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Alzheimer's disease (AD) is a currently incurable neurodegenerative disorder. Several genetic studies have identified a rare variant of triggering receptor expressed on myeloid cells 2 (TREM2) as a risk factor for AD. TREM2 is thought to trigger the microglial response to amyloid plaques. Mouse models have helped elucidate mechanisms through which TREM2 affects microglial function and modulates pathological features of AD. A synthesis of the 35 mouse-model studies included in this review indicates that TREM2 modulates amyloid plaque composition and deposition, microglial morphology and proliferation, neuroinflammation, and tau phosphorylation. TREM2 also acts as a sensor for anionic lipids exposed during neuronal apoptosis and Aß deposition, may improve spatial abilities and memory, and protect against apoptosis. In early stages of AD, TREM2 knock-down reduces expression of proinflammatory cytokines and upregulates anti-inflammatory cytokines but in later stages, TREM2 may contribute to the disease by aggravating neuroinflammation. The results provide insight into TREM2-related mechanisms that may be associated with AD in humans and may aid future development of disease-modifying pharmacological treatments targeting TREM2.
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Doença de Alzheimer/genética , Glicoproteínas de Membrana/fisiologia , Receptores Imunológicos/fisiologia , Doença de Alzheimer/metabolismo , Doença de Alzheimer/patologia , Peptídeos beta-Amiloides/metabolismo , Animais , Proliferação de Células , Citocinas/metabolismo , Modelos Animais de Doenças , Humanos , Inflamação , Mediadores da Inflamação/metabolismo , Camundongos Transgênicos , Microglia/patologia , Microglia/fisiologia , Terapia de Alvo Molecular , Fosforilação , Risco , Proteínas tau/metabolismoRESUMO
MOTIVATION: Mistakes in linking a patient's biological samples with their phenotype data can confound RNA-Seq studies. The current method for avoiding such sample mixups is to test for inconsistencies between biological data and known phenotype data such as sex. However, in DNA studies a common QC step is to check for unexpected relatedness between samples. Here, we extend this method to RNA-Seq, which allows the detection of duplicated samples without relying on identifying inconsistencies with phenotype data. SUMMARY: We present RNASeq_similarity_matrix: an automated tool to generate a sequence similarity matrix from RNA-Seq data, which can be used to visually identify sample mix-ups. This is particularly useful when a study contains multiple samples from the same individual, but can also detect contamination in studies with only one sample per individual. AVAILABILITY: RNASeq_similarity_matrix has been made available as a documented GPL licensed Docker image on www.github.com/nicokist/RNASeq_similarity_matrix. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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SUMMARY: We present software to characterize and rank potential therapeutic (drug) targets with data from public databases and present it in a user-friendly format. By understanding potential obstacles to drug development through the gathering and understanding of this information, combined with robust approaches to target validation to generate therapeutic hypotheses, this approach may provide high quality targets, leading the process of drug development to become more efficient and cost-effective. AVAILABILITY AND IMPLEMENTATION: The information we gather on potential targets concerns small-molecule druggability (ligandability), suitability for large-molecule approaches (e.g. antibodies) or new modalities (e.g. antisense oligonucleotides, siRNA or PROTAC), feasibility (availability of resources such as assays and biological knowledge) and potential safety risks (adverse tissue-wise expression, deleterious phenotypes). This information can be termed 'tractability'. We provide visualization tools to understand its components. TractaViewer is available from https://github.com/NeilPearson-Lilly/TractaViewer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Genoma , Software , Bases de Dados FactuaisRESUMO
MOTIVATION: The datasets generated by DNA methylation analyses are getting bigger. With the release of the HumanMethylationEPIC micro-array and datasets containing thousands of samples, analyses of these large datasets using R are becoming impractical due to large memory requirements. As a result there is an increasing need for computationally efficient methodologies to perform meaningful analysis on high dimensional data. RESULTS: Here we introduce the bigmelon R package, which provides a memory efficient workflow that enables users to perform the complex, large scale analyses required in epigenome wide association studies (EWAS) without the need for large RAM. Building on top of the CoreArray Genomic Data Structure file format and libraries packaged in the gdsfmt package, we provide a practical workflow that facilitates the reading-in, preprocessing, quality control and statistical analysis of DNA methylation data.We demonstrate the capabilities of the bigmelon package using a large dataset consisting of 1193 human blood samples from the Understanding Society: UK Household Longitudinal Study, assayed on the EPIC micro-array platform. AVAILABILITY AND IMPLEMENTATION: The bigmelon package is available on Bioconductor (http://bioconductor.org/packages/bigmelon/). The Understanding Society dataset is available at https://www.understandingsociety.ac.uk/about/health/data upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Metilação de DNA , Software , Genômica , Humanos , Estudos Longitudinais , Fluxo de TrabalhoRESUMO
One of sleep's putative functions is mediation of adaptation to waking experiences. Chronic stress is a common waking experience; however, which specific aspect of sleep is most responsive, and how sleep changes relate to behavioral disturbances and molecular correlates remain unknown. We quantified sleep, physical, endocrine, and behavioral variables, as well as the brain and blood transcriptome in mice exposed to 9 weeks of unpredictable chronic mild stress (UCMS). Comparing 46 phenotypic variables revealed that rapid-eye-movement sleep (REMS), corticosterone regulation, and coat state were most responsive to UCMS. REMS theta oscillations were enhanced, whereas delta oscillations in non-REMS were unaffected. Transcripts affected by UCMS in the prefrontal cortex, hippocampus, hypothalamus, and blood were associated with inflammatory and immune responses. A machine-learning approach controlling for unspecific UCMS effects identified transcriptomic predictor sets for REMS parameters that were enriched in 193 pathways, including some involved in stem cells, immune response, and apoptosis and survival. Only three pathways were enriched in predictor sets for non-REMS. Transcriptomic predictor sets for variation in REMS continuity and theta activity shared many pathways with corticosterone regulation, in particular pathways implicated in apoptosis and survival, including mitochondrial apoptotic machinery. Predictor sets for REMS and anhedonia shared pathways involved in oxidative stress, cell proliferation, and apoptosis. These data identify REMS as a core and early element of the response to chronic stress, and identify apoptosis and survival pathways as a putative mechanism by which REMS may mediate the response to stressful waking experiences.
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Apoptose , Comportamento Animal , Corticosterona/metabolismo , Sono REM , Estresse Psicológico , Animais , Doença Crônica , Eletroencefalografia , Masculino , Camundongos , Camundongos Endogâmicos BALB C , Fenótipo , Transcriptoma , Vigília/fisiologiaRESUMO
BACKGROUND: The number line task assesses the ability to estimate numerical magnitudes. People vary greatly in this ability, and this variability has been previously associated with mathematical skills. However, the sources of individual differences in number line estimation and its association with mathematics are not fully understood. AIMS: This large-scale genetically sensitive study uses a twin design to estimate the magnitude of the effects of genes and environments on: (1) individual variation in number line estimation and (2) the covariation of number line estimation with mathematics. SAMPLES: We used over 3,000 8- to 16-year-old twins from the United States, Canada, the United Kingdom, and Russia, and a sample of 1,456 8- to 18-year-old singleton Russian students. METHODS: Twins were assessed on: (1) estimation of numerical magnitudes using a number line task and (2) two mathematics components: fluency and problem-solving. RESULTS: Results suggest that environments largely drive individual differences in number line estimation. Both genes and environments contribute to different extents to the number line estimation and mathematics correlation, depending on the sample and mathematics component. CONCLUSIONS: Taken together, the results suggest that in more heterogeneous school settings, environments may be more important in driving variation in number line estimation and its association with mathematics, whereas in more homogeneous school settings, genetic effects drive the covariation between number line estimation and mathematics. These results are discussed in the light of development and educational settings.
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Aptidão/fisiologia , Interação Gene-Ambiente , Individualidade , Conceitos Matemáticos , Resolução de Problemas/fisiologia , Adolescente , Criança , Feminino , Humanos , Estudos Longitudinais , MasculinoRESUMO
Human genome-wide association studies (GWAS), transcriptome analyses of animal models, and candidate gene studies have advanced our understanding of the genetic architecture of aggressive behaviors. However, each of these methods presents unique limitations. To generate a more confident and comprehensive view of the complex genetics underlying aggression, we undertook an integrated, cross-species approach. We focused on human and rodent models to derive eight gene lists from three main categories of genetic evidence: two sets of genes identified in GWAS studies, four sets implicated by transcriptome-wide studies of rodent models, and two sets of genes with causal evidence from online Mendelian inheritance in man (OMIM) and knockout (KO) mice reports. These gene sets were evaluated for overlap and pathway enrichment to extract their similarities and differences. We identified enriched common pathways such as the G-protein coupled receptor (GPCR) signaling pathway, axon guidance, reelin signaling in neurons, and ERK/MAPK signaling. Also, individual genes were ranked based on their cumulative weights to quantify their importance as risk factors for aggressive behavior, which resulted in 40 top-ranked and highly interconnected genes. The results of our cross-species and integrated approach provide insights into the genetic etiology of aggression.
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Agressão/fisiologia , Estresse Fisiológico/genética , Animais , Bases de Dados Genéticas , Emoções/fisiologia , Perfilação da Expressão Gênica/métodos , Redes Reguladoras de Genes/genética , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla/métodos , Humanos , Camundongos , Polimorfismo de Nucleotídeo Único/genética , Ratos , Proteína Reelina , Fatores de Risco , Transcriptoma/genéticaRESUMO
An enhanced understanding of the pathophysiology of depression would facilitate the discovery of new efficacious medications. To this end, we examined hippocampal transcriptional changes in rat models of disease and in humans to identify common disease signatures by using a new algorithm for signature-based clustering of expression profiles. The tool identified a transcriptomic signature comprising 70 probesets able to discriminate depression models from controls in both Flinders Sensitive Line and Learned Helplessness animals. To identify disease-relevant pathways, we constructed an expanded protein network based on signature gene products and performed functional annotation analysis. We applied the same workflow to transcriptomic profiles of depressed patients. Remarkably, a 171-probesets transcriptional signature which discriminated depressed from healthy subjects was identified. Rat and human signatures shared the SCARA5 gene, while the respective networks derived from protein-based significant interactions with signature genes contained 25 overlapping genes. The comparison between the most enriched pathways in the rat and human signature networks identified a highly significant overlap (p-value: 3.85 × 10-6) of 67 terms including ErbB, neurotrophin, FGF, IGF, and VEGF signaling, immune responses and insulin and leptin signaling. In conclusion, this study allowed the identification of a hippocampal transcriptional signature of resilient or susceptible responses in rat MDD models which overlapped with gene expression alterations observed in depressed patients. These findings are consistent with a loss of hippocampal neural plasticity mediated by altered levels of growth factors and increased inflammatory responses causing metabolic impairments as crucial factors in the pathophysiology of MDD.
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Transtorno Depressivo Maior/genética , Transtorno Depressivo Maior/fisiopatologia , Peptídeos e Proteínas de Sinalização Intercelular/genética , Transdução de Sinais/genética , Transcriptoma/genética , Animais , Química Encefálica/genética , Biologia Computacional , Perfilação da Expressão Gênica , Regulação da Expressão Gênica/genética , Desamparo Aprendido , Hipocampo/efeitos dos fármacos , Hipocampo/fisiologia , Humanos , Masculino , Ratos , Receptores Depuradores Classe A/genética , Especificidade da EspécieRESUMO
Rare heterozygous coding variants in the triggering receptor expressed in myeloid cells 2 (TREM2) gene, conferring increased risk of developing late-onset Alzheimer's disease, have been identified. We examined the transcriptional consequences of the loss of Trem2 in mouse brain to better understand its role in disease using differential expression and coexpression network analysis of Trem2 knockout and wild-type mice. We generated RNA-Seq data from cortex and hippocampus sampled at 4 and 8 months. Using brain cell-type markers and ontology enrichment, we found subnetworks with cell type and/or functional identity. We primarily discovered changes in an endothelial gene-enriched subnetwork at 4 months, including a shift toward a more central role for the amyloid precursor protein gene, coupled with widespread disruption of other cell-type subnetworks, including a subnetwork with neuronal identity. We reveal an unexpected potential role of Trem2 in the homeostasis of endothelial cells that goes beyond its known functions as a microglial receptor and signaling hub, suggesting an underlying link between immune response and vascular disease in dementia.
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Córtex Cerebral/metabolismo , Regulação da Expressão Gênica , Hipocampo/metabolismo , Glicoproteínas de Membrana/metabolismo , Microglia/metabolismo , Receptores Imunológicos/metabolismo , Animais , Células Endoteliais/metabolismo , Perfilação da Expressão Gênica , Ontologia Genética , Redes Reguladoras de Genes , Masculino , Glicoproteínas de Membrana/genética , Camundongos Knockout , Neurônios/metabolismo , Receptores Imunológicos/genética , Análise de Sequência de RNARESUMO
Individuals with depression differ substantially in their response to treatment with antidepressants. Specific predictors explain only a small proportion of these differences. To meaningfully predict who will respond to which antidepressant, it may be necessary to combine multiple biomarkers and clinical variables. Using statistical learning on common genetic variants and clinical information in a training sample of 280 individuals randomly allocated to 12-week treatment with antidepressants escitalopram or nortriptyline, we derived models to predict remission with each antidepressant drug. We tested the reproducibility of each prediction in a validation set of 150 participants not used in model derivation. An elastic net logistic model based on eleven genetic and six clinical variables predicted remission with escitalopram in the validation dataset with area under the curve 0.77 (95%CI; 0.66-0.88; p = 0.004), explaining approximately 30% of variance in who achieves remission. A model derived from 20 genetic variables predicted remission with nortriptyline in the validation dataset with an area under the curve 0.77 (95%CI; 0.65-0.90; p < 0.001), explaining approximately 36% of variance in who achieves remission. The predictive models were antidepressant drug-specific. Validated drug-specific predictions suggest that a relatively small number of genetic and clinical variables can help select treatment between escitalopram and nortriptyline.
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Biomarcadores/análise , Citalopram/uso terapêutico , Transtorno Depressivo Maior/tratamento farmacológico , Nortriptilina/uso terapêutico , Adulto , Antidepressivos de Segunda Geração/uso terapêutico , Antidepressivos Tricíclicos/uso terapêutico , Transtorno Depressivo Maior/genética , Transtorno Depressivo Maior/patologia , Feminino , Humanos , Masculino , Resultado do TratamentoRESUMO
Individual differences in number sense correlate with mathematical ability and performance, although the presence and strength of this relationship differs across studies. Inconsistencies in the literature may stem from heterogeneity of number sense and mathematical ability constructs. Sample characteristics may also play a role as changes in the relationship between number sense and mathematics may differ across development and cultural contexts. In this study, 4,984 16-year-old students were assessed on estimation ability, one aspect of number sense. Estimation was measured using 2 different tasks: number line and dot-comparison. Using cognitive and achievement data previously collected from these students at ages 7, 9, 10, 12, and 14, the study explored for which of the measures and when in development these links are observed, and how strong these links are and how much these links are moderated by other cognitive abilities. The 2 number sense measures correlated modestly with each other (r = .22), but moderately with mathematics at age 16. Both measures were also associated with earlier mathematics; but this association was uneven across development and was moderated by other cognitive abilities. (PsycINFO Database Record
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Conceitos Matemáticos , Sucesso Acadêmico , Adolescente , Criança , Desenvolvimento Infantil , Cognição , Feminino , Humanos , Masculino , Testes Psicológicos , Análise de Regressão , Seio Sagital SuperiorRESUMO
Response to antidepressant (AD) treatment may be a more polygenic trait than previously hypothesized, with many genetic variants interacting in yet unclear ways. In this study we used methods that can automatically learn to detect patterns of statistical regularity from a sparsely distributed signal across hippocampal transcriptome measurements in a large-scale animal pharmacogenomic study to uncover genomic variations associated with AD. The study used four inbred mouse strains of both sexes, two drug treatments, and a control group (escitalopram, nortriptyline, and saline). Multi-class and binary classification using Machine Learning (ML) and regularization algorithms using iterative and univariate feature selection methods, including InfoGain, mRMR, ANOVA, and Chi Square, were used to uncover genomic markers associated with AD response. Relevant genes were selected based on Jaccard distance and carried forward for gene-network analysis. Linear association methods uncovered only one gene associated with drug treatment response. The implementation of ML algorithms, together with feature reduction methods, revealed a set of 204 genes associated with SSRI and 241 genes associated with NRI response. Although only 10% of genes overlapped across the two drugs, network analysis shows that both drugs modulated the CREB pathway, through different molecular mechanisms. Through careful implementation and optimisations, the algorithms detected a weak signal used to predict whether an animal was treated with nortriptyline (77%) or escitalopram (67%) on an independent testing set. The results from this study indicate that the molecular signature of AD treatment may include a much broader range of genomic markers than previously hypothesized, suggesting that response to medication may be as complex as the pathology. The search for biomarkers of antidepressant treatment response could therefore consider a higher number of genetic markers and their interactions. Through predominately different molecular targets and mechanisms of action, the two drugs modulate the same Creb1 pathway which plays a key role in neurotrophic responses and in inflammatory processes. © 2016 The Authors. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics Published by Wiley Periodicals, Inc.
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Antidepressivos/uso terapêutico , Inibidores da Recaptação de Serotonina e Norepinefrina/farmacologia , Animais , Citalopram/uso terapêutico , Proteína de Ligação ao Elemento de Resposta ao AMP Cíclico , Depressão/tratamento farmacológico , Transtorno Depressivo/tratamento farmacológico , Transtorno Depressivo/genética , Modelos Animais de Doenças , Feminino , Hipocampo , Masculino , Camundongos , Herança Multifatorial/genética , Nortriptilina/uso terapêutico , Farmacogenética , Inibidores Seletivos de Recaptação de Serotonina/uso terapêutico , Inibidores da Recaptação de Serotonina e Norepinefrina/uso terapêutico , Transcriptoma/genética , Resultado do TratamentoRESUMO
The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug.
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Antidepressivos/uso terapêutico , Citalopram/uso terapêutico , Transtorno Depressivo Maior/diagnóstico , Transtorno Depressivo Maior/tratamento farmacológico , Nortriptilina/uso terapêutico , Fatores Etários , Área Sob a Curva , Índice de Massa Corporal , Transtorno Depressivo Maior/epidemiologia , Transtorno Depressivo Maior/fisiopatologia , Feminino , Humanos , Aprendizado de Máquina , Masculino , Personalidade , Medicina de Precisão , Prognóstico , Escalas de Graduação Psiquiátrica , Análise de Regressão , Resultado do TratamentoRESUMO
Despite moderate heritability estimates, the molecular architecture of aggressive behavior remains poorly characterized. This study compared gene expression profiles from a genetic mouse model of aggression with zebrafish, an animal model traditionally used to study aggression. A meta-analytic, cross-species approach was used to identify genomic variants associated with aggressive behavior. The Rankprod algorithm was used to evaluated mRNA differences from prefrontal cortex tissues of three sets of mouse lines (N = 18) selectively bred for low and high aggressive behavior (SAL/LAL, TA/TNA, and NC900/NC100). The same approach was used to evaluate mRNA differences in zebrafish (N = 12) exposed to aggressive or non-aggressive social encounters. Results were compared to uncover genes consistently implicated in aggression across both studies. Seventy-six genes were differentially expressed (PFP < 0.05) in aggressive compared to non-aggressive mice. Seventy genes were differentially expressed in zebrafish exposed to a fight encounter compared to isolated zebrafish. Seven genes (Fos, Dusp1, Hdac4, Ier2, Bdnf, Btg2, and Nr4a1) were differentially expressed across both species 5 of which belonging to a gene-network centred on the c-Fos gene hub. Network analysis revealed an association with the MAPK signaling cascade. In human studies HDAC4 haploinsufficiency is a key genetic mechanism associated with brachydactyly mental retardation syndrome (BDMR), which is associated with aggressive behaviors. Moreover, the HDAC4 receptor is a drug target for valproic acid, which is being employed as an effective pharmacological treatment for aggressive behavior in geriatric, psychiatric, and brain-injury patients. © 2016 Wiley Periodicals, Inc.
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Agressão/fisiologia , Animais , Comportamento Animal/fisiologia , Modelos Animais de Doenças , Perfilação da Expressão Gênica/métodos , Redes Reguladoras de Genes/genética , Genes fos/genética , Genes fos/fisiologia , Camundongos , Comportamento Social , Transcriptoma/genética , Peixe-Zebra/genéticaRESUMO
The spectacular advance in our understanding of the genetic basis of schizophrenia through genome-wide association studies has the potential to identify new leads for drug treatment through improved understanding of disease pathophysiology. However, using these genetic associations successfully in drug development and patient stratification requires further target validation, particularly in understanding which gene(s) is causal in the disease, how the risk variants alter gene function and regulation, and how they fit into disease pathways and networks. If researchers consider the disease network as the target, they need to understand which genes should be targeted and in which modality, in order to modulate pathophysiology and obtain a beneficial effect for the patient. In the present article, we review recent genetic findings in schizophrenia and discuss how these might be validated with biology and integrated with epigenetic and transcriptome data to identify targets that lie within disease networks and pathways. This new understanding of disease biology will also facilitate the development of assays that recapitulate specific aspects of the disease using model organisms and cells. These assays can then be used in screening approaches, which manipulate disease networks or pathological processes to generate and test therapeutic strategies.
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Antipsicóticos/uso terapêutico , Descoberta de Drogas/métodos , Redes Reguladoras de Genes/genética , Variação Genética/genética , Esquizofrenia/tratamento farmacológico , Esquizofrenia/genética , Descoberta de Drogas/tendências , Estudo de Associação Genômica Ampla/métodos , Estudo de Associação Genômica Ampla/tendências , Humanos , Esquizofrenia/diagnósticoRESUMO
Mouse models of aggression have traditionally compared strains, most notably BALB/cJ and C57BL/6. However, these strains were not designed to study aggression despite differences in aggression-related traits and distinct reactivity to stress. This study evaluated expression of genes differentially regulated in a stress (behavioral) mouse model of aggression with those from a recent genetic mouse model aggression. The study used a discovery-replication design using two independent mRNA studies from mouse brain tissue. The discovery study identified strain (BALB/cJ and C57BL/6J) × stress (chronic mild stress or control) interactions. Probe sets differentially regulated in the discovery set were intersected with those uncovered in the replication study, which evaluated differences between high and low aggressive animals from three strains specifically bred to study aggression. Network analysis was conducted on overlapping genes uncovered across both studies. A significant overlap was found with the genetic mouse study sharing 1,916 probe sets with the stress model. Fifty-one probe sets were found to be strongly dysregulated across both studies mapping to 50 known genes. Network analysis revealed two plausible pathways including one centered on the UBC gene hub which encodes ubiquitin, a protein well-known for protein degradation, and another on P38 MAPK. Findings from this study support the stress model of aggression, which showed remarkable molecular overlap with a genetic model. The study uncovered a set of candidate genes including the Erg2 gene, which has previously been implicated in different psychopathologies. The gene networks uncovered points at a Redox pathway as potentially being implicated in aggressive related behaviors.
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Agressão/fisiologia , Comportamento Animal , Animais , Modelos Animais de Doenças , Redes Reguladoras de Genes , Camundongos Endogâmicos BALB C , Camundongos Endogâmicos C57BL , RNA Mensageiro/genética , RNA Mensageiro/metabolismo , Transdução de Sinais/genética , Estresse Psicológico/genética , Regulação para Cima/genéticaRESUMO
BACKGROUND: Several recent behavioural and behavioural genetic studies have investigated the relationship between attention deficit hyperactivity disorder (ADHD) and mathematical ability. The aim of this systematic review was to provide an overview of these studies to date. An emphasis was placed on reviewing results that explored the association between mathematics and the two ADHD components of attention and hyperactivity-impulsivity separately. METHODS: A systematic search of quantitative studies investigating the association between mathematics and ADHD was conducted across five databases (PsychINFO, Web of Science, PubMed, EMBASE, and Scopus). A total of 30 cross-sectional and four longitudinal studies were included in this review. RESULTS: Narrative synthesis of the results was provided using PRISMA guidelines. Taken together, the studies pointed at substantial evidence for a negative association between ADHD symptoms and mathematical ability. This association was particularly marked for the inattentive component of ADHD than for the hyperactive-impulsive component. Evidence from twin studies also showed a significant genetic correlation between mathematics and ADHD, which was greater for the inattentive component of ADHD compared to the hyperactive-impulsive component. CONCLUSIONS: The differential relationship of the hyperactivity-impulsivity and inattention domains with mathematics emphasises the heterogeneity within the disorder and suggests a partially different aetiology of the two ADHD domains. A better understanding of the aetiology of ADHD could help develop more efficient interventions aimed at the reduction of its symptoms. It could also offer an explanatory framework for shortcomings in achievement and inform the development of non-pharmacological intervention strategies.
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Transtorno do Deficit de Atenção com Hiperatividade/psicologia , Cognição , Compreensão , Adolescente , Criança , Avaliação Educacional , Humanos , Comportamento Impulsivo , Estudos Longitudinais , Conceitos MatemáticosRESUMO
BACKGROUND: BALB/cJ is a strain susceptible to stress and extremely susceptible to a defective hedonic impact in response to chronic stressors. The strain offers much promise as an animal model for the study of stress related disorders. We present a comparative hippocampal gene expression study on the effects of unpredictable chronic mild stress on BALB/cJ and C57BL/6J mice. Affymetrix MOE 430 was used to measure hippocampal gene expression from 16 animals of two different strains (BALB/cJ and C57BL/6J) of both sexes and subjected to either unpredictable chronic mild stress (UCMS) or no stress. Differences were statistically evaluated through supervised and unsupervised linear modelling and using Weighted Gene Coexpression Network Analysis (WGCNA). In order to gain further understanding into mechanisms related to stress response, we cross-validated our results with a parallel study from the GENDEP project using WGCNA in a meta-analysis design. RESULTS: The effects of UCMS are visible through Principal Component Analysis which highlights the stress sensitivity of the BALB/cJ strain. A number of genes and gene networks related to stress response were uncovered including the Creb1 gene. WGCNA and pathway analysis revealed a gene network centered on Nfkb1. Results from the meta-analysis revealed a highly significant gene pathway centred on the Ubiquitin C (Ubc) gene. All pathways uncovered are associated with inflammation and immune response. CONCLUSIONS: The study investigated the molecular mechanisms underlying the response to adverse environment in an animal model using a GxE design. Stress-related differences were visible at the genomic level through PCA analysis highlighting the high sensitivity of BALB/cJ animals to environmental stressors. Several candidate genes and gene networks reported are associated with inflammation and neurogenesis and could serve to inform candidate gene selection in human studies and provide additional insight into the pathology of Major Depressive Disorder.
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Encéfalo/metabolismo , Transtorno Depressivo Maior/genética , Hipocampo/metabolismo , Estresse Psicológico/genética , Animais , Encéfalo/fisiopatologia , Transtorno Depressivo Maior/patologia , Modelos Animais de Doenças , Regulação da Expressão Gênica , Hipocampo/fisiopatologia , Humanos , Camundongos , Camundongos Endogâmicos BALB C , Camundongos Endogâmicos C57BL , Biossíntese de Proteínas , Especificidade da EspécieRESUMO
Aggressive behaviour is a major cause of mortality and morbidity. Despite of moderate heritability estimates, progress in identifying the genetic factors underlying aggressive behaviour has been limited. There are currently three genetic mouse models of high and low aggression created using selective breeding. This is the first study to offer a global transcriptomic characterization of the prefrontal cortex across all three genetic mouse models of aggression. A systems biology approach has been applied to transcriptomic data across the three pairs of selected inbred mouse strains (Turku Aggressive (TA) and Turku Non-Aggressive (TNA), Short Attack Latency (SAL) and Long Attack Latency (LAL) mice and North Carolina Aggressive (NC900) and North Carolina Non-Aggressive (NC100)), providing novel insight into the neurobiological mechanisms and genetics underlying aggression. First, weighted gene co-expression network analysis (WGCNA) was performed to identify modules of highly correlated genes associated with aggression. Probe sets belonging to gene modules uncovered by WGCNA were carried forward for network analysis using ingenuity pathway analysis (IPA). The RankProd non-parametric algorithm was then used to statistically evaluate expression differences across the genes belonging to modules significantly associated with aggression. IPA uncovered two pathways, involving NF-kB and MAPKs. The secondary RankProd analysis yielded 14 differentially expressed genes, some of which have previously been implicated in pathways associated with aggressive behaviour, such as Adrbk2. The results highlighted plausible candidate genes and gene networks implicated in aggression-related behaviour.