scholarly journals Topological gene expression networks recapitulate brain anatomy and function

2019 ◽  
Vol 3 (3) ◽  
pp. 744-762 ◽  
Author(s):  
Alice Patania ◽  
Pierluigi Selvaggi ◽  
Mattia Veronese ◽  
Ottavia Dipasquale ◽  
Paul Expert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brain Function ◽  
Resting State Fmri ◽  
Brain Atlas ◽  
Brain Anatomy ◽  
Expression Data ◽  
Related Gene ◽  
Genetic Pathways ◽  
Dopaminergic Pathway ◽  
And Function

Understanding how gene expression translates to and affects human behavior is one of the ultimate goals of neuroscience. In this paper, we present a pipeline based on Mapper, a topological simplification tool, to analyze gene co-expression data. We first validate the method by reproducing key results from the literature on the Allen Human Brain Atlas and the correlations between resting-state fMRI and gene co-expression maps. We then analyze a dopamine-related gene set and find that co-expression networks produced by Mapper return a structure that matches the well-known anatomy of the dopaminergic pathway. Our results suggest that network based descriptions can be a powerful tool to explore the relationships between genetic pathways and their association with brain function and its perturbation due to illness and/or pharmacological challenges.

scholarly journals Topological gene-expression networks recapitulate brain anatomy and function

2018 ◽  
Author(s):  
Alice Patania ◽  
Pierluigi Selvaggi ◽  
Mattia Veronese ◽  
Ottavia Dipasquale ◽  
Paul Expert ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brain Function ◽  
Resting State Fmri ◽  
Brain Atlas ◽  
Brain Anatomy ◽  
Expression Data ◽  
Pharmacological Challenge ◽  
Topological Network ◽  
Dopaminergic Pathway ◽  
And Function

AbstractUnderstanding how gene expression translates to and affects human behaviour is one of the ultimate aims of neuroscience. In this paper, we present a pipeline based on Mapper, a topological simplification tool, to produce and analyze genes co-expression data. We first validate the method by reproducing key results from the literature on the Allen Human Brain Atlas, and the correlations between resting-state fMRI and gene co-expression maps. We then analyze a dopamine-related gene-set and find that co-expression networks produced by Mapper returned a structure that matches the well-known anatomy of the dopaminergic pathway. Our results suggest that topological network descriptions can be a powerful tool to explore the relationships between genetic pathways and their association with brain function and its perturbation due to illness and/or pharmacological challenge.

scholarly journals A practical guide to linking brain-wide gene expression and neuroimaging data

2018 ◽  
Author(s):  
Aurina Arnatkevičiūtė ◽  
Ben D. Fulcher ◽  
Alex Fornito
Keyword(s):  
Gene Expression ◽  
Brain Atlas ◽  
Expression Data ◽  
Analysis Pipeline ◽  
Atlas Data ◽  
Processing Pipeline ◽  
Neuroimaging Data ◽  
Brain Structure And Function ◽  
Expression Atlas ◽  
And Function

AbstractThe recent availability of comprehensive, brain-wide gene expression atlases such as the Allen Human Brain Atlas (AHBA) has opened new opportunities for understanding how spatial variations on the molecular scale relate to the macroscopic neuroimaging phenotypes. A rapidly growing body of literature is demonstrating relationships between gene expression and diverse properties of brain structure and function, but approaches for combining expression atlas data with neuroimaging are highly inconsistent, with substantial variations in how the expression data are processed. The degree to which these methodological variations affect findings is unclear. Here, we outline a seven-step analysis pipeline for relating brain-wide transcriptomic and neuroimaging data and compare how different processing choices influence the resulting data. We suggest that studies using AHBA should work towards a unified data processing pipeline to ensure consistent and reproducible results in this burgeoning field.

scholarly journals Differentially Coexpressed Disease Gene Identification Based on Gene Coexpression Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Xue Jiang ◽  
Han Zhang ◽  
Xiongwen Quan
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Selection ◽  
Coexpression Network ◽  
Expression Data ◽  
Gene Coexpression Network ◽  
Related Gene ◽  
Differential Coexpression ◽  
Gene Coexpression ◽  
Disease Related Gene

Screening disease-related genes by analyzing gene expression data has become a popular theme. Traditional disease-related gene selection methods always focus on identifying differentially expressed gene between case samples and a control group. These traditional methods may not fully consider the changes of interactions between genes at different cell states and the dynamic processes of gene expression levels during the disease progression. However, in order to understand the mechanism of disease, it is important to explore the dynamic changes of interactions between genes in biological networks at different cell states. In this study, we designed a novel framework to identify disease-related genes and developed a differentially coexpressed disease-related gene identification method based on gene coexpression network (DCGN) to screen differentially coexpressed genes. We firstly constructed phase-specific gene coexpression network using time-series gene expression data and defined the conception of differential coexpression of genes in coexpression network. Then, we designed two metrics to measure the value of gene differential coexpression according to the change of local topological structures between different phase-specific networks. Finally, we conducted meta-analysis of gene differential coexpression based on the rank-product method. Experimental results demonstrated the feasibility and effectiveness of DCGN and the superior performance of DCGN over other popular disease-related gene selection methods through real-world gene expression data sets.

scholarly journals Transcriptomic and Cellular Decoding of Regional Brain Vulnerability to Neurodevelopmental Disorders

2019 ◽  
Author(s):  
Jakob Seidlitz ◽  
Ajay Nadig ◽  
Siyuan Liu ◽  
Richard A.I. Bethlehem ◽  
Petra E. Vértes ◽  
...  
Keyword(s):  
Gene Expression ◽  
Spatial Patterns ◽  
Neurodevelopmental Disorders ◽  
Copy Number Variations ◽  
Brain Anatomy ◽  
Specific Cell ◽  
Expression Data ◽  
Regional Brain ◽  
Genetic Risks ◽  
Neuroimaging Data

AbstractNeurodevelopmental disorders are highly heritable and associated with spatially-selective disruptions of brain anatomy. The logic that translates genetic risks into spatially patterned brain vulnerabilities remains unclear but is a fundamental question in disease pathogenesis. Here, we approach this question by integrating (i) in vivo neuroimaging data from patient subgroups with known causal genomic copy number variations (CNVs), and (ii) bulk and single-cell gene expression data from healthy cortex. First, for each of six different CNV disorders, we show that spatial patterns of cortical anatomy change in youth are correlated with spatial patterns of expression for CNV region genes in bulk cortical tissue from typically-developing adults. Next, by transforming normative bulk-tissue cortical expression data into cell-type expression maps, we further link each disorder’s anatomical change map to specific cell classes and specific CNV-region genes that these cells express. Finally, we establish convergent validity of this “transcriptional vulnerability model” by inter-relating patient neuroimaging data with measures of altered gene expression in both brain and blood-derived patient tissue. Our work clarifies general biological principles that govern the mapping of genetic risks onto regional brain disruption in neurodevelopmental disorders. We present new methods that can harness these principles to screen for potential cellular and molecular determinants of disease from readily available patient neuroimaging data.

scholarly journals Photobiomodulation Does Not Influence Maturation and Leads to Mild Improvements in Functional Healing of Mouse Achilles Tendons

2019 ◽  
Author(s):  
Ryan C. Locke ◽  
Elisabeth A. Lemmon ◽  
Ellen Dudzinski ◽  
Sarah C. Kopa ◽  
Julianna M. Wayne ◽  
...  
Keyword(s):  
Gene Expression ◽  
Near Infrared ◽  
Postnatal Growth ◽  
Acute Effect ◽  
Cell Number ◽  
Structure And Function ◽  
Infrared Light ◽  
Related Gene ◽  
Achilles Tenotomy ◽  
And Function

ABSTRACTTendon rupture can occur at any age and is commonly treated non-operatively, yet can result in persisting symptoms. Thus, a need exists to improve non-operative treatments of injured tendons. Photobiomodulation (PBM) therapy has shown promise in the clinic and is hypothesized to stimulate mitochondrial-related metabolism and improve healing. However, the effect of PBM therapy on mitochondrial function during tendon maturation and healing are unknown, and its effect on tendon structure and function remain unclear. In this study, near-infrared light (980:810nm blend, 2.5J/cm2) was applied at low (30mW/cm2) or high (300mW/cm2) irradiance to unilateral Achilles tendons of CD-1 mice during postnatal growth (maturation) as well as adult mice with bilateral Achilles tenotomy (healing). The chronic effect of PBM therapy on tendon structure and function was determined using histology and mechanics, and the acute effect of PBM therapy on mitochondrial-related gene expression was assessed. During maturation and healing, collagen alignment, cell number, and nuclear shape were unaffected by chronic PBM therapy. We found a sex-dependent effect of PBM therapy during healing on mechanical outcomes (e.g., increased stiffness and Young’s modulus for PBM-treated females, and increased strain at ultimate stress for PBM-treated males). Mitochondria-related gene expression was marginally influenced by PBM therapy for both maturation and healing studies. This study was the first to implement PBM therapy during both growth and healing of the murine tendon. PBM therapy resulted in marginal and sex-dependent effects on murine tendon.

scholarly journals A laminar augmented cascading flexible neural forest model for classification of cancer subtypes based on gene expression data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Lianxin Zhong ◽  
Qingfang Meng ◽  
Yuehui Chen ◽  
Lei Du ◽  
Peng Wu
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Small Sample Size ◽  
Small Sample ◽  
Expression Data ◽  
Cancer Subtypes ◽  
Cancer Pathogenesis ◽  
Depth Study ◽  
And Function

Abstract Background Correctly classifying the subtypes of cancer is of great significance for the in-depth study of cancer pathogenesis and the realization of personalized treatment for cancer patients. In recent years, classification of cancer subtypes using deep neural networks and gene expression data has gradually become a research hotspot. However, most classifiers may face overfitting and low classification accuracy when dealing with small sample size and high-dimensional biology data. Results In this paper, a laminar augmented cascading flexible neural forest (LACFNForest) model was proposed to complete the classification of cancer subtypes. This model is a cascading flexible neural forest using deep flexible neural forest (DFNForest) as the base classifier. A hierarchical broadening ensemble method was proposed, which ensures the robustness of classification results and avoids the waste of model structure and function as much as possible. We also introduced an output judgment mechanism to each layer of the forest to reduce the computational complexity of the model. The deep neural forest was extended to the densely connected deep neural forest to improve the prediction results. The experiments on RNA-seq gene expression data showed that LACFNForest has better performance in the classification of cancer subtypes compared to the conventional methods. Conclusion The LACFNForest model effectively improves the accuracy of cancer subtype classification with good robustness. It provides a new approach for the ensemble learning of classifiers in terms of structural design.

scholarly journals Bioinformatic Analysis of Peripheral Blood miRNA of Breast Cancer Patients in Relation With Anthracycline Cardiotoxicity

2019 ◽  
Author(s):  
Wang Yadi ◽  
Chen Shurui ◽  
Zhang Tong ◽  
Chen Suxian ◽  
Tong Qing ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Patients ◽  
Cancer Chemotherapy ◽  
Bioinformatic Analysis ◽  
Expression Data ◽  
Breast Cancer Patients ◽  
Related Gene ◽  
Anthracycline Cardiotoxicity ◽  
Breast Cancer Chemotherapy

Abstract The current diagnostic methods and treatments still fail to lower the incidence of anthracycline-induced cardiotoxicity effectively. In this study, we aimed to (1) analyze the cardiotoxicity-related genes after breast cancer chemotherapy in gene expression database and (2) carry out bioinformatic analysis to identify cardiotoxicity-related abnormal expressions, the biomarkers of such abnormal expressions, and the key regulatory pathways after breast cancer chemotherapy. Cardiotoxicity-related gene expression data (GSE40447) after breast cancer chemotherapy was acquired from the GEO database. The biomarker expression data of women with chemotherapy-induced cardiotoxicity (group A), chemotherapy history but no cardiotoxicity (group B), and confirmatory diagnosis of breast cancer but normal ejection fraction before chemotherapy (group C) were analyzed to obtain the mRNA with differential expressions and predict the miRNAs regulating the differential expressions. The miRanda formula and functional enrichment analysis were used to screen abnormal miRNAs. Then, the gene ontology (GO) analysis was adapted to further screen the miRNAs related to cardiotoxicity after breast cancer chemotherapy. The data of differential analysis of biomarker expression of groups A, B, and C using the GSE40447-related gene expression profile database showed that there were 30 intersection genes. The differentially expressed mRNAs were predicted using the miRanda and TargetScan software, and a total of 2978 miRNAs were obtained by taking the intersections. Further, the GO analysis and targeted regulatory relationship between miRNA and target genes were used to establish miRNA-gene interaction network to screen and obtain 7 cardiotoxicity-related miRNAs with relatively high centrality, including hsa-miR-4638-3p, hsa-miR-5096, hsa-miR-4763-5p, hsa-miR-1273g-3p, hsa-miR6192, hsa-miR-4726-5p and hsa-miR-1273a. Among them, hsa-miR-4638-3p and hsa-miR-1273g-3p had the highest centrality. The PCR verification results were consistent with those of the chip data. There are differentially expressed miRNAs in the peripheral blood of breast cancer patients with anthracycline cardiotoxicity. Among them, hsa-miR-4638-3p and hsa-miR-1273g-3p are closely associated with the onset of anthracycline cardiotoxicity in patients with breast cancer. Mining, integrating, and validating effective information resources of biological gene chips can provide a new direction for further studies on the molecular mechanism of anthracycline cardiotoxicity.

scholarly journals Effect of deoxynivalenol on apoptosis, barrier function, and expression levels of genes involved in nutrient transport, mitochondrial biogenesis and function in IPEC-J2 cells

2017 ◽  
Vol 6 (6) ◽  
pp. 866-877 ◽  
Author(s):  
Peng Liao ◽  
Meifang Liao ◽  
Ling Li ◽  
Bie Tan ◽  
Yulong Yin
Keyword(s):  
Gene Expression ◽  
Mitochondrial Biogenesis ◽  
Barrier Function ◽  
Nutrient Transport ◽  
Nutrient Utilization ◽  
Related Gene ◽  
Expression Levels ◽  
And Function

DON could affect apoptosis, barrier function, nutrient utilization, as well as mitochondrial biogenesis and function-related gene expression in the IPEC-J2.

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