The genetic factor structure of a range of learning measures was explored in twin children, recruited in preschool and followed to Grade 2 (total N = 2084). Measures of orthographic learning and word reading were included in the analyses to determine how these patterned with the learning processes. An exploratory factor analysis of the genetic correlations among the variables indicated a three-factor model. Vocabulary tests loaded on the first factor, the Grade 2 measures of word reading and orthographic learning, plus preschool letter knowledge, loaded on the second, and the third was characterized by tests of verbal short-term memory. The three genetic factors correlated, with the second (print) factor showing the most specificity. We conclude that genetically-influenced learning processes underlying print-speech integration, foreshadowed by preschool letter knowledge, have a degree of independence from genetic factors affecting spoken language. We also argue that the psychology and genetics of associative learning be afforded a more central place in studies of reading (dis)ability, and suggest some links to molecular studies of the genetics of learning.
Molecular and electrophysiological properties of NMDARs suggest that they may be the Hebbian “coincidence detectors” hypothesized to underlie associative learning. Because of the nonspecificity of drugs that modulate NMDAR function or the relatively chronic genetic manipulations of various NMDAR subunits from mammalian studies, conclusive evidence for such an acute role for NMDARs in adult behavioral plasticity, however, is lacking. Moreover, a role for NMDARs in memory consolidation remains controversial.
The Drosophila genome encodes two NMDAR homologs, dNR1 and dNR2. When coexpressed in Xenopus oocytes or Drosophila S2 cells, dNR1 and dNR2 form functional NMDARs with several of the distinguishing molecular properties observed for vertebrate NMDARs, including voltage/Mg2+-dependent activation by glutamate. Both proteins are weakly expressed throughout the entire brain but show preferential expression in several neurons surrounding the dendritic region of the mushroom bodies. Hypomorphic mutations of the essential dNR1 gene disrupt olfactory learning, and this learning defect is rescued with wild-type transgenes. Importantly, we show that Pavlovian learning is disrupted in adults within 15 hr after transient induction of a dNR1 antisense RNA transgene. Extended training is sufficient to overcome this initial learning defect, but long-term memory (LTM) specifically is abolished under these training conditions.
Our study uses a combination of molecular-genetic tools to (1) generate genomic mutations of the dNR1 gene, (2) rescue the accompanying learning deficit with a dNR1+ transgene, and (3) rapidly and transiently knockdown dNR1+ expression in adults, thereby demonstrating an evolutionarily conserved role for the acute involvement of NMDARs in associative learning and memory.
To consider recent findings from quantitative genetic research in the context of molecular genetic research, especially genome-wide association studies. We focus on findings that go beyond merely estimating heritability. We use learning abilities and disabilities as examples.
Recent twin research in the area of learning abilities and disabilities was reviewed.
Three findings from quantitative genetic research stand out for their far-reaching implications for child and adolescent psychiatry. First, common disorders such as learning difficulties are the quantitative extreme of the same genetic factors responsible for genetic influence throughout the normal distribution (the Common Disorders are Quantitative Traits Hypothesis). Second, the same set of genes is largely responsible for genetic influence across diverse learning and cognitive abilities and disabilities (the Generalist Genes Hypothesis). Third, experiences are just as influenced genetically as are behaviors and genetic factors mediate associations between widely used measures of the environment and behavioural outcomes (the Nature of Nurture Hypothesis).
Quantitative genetics can go far beyond the rudimentary ‘how much’ question about nature versus nurture, and can continue to provide important findings in the era of molecular genetics.
Quantitative genetics; molecular genetics; twin studies; learning abilities; disabilities
We identified the neurons comprising the Drosophila mushroom body (MB), an associative center in invertebrate brains, and provide a comprehensive map describing their potential connections. Each of the 21 MB output neuron (MBON) types elaborates segregated dendritic arbors along the parallel axons of ∼2000 Kenyon cells, forming 15 compartments that collectively tile the MB lobes. MBON axons project to five discrete neuropils outside of the MB and three MBON types form a feedforward network in the lobes. Each of the 20 dopaminergic neuron (DAN) types projects axons to one, or at most two, of the MBON compartments. Convergence of DAN axons on compartmentalized Kenyon cell–MBON synapses creates a highly ordered unit that can support learning to impose valence on sensory representations. The elucidation of the complement of neurons of the MB provides a comprehensive anatomical substrate from which one can infer a functional logic of associative olfactory learning and memory.
One of the key goals of neuroscience is to understand how specific circuits of brain cells enable animals to respond optimally to the constantly changing world around them. Such processes are more easily studied in simpler brains, and the fruit fly—with its small size, short life cycle, and well-developed genetic toolkit—is widely used to study the genes and circuits that underlie learning and behavior.
Fruit flies can learn to approach odors that have previously been paired with food, and also to avoid any odors that have been paired with an electric shock, and a part of the brain called the mushroom body has a central role in this process. When odorant molecules bind to receptors on the fly's antennae, they activate neurons in the antennal lobe of the brain, which in turn activate cells called Kenyon cells within the mushroom body. The Kenyon cells then activate output neurons that convey signals to other parts of the brain.
It is known that relatively few Kenyon cells are activated by any given odor. Moreover, it seems that a given odor activates different sets of Kenyon cells in different flies. Because the association between an odor and the Kenyon cells it activates is unique to each fly, each fly needs to learn through its own experiences what a particular pattern of Kenyon cell activation means.
Aso et al. have now applied sophisticated molecular genetic and anatomical techniques to thousands of different transgenic flies to identify the neurons of the mushroom body. The resulting map reveals that the mushroom body contains roughly 2200 neurons, including seven types of Kenyon cells and 21 types of output cells, as well as 20 types of neurons that use the neurotransmitter dopamine. Moreover, this map provides insights into the circuits that support odor-based learning. It reveals, for example, that the mushroom body can be divided into 15 anatomical compartments that are each defined by the presence of a specific set of output and dopaminergic neuron cell types. Since the dopaminergic neurons help to shape a fly's response to odors on the basis of previous experience, this organization suggests that these compartments may be semi-autonomous information processing units.
In contrast to the rest of the insect brain, the mushroom body has a flexible organization that is similar to that of the mammalian brain. Elucidating the circuits that support associative learning in fruit flies should therefore make it easier to identify the equivalent mechanisms in vertebrate animals.
mushroom body; olfactory learning; associative memory; neuronal circuits; dopamine; plasticity; D. melanogaster
Studies suggest that genetic factors are associated with the etiology of learning disabilities. Incontinentia Pigmenti (IP, OMIM#308300), which is caused by mutations of the IKBKG/NEMO gene, is a rare X-linked genomic disorder (1∶10000/20∶000) that affects the neuroectodermal tissues. It always affects the skin and sometimes the hair, teeth, nails, eyes and central nervous system (CNS). Data from IP patients demonstrate the heterogeneity of the clinical phenotype; about 30% have CNS manifestations. This extreme variability suggests that IP patients might also have learning disabilities. However, no studies in the literature have evaluated the cognitive profile of IP patients. In fact, the learning disability may go unnoticed in general neurological analyses, which focus on major disabling manifestations of the CNS. Here, we investigated the neuropsychological outcomes of a selected group of IP-patients by focusing on learning disabilities. We enrolled 10 women with IP (7 without mental retardation and 3 with mild to severe mental retardation) whose clinical diagnosis had been confirmed by the presence of a recurrent deletion in the IKBKG/NEMO gene. The participants were recruited from the Italian patients' association (I.P.A.SS.I. Onlus). They were submitted to a cognitive assessment that included the Wechsler Adult Intelligence scale and a battery of tests examining reading, arithmetic and writing skills. We found that 7 patients had deficits in calculation/arithmetic reasoning and reading but not writing skills; the remaining 3 had severe to mild intellectual disabilities. Results of this comprehensive evaluation of the molecular and psychoneurological aspects of IP make it possible to place “learning disabilities” among the CNS manifestations of the disease and suggest that the IKBKG/NEMO gene is a genetic determinant of this CNS defect. Our findings indicate the importance of an appropriate psychoneurological evaluation of IP patients, which includes early assessment of learning abilities, to prevent the onset of this deficit.
Visual and verbal learning in a genetic metabolic disorder (cystinosis) were examined in the following three studies. The goal of Study I was to provide a normative database and establish the reliability and validity of a new test of visual learning and memory (Visual Learning and Memory Test; VLMT) that was modeled after a widely used test of verbal learning and memory (California Verbal Learning Test; CVLT). One hundred seventy-two neurologically intact individuals ages 5 years through 50 years were administered the VLMT and the CVLT. Normative data were collected and the results suggested that the VLMT is a reliable and valid new measure of visual learning and memory. The aim of Study II was to examine possible dissociations between verbal and visual learning and memory performances in individuals with cystinosis as well as to assess changes in performance as individuals with the disorder age. Thirty-seven individuals with cystinosis and 37 matched controls were administered a new test of visual learning and memory (Visual Learning and Memory Test; VLMT) and the California Verbal Learning Test (CVLT). Individuals with cystinosis performed at a lower level than controls on almost all indices of visual learning and memory while no differences were found between the groups on the verbal measure. Examination of the results on the VLMT indicated that the visual learning and memory impairment in cystinosis may result from difficulty with processing visual information quickly. Study III aimed to remediate the observed visual learning and memory deficit by implementing an intervention that increased the exposure time for visual stimuli. Fifteen individuals with cystinosis were administered a version of the VLMT in which the stimuli were exposed for 3-seconds rather than 1-second. Fifteen matched controls were administered the 1-second version of the VLMT. The results of Study III indicated that by increasing the exposure time for each visual stimulus, individuals with cystinosis were able to perform at the same level as control subjects. This is the first study to demonstrate impaired visual learning and spared verbal learning in individuals with cystinosis. These results may provide the foundation for designing cognitive interventions, may lead to further hypotheses regarding the underlying mechanism of the observed visual learning and memory deficit, and have implications for a greater understanding of gene-behavior relationships.
cystinosis; learning; memory; CVLT; white matter; non-verbal learning disability; lysosomal storage disease; visuospatial
Electrochemotherapy is an effective approach in local tumour treatment employing locally applied high-voltage electric pulses in combination with chemotherapeutic drugs. In planning and performing electrochemotherapy a multidisciplinary expertise is required and collaboration, knowledge and experience exchange among the experts from different scientific fields such as medicine, biology and biomedical engineering is needed. The objective of this study was to develop an e-learning application in order to provide the educational content on electrochemotherapy and its underlying principles and to support collaboration, knowledge and experience exchange among the experts involved in the research and clinics.
The educational content on electrochemotherapy and cell and tissue electroporation was based on previously published studies from molecular dynamics, lipid bilayers, single cell level and simplified tissue models to complex biological tissues and research and clinical results of electrochemotherapy treatment. We used computer graphics such as model-based visualization (i.e. 3D numerical modelling using finite element method) and 3D computer animations and graphical illustrations to facilitate the representation of complex biological and physical aspects in electrochemotherapy. The e-learning application is integrated into an interactive e-learning environment developed at our institution, enabling collaboration and knowledge exchange among the users. We evaluated the designed e-learning application at the International Scientific workshop and postgraduate course (Electroporation Based Technologies and Treatments). The evaluation was carried out by testing the pedagogical efficiency of the presented educational content and by performing the usability study of the application.
The e-learning content presents three different levels of knowledge on cell and tissue electroporation. In the first part of the e-learning application we explain basic principles of electroporation process. The second part provides educational content about importance of modelling and visualization of local electric field in electroporation-based treatments. In the third part we developed an interactive module for visualization of local electric field distribution in 3D tissue models of cutaneous tumors for different parameters such as voltage applied, distance between electrodes, electrode dimension and shape, tissue geometry and electric conductivity. The pedagogical efficiency assessment showed that the participants improved their level of knowledge. The results of usability evaluation revealed that participants found the application simple to learn, use and navigate. The participants also found the information provided by the application easy to understand.
The e-learning application we present in this article provides educational material on electrochemotherapy and its underlying principles such as cell and tissue electroporation. The e-learning application is developed to provide an interactive educational content in order to simulate the "hands-on" learning approach about the parameters being important for successful therapy. The e-learning application together with the interactive e-learning environment is available to the users to provide collaborative and flexible learning in order to facilitate knowledge exchange among the experts from different scientific fields that are involved in electrochemotherapy. The modular structure of the application allows for upgrade with new educational content collected from the clinics and research, and can be easily adapted to serve as a collaborative e-learning tool also in other electroporation-based treatments such as gene electrotransfer, gene vaccination, irreversible tissue ablation and transdermal gene and drug delivery. The presented e-learning application provides an easy and rapid approach for information, knowledge and experience exchange among the experts from different scientific fields, which can facilitate development and optimisation of electroporation-based treatments.
Vocal learning is a rare and complex behavioral trait that serves as a basis for the acquisition of human spoken language. In songbirds, vocal learning and production depend on a set of specialized brain nuclei known as the song system.
Using high-throughput functional genomics we have identified ∼200 novel molecular markers of adult zebra finch HVC, a key node of the song system. These markers clearly differentiate HVC from the general pallial region to which HVC belongs, and thus represent molecular specializations of this song nucleus. Bioinformatics analysis reveals that several major neuronal cell functions and specific biochemical pathways are the targets of transcriptional regulation in HVC, including: 1) cell-cell and cell-substrate interactions (e.g., cadherin/catenin-mediated adherens junctions, collagen-mediated focal adhesions, and semaphorin-neuropilin/plexin axon guidance pathways); 2) cell excitability (e.g., potassium channel subfamilies, cholinergic and serotonergic receptors, neuropeptides and neuropeptide receptors); 3) signal transduction (e.g., calcium regulatory proteins, regulators of G-protein-related signaling); 4) cell proliferation/death, migration and differentiation (e.g., TGF-beta/BMP and p53 pathways); and 5) regulation of gene expression (candidate retinoid and steroid targets, modulators of chromatin/nucleolar organization). The overall direction of regulation suggest that processes related to cell stability are enhanced, whereas proliferation, growth and plasticity are largely suppressed in adult HVC, consistent with the observation that song in this songbird species is mostly stable in adulthood.
Our study represents one of the most comprehensive molecular genetic characterizations of a brain nucleus involved in a complex learned behavior in a vertebrate. The data indicate numerous targets for pharmacological and genetic manipulations of the song system, and provide novel insights into mechanisms that might play a role in the regulation of song behavior and/or vocal learning.
Animals with rudimentary innate abilities require substantial learning to transform those abilities into useful skills, where a skill can be considered as a set of sensory–motor associations. Using linear neural network models, it is proved that if skills are stored as distributed representations, then within-lifetime learning of part of a skill can induce automatic learning of the remaining parts of that skill. More importantly, it is shown that this “free-lunch” learning (FLL) is responsible for accelerated evolution of skills, when compared with networks which either 1) cannot benefit from FLL or 2) cannot learn. Specifically, it is shown that FLL accelerates the appearance of adaptive behaviour, both in its innate form and as FLL-induced behaviour, and that FLL can accelerate the rate at which learned behaviours become innate.
Some behaviours are purely innate (e.g., blinking), whereas other, “apparently innate,” behaviours require a degree of learning to refine them into a useful skill (e.g., nest building). In terms of biological fitness, it matters how quickly such learning occurs, because time spent learning is time spent not eating, or time spent being eaten, both of which reduce fitness. Using artificial neural networks as model organisms, it is proven that it is possible for an organism to be born with a set of “primed” connections which guarantee that learning part of a skill induces automatic learning of other skill components, an effect known as free-lunch learning (FLL). Critically, this effect depends on the assumption that associations are stored as distributed representations. Using a genetic algorithm, it is shown that primed organisms can evolve within 30 generations. This has three important consequences. First, primed organisms learn quickly, which increases their fitness. Second, the presence of FLL effectively accelerates the rate of evolution, for both learned and innate skill components. Third, FLL can accelerate the rate at which learned behaviours become innate. These findings suggest that species may depend on the presence of distributed representations to ensure rapid evolution of adaptive behaviours.
Introduction: This study presents our online-teaching material within the k-MED project (Knowledge in Medical Education) at the university of Marburg. It is currently organized in five e-learning modules: cytogenetics, chromosomal aberrations, formal genetics, fundamentals of molecular diagnostics, and congenital abnormalities and syndromes. These are basic courses intended to do the educational groundwork, which will enable academic teachers to concentrate on more sophisticated topics during their lectures.
Methods: The e-learning modules have been offered to a large group of about 3300 students during four years at the Faculty of Medicine in Marburg. The group consists of science students (human biology) and medical students in the preclinical or the clinical period, respectively. Participants were surveyed on acceptance by evaluating user-tracking data and questionnaires.
Results and Conclusion: Analysis of the evaluation data proofs the broad acceptance of the e-learning modules during eight semesters. The courses are in stable or even increasing use from winter term 2005/06 until spring term 2009.
Conclusion: Our e-learning-model is broadly accepted among students with different levels of knowledge at the Faculty of Medicine in Marburg. If the e-learning courses are maintained thoroughly, minor adaptations can increase acceptance and usage even furthermore. Their use should be extended to the medical education of technical assistances and nurses, who work in the field of human genetics.
Human genetics; e-Learning; evaluation; multimedia
Behavioral inflexibility is a feature of schizophrenia, attention deficit-hyperactivity disorder, and behavior addictions that likely results from heritable deficits in the inhibitory control over behavior. Here, we investigate the genetic basis of individual differences in flexibility, measured using an operant reversal learning task.
We quantified discrimination acquisition and subsequent reversal learning in a cohort of 51 BXD strains of mice (2–5 mice/strain, N = 176) for which we have matched data on sequence, gene expression in key CNS regions, and neuroreceptor levels.
Strain variation in trials to criterion on acquisition and reversal was high, with moderate heritability (~0.3). Acquisition and reversal learning phenotypes did not covary at the strain level, suggesting that these traits are effectively under independent genetic control. Reversal performance did covary with dopamine D2 receptor levels in the ventral midbrain, consistent with a similar observed relationship between impulsivity and D2 receptors in humans. Reversal, but not acquisition, is linked to a locus on mouse chromosome 10 with a peak LRS at 86.2Mb (p <.05 genome-wide). Variance in mRNA levels of select transcripts expressed in neocortex, hippocampus, and striatum correlated with the reversal learning phenotype, including Syn3, Nt5dc3 and Hcfc2.
This work demonstrates the clear trait independence between, and genetic control of, discrimination acquisition and reversal and illustrates how globally coherent data sets for a single panel of highly-related strains can be interrogated and integrated to uncover genetic sources and molecular and neuropharmacological candidates of complex behavioral traits relevant to human psychopathology.
cognitive flexibility; impulsivity; response inhibition; genetics; quantitative trait loci; recombinant inbred mice
What is particularly worth remembering about a traumatic experience is what brought it about, and what made it cease. For example, fruit flies avoid an odor which during training had preceded electric shock punishment; on the other hand, if the odor had followed shock during training, it is later on approached as a signal for the relieving end of shock. We provide a neurogenetic analysis of such relief learning. Blocking, using UAS-shibirets1, the output from a particular set of dopaminergic neurons defined by the TH-Gal4 driver partially impaired punishment learning, but left relief learning intact. Thus, with respect to these particular neurons, relief learning differs from punishment learning. Targeting another set of dopaminergic/serotonergic neurons defined by the DDC-Gal4 driver on the other hand affected neither punishment nor relief learning. As for the octopaminergic system, the tbhM18 mutation, compromising octopamine biosynthesis, partially impaired sugar-reward learning, but not relief learning. Thus, with respect to this particular mutation, relief learning, and reward learning are dissociated. Finally, blocking output from the set of octopaminergic/tyraminergic neurons defined by the TDC2-Gal4 driver affected neither reward, nor relief learning. We conclude that regarding the used genetic tools, relief learning is neurogenetically dissociated from both punishment and reward learning. This may be a message relevant also for analyses of relief learning in other experimental systems including man.
dopamine; fruit fly; octopamine; olfaction; reinforcement signaling; relief learning
The new view of cognitive neuropsychology that considers not just case studies of rare severe disorders but also common disorders, as well as normal variation and quantitative traits, is more amenable to recent advances in molecular genetics, such as genome-wide association studies, and advances in quantitative genetics, such as multivariate genetic analysis. A surprising finding emerging from multivariate quantitative genetic studies across diverse learning abilities is that most genetic influences are shared: they are ‘generalist’, rather than ‘specialist’.
We exploited widespread access to inexpensive and fast Internet connections in the United Kingdom to assess over 5000 pairs of 12-year-old twins from the Twins Early Development Study (TEDS) on four distinct batteries: reading, mathematics, general cognitive ability (g) and, for the first time, language.
Genetic correlations remain high among all of the measured abilities, with language as highly correlated genetically with g as reading and mathematics.
Despite developmental upheaval, generalist genes remain important into early adolescence, suggesting optimal strategies for molecular genetic studies seeking to identify the genes of small effect that influence learning abilities and disabilities.
Learning Ability; Intelligence; Reading; Mathematics; Language; Development; Adolescence; Genetics; Twins
While patients with schizophrenia display an overall probabilistic category learning performance deficit, the extent to which this deficit occurs in unaffected siblings of patients with schizophrenia is unknown. There are also discrepant findings regarding probabilistic category learning acquisition rate and performance in patients with schizophrenia.
A probabilistic category learning test was administered to 108 patients with schizophrenia, 82 unaffected siblings, and 121 healthy participants.
Patients with schizophrenia displayed significant differences from their unaffected siblings and healthy participants with respect to probabilistic category learning acquisition rates. Although siblings on the whole failed to differ from healthy participants on strategy and quantitative indices of overall performance and learning acquisition, application of a revised learning criterion enabling classification into good and poor learners based on individual learning curves revealed significant differences between percentages of sibling and healthy poor learners: healthy (13.2%), siblings (34.1%), patients (48.1%), yielding a moderate relative risk.
These results clarify previous discrepant findings pertaining to probabilistic category learning acquisition rate in schizophrenia and provide the first evidence for the relative risk of probabilistic category learning abnormalities in unaffected siblings of patients with schizophrenia, supporting genetic underpinnings of probabilistic category learning deficits in schizophrenia. These findings also raise questions regarding the contribution of antipsychotic medication to the probabilistic category learning deficit in schizophrenia. The distinction between good and poor learning may be used to inform genetic studies designed to detect schizophrenia risk alleles.
schizophrenia; relative risk; cognition; probability learning; caudate nucleus; antipsychotics
Operant (instrumental) and classical (Pavlovian) conditioning are taught as the simplest forms of associative learning. Recent research in several invertebrate model systems has now accumulated evidence that the dichotomy is not as simple as it seemed. During operant learning in the fruit fly Drosophila, at least two genetically distinct learning systems interact dynamically. Inspired by analogous results in three other research fields, we propose to term one of these systems world-learning (assigning value to sensory stimuli) and the other self-learning (assigning value to a specific action or movement). During the goal-directed phase of operant learning, world-learning inhibits self-learning (in Drosophila via the mushroom-body neuropil), to allow for flexible generalization. Extended training overcomes this inhibition in a phase transition akin to habit formation in vertebrates, allowing self-learning to transform spontaneous actions to habitual responses. In part, these insights were achieved by reducing operant experiments beyond the traditional set-ups (i.e., ‘pure’ operant learning) and using modern, molecular and/or genetic model systems.
insect; flight; choice; decision-making; spontaneity; behavior; memory
As research into the neurobiology of language has focused primarily on the systems level, fewer studies have examined the link between molecular genetics and normal variations in language functions. Because the ability to learn a language varies in adults and our genetic codes also vary, research linking the two provides a unique window into the molecular neurobiology of language. We consider a candidate association between the dopamine receptor D2 gene (DRD2) and linguistic grammar learning. DRD2-TAQ-IA polymorphism (rs1800497) is associated with dopamine receptor D2 distribution and dopamine impact in the human striatum, such that A1 allele carriers show reduction in D2 receptor binding relative to carriers who are homozygous for the A2 allele. The individual differences in grammatical rule learning that are particularly prevalent in adulthood are also associated with striatal function and its role in domain-general procedural memory. Therefore, we reasoned that procedurally-based grammar learning could be associated with DRD2-TAQ-IA polymorphism. Here, English-speaking adults learned artificial concatenative and analogical grammars, which have been respectively associated with procedural and declarative memory. Language learning capabilities were tested while learners’ neural hemodynamic responses were simultaneously measured by fMRI. Behavioral learning and brain activation data were subsequently compared with the learners’ DRD2 (rs1800497) genotype. Learners who were homozygous for the A2 allele were better at concatenative (but not analogical) grammar learning and had higher striatal responses relative to those who have at least one A1 allele. These results provide preliminary evidence for the neurogenetic basis of normal variations in linguistic grammar learning and its link to domain-general functions.
Variants of the contactin associated protein-like 2 (Cntnap2) gene are risk factors for language-related disorders including autism spectrum disorder, specific language impairment, and stuttering. Songbirds are useful models for study of human speech disorders due to their shared capacity for vocal learning, which relies on similar cortico-basal ganglia circuitry and genetic factors. Here, we investigate Cntnap2 protein expression in the brain of the zebra finch, a songbird species in which males, but not females, learn their courtship songs. We hypothesize that Cntnap2 has overlapping functions in vocal learning species, and expect to find protein expression in song-related areas of the zebra finch brain. We further expect that the distribution of this membrane-bound protein may not completely mirror its mRNA distribution due to the distinct subcellular localization of the two molecular species. We find that Cntnap2 protein is enriched in several song control regions relative to surrounding tissues, particularly within the adult male, but not female, robust nucleus of the arcopallium (RA), a cortical song control region analogous to human layer 5 primary motor cortex. The onset of this sexually dimorphic expression coincides with the onset of sensorimotor learning in developing males. Enrichment in male RA appears due to expression in projection neurons within the nucleus, as well as to additional expression in nerve terminals of cortical projections to RA from the lateral magnocellular nucleus of the nidopallium. Cntnap2 protein expression in zebra finch brain supports the hypothesis that this molecule affects neural connectivity critical for vocal learning across taxonomic classes.
autism; birdsong; Caspr2; speech; zebra finch
Dysregulation of genetic factors such as microRNAs (miRNAs) and mRNAs has been widely shown to be associated with cancer progression and development. In particular, miRNAs and mRNAs cooperate to affect biological processes, including tumorigenesis. The complexity of miRNA-mRNA interactions presents a major barrier to identifying their co-regulatory roles and functional effects. Thus, by computationally modeling these complex relationships, it may be possible to infer the gene interaction networks underlying complicated biological processes.
We propose a data-driven, hypergraph structural method for constructing higher-order miRNA-mRNA interaction networks from cancer genomic profiles. The proposed model explicitly characterizes higher-order relationships among genetic factors, from which cooperative gene activities in biological processes may be identified. The proposed model is learned by iteration of structure and parameter learning. The structure learning efficiently constructs a hypergraph structure by generating putative hyperedges representing complex miRNA-mRNA modules. It adopts an evolutionary method based on information-theoretic criteria. In the parameter learning phase, the constructed hypergraph is refined by updating the hyperedge weights using the gradient descent method. From the model, we produce biologically relevant higher-order interaction networks showing the properties of primary and metastatic prostate cancer, as candidates of potential miRNA-mRNA regulatory circuits.
Our approach focuses on potential cancer-specific interactions reflecting higher-order relationships between miRNAs and mRNAs from expression profiles. The constructed miRNA-mRNA interaction networks show oncogenic or tumor suppression characteristics, which are known to be directly associated with prostate cancer progression. Therefore, the hypergraph-based model can assist hypothesis formulation for the molecular pathogenesis of cancer.
miRNA-mRNA interaction networks; Hypergraph-based model; Higher-order gene modules; Evolutionary learning; Cancer genomics data analysis
Complex traits such as obesity are manifestations of intricate interactions of multiple genetic factors. However, such relationships are difficult to identify. Thanks to the recent advance in high-throughput technology, a large amount of data has been collected for various complex traits, including obesity. These data often measure different biological aspects of the traits of interest, including genotypic variations at the DNA level and gene expression alterations at the RNA level. Integration of such heterogeneous data provides promising opportunities to understand the genetic components and possibly genetic architecture of complex traits. In this paper, we propose a machine learning based method, module-guided Random Forests (mgRF), to integrate genotypic and gene expression data to investigate genetic factors and molecular mechanism underlying complex traits. mgRF is an augmented Random Forests method enhanced by a network analysis for identifying multiple correlated variables of different types. We applied mgRF to genetic markers and gene expression data from a cohort of F2 female mouse intercross. mgRF outperformed several existing methods in our extensive comparison. Our new approach has an improved performance when combining both genotypic and gene expression data compared to using either one of the two types of data alone. The resulting predictive variables identified by mgRF provide information of perturbed pathways that are related to body weight. More importantly, the results uncovered intricate interactions among genetic markers and genes that have been overlooked if only one type of data was examined. Our results shed light on genetic mechanisms of obesity and our approach provides a promising complementary framework to the “genetics of gene expression” analysis for integrating genotypic and gene expression information for analyzing complex traits.
Obesity has become a perilous global epidemic that can lead to complex diseases, such as diabetes and cardiovascular diseases. Much effort has been devoted to the studies of the genetic mechanisms that pillow the manifestation of obesity. Although a large quantity of experimental data has been accumulated lately using high-throughput techniques, our understanding of genetic mechanisms of obesity is still limited. The proposed method is motivated to address three critical issues that have impeded the existing methods. The first is the curse of dimensionality in selecting a subset of genetic elements related to the traits of interest from a large number of candidates. The second is genetic multiplicity underlying non-Mendelian traits, in which multiple genes are in interplay. The third issue is the integration of data from multiple sources in light of genetic multiplicity and curse of dimensionality. Here, we propose a new method, which augments the Random Forests method with a network-based analysis, to integrate genotypic and gene expression information and identify correlated multiple genetic elements underlying mouse weight. Our results shed light on complex genetic interactions underlying obesity, which can form viable hypotheses worthy of further investigation.
Understanding the root molecular and genetic causes driving complex traits is a fundamental challenge in genomics and genetics. Numerous studies have used variation in gene expression to understand complex traits, but the underlying genomic variation that contributes to these expression changes is not well understood. In this study, we developed a framework to integrate gene expression and genotype data to identify biological differences between samples from opposing complex trait classes that are driven by expression changes and genotypic variation. This framework utilizes pathway analysis and multi-task learning to build a predictive model and discover pathways relevant to the complex trait of interest. We simulated expression and genotype data to test the predictive ability of our framework and to measure how well it uncovered pathways with genes both differentially expressed and genetically associated with a complex trait. We found that the predictive performance of the multi-task model was comparable to other similar methods. Also, methods like multi-task learning that considered enrichment analysis scores from both data sets found pathways with both genetic and expression differences related to the phenotype. We used our framework to analyze differences between estrogen receptor (ER) positive and negative breast cancer samples. An analysis of the top 15 gene sets from the multi-task model showed they were all related to estrogen, steroids, cell signaling, or the cell cycle. Although our study suggests that multi-task learning does not enhance predictive accuracy, the models generated by our framework do provide valuable biological pathway knowledge for complex traits.
The Val66Met polymorphism in the brain-derived neurotropic factor (BDNF) gene results in alterations in fear extinction behavior in both human populations and mouse models. However, it is not clear whether this polymorphism plays a similar role in extinction of appetitive behaviors. Therefore, we examined operant learning and extinction of both food and cocaine self-administration behavior in an inbred genetic knock-in mouse strain expressing the variant Bdnf. These mice provide a unique opportunity to relate alterations in aversive and appetitive extinction learning as well as provide insight into how human genetic variation can lead to differences in behavior. BDNFMet/Met mice exhibited a severe deficit in operant learning as evidenced by an inability to learn the food self-administration task. Therefore, extinction experiments were performed comparing wildtype (BDNFVal/Val) animals to mice heterozygous for the Met allele (BDNFVal/Met), which did not differ in food or cocaine self-administration behavior. In contrast to the deficit in fear extinction previously demonstrated in these mice, we found that BDNFVal/Met mice exhibited more rapid extinction of cocaine responding compared to wildtype mice. No differences were found between the genotypes in the extinction of food self-administration behavior or the reinstatement of cocaine seeking, indicating the effect is specific to extinction of cocaine responding. These results suggest that the molecular mechanisms underlying aversive and appetitive extinction are distinct from one another and BDNF may play opposing roles in the two phenomena.
Cocaine; Extinction; BDNF; Single-nucleotide polymorphism; Neurotrophin; Self-Administration
The gene encoding the forkhead box transcription factor, FOXP2, is essential for developing the full articulatory power of human language. Mutations of FOXP2 cause developmental verbal dyspraxia (DVD), a speech and language disorder that compromises the fluent production of words and the correct use and comprehension of grammar. FOXP2 patients have structural and functional abnormalities in the striatum of the basal ganglia, which also express high levels of FOXP2. Since human speech and learned vocalizations in songbirds bear behavioral and neural parallels, songbirds provide a genuine model for investigating the basic principles of speech and its pathologies. In zebra finch Area X, a basal ganglia structure necessary for song learning, FoxP2 expression increases during the time when song learning occurs. Here, we used lentivirus-mediated RNA interference (RNAi) to reduce FoxP2 levels in Area X during song development. Knockdown of FoxP2 resulted in an incomplete and inaccurate imitation of tutor song. Inaccurate vocal imitation was already evident early during song ontogeny and persisted into adulthood. The acoustic structure and the duration of adult song syllables were abnormally variable, similar to word production in children with DVD. Our findings provide the first example of a functional gene analysis in songbirds and suggest that normal auditory-guided vocal motor learning requires FoxP2.
Do special “human” genes provide the biological substrate for uniquely human traits, such as language? Genetic aberrations of the human FoxP2 gene impair speech production and comprehension, yet the relative contributions of FoxP2 to brain development and function are unknown. Songbirds are a useful model to address this because, like human youngsters, they learn to vocalize by imitating the sounds of their elders. Previously, we found that when young zebra finches learn to sing or when adult canaries change their song seasonally, FoxP2 is up-regulated in Area X, a brain region important for song plasticity. Here, we reduced FoxP2 levels in Area X before zebra finches started to learn their song, using virus-mediated RNA interference for the first time in songbird brains. Birds with experimentally lowered levels of FoxP2 imitated their tutor's song imprecisely and sang more variably than controls. FoxP2 thus appears to be critical for proper song development. These results suggest that humans and birds may employ similar molecular substrates for vocal learning, which can now be further analyzed in an experimental animal system.
The FoxP2 gene, which is essential for human speech and language, is also required for proper song development in songbirds, raising the possibility that songbirds and humans share molecular pathways for learned vocalizations.
In Pavlovian conditioning, animals learn to associate initially neutral stimuli with positive or negative outcomes, leading to appetitive and aversive learning respectively. The honeybee (Apis mellifera) is a prominent invertebrate model for studying both versions of olfactory learning and for unraveling the influence of genotype. As a queen bee mates with about 15 males, her worker offspring belong to as many, genetically-different patrilines. While the genetic dependency of appetitive learning is well established in bees, it is not the case for aversive learning, as a robust protocol was only developed recently. In the original conditioning of the sting extension response (SER), bees learn to associate an odor (conditioned stimulus - CS) with an electric shock (unconditioned stimulus - US). This US is however not a natural stimulus for bees, which may represent a potential caveat for dissecting the genetics underlying aversive learning. We thus first tested heat as a potential new US for SER conditioning. We show that thermal stimulation of several sensory structures on the bee’s body triggers the SER, in a temperature-dependent manner. Moreover, heat applied to the antennae, mouthparts or legs is an efficient US for SER conditioning. Then, using microsatellite analysis, we analyzed heat sensitivity and aversive learning performances in ten worker patrilines issued from a naturally inseminated queen. We demonstrate a strong influence of genotype on aversive learning, possibly indicating the existence of a genetic determinism of this capacity. Such determinism could be instrumental for efficient task partitioning within the hive.
A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning) that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not already well studied. Many of these processes are well studied in some organism, but not necessarily in an investigator's organism of interest. Sequence-based search methods (e.g. BLAST) have been used to transfer such annotation information between organisms. We demonstrate that functional genomics can complement traditional sequence similarity to improve the transfer of gene annotations between organisms. Our method transfers annotations only when functionally appropriate as determined by genomic data and can be used with any prediction algorithm to combine transferred gene function knowledge with organism-specific high-throughput data to enable accurate function prediction.
We show that diverse state-of-art machine learning algorithms leveraging functional knowledge transfer (FKT) dramatically improve their accuracy in predicting gene-pathway membership, particularly for processes with little experimental knowledge in an organism. We also show that our method compares favorably to annotation transfer by sequence similarity. Next, we deploy FKT with state-of-the-art SVM classifier to predict novel genes to 11,000 biological processes across six diverse organisms and expand the coverage of accurate function predictions to processes that are often ignored because of a dearth of annotated genes in an organism. Finally, we perform in vivo experimental investigation in Danio rerio and confirm the regulatory role of our top predicted novel gene, wnt5b, in leftward cell migration during heart development. FKT is immediately applicable to many bioinformatics techniques and will help biologists systematically integrate prior knowledge from diverse systems to direct targeted experiments in their organism of study.
Due to technical and ethical challenges many human diseases or biological processes are studied in model organisms. Discoveries in these organisms are then transferred back to human or other model organisms. Traditional methods for transferring novel gene function annotations have relied on finding genes with high sequence similarity believed to share evolutionary ancestry. However, sequence similarity does not guarantee a shared functional role in molecular pathways. In this study, we show that functional genomics can complement traditional sequence similarity measures to improve the transfer of gene annotations between organisms. We coupled our knowledge transfer method with current state-of-the-art machine learning algorithms and predicted gene function for 11,000 biological processes across six organisms. We experimentally validated our prediction of wnt5b's involvement in the determination of left-right heart asymmetry in zebrafish. Our results show that functional knowledge transfer can improve the coverage and accuracy of machine learning methods used for gene function prediction in a diverse set of organisms. Such an approach can be applied to additional organisms, and will be especially beneficial in organisms that have high-throughput genomic data with sparse annotations.
Studies of natural animal populations reveal widespread evidence for the diffusion of novel behaviour patterns, and for intra- and inter-population variation in behaviour. However, claims that these are manifestations of animal ‘culture’ remain controversial because alternative explanations to social learning remain difficult to refute. This inability to identify social learning in social settings has also contributed to the failure to test evolutionary hypotheses concerning the social learning strategies that animals deploy.
We present a solution to this problem, in the form of a new means of identifying social learning in animal populations. The method is based on the well-established premise of social learning research, that - when ecological and genetic differences are accounted for - social learning will generate greater homogeneity in behaviour between animals than expected in its absence. Our procedure compares the observed level of homogeneity to a sampling distribution generated utilizing randomization and other procedures, allowing claims of social learning to be evaluated according to consensual standards. We illustrate the method on data from groups of monkeys provided with novel two-option extractive foraging tasks, demonstrating that social learning can indeed be distinguished from unlearned processes and asocial learning, and revealing that the monkeys only employed social learning for the more difficult tasks. The method is further validated against published datasets and through simulation, and exhibits higher statistical power than conventional inferential statistics.
The method is potentially a significant technological development, which could prove of considerable value in assessing the validity of claims for culturally transmitted behaviour in animal groups. It will also be of value in enabling investigation of the social learning strategies deployed in captive and natural animal populations.