Hot topics in behavioral and neural genetics

The Fourth Annual Meeting of the International Behavioral and Neural Genetics Society (IBANGS) was held in San Diego, CA, USA on November 8–10, 2001, as an official satellite symposium of the Annual Meeting of the Society for Neuroscience (SFN). The meeting was a huge success, attracting over 100 participants from many different countries. During the meeting, the first issue of the new journal, Genes, Brain and Behavior (or G2B for short), was presented to the membership and then during the subsequent SFN meeting to the neuroscience community as a whole and was generally very well received. The IBANGS meeting was not only a success quantitatively, but also qualitatively. In what follows, several participants report on the highlights of the different sessions of the meeting. Obviously, not all presentations could be discussed. However, the program and abstracts of the meeting (as well as those of previous meetings) will remain available online at the IBANGS website (http://www.ibngs.org). The molecular traces of memories are subtle and the successful hunt for these ghosts in the machine requires experimental finesse, tenacity, and effective models. The 2001 San Diego IBANGS meeting opened with presentations by Mark Mayford (Scripps Research Institute) and Josh Dubnau (Cold Spring Harbor) on the potential of harnessing microarrays to reveal the molecular footprints of long-term memory. Mayford's presentation (‘Microarrays, mutants and memory’) highlighted a number of the technical and conceptual hurdles that need to be leapt over, or side-stepped, in order to define the set of genes whose expression is modulated during learning. Dubnau's presentation (‘Functional genomics of memory in Drosophila’) evoked yet more fly envy among most mammals in the audience and affirmed the statistical maxim of strength in numbers. The key technical issue can be crudely rephrased as questions about the size and location of an engram. Is memory distributed or focal? Does long-term memory involve limited populations of cells and synapses – the so-called sparse encoding model – or does memory have a larger cellular and synaptic impact? If laying down a new memory involves tweaking a few spines and synapses then the typical ‘A vs. B’ whole tissue comparisons (a chunk of striatum/amygdala/hippocampus or the whole fly head in Mayford's and Dubnau's cases, respectively) may be frustrating and unfruitful. RNA profiling may need to achieve quantal levels of resolution and very high signal-to-noise (S : N) ratios. The Affymetrix microarrays used by both Mayford and Dubnau are sensitive down to sub picomolar concentrations – five to 10 transcripts per cell – but data generated by single arrays are still noisy, and detecting the modulation of low abundance transcripts requires a large population of responsive cells and a large number of arrays. S : N ratios are a function of the square root of N, and in those cases in which N is a $500 microarray one needs conviction, deep pockets, and statistical savvy to highlight the right set of genes. The lingering question is whether strength in numbers will be sufficient to detect and read the molecular signature of the engram. Mayford's array experiments started with a well-designed fear conditioning paradigm intended to trigger changes in the amygdala. The experiments exploited four treatment groups: (1) paired-Pavlovian tone and shock; (2) semirandom unpaired tone and shock; (3) tone without shock; (4) handling control. This design enabled Mayford and colleagues to dissociate stress, electrical shock, and the conditioning to the cue and its context (a box) itself at the transcript level. The scientific hypothesis driving the research is that the transfer of a memory from short-term to long-term memory involves gene transcription triggered by a CREB/calmodulin binding protein-dependent pathway and bursts in nuclear calcium ion concentration. The microarrays were run in triplicate using tissue that included most of the lateral amygdala. The mildest treatment (handling alone) is apparently sufficiently stressful to mice to modulate gene expression in the amygdala. Surprisingly, the addition of a highly aversive foot shock did not modulate gene expression anymore than simple handling. While 21 genes were nominated by the Pavlovian conditioning paradigm using the criteria described by Sandberg et al. (2000; > 1.8x change in three out of four arrays), none of these candidates survived more stringent secondary screens. The apparent lack of a significant response may be owing to sparse encoding of memory combined with still low power and low S : N ratio of first generation microarray data generated using small numbers of mice (∼four inbred mice per group). Josh Dubnau, an investigator working with Tim Tully at Cold Spring Harbor Laboratory, provided a note of optimism. Thousands of Drosophila (wildtype, amnesiac and nalyot mutants) were taught simple odor discrimination tricks (motivated by shock) that involve CREB-dependent synaptic changes in the mushroom body. The mushroom bodies are a large part of the fly's brain, and consist of about 5000 cells or 5–10% of the brain. Pools of message extracted from 1000 flash-frozen heads of trained or untrained flies were hybridized to Affymetrix arrays that screen about 1500 transcripts or 10% of the fly genome. Given the high noise level of single array data, Dubnau and colleagues pooled data from 10 arrays per group, a tactic that allowed them to use conventional parametric statistics to search for differences. An appreciable number of transcripts (∼176) were nominated in this process. A spot check of 21 of these genes by quantitative polymerase chain reaction (PCR) confirmed about 30%. Just over 10 of the nominees contained CRE promoter sites consistent with a role of cAMP responsive element binding protein (CREB) in the memory consolidation process. milord and approximately half of the genes in the extended milord network were highlighted by the analysis. milord itself is expressed in the mushroom bodies and calyces, a finding that makes sense functionally and anatomically for an olfactory learning task. The power of an array analysis of 1000 heads and 10 arrays is clearly sufficient to generate highly informative lists of strong candidates involved in memory consolidation. The hope among many of us is that there will be sufficient conservation of memory networks that we will be able to carry out comparisons of these same molecules and pathway in rodent models of learning and memory. No biological process or phenotypic trait can be understood without a detailed knowledge of the underlying genetic architecture. This requires isolating all of the genes whose products contribute to the generation of a phenotype or process, discovering the cellular/biochemical functions of each gene or gene product and understanding the complexity of epistatic interactions among the network of genes. Historically, geneticists have tried to tackle this daunting task via the separate use of two very different approaches: a quantitative-genetic analysis of naturally occurring variation on the one hand (the ‘Hirschian’ method) and a single-gene mutant approach relying on induced mutations on the other hand (the ‘Benzerian’ method; Tully 1996). The symposium on Drosophila neurobehavioral genetics provided a vivid illustration of the timeliness and necessity of a synthesis of the two approaches for the study of behavior. The effort to genetically dissect complex behaviors, which began in the 1960s, has been very difficult. One reason for this is pleiotropy. Most gene products function in numerous biological processes and therefore null mutations often result in severe and complex defects that preclude establishment of simple gene-to-behavior causality. Thus, behaviorally specific phenotypes likely result from unusually subtle alleles. Ralph Greenspan (University of California, San Diego) offered a dramatic illustration of this point. As he noted, very small magnitude changes in enzymatic activity can cause behaviorally specific defects. A 20% decrease in CaM kinase II activity, for instance, results in a deficit in courtship conditioning. In contrast, a 10% reduction in activity permits the initial courtship conditioning but disrupts retention. However, null alleles of CaMKII are lethal. As Greenspan pointed out, the finding that subtle biochemical defects underlie behavioral variation seems to be the rule rather than the exception. A mere 10% alteration in PKG activity resulting from a hypomorphic allele in the foraging (for) locus can also dramatically alter behavior, in this case causing a switch between two food search strategies (see also below). Thus, genetic variants with behaviorally specific phenotypes might only result from ‘special’ mutant alleles whose pleiotropies do not interfere with quantification of a behavioral phenotype. Might this pose a problem for mutagenesis screens for large magnitude effects? This problem is confounded by a second one: genes are not biologically isolated from each other, but instead exist as components of complex interacting networks. While this notion is not new, it was aptly illustrated by work in which Greenspan used DNA microarrays to profile the gene expression in the high and low geotaxis lines originally selected by Jerry Hirsch. This remarkable experimental fusion of pre- and postgenomic approaches showed that quantitative alterations in geotaxis are associated with very complex genome-wide compensation in gene expression. Capitalizing on the genetic resources available to the modern Drosophilist, it was subsequently demonstrated that mutations in several of the differentially expressed genes actually entail altered the geotactic behavior. Thus, even subtle alleles can alter a precarious genomic balance causing numerous compensatory changes in the regulation of many different genes until a new balance is achieved. Attempting to map and understand these myriad gene interactions is a daunting task. As a result of this genomic pleiotropy, Greenspan argues, it will be very difficult to draw simple mechanistic conclusions about the role of a gene or pathway in the behavior of wild populations based solely on the effects of lesions in single genes. Instead, we need to keep in mind the complexity of multi-gene interactions that might underlie a behavioral phenotype. While this is a rather quantitative-genetic outlook, it still strives to understand biochemical/molecular mechanisms of complex traits. Next, Trudy Mackay (North Carolina State University) showed several beautiful illustrations of the power of quantitative-genetic approaches when combined with the formidable genetic toolbox resulting from many years of single gene approaches in Drosophila. Using classical breeding schemes, Mackay and her coworkers first selected for phenotypic extremes of behaviors such as locomotion, mating speed and olfaction. Next they used quantitative trait locus (QTL) mapping to link these behaviors to chromosomal regions. At this stage, most quantitative geneticists hit a roadblock in their gene discovery effort. As Mackay pointed out, even a 5–10 cM region of the fly genome (rather small in QTL terms) will typically contain 1000 genes. This is far too many to rely on candidate gene approaches to find the relevant gene. Mackay solved this seemingly insurmountable difficulty by a technique she calls quantitative complementation which derives from deficiency mapping, a common trick in the world of Drosophila genetics. Numerous deletion mutants (deficiency stocks) are currently available. Each line contains a small deletion (which in many cases is well mapped to the physical genome) that removes a more manageable number of genes. Deficiency mapping involves finding the smallest deficiency region that fails to complement a mutant of interest (in this case a QTL). Mackay and her coworkers used this approach to great success. In the case of a QTL for olfactory behavior, for instance, they were able to reduce the candidate gene list by several orders of magnitude to roughly 10 genes. It is worth noting that this process also revealed that this olfactory QTL, which originally did not appear to be sex specific, could be broken down into two linked QTL, one of which was male specific, and the other female specific. In other words, the original QTL was caused by at least two distinct but linked genes. By testing all extant mutations in genes in this region they were able to find one that failed to complement the female specific olfactory defect. This gene, named vanaso, encodes a novel protein with a predicted guanosine triphosphate (GTP) binding region. In what was surely an intellectually satisfying result, Mackay and her lab team found that the vanaso expression appears specific to the third antennal segment. This success story points to the potential synergy of QTL mapping and forward mutagenesis in the context of a genetic powerhouse like D. melanogaster. Could this same philosophical outlook succeed in identifying the allelic variation underlying naturally occurring behavioral variation? Craig Riedl (University of Toronto) argued that it could. In addition to choosing animals exhibiting phenotypic extremes in behavior, Marla Sokolowski and her lab team have taken advantage of naturally occurring variations to dissect the genetic architecture of behaviors such as geotaxis, locomotion, pupation position and foraging for food. So far, the best success story involves a gene involved in larval foraging behavior. Wild populations of flies seem to fall into two groups that use different feeding strategies. The so-called ‘sitters’ have normal locomotion, but tend to feed in one place. In contrast, ‘rovers’ tend to move around in search of food. Sokolowski and her coworkers were able to map this behavioral phenotype to the 24A3-4 region of ChrII. Using a candidate gene approach, they were next able to show that an extant mutation in a cGMP-dependent kinase (which mapped to the same locus; Kalderon & Rubin 1989) also altered this larval feeding strategy. Sokolowski and her coworkers have demonstrated that the switch from ‘roving’ to ‘sitting’ behavior is the result of a mutation in a single gene called for, which encodes this cGMP-dependent kinase. Notably, just a 10% reduction (a defect of Greenspanian magnitude, see above) in PKG activity causes a switch from rover to sitter behavior. Again the combination of quantitative genetics and single gene mutant approaches facilitated the establishment of a causal link between a gene and behavior. Riedl also described ongoing work in the Sokolowski group to apply the same approaches to the discovery of genes involved in several other behaviors such as the distance larvae pupate from the food and the localization of modifiers of the foraging gene. While these behaviors have previously been analyzed using quantitative genetic approaches, the usual roadblock was reached because the alleles involved had relatively small effects in natural populations: they could not be mapped. Only with the QTL mapping approaches that Mackay has developed has it been possible to now go back to these phenotypes and localize the genes involved. It is notable that for three out of the four QTL analyses that Riedl presented (pupation distance, foraging behavior, and diapause) only a single significant QTL was found. In the fourth case (geotaxis), four QTL were found. The chromosomal localization of these single QTL corresponded well with that derived from Sokolowski's previous quantitative analyses of these traits. That they found significant QTL suggests that there are genes in natural populations that have measurable effects on behavior. These genes are well on the road to being mapped using quantitative complementation analysis combined with standard single gene mutant analyses (the Mackay approach). As all three talks in this symposium illustrated, a comprehensive biological understanding of behavioral traits is refractory both to quantitative genetic and to single-gene mutational analysis on their own. The richness of well-characterized behavioral paradigms, the wealth of genetic tools and the recent revolution in genomic informatics provide the means to do integrative neurobehavioral research in Drosophila. One can envision the concordant use of selective breeding and forward mutagenesis together with the potential to monitor the myriad genomic responses to these perturbations. The future of behavior-genetics seems to promise a marriage of quantitative and single gene approaches. This will be an arranged marriage brokered by those who understand and appreciate both experimental strategies. Over the past few decades a lot of energy has been expended on the refinement of clinical descriptions and definitions of psychiatric disorders in humans. This has led to an increasing awareness of the complexities of these disorders, including overlap between them. It comes as no surprise therefore that despite considerable effort only limited progress has been made in understanding the nature of the inherited causative neurobiology. One approach that is likely to increase power to find vulnerability genes is to utilize dimensional or quantitative measures and associated biological traits, including intermediate phenotypes, which may be influenced by variation at fewer genes and may mediate different aspects of the disease. So, how does one put theory into practice? The participants in this symposium discussed the ways in which the use of intermediate phenotypes is beginning to reap results in candidate gene analysis for schizophrenia, alcoholism and anxiety disorders. Intermediate phenotypes for alcoholism can be identified within premorbid drug-seeking behavior, acute responses to alcohol consumption and long-term tolerance and craving. David Goldman (NIAAA) started off by reviewing the genetic variation in alcohol metabolism and response to alcohol consumption. Recognition of the intermediate phenotype, facial flushing in response to alcohol, led to the elucidation of the genetic variation in alcohol metabolizing genes. Approximately half the population of South-East Asians have two different genetic variants: ADH2*2, which increases the metabolism of ethanol to acetaldehyde, and ALDH2*2 which inactivates ALDH, the enzyme that converts acetaldehyde to acetate. This means that ingestion of even small amounts of ethanol elevates acetaldehyde levels and produces an unpleasant systemic reaction, including flushing, headache and tachycardia. A dimensionally measured lower subjective and physiological response to alcohol in alcohol-naïve sons of alcoholics is associated with a four-fold increase in the risk of future genes for in ethanol response may be found in the and which are also key in an phenotype in one of Goldman the for an promoter the allele of which has been associated with dimensional measures of and The allele of a of has been associated with alcohol in combination with the of Goldman described whole genome on and which the and in and on the region on in the alcohol and alcohol to a In both populations the location of the locus was to be within the or in the between the and the gene for alcohol phenotypes for alcoholism and anxiety disorders – intermediate mechanisms in the – are the low and the potential is the at in one of 10 is a highly of the in which the is or are in response to or and are sensitive measures of An finding is that the a of and is of lower in alcoholics and alcohol-naïve of (NIAAA) presented recent work from on and both groups from the that is three more common in alcoholics than and is in alcoholics who have anxiety disorders. with anxiety disorders is associated not only with but also with These and intermediate phenotypes are now being used in candidate gene analysis. A candidate for alcoholism and anxiety vulnerability is the gene for a enzyme involved in the metabolism of and in the group found that the low activity allele of the and the was associated both with and a dimensional of in both and that the of be via in anxiety disorders, which increases activity in the (the of is associated with power and are likely to A of is function of the are several intermediate phenotypes which might be used to genetic on working and and measures presented results from a large study of and and their showed significant in working as measured by the In during working memory demonstrated that both and their showed for a level of their is with Thus, working memory appears to be a intermediate phenotype for vulnerability to An candidate gene is which the of response during working memory. they the of the with both and in this data found not only was the high activity allele to the in it was also associated with on the and of the in of in a of showed that the low activity allele predicted a more response in the most likely by The is involved in memory. a of is in the of and their a of synaptic which may underlie is facilitated by the The was for a group found that the was associated with there was no on the risk for that the result may be owing to effects and limited or that the memory problem in is encoding and that may be more for or consolidation. The presentation as a potential intermediate phenotype for and a variation in is a of the effects of by on is associated with activity to the in (NIAAA) a of that it is best measured by the analysis. at the effects of and on in four groups: and In all groups with and had than and had the same levels of which were than in and results were presented which showed that is associated with and this may be a trait is in alcoholics but this may be a of demonstrated that the of the owing to and effects by a analysis the of at least two distributed population means and suggests a trait for may be modulated by genes for and described with which showed that but the response was with as measured by the response to The neurobehavioral genetics began by a presentation by on the effects of and on behavior and gene expression. and colleagues used a of behavioral to the effects of was by a a and and by the animals three to one of of a an and In these isolated animals on most of these to Thus, these results the notion that functions as well as on testing for by use of microarray gene expression in the of the between isolated were found. These data that may entail and to long-term changes in behavioral This also the of the use of these mice as models for psychiatric disorders and as tools for Next, Research Institute) presented data on that behavior in a new mutant called of the calcium gene, on This mutant was from a screen null mice were used to In this have and show as These within of with only subtle changes in are and and colleagues were able to show by a of that a gene on and derived from can increase the expression of this In the also of mice with one null allele were as to in This was by one from (University on the of neurobehavioral phenotypes in the and In for behavioral responses to and for to were or by a of food In an conditioning mice showed a for showed an However, when food was mice showed a for rather than an of and was measured with and without a food A food caused the but not the to increase its at a low of comparisons of with also that

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.