region also contains one of the three catalytic subunits of cyclic AMP-dependent PKA, AmPKA-C1, a gene known to affect behavior in flies and mammals, including behavioral responses to alcohol, learning, and locomotor rhythm (Eisenhardt et al. 2001). Finally, the ortholog of homer, a dendritic gene involved in calcium signaling and synaptic plasticity, is closely linked to AmPKA-C1 (Diagana et al. 2002; Szumlinski et al. 2004).
Measurement of gene expression by qRT-PCR for defensive-behavior candidate genes showed several interesting trends. There were few statistically significant differences, because in this first screen, families were chosen that had divergent behavior but were genotypically diverse. Variation in expression between individuals was high, but numerical differences indicated a trend towards higher gene expression in older defensive bees. Only 14-3-3 Epsilon showed significantly higher transcript levels in the defensive family of bees. In contrast, the CG8165 ortholog, a putative jumonji-domain transcription factor, had numerically higher levels in newly emerged defensive adults, yet significantly lower levels in older defensive bees (Table 3). This suggests an earlier peak in expression of this transcription factor in defensive bees. It was interesting that the mRNA levels of the GABA-B-R1 receptor, part of the major inhibitory pathway of neural signaling, were significantly lower in newly emerged defensive bees. Transcript levels of seven other genes showed trends for higher expression in defensive bees at the >1.3-fold level. At sting1, an unknown carboxylesterase showed the greatest numerical expression difference of all, but its levels also showed the highest interindividual variability, resulting in no significant difference between families. As previously stated, this gene also showed the highest allelic variability in cDNA sequence. At sting2, both the ortholog of Dmel/discs lost and AmArr4 (arrestin) trended towards higher expression in defensive bees. Three other genes showed high numerical but not significantly different levels in defensive bees: oxysterol binding protein, the Dmel/tango ortholog, and the Am5HT7 serotonin receptor (1.7-fold higher). Eight of the 19 genes tested appear to be more highly expressed in older defensive bees at 1.3-fold or higher although only one is significantly so. The fact that so many of these linked genes trended towards higher mRNA levels in defensive bees may mean that they are regionally regulated. More sampling of other bees and genomic regions is necessary. Some genes may be expressed at higher levels in high-defensive bees because of elevated metabolic rates (Harrison et al. 2005). Presumably, genes that influence behavior will show differences in expression levels of protein at some stage between low- and high-defensive alleles. However, demonstration of differences in transcript levels does not prove a causal connection to the phenotype. Conversely, failure to find a difference in transcript level does not disprove a causal connection to behavior and could be a consequence of not sampling the most relevant developmental stage, or that small differences in expression are still relevant to the phenotype.
The advantage of high recombination rates
By use of genome sequence and linkage maps, we reduced the list of candidate genes for honeybee foraging and defensive behavior from 10,157 (the current number of predicted peptides) to just 17 to 61 per QTL. As a consequence of high recombination rates, this level of resolution within 97% CIs was achieved in relatively large genetic distances, averaging 40 cM per CI. Table 4 shows a comparison of our results to a study in which QTLs for ovariole number were mapped in Drosophila. Because of the low recombination rate in Drosophila, CIs totaling 158 cM represented half the genome in this cross and contained 9,100 genes (Orgogozo et al. 2006). But regions of similar size influencing foraging and defensive specialization in the bee contained only 113 and 128 genes, respectively. Other model organisms would provide less drastic comparisons. For example, a 40-cM window in mouse would be expected to contain about ten times as many genes compared to the bee, or about 510 genes (assuming a total of 20,000 genes evenly distributed over 2800 Mb and a recombination rate of 0.56 cm/Mb). Of course, results will depend on local gene distributions and recombination rates. In practice, it is usually necessary to map a QTL to within 1 cM in mammalian species to reduce the list to five to ten candidate genes (e.g. Talbot et al. 2003; Flint et al. 2005). The genetic size of our QTL intervals compared to the size of the bee linkage map suggests that we should find 320 and 236 genes for the pln QTL and sting QTL, respectively, but instead, we found less than half this number (Table 4). The expected number of genes based on physical distance is closer to the observed value. The discrepancy between these predictions can be explained by higher than average recombination rates in the QTL regions. The average recombination rates for the pln and sting QTL intervals are 28.2 and 32.8 cM/Mb, respectively, but the genome average is 19 cM/Mb. Consequently, there is a little less than one gene per centimorgan in the QTL regions. Our analyses do involve some degree of uncertainty. First of all, the estimate of recombination rate in QTL regions does not take into account sequence groups that have not yet been assigned to chromosomes (21% of the genome; HGSC 2006) that may lie between assigned groups. Therefore, recombination rates actually may be somewhat higher in QTL regions than we estimated. Sequence groups missing within CIs also may contain additional genes. In addition, the annotated set of 10,157 genes is a high-confidence set and eventually, may increase by several thousands as genes that are more novel in sequence are added (e.g., Drosophila has a gene count of about 13,000). Finally, our analyses focus on conserved genes of known function. A previous study of 81 Kb of sequence linked to sting2 revealed 13 expressed transcripts, none of which showed homology to known genes (Lobo et al. 2003). It cannot be ruled out that these behaviors may be at least partly influenced by completely novel genes.
Table 4Comparison of number of candidate genes in QTL confidence intervals from Apis and DrosophilaaInterval Size (cM)bGenusTraitNumber of QTLPredicted no. of genes based on genetic or physical sizecActual no. of genes145ApisPollen foraging4320222113107ApisDefensive behavior3236145128158DrosophilaOvariole number56,800–9,100aData for Drosophila are derived from a study of QTL for ovariole number in an interspecific cross between D. simulans and D. sechellia (Orgogozo et al. 2006). Data for Apis are from this report.bCumulative size of all confidence intervals.cCalculation of number of genes expected based on genetic distance was done by multiplying the proportion of total genetic distance that an interval contains by the number of predicted genes. An estimate based on physical distance in the honeybee is based on the current amount of genome sequence assembly (235 Mb).
Conclusions
Our findings lead us to propose that foraging division of labor (Fig. 1) is influenced by a gene network involving IIS (Fig. 4). This is just a hypothesis, but a testable one. We also suggest that the genetically variable defensive responses of bees may be explained by allelic differences in neuronal transcription factors and genes involved in G protein-coupled signaling pathways. The potential involvement of the Am5HT7 serotonin receptor in defensive/aggressive behavior implies that the bee may be used to elucidate a role of serotonin in novelty-seeking (guarding) behavior and that this behavior could be modified by specific agonists and antagonists of this receptor subtype. Linkage mapping at a finer scale using many single nucleotide polymorphisms combined with genome-wide expression assays could be the next step in finding the sequences responsible for behavioral variation. The bee is likely to become an important species in this process and may become the first invertebrate model for understanding how gene-regulation of life histories are remodeled by social evolution.

behavior genetics
candidate genes
recombination rate
insulin-like signaling
aggressive behavior
foraging behavior
apis mellifera


Local staging of rectal cancer: the current role of MRI

With the advent of powerful gradient coil systems and high-resolution surface coils, magnetic resonance imaging (MRI) has recently extended its role in the staging of rectal cancer. MRI is superior to endorectal ultrasound, the most widely used staging modality in patients with rectal tumors, in that it visualizes not only the intestinal wall but also the surrounding pelvic anatomy. The crucial advantage of MRI is not that it enables exact T-staging but precise evaluation of the topographic relationship of a tumor to the mesorectal fascia. This fascia is the most important anatomic landmark for the feasibility of total mesorectal excision, which has evolved into the standard operative procedure for the resection of cancer located in the middle or lower third of the rectum. MRI is currently the only imaging modality that is highly accurate in predicting whether or not it is likely that a tumor-free margin can be achieved and thus provides important information for planning of an effective therapeutic strategy, especially in patients with advanced rectal cancer.
Introduction
Colorectal cancer is the third most common cancer worldwide [1, 2]. In the United States, about 145,000 new cases and 56,000 deaths were estimated for 2005 [1]. In recent years, mortality rates have decreased due to major changes in therapeutic management, in particular the standardization of the operative procedure and the introduction of adjuvant and neoadjuvant therapy [1].
Colorectal cancer primarily develops from adenomatous polyps over a period of 10–15 years, known as the adenoma-carcinoma sequence [3]. The incidence of polyps increases with age and the risk of malignant transformation of a polyp markedly increases with its diameter. The rate of malignant transformation is about 1% for polyps less than 1 cm in diameter, but 10% for larger ones [4, 5]. Around 40–50% of colorectal cancers are located in the rectum.
Rectal cancer is defined as a tumor whose aboral margin measured with the rigid rectoscope is 16 cm or less from the anocutaneous line. This distance serves to classify rectal cancer into tumors of the upper third (12–16 cm), the middle third (6–12 cm), and the lower third (<6 cm) [6] according to the UICC.
The mesorectal fascia is an important anatomic landmark for the diagnostic evaluation of local tumor extent [7] (Fig. 1b). The fascia is a connective tissue sheath that encloses the rectum and the perirectal fatty tissue, including lymph nodes and lymphatic vessels down to the pelvic floor and acts as a natural barrier for tumor spread. The ability to visualize the mesorectal fascia on CT images has been described more than 20 years ago [8]. MRI currently is the most advanced staging modality able to depict the fascia and its relation to the tumor margins precisely. The following article will give an overview of the staging modalities currently used in rectal cancer staging, with an emphasis on the role of MRI and its significance for planning an effective therapeutical strategy for the individual patient.
Fig. 1a Axial T2-weighted FSE (TSE) sequence of the pelvis depicting the layers of the rectal wall. The mucosa and submucosa can be visualized as a relatively hyperintense band (arrows). The hypointense line (arrowheads) represents the muscularis propria. b Axial T2-weighted FSE (TSE) sequence. The mesorectal fascia can be visualized as a thin line (arrowheads), enveloping the mesorectal compartment, containing the rectum, mesorectal fat, blood vessels, lymphatic vessels and nodes
Therapeutic options
Surgery
The anatomic position of the rectum in the true pelvis and its vicinity to adjacent anatomy, in particular the sphincter muscles, poses a challenge for the surgeon regardless of the surgical technique used. Surgical treatment of rectal cancer is a difficult balancing act between minimizing the risk of local recurrence and the preservation of anorectal and genitourinary function.
Total mesorectal excision (TME)
The introduction of standardized TME [9] has considerably improved prognosis in patients with cancer located in the middle or lower third of the rectum. Using this operative technique, the rectum is resected together with all surrounding lymphatic pathways, lymph nodes, mesorectal fatty tissue, and the mesorectal fascia while the parietal pelvis fascia and the pelvic splanchnic nerves (nervi erigentes) are spared. The widespread introduction of TME has markedly reduced the rate of non-continence-preserving abdominoperineal operations for rectal cancer.
Local excision is an option in patients with very small, well to moderately differentiated tumors that are confined to the mucosa and submucosa [10]. Techniques used for local excision are transanal surgical tumor removal and endoscopic microsurgical tumor ablation. Only a few patients are candidates for local excision (about 5%) and these must be selected with great care.
Circumferential resection margin (CRM) and local recurrence
The local recurrence rate after surgery performed with curative intent ranges between 3% and 32% [11]. For TME, some studies report local recurrence rates that are markedly below 10% [12–14]. Lateral circumferential tumor extent is a much more important prognostic factor for local recurrence than longitudinal tumor extent. Incomplete resection of the lateral tumor margins is now considered the most important cause of local recurrence [15–17]. In a study by Quirke et al. [15], 83% of the patients with a positive CRM had local tumor recurrence. Hence, the topographic relationship of the tumor to the mesorectal fascia that serves as a natural barrier and anatomic landmark for TME is the most important criterion in local tumor staging for therapeutic decision making.
Adjuvant/neoadjuvant therapy
The aims of adjuvant or neoadjuvant therapy are to enable or facilitate total tumor resection even in advanced disease, to prevent local tumor recurrence, and to minimize the risk of distant metastases. Adjuvant or neoadjuvant therapy leads to downstaging of the tumor [18, 19] in terms of its T and N stages, and 20% of patients even show complete tumor regression (sterilization) [20].
The timing of adjuvant or neoadjuvant therapy is still a matter of debate. Based on the results of two large studies [21, 22], preoperative radiotherapy alone or combined radiochemotherapy is the preferred option for tumors of the middle or lower rectum in Europe. One of these studies, performed in Scandinavia [21], showed that a short cycle of preoperative radiotherapy reduces the local recurrence rate from 27% to 11%. The second study showed that even patients who underwent TME, which already has a lower recurrence rate than other operative approaches, benefit from preoperative radiotherapy [22]. Preoperative irradiation significantly reduced local recurrence compared to the group treated by TME only. In the United States, adjuvant therapy consisting of combined postoperative radiochemotherapy is favored for patients with T3 and/or N1 tumors. [23].
Local tumor staging
Tumor staging is crucial for the prognosis and planning of therapy in the individual patient and aims at precisely determining the extent of the tumor as a basis for deciding whether surgery alone or surgery in combination with neoadjuvant therapy is the most suitable strategy. Of course, it is of great importance to avoid overtreatment or undertreatment of the patient. To reach a high level of accuracy in rectal cancer staging and to develop an adequate individual strategy for therapy, it is indispensable to establish a multidisciplinary team [24]. Rectal cancer staging is now mostly based on the TNM and UICC staging systems [6] (Tables 1, 2), which have largely replaced the older Dukes classification. The most important anatomic structure on which staging is based using these staging systems is the lamina muscularis propria. While T1 rectal carcinomas are confined to the mucosa and submucosa, T2 tumors invade the muscularis propria (Figs. 2a–c and 3). A T3 cancer is defined as a tumor extending beyond the lamina muscularis propria (Figs. 4, 5). However, none of the staging systems takes into account the fact that the T3 tumors are a very heterogeneous group, comprising tumors that just barely extend beyond the lamina muscularis propria as well as tumors that extend to or invade the mesorectal fascia (Figs. 4, 5) without further subclassification. The therapeutically important topographic relationship of the lateral tumor margins to the mesorectal fascia is not taken into consideration. An adequate, state-of-the-art staging classification should be able to precisely determine this relationship and to predict whether a tumor-free CRM is likely to be achieved or not. In this way one would be able to differentiate patients with minimal mesorectal infiltration in whom neoadjuvant therapy is not mandatory from patients who would definitely benefit from neoadjuvant therapy because the mesorectal fascia is infiltrated or at risk. T4 rectal cancers are defined as tumors, that reach the peritoneal surface or adjacent organs (Figs. 6a,b, 7, 8).
Table 1TNM classification for colorectal cancerTypeDescriptionT1Tumor involves submucosaT2Tumor involves muscularis propriaT3Tumor beyond muscularis propriaT4Tumor reaches peritoneal surface or invades adjacent organN0No involved nodesN1Up to three perirectal/colic nodesN2Four or more perirectal/colic nodesTable 2UICC staging of rectal carcinomaStageDescriptionStage 0TisN0M0Stage IT1N0M0T2N0M0Stage IIAT3N0M0 BT4N0M0Stage IIIAT1, T2N1M0 BT3, T4N1M0 CEvery TN2M0Stage IVEvery TEvery NM1Fig. 2a Paraxial T2-weighted FSE (TSE) sequence. T1/2 rectal cancer. The relatively hyperintense intraluminal tumor (arrowhead) is confined to the rectal wall. Tumor invasion of the mesorectum is not visible. b Paraxial 3D-MPR and c intraluminal (virtual endoscopy) CT reconstuctions after rectal insufflation of CO2 showing the same tumor as aFig. 3Paraxial T2-weighted FSE (TSE) sequence. Tumor of the rectal wall. Fibrous strands into the mesorectum represent desmoplastic reaction (arrow). A differention between desmoplastic reaction and tumor infiltration of the mesorectum can be difficultFig. 4Paraxial T2 weighted FSE (TSE) sequence. A T3 rectal cancer breached through the muscularis propria (arrow) and invades the mesorectum. The tumor does not reach the mesorectal fascia (arrowheads). A tumor-free CRM can be expectedFig. 5Paraxial T2-weighted FSE (TSE) sequence. A T3 rectal cancer widely invades the mesorectum. A tumor deposit (arrow) is located directly adjacent to the mesorectal fascia (arrowhead). A tumor-free resection margin cannot be predictedFig. 6a Paraxial T2-weighted FSE (TSE) sequence and b sagittal T2-weighted FSE (TSE) sequence of a T4 cancer located in the upper third of the rectum invading the uterus (arrows)Fig. 7Paraxial T2-weighted FSE (TSE) sequence of a low T4 rectal cancer with infiltration of the levator ani muscle (arrow)Fig. 8Recurrent rectal cancer. Paraxial T1-weighted SE sequence with fat suppression after i.v. apllication of gadopentetate-dimelglumine at a dosage of 0.2 mmol/kg body weight. The large extraluminal tumor shows central necrosis (arrowhead) and reaches the right pelvic wall
Staging modalities
Endorectal ultrasound (EUS)
EUS is the oldest and most widely used imaging technique for evaluating the local extent of rectal cancer. EUS depicts the anatomic layers of the rectal wall with a high degree of accuracy and thus enables precise determination of the tumor extent in relation to the different wall layers. Reported accuracy rates of transrectal ultrasound in assessing the T stage are in the range of 69–97% [25–35]. EUS is most suitable for evaluating early rectal cancer while it is limited in assessing more advanced tumors. Although EUS allows the identification of transmural tumor growth, exact determination of the circumferential tumor spread and—even more important—depiction of the relation between the edges of the tumor and the mesorectal fascia is often not possible due to the limited scan depth caused by the high frequencies used. Moreover, the accuracy varies widely with the examiner’s experience [28, 36].
Computed tomography (CT)
Most older studies report rather low accuracy rates of only 52–70% [32, 37–41] for T-staging by CT. It is remarkable that accuracy levels reported in studies including less advanced tumors were considerably lower compared with those including only advanced tumor stages. The poor accuracy of CT in the staging of superficial tumors is mainly attributable to the fact that these studies used conventional CT protocols with low spatial and contrast resolution. The accuracy has since been improved by the advent of the multislice technique (MSCT). In a study of 92 patients by Kulinna et al. [42], T-staging using MSCT was found to have an accuracy of 86%, while Filippone et al. [43] found an accuracy of 83% in a study of 41 patients. If one takes into account that four-row CT scanners were used in these studies, it is evident that further improvement is to be expected from state-of-the-art CT scanners with up to 64 detector rows that are already in use today. Hence, the role of MSCT in the local staging of rectal cancer remains to be defined. CT is superior to both EUS and MRI in that the scan typically covers the entire abdomen and pelvis and thus also allows evaluation of the liver, the most important target organ of hematogenic metastatic spread of rectal cancer.
MRI
It is undisputed that MRI is the imaging modality with the highest soft-tissue contrast. This is why MRI is also used for staging rectal cancer. However, initial results with MRI were disappointing, with accuracies in T-staging reported in older studies ranging between 58 and 74% [39, 44–46]. These rather poor results are primarily due to the poor spatial resolution achieved with the whole-body coil systems used in these studies. When endorectal coils are used, MRI has similar accuracies as EUS [31, 47–49]. MRI using endorectal coil systems is comparable to EUS in that it allows highly accurate differentiation of the layers of the intestinal wall. However, endorectal coils also have a number of disadvantages. As with EUS, the field of view (FOV) is rather small and only allows adequate evaluation of early stages of rectal cancer because the evaluation of surrounding pelvic anatomy is limited. In patients with advanced tumors, insertion of the coil system may be impossible or is very painful. Another disadvantage is the high cost of endorectal coils, which are usually disposable.
The advent of powerful gradient systems and, above all, the development of high-resolution phased array surface coil systems in recent years brought the breakthrough in the staging of rectal cancer by MRI. The use of these phased-array surface coils combines a very high spatial resolution with a large FOV that allows not only detailed evaluation of the intestinal wall but also depicts surrounding anatomy including the mesorectal fascia.
Imaging technique
Rectal cancer staging by MRI is rather fast and straightforward. No special patient preparation is required. Some authors recommend administration of a positive or negative enteral contrast medium, but this seems not to be necessary as suggested by current data in the literature. A study published only recently even indicated that rectal distension significantly reduces the distance between the rectal wall and the mesorectal fascia and that this might impact on the ability of MRI to predict accurately the distance between the tumor and the potential resection margin [50].
At our department, we administer a spasmolytic agent (butylscopolamine) at a dose of 20–40 mg to prevent artifacts caused by peristalsis of the small intestine and to distend the sigmoid and rectum. The agent has a short half-life and is therefore injected intramuscularly immediately before MRI.
For efficient planning of the pulse sequences to be employed, the radiologist performing the examination should beforehand obtain information about the approximate tumor localization (distance from anocutaneous line in cm) from the referring surgeon and ask the patient about any previous surgery or diseases of the pelvic organs.
The patient is positioned comfortably on the back and a phased-array surface coil is placed on the pelvis in such a way that the lower edge of the coil comes to lie well below the pubic bone. The coil is kept in place with belts and the patient is then advanced head-first into the bore of the magnet.
Following the usual localizer scans, a sagittal T2-weighted half-Fourier single shot turbo spin-echo (SSFSE, HASTE) sequence with a large field of view (FOV) should be acquired to obtain an overview and for planning of the subsequent sequences (e.g. TR ∞, TE 62 ms, slice thickness 5 mm, FOV 255×340 mm, matrix size 116×256, voxel size 2.2×1.3×5 mm). Precise tumor localization is then achieved with an axial T2-weighted fast spin-echo (FSE) or turbo spin-echo (TSE) sequence with a large FOV and a slice thickness of 5 mm (e.g. TR 4,170 ms, TE 98 ms, FOV 300×220 mm, matrix 282×512, voxel size 0.8×0.6×5 mm). At the core of the examination is a high-resolution T2-weighted TSE sequence with a small FOV and a slice thickness of 3 mm (e.g. TR 3,570 ms, TE 68 ms, FOV 180×180 mm, matrix 179×256, voxel size 1.0×0.7×3 mm). It is mandatory to place the slices perpendicular to the longitudinal axis of the tumor or the intestinal lumen in the vicinity of the tumor. With this sequence, it is possible to precisely evaluate the tumor and its relationship to the intestinal wall, mesorectal fascia, the pelvic organs, and possibly also to the peritoneal fold. Moreover, mesorectal lymph nodes in the immediate vicinity of the tumor can be evaluated. For visualization of more distant lymph nodes in our institution a T1 to proton-density-weighted two-dimensional (2D) TSE sequence with a short echo train length (e.g. 3 or 5) in axial orientation (e.g. TR 1,980 ms, TE 10 ms, slice thickness 5 mm, FOV 300×225 mm, matrix 219×512, voxel size 1× 0.6×5 mm), which covers the entire area up to the aortic bifurcation is used. Alternatively, a T1-weighted 3D gradient-echo sequence can be used for this purpose, allowing for the reconstruction of thinner slices. Possible infiltration of the anal sphincter muscles in patients with low tumors is evaluated using a coronal T2-weighted FSE (TSE) sequence (e.g. TR 3,570 ms, TE 68 ms, FOV 180×180, matrix 179×256, voxel size 1.0×0.7×3 mm) positioned parallel to the longitudinal axis of the anal canal. Current data in the literature suggests that intravenous contrast medium administration does not improve staging of rectal tumors by MRI [51, 52].
Since differentiation with the T2-weighted sequences is based on the contrast between the high-signal-intensity mesorectal fatty tissue and the rather low signal intensity of the tumor, spectral fat suppression techniques are not needed. The duration of the MRI protocol as just outlined is about 25–30 min, including planning.
T-staging
Although the introduction of phased-array coil systems has improved the accuracy of MRI in staging rectal cancer, even more recent studies report accuracies of only 67–86 % for T-staging [53–56]. These disappointing results are primarily due to the poor differentiation of T1/2 cancer from so-called borderline T3 cancer, where it is often not possible to distinguish true mesorectal tumor invasion from desmoplastic reactions (Fig. 3) [49, 54, 57]. Desmoplastic reactions are reactive tissue alterations which often occur in the immediate surrounding of tumors, most frequently resulting in fibrotic extensions that may contain tumor cells or not. The failure to differentiate between desmoplastic reactions and tumor growth is not specific to MRI but is also a well-known problem in rectal cancer staging with EUS [27]. Clinically and therapeutically, however, this differentiation is of minor importance. As already mentioned, it is much more important to precisely describe the relationship of the tumor to the mesorectal fascia, representing the anticipated resection plane for TME in order to assess the likelihood of a tumor-free CRM. Several recent studies have confirmed that MRI is highly suited to provide this information [54, 57–60]. In a study of 43 patients, Bissett et al. [59] found good agreement between preoperative MRI and histopathology with regard to the demonstration of tumor penetration through the mesorectal fascia (accuracy: 95%). These results are underlined by the studies of Beets-Tan et al. [54, 61], who investigated 76 patients and likewise found preoperative MRI to be highly accurate in assessment of the CRM. The agreement was 100% in T4 tumors, and 97% and 93% for both readers in tumors with a histologically determined tumor-free CRM >10 mm. Regression analysis for histologically determined margins of 1–10 mm demonstrated that a tumor-free resection margin of 2 mm was predicted with an accuracy of 97% if the distance between tumor and mesorectal fascia measured by MRI was at least 6 mm. It is noteworthy that this study likewise showed only moderate results with regard to T-staging (accuracy of 83% and 67% for the two readers) [54, 61]. In a study of 98 patients published by Brown and co-workers in 2003, the agreement between MRI and histology in assessment of the CRM was 92% [60]. These figures indicate that MRI allows accurate prediction of the CRM status after resection. The expected CRM can be described as involved if tumor invasion of the mesorectal fascia is visible or the tumor has a proximity of 1 mm or less to the mesorectal fascia. A tumor-free CRM can be assumed with a high degree of accuracy if the shortest distance from the maximum tumor extension, a mesorectal tumor deposit or a suspect lymph node in the mesorectum is more than 6 mm [54]. The role of tumors that extend towards the mesorectal fascia to a distance of less than 5 mm on MR images remains controversial.
The study by Brown et al. [60] also suggests that other important prognostic factors besides the CRM are the infiltration of extramural veins and possible infiltration of the peritoneal fold and that these can also be identified by preoperative MRI.
A study by Oberholzer and co-workers published in 2005 has shown that parallel imaging techniques do not compromise diagnostic accuracy with regard to the assessment of the CRM, but can considerable shorten the examination [62].
N-staging
Identification of metastatic lymph nodes is the greatest challenge in preoperative staging of rectal cancer, regardless of the modality used (Figs. 9, 10, 11). Exact staging is important because the number of metastatic nodes has been shown to affect the prognosis [63]. Involvement of lymph nodes in the vicinity of the mesorectal fascia is associated with a higher risk of local recurrence [16]. In patients with metastatic nodes outside the mesorectal fascia, extended lymph node resection with additional removal of the internal iliac nodes becomes necessary [64]. This lymph node group is not removed when regular TME is performed. A special problem associated with identifying lymphatic involvement in rectal cancer is that lymph node size is not a reliable criterion for metastatic involvement because micrometastasis in normal-sized lymph nodes is common [65, 66].
Fig. 9Sagittal T2-weighted FSE (TSE) sequence. The enlarged mesorectal lymph node (arrow) shows heterogenous signal intensity indicating tumor invasionFig. 10Axial PD-weighted FSE (TSE) sequence. A large lymph node metastasis (asterisk) located below the aortic bifurcation in a patient with rectal cancerFig. 11Axial T2-weighted FSE (TSE) sequence. Inguinal lymph node metastases (arrows) in a patient with low rectal cancer
The accuracy rates reported in the literature for N-staging by the different imaging modalities vary widely (EUS: 61–80% [10, 25, 26, 30–32, 34, 35, 37, 67], CT: 56–79% [32, 39, 68, 69], MRI: 57–85% [32, 39, 47, 53, 55, 60]. In a current meta-analysis including 84 studies, Lahaye et al. [70] found EUS to be slightly superior in assessing nodal status, but there were altogether no significant differences between the three staging modalities investigated. In summary, these results suggest that none of the imaging procedures currently in use enables reliable detection of metastatic lymph nodes.
In their study of MRI with histologic correlation, Brown et al. [71] identified an irregular contour and inhomogeneous signal to be the most reliable MRI criteria for lymph node metastasis (Fig. 9).
Future perspectives
USPIO
A new promising approach to detect metastatic lymph nodes by MRI is imaging in combination with ultrasmall superparamagnetic iron oxide particles (USPIO) as a contrast medium for systemic MR lymphography (Fig. 12a,b). Following intravenous administration, the particles are phagocytozed by nodal macrophages and, due to susceptibility effects, cause a signal decrease in normal or reactively changed lymph nodes on T2- and T2*-weighted images, which are usually acquired 24 h after administration of USPIO [72]. USPIO agents are currently under clinical evaluation and are not yet clinically available. Initial results of a study investigating this new approach in mesorectal lymph nodes are promising [73]. Further studies are needed to show whether USPIO can significantly improve lymph node staging by MRI.
Fig. 12a Axial PD-weighted sequence of the pelvis. Two small lymph nodes are visible adjacent to the iliac vessels (arrows). b Axial T2*-weighted gradient echo sequence acquired 24 hours after i.v. infusion of USPIO (Sinerem). The two lymph nodes (arrows) show homogenous signal decrease indicating normal lymphatic tissue. As USPIO agents are currently under clinical evaluation and are not yet clinically available, this image was acquired during a clinical trial
Whole-body MRI
The recent introduction of powerful whole-body MRI systems enables imaging of the whole body in a single session through repeated table movement. Several studies have already demonstrated the benefit of this approach for a variety of diagnostic queries in oncologic patients [74–77]. This technique may also be used for rectal cancer staging in the future and allow local staging and whole-body staging in a single session. In this way it would become possible to also evaluate the liver as the primary target organ of hematogenic spread of rectal cancer. The potential of parallel imaging to shorten the examination that has already been mentioned would be of particular significance in this respect [62].
Diffusion/perfusion-weighted MRI and PET
It has long been known that the pathophysiology and aggressiveness of a tumor are determined not only by the macroscopic tumor extent but also by other factors such as tumor microcirculation and angiogenesis. Several studies have shown the potential of diffusion- or perfusion-weighted imaging to indirectly determine these factors and to thus predict the response to adjuvant or neoadjuvant therapy [78–81]. However, extensive further research is necessary before the routine clinical use of these new techniques.
Recent studies have indicated that PET is able to predict response to neoadjuvant treatment of locally advanced rectal cancer with a high degree of accuracy [82].
Conclusion
The advances that have been made in the treatment of rectal cancer in recent years and that have considerably improved the prognosis of affected patients rely on differentiated pretherapeutic tumor staging. Despite its known limitations in T-staging, MRI is currently the only imaging modality that enables highly accurate evaluation of the topographic relationship between lateral tumor extent and the mesorectal fascia and to thus make a prediction about the CRM. In this way it is possible to carefully select those patients who will benefit from neoadjuvant therapy and to avoid overtreatment or undertreatment.

staging
rectal cancer
mri
rectal carcinoma


Is melatonin helpful in stopping the long-term use of hypnotics? A discontinuation trial

Objective To find out if administration of melatonin facilitates discontinuation of benzodiazepine (BD) therapy in patients with insomnia.
Impact of findings on practice
stopping long-term use of benzodiazepine hypnotics is difficult
users of a high daily dose might be more responsive to the effect of melatonin on the discontinuation of benzodiazepines
investigators should take into account the difficulty of recruiting patients for studies on discontinuing the use of benzodiazepines
Introduction
The point prevalence of the use of sleeping medication, often benzodiazepines (BD), in countries such as the Netherlands is approximately 6% [1], with over one-third being long-term users. This does not meet the standard of the Dutch Society of General Practitioners, which advises that hypnotics should not be prescribed longer than for a period of 10 days [2]. After a minimal intervention strategy such as sending an educational letter, one in every four users stops [3–5]. As an aid to discontinuation, several treatments have been used, e.g., certain drugs [6] and cognitive therapy [7], without added value.
The circadian rhythm in humans is controlled by the endogenous biological clock, located in the suprachiasmatic nuclei of the hypothalamus and influenced by light and darkness to the body [8]. Suprachiasmatic projections regulate the pineal gland and its production of melatonin. The primary physiological function of melatonin is to convey information about the daily cycle of light and darkness to the body’s physiology. Melatonin production changes with age and is lower in the elderly. Its secretion is high during the night and has an effect on falling asleep. There is evidence that low doses of melatonin improve initial sleep quality in selected elderly insomniacs [9]. Some BDs have shown to have a negative effect on melatonin production in the pineal gland [8].
Garfinkel et al. [10] investigated stopping the chronic use of BD sleeping medication with the help of melatonin (5 mg). In their randomized discontinuation trial melatonin appeared efficacious: 14 of 18 (78%) patients on melatonin stopped their BD compared to 4 out of 16 (25%) in a placebo control group.
A study in elderly patients of Cardinali et al. [11] does not support efficacy of melatonin to reduce the use of benzodiazepines in low doses. Forty-five patients were randomized to receive either melatonin (3 mg) or placebo for six weeks. In two steps BD was tapered off and stopped after four weeks. Several sleep parameters were assessed and found not to be different for both groups.
The administration of melatonin as a sleeping medication is a contradictory tool to help people discontinue use of BDs.
Aim of the study. The main research question in our trial was whether melatonin is helpful in discontinuing the use of BD sleeping medication, looking at the stopping rate. Secondary objectives are finding the possible influence of other variables on stopping, such as age, gender, period of BD use, and dependence on BD. Besides, we investigated a putative shift of addiction, e.g., to alcohol or smoking.
Method
Our study was designed as a randomized placebo-controlled discontinuation trial. We gathered data in nine general practices (2001–2004). The patients, their GPs and the principal investigator were blinded for the study medication. All practices were located in Maastricht, in the south of the Netherlands.
Included are adult patients who used BD as a sleeping medication for more than three months (defined as long-term use) with a minimum use of three days per week. Patients were selected via their GPs, in six practices through the desk prescriptions, in two on the basis of a pharmacy print out, and in one through the problem list in the medical file on the ICPC-code P17 (drug addiction) [12]. Exclusion criteria were the use of more than one BD at the same time, use of another type of sleep medication, use of stimulants and, according to their GP, alcohol misuse, serious mental/somatic disease or unfit to participate.
All patients who were selected received an invitation letter from their GP that also informed them of the disadvantages of long-term use of BD. A short questionnaire was added to check the inclusion criteria. All respondents meeting the inclusion criteria received by mail a questionnaire (T0): about the use of sleeping medication, sleep complaints (SWEL) [13], sleep quality [14], general health experience (RAND-36) [15], BD dependence (Bendep-SRQ) [16] and habit formation (amount of alcoholic consumptions and/or cigarettes/cigars per day). The sleep wake experience list (SWEL) is a validated instrument to study chronic sleep complaints (insomnia, hypersomnia or the combination of those two). The benzodiazepine dependence self-report questionnaire (Bendep-SRQ) is a validated instrument to measure BD dependency, scoring in subscales the degree of awareness of problematic BD use, degree of preoccupation with respect to the availability of BDs, degree of lack of compliance with the therapeutic BD regimen, and the degree of unambiguity of experienced BD withdrawal (Bendep-SRQ, scale 4)
At the end of the questionnaire, we asked specifically whether the patient was willing to discontinue use of BDs. Participants had to send back an informed consent form. All participants were checked for liver and kidney function and their urine was tested for BD with a fluorescence polarization immunoassay (FPIA) [17].
After returning the informed consent, patients were included for random allocation.
The use of sleeping medication at the start of the study was compared with the defined daily dose (DDD) of the patient’s hypnotic. It was expressed as a quotient of prescribed daily dose (PDD) relative to the defined daily dose, i.e., PDD/DDD. The PDD was based on the completed questionnaire (T0). We created three categories using this quotient: low (quotient ≤ 0.5), average (0.5 < quotient < 1.0) and high use (quotient ≥ 1.0).
All participants received a stopping scheme of their BD via their own pharmacy. Their BD was converted to an equivalent dose of diazepam [18] that was stabilized for two weeks and then further converted every two weeks to 75%, 50%, 25%, 12.5% and 0% of the original dose. We added 5 mg melatonin or placebo which had to be taken 4 h before patients went to bed. After stopping BD we continued the use of melatonin or placebo for six more weeks. Furthermore a questionnaire was sent at 18 weeks (T1), 26 weeks (T2) and 52 weeks (T3) after the beginning of the discontinuation. All questionnaires contained the same set of instruments. Also urine samples for BD determination were required at T1 and T3.
Results
The GPs selected 503 patients for our trial, 138 males and 365 females. Of these, 80% responded to our invitation letter (Fig. 1). Six patients reported to have stopped their use of sleeping medication already. Sixteen patients used two or more BDs, and were excluded from the study. Of all responders, 124 appeared to be willing to participate.
Fig. 1Selection of patients and response/non-response; participants
Receiving the first questionnaire of these 124 patients, 22 did not respond, 56 decided not to participate and eight patients did not meet the inclusion criteria (six of whom had already stopped the BD use). At the end, only 38 participants (16 males and 22 females) were indicated to take part in the study. They were randomly allocated to melatonin or placebo.
The participants compared to the non-participants were more often males (40% vs. 25%), and had a lower age (<50 years, 16% vs. 5%) and less elderly (>80 years, 5% vs. 18%). Of the total group, 76% were 60 years or older.
The characteristics of all participants are listed in Table 1. No differences were found between the melatonin and the placebo group. The sleep quality was rated bad in 58% of the cases. According to the SWEL 10 of the 38 participants reported that they did not have any sleeping complaints (any more) Of the other 28, 18 complained of insomnia, three of hypersomnia, and seven of a combination of both. Most participants (76%) used a low dose of BD (PDD/DDD < 1.0). According to the score on the Bendep-SRQ list, 61% of the participants were aware that they had problematic BD use.
Table 1Characteristics of the 38 participants, and for melatonin and placebo separatelyMelatonin group n = 20 numbers (%)Placebo group n = 18 numbers (%)Total participants n = 38 numbers (%)GenderMale6 (30)10 (56)16 (42)Female14 (70)8 (44)22 (58)Age in years<503 (15)3 (17)6 (16)50–593 (15)3 (17)6 (16)60–696 (30)7 (39)13 (34)70−797 (35)4 (22)11 (29)≥801 (5)1 (5)2 (5)Health insuranceNational health insurance16 (80)18 (100)34 (90)Private insurance4 (20)–4 (10)Sleep qualityGood7 (35)5 (28)12 (32)Bad11 (55)11 (61)22 (58)2 missing2 missing4 missingPeriod of use of BD<1 year–––1−5 years7 (35)7 (39)14 (37)6−9 years4 (20)4 (22)8 (21)≥10 years9 (45)6 (34)15 (39)1 missing1 missingDaily use of BD (PDD/DDD)≤0.5 (low)11 (55)14 (78)25 (66)0.5–1.0 (moderate)4 (20)–4 (10)≥1.0 (high)5 (25)4 (22)9 (24)SmokingYes9 (45)7 (39)16 (42)No10 (50)11 (61)21 (55)1 missing1 missingAlcohol useYes15 (75)15 (83)30 (79)No5 (25)3 (17)8 (21)Body mass index (BMI)Normal8 (40)10 (56)18 (47)BMI > 2512 (60)8 (44)20 (53)Problematic BD use (subscale of Bendep-SRQ)Low6 (30)2 (11)8 (21)Average3 (15)2 (11)5 (13)High11 (55)13 (72)23 (61)1 missing1 missingNone of the differences were statically significant at the 5% level (χ2-test)
Twenty-two participants had tried to stop BD use in the past, and 12 of them experienced a high level of withdrawal symptoms.
In total 21 patients discontinued the use of BD after the taper off: 12 in the melatonin group and nine in the placebo group (Table 2). After one year 15 patients remained (40%) without BD sleeping medication (eight out of 20 in the melatonin group and seven out of 18 in the placebo group). This difference was not significant.
Table 2Follow up after one year. Use of BD sleeping medication after the taper off and during follow up. Separate data for the melatonin and placebo groupStopped on T1Did not resume on T2Definitely stopped on T3Melatonin *** on T3Melatonin group (n = 20)12108*2Placebo group (n = 18)977* (1**)1Total2117153T1 = at the time of stopping the use of melatonin or placebo (six weeks after the taper off of BD)T2 = after six monthsT3 = after one year* One person in the placebo group and one in the melatonin group stopped participation in the trial after the taper off. According to their GPs, they no longer used any BD at T3** One person had resumed using BD at T2, but stopped again at T3. He is not counted as a definite stopper*** Three persons received melatonin from their GP after discontinuing the use of melatonin in the trial
The urine analysis showed that two patients in the placebo group, who had reported that they had stopped using BD, were still positive. They had received new prescriptions of BD at one and three months after T3, although at a lower dose.
At the end of the trial three persons used melatonin, two in the melatonin group and one in the placebo group. Of the patients who had stopped, four had switched to a herb product (valerian, Valdispert®) or homeopathic product (Nervogin®, passiflora).
The patients who had totally discontinued BD use were compared with the non-stoppers in terms of gender, age, weight, period of use, PDD, sleeping quality, general health experience and awareness of problematic use. We found small differences, which were not statistically significant at the α = 0.05 level (Table 3). Of the patients who had already used BD for five years or more, 35% stopped compared to 43% of the patients with a shorter period of use. For high daily use (PDD/DDD ≥ 1), this was 33% compared to 41% for daily use <1. Of the patients who were aware of their problematic use, 54% stopped compared to 29% among the others.
Table 3Putative indicators of discontinuation of benzodiazepines (numbers and percentages)Definite stoppersNon-stoppersTotalMale6 (38%)10 (62%)16Female9 (41%)13 (59%)22Age <656 (33%)12 (67%)18Age 65+9 (45%)11 (55%)20Body mass index <256 (33%)12 (67%)18Body mass index ≥259 (45%)11 (55%)20Period of use <5 year6 (43%)8 (57%)14Period of use ≥5 year8 (35%)15 (65%)23 (1 missing)PDD/DDD < 1.012 (41%)17 (59%)29PDD/DDD ≥ 1.03 (33%)6 (67%)9Awareness of problematic use, low7 (29%)17 (71%)24Awareness of problematic use, high7 (54%)6 (46%)13 (1 missing)General health, low6 (40%)9 (60%)15General health, high9 (41%)13 (59%)22 (1 missing)Sleeping quality, bad8 (36%)14 (64%)22Sleeping quality, good6 (50%)6 (50%)12 (4 missing)None of the differences were statistically significant at the 5% level (χ2 test)
Four of the 19 patients who had stopped at T1 reported a high level of withdrawal symptoms during the taper off. We have no data for two people because they did not complete the questionnaire.
To measure the effects of discontinuation we compared the scores of T0 (initial measurement) with those at T3 (final measurement). In terms of health experience, there were no differences between the stoppers and non stoppers according to the scores on nine scales of the RAND questionnaire, sleeping quality, alcohol consumption, or smoking.
Discussion
In our trial, we could not confirm the positive outcome of Garfinkel et al. [10]. Our findings are in agreement with those of Cardinali et al. [11].
The success rate of our study was 40%, higher than of that of spontaneous stopping (10–15%) [19, 20] and stopping after an informative letter or minimal intervention strategy (25%) [3–5]. This may be caused by the use of our taper-off program, the selection by some GPs and the selective response, including the awareness of problematic use (in two-thirds of the participants). In general, readiness to stop using BD sleeping medication was low. In our trial we fell well short of the discontinuation rate in the melatonin group of the trial of Garfinkel et al. (14 out of 18).
After a minimum intervention, it appears that of the stoppers after two years only half remain without this medication [21]. In our trial, 55% of the participants had stopped shortly after the taper off. However, after a year this dropped to 40%. We omitted any supportive measures during follow up as much as possible in order to measure the pure effect of melatonin.
The gender difference in our selected population (n = 503) is comparable to figures in the literature [1]. BD use among Garfinkel et al.’s patients was higher than ours (only nine of our patients had PPD/DDD ≥ 1.0). It has been shown that patients on a rather low dose of BD discontinue their use more easily and resume their BD use less rapidly [21]. For patients with a high daily use melatonin may still be valuable. If there will be future discontinuation trials there is reason to especially include patients who use a high dose of BD.
In our trial, in contrast to that of Garfinkel et al., at the end only 9% still used melatonin. Four participants changed their use to a homeopathic or phytotherapeutic medication, which indicates a basic need to use something for their sleeping problem.
Half of the patients who tried to stop by themselves before participating in our study, without taper off, suffered from withdrawal symptoms. In our trial this was 25%, showing the advantage of gradual tapering. We did not find a shift of addiction behavior, sleep quality or general health among stoppers of BD.
Conclusions
Our trial does not provide conclusive evidence that melatonin is helpful for BD discontinuation.
The overall question about the effectiveness of intervention remains. Another trial, in contrast to the first trial, shows a negative result. One should consider that the results of all three trials are influenced by a possible selection bias or lack of power, due to the low number of participants.
Further investigation is necessary, with special attention to the effect of the daily dose on stopping the use of BD. Finally one should take into account the difficulty of recruiting BD users for stop studies.

melatonin
hypnotics
discontinuation
the netherlands
long-term benzodiazepine use
primary care