scholarly journals Excitatory/inhibitory imbalance in autism: the role of glutamate and GABA gene-sets in symptoms and cortical brain structure

2021 ◽  
Author(s):  
Viola Hollestein ◽  
Geert Poelmans ◽  
Natalie Forde ◽  
Christian F Beckmann ◽  
Christine Ecker ◽  
...  
Keyword(s):  
Gene Expression ◽  
Sensory Processing ◽  
Brain Structure ◽  
Symptom Severity ◽  
Expression Profiles ◽  
Brain Regions ◽  
Gene Set Analysis ◽  
Gene Set ◽  
Gene Sets ◽  
Autism Symptomatology

Background: The excitatory/inhibitory (E/I) imbalance hypothesis posits that an imbalance between excitatory (glutamatergic) and inhibitory (GABAergic) mechanisms underlies the behavioral characteristics of autism spectrum disorder (autism). However, how E/I imbalance arises and how it may differ across autism symptomatology and brain regions is not well understood. Methods: We used innovative analysis methods - combining competitive gene-set analysis and gene-expression profiles in relation to cortical thickness (CT)- to investigate the relationship between genetic variance, brain structure and autism symptomatology of participants from the EU-AIMS LEAP cohort (autism=360, male/female=259/101; neurotypical control participants=279, male/female=178/101) aged 6 to 30 years. Competitive gene-set analysis investigated associations between glutamatergic and GABAergic signaling pathway gene-sets and clinical measures, and CT. Additionally, we investigated expression profiles of the genes within those sets throughout the brain and how those profiles relate to differences in CT between autistic and neurotypical control participants in the same regions. Results: The glutamate gene-set was associated with all autism symptom severity scores on the Autism Diagnostic Observation Schedule-2 (ADOS-2) and the Autism Diagnostic Interview-Revised (ADI-R) within the autistic group, while the GABA set was associated with sensory processing measures (using the SSP subscales) across all participants. Brain regions with greater gene expression of both glutamate and GABA genes showed greater differences in CT between autistic and neurotypical control participants. Conclusions: Our results suggest crucial roles for glutamate and GABA genes in autism symptomatology as well as CT, where GABA is more strongly associated with sensory processing and glutamate more with autism symptom severity. 

scholarly journals Distance-correlation based gene set analysis in longitudinal studies

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Jiehuan Sun ◽  
Jose D. Herazo-Maya ◽  
Xiu Huang ◽  
Naftali Kaminski ◽  
Hongyu Zhao
Keyword(s):  
Gene Expression ◽  
Disease Progression ◽  
Clinical Outcomes ◽  
Expression Profiles ◽  
Gene Set Analysis ◽  
Related Gene ◽  
Disease Etiology ◽  
Distance Correlation ◽  
Gene Set ◽  
Gene Sets

AbstractLongitudinal gene expression profiles of subjects are collected in some clinical studies to monitor disease progression and understand disease etiology. The identification of gene sets that have coordinated changes with relevant clinical outcomes over time from these data could provide significant insights into the molecular basis of disease progression and lead to better treatments. In this article, we propose a Distance-Correlation based Gene Set Analysis (dcGSA) method for longitudinal gene expression data. dcGSA is a non-parametric approach, statistically robust, and can capture both linear and nonlinear relationships between gene sets and clinical outcomes. In addition, dcGSA is able to identify related gene sets in cases where the effects of gene sets on clinical outcomes differ across subjects due to the subject heterogeneity, remove the confounding effects of some unobserved time-invariant covariates, and allow the assessment of associations between gene sets and multiple related outcomes simultaneously. Through extensive simulation studies, we demonstrate that dcGSA is more powerful of detecting relevant genes than other commonly used gene set analysis methods. When dcGSA is applied to a real dataset on systemic lupus erythematosus, we are able to identify more disease related gene sets than other methods.

Utilizing Cancer - Functional Gene Set - Compound Networks to Identify Putative Drugs for Breast Cancer

2018 ◽  
Vol 21 (2) ◽  
pp. 74-83
Author(s):  
Tzu-Hung Hsiao ◽  
Yu-Chiao Chiu ◽  
Yu-Heng Chen ◽  
Yu-Ching Hsu ◽  
Hung-I Harry Chen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Cancer Therapy ◽  
Cancer Treatment ◽  
Cancer Survival ◽  
Expression Profiles ◽  
Functional Gene ◽  
Gene Set Enrichment Analysis ◽  
Gene Set ◽  
Gene Sets

Aim and Objective: The number of anticancer drugs available currently is limited, and some of them have low treatment response rates. Moreover, developing a new drug for cancer therapy is labor intensive and sometimes cost prohibitive. Therefore, “repositioning” of known cancer treatment compounds can speed up the development time and potentially increase the response rate of cancer therapy. This study proposes a systems biology method for identifying new compound candidates for cancer treatment in two separate procedures. Materials and Methods: First, a “gene set–compound” network was constructed by conducting gene set enrichment analysis on the expression profile of responses to a compound. Second, survival analyses were applied to gene expression profiles derived from four breast cancer patient cohorts to identify gene sets that are associated with cancer survival. A “cancer–functional gene set– compound” network was constructed, and candidate anticancer compounds were identified. Through the use of breast cancer as an example, 162 breast cancer survival-associated gene sets and 172 putative compounds were obtained. Results: We demonstrated how to utilize the clinical relevance of previous studies through gene sets and then connect it to candidate compounds by using gene expression data from the Connectivity Map. Specifically, we chose a gene set derived from a stem cell study to demonstrate its association with breast cancer prognosis and discussed six new compounds that can increase the expression of the gene set after the treatment. Conclusion: Our method can effectively identify compounds with a potential to be “repositioned” for cancer treatment according to their active mechanisms and their association with patients’ survival time.

Linear Combination Test for Hierarchical Gene Set Analysis

2011 ◽  
Vol 10 (1) ◽  
Author(s):  
Xiaoming Wang ◽  
Irina Dinu ◽  
Wei Liu ◽  
Yutaka Yasui
Keyword(s):  
Gene Expression ◽  
Linear Combination ◽  
Covariance Matrix ◽  
Gene Set Analysis ◽  
Gene Set ◽  
Hotelling's T2 ◽  
Gene Sets ◽  
Hotelling’S T2 ◽  
Combination Test ◽  
Linear Combination Test

Gene-set analysis (GSA) aims to identify sets of differentially expressed genes by a phenotype in DNA microarray studies. Challenges occur due to the salient characteristics of the data: (1) the number of genes is far larger than the number of observations; (2) gene expression measurements, especially within each gene set, can be highly correlated; and (3) the number of gene sets that can be examined is large and increasing rapidly. These challenges call for gene-set testing procedures that have both efficiency in computation for large GSAs and high power in the presence of the high correlation.We propose a new GSA approach called Linear Combination Test (LCT), incorporating the covariance matrix estimator of gene expression into the test statistic. The proposed LCT and two other GSA methods, a mod-ification of Hotelling’s T2 using a shrinkage covariance matrix and our SAM-GS (Dinu et. al. 2007), the two methods that have been reported by Tsai and Chen (2009) to perform best in terms of power, are evaluated in simulation studies and a real microarray study. The LCT method is more computationally efficient than the modified Hotelling’s T2 and approximates the superb power of the modified Hotelling’s T2. LCT is slightly faster than SAM-GS, but more powerful, due to incorporating the covariance matrix estimator. An extra step to enhance the interpretation of GSA results is also proposed in the form of a hierarchical LC (HLC) testing procedure, providing scientists useful hierarchical information on gene sets that LCT identified as differentially expressed.Availability: A free R-code to perform LCT-GSA and HLC test is available at http://www.ualberta.ca/~yyasui/homepage.html.

scholarly journals TOXPANEL: A Gene-Set Analysis Tool to Assess Liver and Kidney Injuries

2021 ◽  
Vol 12 ◽  
Author(s):  
Patric Schyman ◽  
Zhen Xu ◽  
Valmik Desai ◽  
Anders Wallqvist
Keyword(s):  
Gene Expression ◽  
Kidney Injury ◽  
Fold Change ◽  
Gene Set Analysis ◽  
Analysis Tool ◽  
Gene Set ◽  
Physiological Range ◽  
Gene Sets ◽  
Liver And Kidney

Gene-set analysis is commonly used to identify trends in gene expression when cells, tissues, organs, or organisms are subjected to conditions that differ from those within the normal physiological range. However, tools for gene-set analysis to assess liver and kidney injury responses are less common. Furthermore, most websites for gene-set analysis lack the option for users to customize their gene-set database. Here, we present the ToxPanel website, which allows users to perform gene-set analysis to assess liver and kidney injuries using activation scores based on gene-expression fold-change values. The results are graphically presented to assess constituent injury phenotypes (histopathology), with interactive result tables that identify the main contributing genes to a given signal. In addition, ToxPanel offers the flexibility to analyze any set of custom genes based on gene fold-change values. ToxPanel is publically available online at https://toxpanel.bhsai.org. ToxPanel allows users to access our previously developed liver and kidney injury gene sets, which we have shown in previous work to yield robust results that correlate with the degree of injury. Users can also test and validate their customized gene sets using the ToxPanel website.

scholarly journals Application of biclustering of gene expression data and gene set enrichment analysis methods to identify potentially disease causing nanomaterials

2015 ◽  
Vol 6 ◽  
pp. 2438-2448 ◽  
Author(s):  
Andrew Williams ◽  
Sabina Halappanavar
Keyword(s):  
Gene Expression ◽  
Pulmonary Fibrosis ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Data Driven ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Analysis Methods ◽  
Gene Sets

Background: The presence of diverse types of nanomaterials (NMs) in commerce is growing at an exponential pace. As a result, human exposure to these materials in the environment is inevitable, necessitating the need for rapid and reliable toxicity testing methods to accurately assess the potential hazards associated with NMs. In this study, we applied biclustering and gene set enrichment analysis methods to derive essential features of altered lung transcriptome following exposure to NMs that are associated with lung-specific diseases. Several datasets from public microarray repositories describing pulmonary diseases in mouse models following exposure to a variety of substances were examined and functionally related biclusters of genes showing similar expression profiles were identified. The identified biclusters were then used to conduct a gene set enrichment analysis on pulmonary gene expression profiles derived from mice exposed to nano-titanium dioxide (nano-TiO2), carbon black (CB) or carbon nanotubes (CNTs) to determine the disease significance of these data-driven gene sets. Results: Biclusters representing inflammation (chemokine activity), DNA binding, cell cycle, apoptosis, reactive oxygen species (ROS) and fibrosis processes were identified. All of the NM studies were significant with respect to the bicluster related to chemokine activity (DAVID; FDR p-value = 0.032). The bicluster related to pulmonary fibrosis was enriched in studies where toxicity induced by CNT and CB studies was investigated, suggesting the potential for these materials to induce lung fibrosis. The pro-fibrogenic potential of CNTs is well established. Although CB has not been shown to induce fibrosis, it induces stronger inflammatory, oxidative stress and DNA damage responses than nano-TiO2 particles. Conclusion: The results of the analysis correctly identified all NMs to be inflammogenic and only CB and CNTs as potentially fibrogenic. In addition to identifying several previously defined, functionally relevant gene sets, the present study also identified two novel genes sets: a gene set associated with pulmonary fibrosis and a gene set associated with ROS, underlining the advantage of using a data-driven approach to identify novel, functionally related gene sets. The results can be used in future gene set enrichment analysis studies involving NMs or as features for clustering and classifying NMs of diverse properties.

scholarly journals Disease and phenotype gene set analysis of disease-based gene expression in mouse and human

2010 ◽  
Vol 42A (2) ◽  
pp. 162-167 ◽  
Author(s):  
Supriyo De ◽  
Yongqing Zhang ◽  
John R. Garner ◽  
S. Alex Wang ◽  
Kevin G. Becker
Keyword(s):  
Gene Expression ◽  
Human Disease ◽  
Complex Disease ◽  
Population Based ◽  
Gene Set Analysis ◽  
Common Disease ◽  
Analysis Tool ◽  
Gene Set ◽  
Disease Phenotypes ◽  
Gene Sets

The genetic contributions to common disease and complex disease phenotypes are pleiotropic, multifactorial, and combinatorial. Gene set analysis is a computational approach used in the analysis of microarray data to rapidly query gene combinations and multifactorial processes. Here we use novel gene sets based on population-based human genetic associations in common human disease or experimental genetic mouse models to analyze disease-related microarray studies. We developed a web-based analysis tool that uses these novel disease- and phenotype-related gene sets to analyze microarray-based gene expression data. These gene sets show disease and phenotype specificity in a species-specific and cross-species fashion. In this way, we integrate population-based common human disease genetics, mouse genetically determined phenotypes, and disease or phenotype structured ontologies, with gene expression studies relevant to human disease. This may aid in the translation of large-scale high-throughput datasets into the context of clinically relevant disease phenotypes.

scholarly journals To select relevant features for longitudinal gene expression data by extending a pathway analysis method

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1166 ◽  
Author(s):  
Suyan Tian ◽  
Chi Wang ◽  
Howard H. Chang
Keyword(s):  
Feature Selection ◽  
Expression Profiles ◽  
Simulated Data ◽  
Biological Information ◽  
Gene Set Analysis ◽  
Analysis Method ◽  
Gene Set ◽  
Gene Sets ◽  
Selection Algorithms ◽  
Selection Of

The emerging field of pathway-based feature selection that incorporates biological information conveyed by gene sets/pathways to guide the selection of relevant genes has become increasingly popular and widespread. In this study, we adapt a gene set analysis method – the significance analysis of microarray gene set reduction (SAMGSR) algorithm to carry out feature selection for longitudinal microarray data, and propose a pathway-based feature selection algorithm – the two-level SAMGSR method. By using simulated data and a real-world application, we demonstrate that a gene’s expression profiles over time can be considered as a gene set. Thus a suitable gene set analysis method can be utilized or modified to execute the selection of relevant genes for longitudinal omics data. We believe this work paves the way for more research to bridge feature selection and gene set analysis with the development of novel pathway-based feature selection algorithms.

scholarly journals Statistical Approach of Gene Set Analysis with Quantitative Trait Loci for Crop Gene Expression Studies

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 945
Author(s):  
Samarendra Das ◽  
Shesh N. Rai
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Statistical Approach ◽  
Gene Set Analysis ◽  
Expression Data ◽  
Expression Study ◽  
Gene Set ◽  
Gene Sets ◽  
Expression Studies ◽  
Gene Expression Studies

Genome-wide expression study is a powerful genomic technology to quantify expression dynamics of genes in a genome. In gene expression study, gene set analysis has become the first choice to gain insights into the underlying biology of diseases or stresses in plants. It also reduces the complexity of statistical analysis and enhances the explanatory power of the obtained results from the primary downstream differential expression analysis. The gene set analysis approaches are well developed in microarrays and RNA-seq gene expression data analysis. These approaches mainly focus on analyzing the gene sets with gene ontology or pathway annotation data. However, in plant biology, such methods may not establish any formal relationship between the genotypes and the phenotypes, as most of the traits are quantitative and controlled by polygenes. The existing Quantitative Trait Loci (QTL)-based gene set analysis approaches only focus on the over-representation analysis of the selected genes while ignoring their associated gene scores. Therefore, we developed an innovative statistical approach, GSQSeq, to analyze the gene sets with trait enriched QTL data. This approach considers the associated differential expression scores of genes while analyzing the gene sets. The performance of the developed method was tested on five different crop gene expression datasets obtained from real crop gene expression studies. Our analytical results indicated that the trait-specific analysis of gene sets was more robust and successful through the proposed approach than existing techniques. Further, the developed method provides a valuable platform for integrating the gene expression data with QTL data.

Gene Set Correlation Analysis and Visualization Using Gene Expression Data

2020 ◽  
Vol 15 ◽  
Author(s):  
Chen-An Tsai ◽  
James J. Chen
Keyword(s):  
Gene Expression ◽  
Correlation Analysis ◽  
Gene Expression Data ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Superior Performance ◽  
Expression Data ◽  
Gene Set ◽  
Gene Sets ◽  
Set Correlation

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways. Methods: We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the costructure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods. Result and Conclusion: We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

scholarly journals Peripheral Blood Gene Expression and IgG Glycosylation Profiles as Markers of Tocilizumab Treatment in Rheumatoid Arthritis

2012 ◽  
Vol 39 (5) ◽  
pp. 916-928 ◽  
Author(s):  
BERTALAN MESKO ◽  
SZILARD POLISKA ◽  
SZILVIA SZAMOSI ◽  
ZOLTAN SZEKANECZ ◽  
JANOS PODANI ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
Peripheral Blood ◽  
Multiple Testing ◽  
Mononuclear Cells ◽  
Expression Profiles ◽  
Quantitative Polymerase Chain Reaction ◽  
Response To Treatment ◽  
Peripheral Blood Mononuclear ◽  
Gene Sets

Objective.Tocilizumab, a humanized anti-interleukin-6 receptor monoclonal antibody, has recently been approved as a biological therapy for rheumatoid arthritis (RA) and other diseases. It is not known if there are characteristic changes in gene expression and immunoglobulin G glycosylation during therapy or in response to treatment.Methods.Global gene expression profiles from peripheral blood mononuclear cells of 13 patients with RA and active disease at Week 0 (baseline) and Week 4 following treatment were obtained together with clinical measures, serum cytokine levels using ELISA, and the degree of galactosylation of the IgG N-glycan chains. Gene sets separating responders and nonresponders were tested using canonical variates analysis. This approach also revealed important gene groups and pathways that differentiate responders from nonresponders.Results.Fifty-nine genes showed significant differences between baseline and Week 4 and thus correlated with treatment. Significantly, 4 genes determined responders after correction for multiple testing. Ten of the 12 genes with the most significant changes were validated using real-time quantitative polymerase chain reaction. An increase in the terminal galactose content of N-linked glycans of IgG was observed in responders versus nonresponders, as well as in treated samples versus samples obtained at baseline.Conclusion.As a preliminary report, gene expression changes as a result of tocilizumab therapy in RA were examined, and gene sets discriminating between responders and nonresponders were found and validated. A significant increase in the degree of galactosylation of IgG N-glycans in patients with RA treated with tocilizumab was documented.

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