Signal Attenuation as a Rat Model of Obsessive Compulsive Disorder

In the signal attenuation rat model of obsessive-compulsive disorder (OCD), lever-pressing for food is followed by the presentation of a compound stimulus which serves as a feedback cue. This feedback is later attenuated by repeated presentations of the stimulus without food (without the rat emitting the lever-press response). In the next stage, lever-pressing is assessed under extinction conditions (i.e., no food is delivered). At this stage rats display two types of lever-presses, those that are followed by an attempt to collect a reward, and those that are not. The latter are the measure of compulsive-like behavior in the model. A control procedure in which rats do not experience the attenuation of the feedback cue serves to distinguish between the effects of signal attenuation and of extinction. The signal attenuation model is a highly validated model of OCD and differentiates between compulsive-like behaviors and behaviors that are repetitive but not compulsive. In addition the measures collected during the procedure eliminate alternative explanations for differences between the groups being tested, and are quantitative, unbiased and unaffected by inter-experimenter variability. The major disadvantages of this model are the costly equipment, the fact that it requires some technical know-how and the fact that it is time-consuming compared to other models of OCD (11 days). The model may be used for detecting the anti- or pro-compulsive effects of pharmacological and non-pharmacological manipulations and for studying the neural substrate of compulsive behavior.     

Partitioning the Heritability of Tourette Syndrome and Obsessive Compulsive Disorder Reveals Differences in Genetic Architecture

The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained by all SNPs for two phenotypically-related neurobehavioral disorders, obsessive-compulsive disorder (OCD) and Tourette Syndrome (TS), using GCTA. Our analysis yielded a heritability point estimate of 0.58 (se = 0.09, p = 5.64e-12) for TS, and 0.37 (se = 0.07, p = 1.5e-07) for OCD. In addition, we conducted multiple genomic partitioning analyses to identify genomic elements that concentrate this heritability. We examined genomic architectures of TS and OCD by chromosome, MAF bin, and functional annotations. In addition, we assessed heritability for early onset and adult onset OCD. Among other notable results, we found that SNPs with a minor allele frequency of less than 5% accounted for 21% of the TS heritability and 0% of the OCD heritability. Additionally, we identified a significant contribution to TS and OCD heritability by variants significantly associated with gene expression in two regions of the brain (parietal cortex and cerebellum) for which we had available expression quantitative trait loci (eQTLs). Finally we analyzed the genetic correlation between TS and OCD, revealing a genetic correlation of 0.41 (se = 0.15, p = 0.002). These results are very close to previous heritability estimates for TS and OCD based on twin and family studies, suggesting that very little, if any, heritability is truly missing (i.e., unassayed) from TS and OCD GWAS studies of common variation. The results also indicate that there is some genetic overlap between these two phenotypically-related neuropsychiatric disorders, but suggest that the two disorders have distinct genetic architectures.     

Investigation of Attentional Bias in Obsessive Compulsive Disorder with and without Depression in Visual Search

Whether Obsessive Compulsive Disorder (OCD) is associated with an increased attentional bias to emotive stimuli remains controversial. Additionally, it is unclear whether comorbid depression modulates abnormal emotional processing in OCD. This study examined attentional bias to OC-relevant scenes using a visual search task. Controls, non-depressed and depressed OCD patients searched for their personally selected positive images amongst their negative distractors, and vice versa. Whilst the OCD groups were slower than healthy individuals in rating the images, there were no group differences in the magnitude of negative bias to concern-related scenes. A second experiment employing a common set of images replicated the results on an additional sample of OCD patients. Although there was a larger bias to negative OC-related images without pre-exposure overall, no group differences in attentional bias were observed. However, OCD patients subsequently rated the images more slowly and more negatively, again suggesting post-attentional processing abnormalities. The results argue against a robust attentional bias in OCD patients, regardless of their depression status and speak to generalized difficulties disengaging from negative valence stimuli. Rather, post-attentional processing abnormalities may account for differences in emotional processing in OCD.     

Integrating Linguistics,Social Structure,and Geography to Model Genetic Diversity within India

India represents an intricate tapestry of population substructure shaped by geography, language, culture, and social stratification. Although geography closely correlates with genetic structure in other parts of the world, the strict endogamy imposed by the Indian caste system and the large number of spoken languages add further levels of complexity to understand Indian population structure. To date, no study has attempted to model and evaluate how these factors have interacted to shape the patterns of genetic diversity within India. We merged all publicly available data from the Indian subcontinent into a data set of 891 individuals from 90 well-defined groups. Bringing together geography, genetics, and demographic factors, we developed Correlation Optimization of Genetics and Geodemographics to build a model that explains the observed population genetic substructure. We show that shared language along with social structure have been the most powerful forces in creating paths of gene flow in the subcontinent. Furthermore, we discover the ethnic groups that best capture the diverse genetic substructure using a ridge leverage score statistic. Integrating data from India with a data set of additional 1,323 individuals from 50 Eurasian populations, we find that Indo-European and Dravidian speakers of India show shared genetic drift with Europeans, whereas the Tibeto-Burman speaking tribal groups have maximum shared genetic drift with East Asians.     

Integrating Genetic Data and Demographic Modeling to Facilitate Conservation of Small,Isolated Mountain Goat Populations

Acquisition of field data and analytical methods needed for conservation and management of wildlife populations represent significant challenges, particularly for species that inhabit landscapes that are difficult to access or species that persist in small, isolated populations. In such instances, integrating diverse and complementary data streams, such as genetic and non-genetic data, can advance our understanding of population dynamics and associated management implications. We examined how genetic and morphologic data can be used to articulate population structure of a low-density, peninsular population of mountain goats (Oreamnos americanus) on the Cleveland Peninsula, Alaska, USA, and surrounding areas, 2005–2018. We then use a population demographic modeling approach to examine how the use of population structure information influences sustainable harvest quotas, as compared to a panmictic, null model. Specifically, we conducted extensive field sampling of genetic (n = 446) and morphologic (i.e., horn length, n = 371) data to characterize population structure. We conducted demographic analyses and examined harvest modeling scenarios using a sex- and age-specific matrix population modeling approach. Genetic and morphologic data analyses suggested peninsular subpopulations were demographically isolated, relative to surrounding mainland populations. Specifically, genetic structuring was evident and followed an isolation-by-distance, stepping-stone pattern indicating limited interchange, low effective population sizes, and reduced genetic diversity along a peninsular extremity to mainland gradient. Harvest modeling indicated that overharvest would likely occur if the panmictic, null model was used to guide harvest because the smallest genetically defined population at the peninsular extremity was too small to permit any level of sustainable harvest. Our analyses illustrate the importance of using genetic and morphologic data, in combination with demographic modeling, to quantitatively delineate population boundaries and dynamics for ensuring viability of small, isolated populations. © 2020 The Wildlife Society.     

Connection between Genetic and Clinical Data in Bipolar Disorder

Complex diseases may be associated with combinations of changes in DNA, where the single change has little impact alone. In a previous study of patients with bipolar disorder and controls combinations of SNP genotypes were analyzed, and four large clusters of combinations were found to be significantly associated with bipolar disorder. It has now been found that these clusters may be connected to clinical data.     

Integrating Mouse and Human Genetic Data to Move beyond GWAS and Identify Causal Genes in Cholesterol Metabolism

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A New Approach to Investigate the Association between Brain Functional Connectivity and Disease Characteristics of Attention-Deficit/Hyperactivity Disorder: Topological Neuroimaging Data Analysis

Background

Attention-deficit/hyperactivity disorder (ADHD) is currently diagnosed by a diagnostic interview, mainly based on subjective reports from parents or teachers. It is necessary to develop methods that rely on objectively measureable neurobiological data to assess brain-behavior relationship in patients with ADHD. We investigated the application of a topological data analysis tool, Mapper, to analyze the brain functional connectivity data from ADHD patients.

Methods

To quantify the disease severity using the neuroimaging data, the decomposition of individual functional networks into normal and disease components by the healthy state model (HSM) was performed, and the magnitude of the disease component (MDC) was computed. Topological data analysis using Mapper was performed to distinguish children with ADHD (n = 196) from typically developing controls (TDC) (n = 214).

Results

In the topological data analysis, the partial clustering results of patients with ADHD and normal subjects were shown in a chain-like graph. In the correlation analysis, the MDC showed a significant increase with lower intelligence scores in TDC. We also found that the rates of comorbidity in ADHD significantly increased when the deviation of the functional connectivity from HSM was large. In addition, a significant correlation between ADHD symptom severity and MDC was found in part of the dataset.

Conclusions

The application of HSM and topological data analysis methods in assessing the brain functional connectivity seem to be promising tools to quantify ADHD symptom severity and to reveal the hidden relationship between clinical phenotypic variables and brain connectivity.     

Neuroimaging Study Designs,Computational Analyses and Data Provenance Using the LONI Pipeline

Modern computational neuroscience employs diverse software tools and multidisciplinary expertise to analyze heterogeneous brain data. The classical problems of gathering meaningful data, fitting specific models, and discovering appropriate analysis and visualization tools give way to a new class of computational challenges—management of large and incongruous data, integration and interoperability of computational resources, and data provenance. We designed, implemented and validated a new paradigm for addressing these challenges in the neuroimaging field. Our solution is based on the LONI Pipeline environment [3], [4], a graphical workflow environment for constructing and executing complex data processing protocols. We developed study-design, database and visual language programming functionalities within the LONI Pipeline that enable the construction of complete, elaborate and robust graphical workflows for analyzing neuroimaging and other data. These workflows facilitate open sharing and communication of data and metadata, concrete processing protocols, result validation, and study replication among different investigators and research groups. The LONI Pipeline features include distributed grid-enabled infrastructure, virtualized execution environment, efficient integration, data provenance, validation and distribution of new computational tools, automated data format conversion, and an intuitive graphical user interface. We demonstrate the new LONI Pipeline features using large scale neuroimaging studies based on data from the International Consortium for Brain Mapping [5] and the Alzheimer''s Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu.     

Integrating Genetic,Environmental, and Social Networks to Reveal Transmission Pathways of a Dolphin Foraging Innovation

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Developing Neuroimaging Phenotypes of the Default Mode Network in PTSD: Integrating the Resting State,Working Memory,and Structural Connectivity

Complementary structural and functional neuroimaging techniques used to examine the Default Mode Network (DMN) could potentially improve assessments of psychiatric illness severity and provide added validity to the clinical diagnostic process. Recent neuroimaging research suggests that DMN processes may be disrupted in a number of stress-related psychiatric illnesses, such as posttraumatic stress disorder (PTSD).Although specific DMN functions remain under investigation, it is generally thought to be involved in introspection and self-processing. In healthy individuals it exhibits greatest activity during periods of rest, with less activity, observed as deactivation, during cognitive tasks, e.g., working memory. This network consists of the medial prefrontal cortex, posterior cingulate cortex/precuneus, lateral parietal cortices and medial temporal regions.Multiple functional and structural imaging approaches have been developed to study the DMN. These have unprecedented potential to further the understanding of the function and dysfunction of this network. Functional approaches, such as the evaluation of resting state connectivity and task-induced deactivation, have excellent potential to identify targeted neurocognitive and neuroaffective (functional) diagnostic markers and may indicate illness severity and prognosis with increased accuracy or specificity. Structural approaches, such as evaluation of morphometry and connectivity, may provide unique markers of etiology and long-term outcomes. Combined, functional and structural methods provide strong multimodal, complementary and synergistic approaches to develop valid DMN-based imaging phenotypes in stress-related psychiatric conditions. This protocol aims to integrate these methods to investigate DMN structure and function in PTSD, relating findings to illness severity and relevant clinical factors.     

Paternal Genetic Contribution Influences Fetal Vulnerability to Maternal Alcohol Consumption in a Rat Model of Fetal Alcohol Spectrum Disorder

Background

Fetal alcohol exposure causes in the offspring a collection of permanent physiological and neuropsychological deficits collectively termed Fetal Alcohol Spectrum Disorder (FASD). The timing and amount of exposure cannot fully explain the substantial variability among affected individuals, pointing to genetic influences that mediate fetal vulnerability. However, the aspects of vulnerability that depend on the mother, the father, or both, are not known.

Methodology/Principal Findings

Using the outbred Sprague-Dawley (SD) and inbred Brown Norway (BN) rat strains as well as their reciprocal crosses, we administered ethanol (E), pair-fed (PF), or control (C) diets to the pregnant dams. The dams'' plasma levels of free thyroxine (fT4), triiodothyronine (T3), free T3 (fT3), and thyroid stimulating hormone (TSH) were measured to elucidate potential differences in maternal thyroid hormonal environment, which affects specific aspects of FASD. We then compared alcohol-exposed, pair fed, and control offspring of each fetal strain on gestational day 21 (G21) to identify maternal and paternal genetic effects on bodyweight and placental weight of male and female fetuses.

Conclusions

SD and BN dams exhibited different baseline hypothalamic-pituitary-thyroid function. Moreover, the thyroid function of SD dams was more severely affected by alcohol consumption while that of BN dams was relatively resistant. This novel finding suggests that genetic differences in maternal thyroid function are one source of maternal genetic effects on fetal vulnerability to FASD. The fetal vulnerability to decreased bodyweight after alcohol exposure depended on the genetic contribution of both parents, not only maternal contribution as previously thought. In contrast, the effect of maternal alcohol consumption on placental weight was consistent and not strain-dependent. Interestingly, placental weight in fetuses with different paternal genetic contributions exhibited opposite responses to caloric restriction (pair feeding). In summary, these novel findings demonstrate both maternal and paternal genetic contributions to in utero vulnerability to alcohol, refining our understanding of the genetically-based heterogeneity seen in human FASD.     

Genetic Predictors of Response to Serotonergic and Noradrenergic Antidepressants in Major Depressive Disorder: A Genome-Wide Analysis of Individual-Level Data and a Meta-Analysis

Background

It has been suggested that outcomes of antidepressant treatment for major depressive disorder could be significantly improved if treatment choice is informed by genetic data. This study aims to test the hypothesis that common genetic variants can predict response to antidepressants in a clinically meaningful way.

Methods and Findings

The NEWMEDS consortium, an academia–industry partnership, assembled a database of over 2,000 European-ancestry individuals with major depressive disorder, prospectively measured treatment outcomes with serotonin reuptake inhibiting or noradrenaline reuptake inhibiting antidepressants and available genetic samples from five studies (three randomized controlled trials, one part-randomized controlled trial, and one treatment cohort study). After quality control, a dataset of 1,790 individuals with high-quality genome-wide genotyping provided adequate power to test the hypotheses that antidepressant response or a clinically significant differential response to the two classes of antidepressants could be predicted from a single common genetic polymorphism. None of the more than half million genetic markers significantly predicted response to antidepressants overall, serotonin reuptake inhibitors, or noradrenaline reuptake inhibitors, or differential response to the two types of antidepressants (genome-wide significance p<5×10⁻⁸). No biological pathways were significantly overrepresented in the results. No significant associations (genome-wide significance p<5×10⁻⁸) were detected in a meta-analysis of NEWMEDS and another large sample (STAR*D), with 2,897 individuals in total. Polygenic scoring found no convergence among multiple associations in NEWMEDS and STAR*D.

Conclusions

No single common genetic variant was associated with antidepressant response at a clinically relevant level in a European-ancestry cohort. Effects specific to particular antidepressant drugs could not be investigated in the current study. Please see later in the article for the Editors'' Summary     

Structural,Genetic, and Functional Signatures of Disordered Neuro-Immunological Development in Autism Spectrum Disorder

Background

Numerous linkage studies have been performed in pedigrees of Autism Spectrum Disorders, and these studies point to diverse loci and etiologies of autism in different pedigrees. The underlying pattern may be identified by an integrative approach, especially since ASD is a complex disorder manifested through many loci.

Method

Autism spectrum disorder (ASD) was studied through two different and independent genome-scale measurement modalities. We analyzed the results of copy number variation in autism and triangulated these with linkage studies.

Results

Consistently across both genome-scale measurements, the same two molecular themes emerged: immune/chemokine pathways and developmental pathways.

Conclusion

Linkage studies in aggregate do indeed share a thematic consistency, one which structural analyses recapitulate with high significance. These results also show for the first time that genomic profiling of pathways using a recombination distance metric can capture pathways that are consistent with those obtained from copy number variations (CNV).     

Integrating Genomics and Proteomics Data to Predict Drug Effects Using Binary Linear Programming

The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound''s effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound''s effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds (trichostatin A, MS-275, staurosporine, and digoxigenin) according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound''s efficacy.     

Diseases Severity,Genetic Variation,and Pathogenicity of Ceratocystis Wilt on Lansium domesticum in South Sumatra,Indonesia

Ceratocystis wilt disease has caused significant mortality in duku (Lansium domesticum) since 2014 and has now spread to all districts in South Sumatra, Indonesia. Recently, 16 isolates from duku representing populations from various districts in South Sumatra were isolated. Analysis for the morphological characteristic of the isolate showed that the population has a uniform morphology. Genetic analysis based on internal transcribed spacer (ITS) and β-tubulin sequences verified that the population has being dominated by the ITS5 haplotype of Ceratocystis fimbriata and a new ITS group, the ITS7b haplotype that was localized in Musi Banyuasin. Both haplotypes were highly pathogenic to duku. Inoculation tests on various forest and agroforestry plant hosts showed that both haplotypes were highly pathogenic to Acacia mangium, moderately pathogenic to Acacia carsicarpa, Eucalyptus urophylla, and Melaleuca cajuputi, but weakly pathogenic to Dyera costulata, Hevea brasiliensis, and Alstonia scholaris. Therefore, this pathogen becomes a serious threat to Indonesia’s biodiversity due to its ability to infect forest and agroforestry plants, especially the indigenous ones.     

综合ChIP-chip数据、基因敲除数据和表达谱数据重构基因调控网络

揭示生物体内在的调控机制是生物信息学的一项重要研究内容．各种高通量生物数据的涌现,为从基因组的尺度上重构基因调控网络提供了可能．由于单数据源仅能提供关于调控关系的片面信息且存在噪声,因此整合多种生物学数据的方法有望得到可靠性较高的调控网络．提出了一种综合ChIP-chip数据、knock out (敲除)数据和各种条件下的表达谱数据来推断调控关系的新方法．ChIP-chip数据和knock out 数据能分别提供转录因子和目标基因对关系的直接物理结合和功能关系的证据,这两类数据的整合有望获得较高的识别准确率．但这两类数据的重合性通常较低,基于共调控的基因通常具有较高的表达相似性这一假设,在一定程度上降低了这两类数据重合性较低所带来的影响．算法所识别的大部分调控关系都被YEASTRACT,高质量ChIP-chip数据和文献所验证,从而证明了该方法在调控关系的预测上具有较高的准确性．与其他方法的比较,也表明了该方法具有较高的预测性能．     

Integrating Multibeam Backscatter Angular Response,Mosaic and Bathymetry Data for Benthic Habitat Mapping

Multibeam echosounders (MBES) are increasingly becoming the tool of choice for marine habitat mapping applications. In turn, the rapid expansion of habitat mapping studies has resulted in a need for automated classification techniques to efficiently map benthic habitats, assess confidence in model outputs, and evaluate the importance of variables driving the patterns observed. The benthic habitat characterisation process often involves the analysis of MBES bathymetry, backscatter mosaic or angular response with observation data providing ground truth. However, studies that make use of the full range of MBES outputs within a single classification process are limited. We present an approach that integrates backscatter angular response with MBES bathymetry, backscatter mosaic and their derivatives in a classification process using a Random Forests (RF) machine-learning algorithm to predict the distribution of benthic biological habitats. This approach includes a method of deriving statistical features from backscatter angular response curves created from MBES data collated within homogeneous regions of a backscatter mosaic. Using the RF algorithm we assess the relative importance of each variable in order to optimise the classification process and simplify models applied. The results showed that the inclusion of the angular response features in the classification process improved the accuracy of the final habitat maps from 88.5% to 93.6%. The RF algorithm identified bathymetry and the angular response mean as the two most important predictors. However, the highest classification rates were only obtained after incorporating additional features derived from bathymetry and the backscatter mosaic. The angular response features were found to be more important to the classification process compared to the backscatter mosaic features. This analysis indicates that integrating angular response information with bathymetry and the backscatter mosaic, along with their derivatives, constitutes an important improvement for studying the distribution of benthic habitats, which is necessary for effective marine spatial planning and resource management.