scholarly journals Uncovering Statistical Links Between Gene Expression and Structural Connectivity Patterns in the Mouse Brain

2021 ◽  
Author(s):  
Nestor Timonidis ◽  
Alberto Llera ◽  
Paul H. E. Tiesinga
Keyword(s):  
Gene Expression ◽  
Mouse Brain ◽  
Structural Connectivity ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Synaptic Function ◽  
Reticular Nucleus ◽  
Sources Of Information ◽  
Independent Components ◽  
Brain Connectome

AbstractFinding links between genes and structural connectivity is of the utmost importance for unravelling the underlying mechanism of the brain connectome. In this study we identify links between the gene expression and the axonal projection density in the mouse brain, by applying a modified version of the Linked ICA method to volumetric data from the Allen Institute for Brain Science for identifying independent sources of information that link both modalities at the voxel level. We performed separate analyses on sets of projections from the visual cortex, the caudoputamen and the midbrain reticular nucleus, and we determined those brain areas, injections and genes that were most involved in independent components that link both gene expression and projection density data, while we validated their biological context through enrichment analysis. We identified representative and literature-validated cortico-midbrain and cortico-striatal projections, whose gene subsets were enriched with annotations for neuronal and synaptic function and related developmental and metabolic processes. The results were highly reproducible when including all available projections, as well as consistent with factorisations obtained using the Dictionary Learning and Sparse Coding technique. Hence, Linked ICA yielded reproducible independent components that were preserved under increasing data variance. Taken together, we have developed and validated a novel paradigm for linking gene expression and structural projection patterns in the mouse mesoconnectome, which can power future studies aiming to relate genes to brain function.

scholarly journals Structural covariance of brain region volumes is associated with both structural connectivity and transcriptomic similarity

2017 ◽  
Author(s):  
Yohan Yee ◽  
Darren J. Fernandes ◽  
Leon French ◽  
Jacob Ellegood ◽  
Lindsay S. Cahill ◽  
...  
Keyword(s):  
Gene Expression ◽  
Mouse Brain ◽  
Brain Region ◽  
Large Scale ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Reticular Nucleus ◽  
Structural Covariance ◽  
The Brain ◽  
Structural Volume

AbstractAn organizational pattern seen in the brain, termed structural covariance, is the statistical association of pairs of brain regions in their anatomical properties. These associations, measured across a population as covariances or correlations usually in cortical thickness or volume, are thought to reflect genetic and environmental underpinnings.Here, we examine the biological basis of structural volume covariance in the mouse brain. We first examined large scale associations between brain region volumes using an atlas-based approach that parcellated the entire mouse brain into 318 regions over which correlations in volume were assessed, for volumes obtained from 153 mouse brain images via high-resolution MRI. We then used a seed-based approach and determined, for 108 different seed regions across the brain and using mouse gene expression and connectivity data from the Allen Institute for Brain Science, the variation in structural covariance data that could be explained by distance to seed, transcriptomic similarity to seed, and connectivity to seed.We found that overall, correlations in structure volumes hierarchically clustered into distinct anatomical systems, similar to findings from other studies and similar to other types of networks in the brain, including structural connectivity and transcriptomic similarity networks. Across seeds, this structural covariance was significantly explained by distance (17% of the variation, up to a maximum of 49% for structural covariance to the visceral area of the cortex), transcriptomic similarity (13% of the variation, up to maximum of 28% for structural covariance to the primary visual area) and connectivity (15% of the variation, up to a maximum of 36% for structural covariance to the intermediate reticular nucleus in the medulla) of covarying structures. Together, distance, connectivity, and transcriptomic similarity explained 37% of structural covariance, up to a maximum of 63% for structural covariance to the visceral area. Additionally, this pattern of explained variation differed spatially across the brain, with transcriptomic similarity playing a larger role in the cortex than subcortex, while connectivity explains structural covariance best in parts of the cortex, midbrain, and hindbrain. These results suggest that both gene expression and connectivity underlie structural volume covariance, albeit to different extents depending on brain region, and this relationship is modulated by distance.

scholarly journals HuoXueJieDu Formula Alleviates Diabetic Retinopathy in Rats by Inhibiting SOCS3-STAT3 and TIMP1-A2M Pathways

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hongliang Wang ◽  
Wei Xing ◽  
Shijie Tang ◽  
Zhenglin Wang ◽  
Tiantian Lv ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetic Retinopathy ◽  
Immunohistochemical Staining ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Diabetic Rats ◽  
Pathway Enrichment Analysis ◽  
Protective Effects ◽  
Matrix Metabolism

HuoXueJieDu (HXJD) formula exerts protective effects against diabetic retinopathy (DR) in rats, but its underlying mechanism remains unknown. In the present study, the diabetic rats were established using streptozocin. The administration of HXJD was initiated at 20 weeks after diabetes induction and continued for 12 weeks. Whole genome expression profiles in rat retinas were examined using microarray technology. Differential gene expression and pathway enrichment analysis were conducted on the microarray data, with validation through real-time PCR and immunohistochemical staining. The results showed that 170 genes and several IPA canonical pathways related to inflammation, matrix metabolism, and phototransduction were regulated by HXJD. PCR validation of selected genes, including SOCS3, STAT3, TIMP1, and A2M, confirmed the gene expression changes influenced by HXJD. In addition, the immunohistochemical staining results suggested that critical members of the SOCS3-STAT3 pathway were also affected by HXJD. Taken together, these results indicated that SOCS3-STAT3 and TIMP1-A2M pathways might mediate the alleviation of HXJD activities in rats with diabetic retinopathy.

Expression Analyses of POGZ, A Responsible Gene for Neurodevelopmental Disorders, during Mouse Brain Development

2019 ◽  
Vol 41 (1-2) ◽  
pp. 139-148 ◽  
Author(s):  
Kyoko Ibaraki ◽  
Nanako Hamada ◽  
Ikuko Iwamoto ◽  
Hidenori Ito ◽  
Noriko Kawamura ◽  
...  
Keyword(s):  
Gene Expression ◽  
Brain Development ◽  
Mouse Brain ◽  
Neurodevelopmental Disorders ◽  
Hippocampal Neurons ◽  
Morphological Characterization ◽  
Synaptic Function ◽  
Early Developmental Stage ◽  
Heterochromatin Protein 1 ◽  
Biochemical Fractionation

POGZ is a heterochromatin protein 1 α-binding protein and regulates gene expression. On the other hand, accumulating pieces of evidence indicate that the POGZ gene abnormalities are involved in various neurodevelopmental disorders. In this study, we prepared a specific antibody against POGZ, anti-POGZ, and carried out biochemical and morphological characterization with mouse brain tissues. Western blotting analyses revealed that POGZ is expressed strongly at embryonic day 13 and then gradually decreased throughout the brain development process. In immunohistochemical analyses, POGZ was found to be enriched in cerebrocortical and hippocampal neurons in the early developmental stage. The nuclear expression was also detected in Purkinje cells in cerebellum at postnatal day (P)7 and P15 but disappeared at P30. In primary cultured hippocampal neurons, while POGZ was distributed mainly in the nucleus, it was also visualized in axon and dendrites with partial localization at synapses in consistency with the results obtained in biochemical fractionation analyses. The obtained results suggest that POGZ takes part in the regulation of synaptic function as well as gene expression during brain development.

scholarly journals Scaling of gene transcriptional gradients with brain size across mouse development

2020 ◽  
Author(s):  
Lau Hoi Yan Gladys ◽  
Alex Fornito ◽  
Ben D. Fulcher
Keyword(s):  
Gene Expression ◽  
Mouse Brain ◽  
Brain Size ◽  
Structural Connectivity ◽  
Adult Brain ◽  
Brain Atlas ◽  
Mouse Development ◽  
Time Points ◽  
Scaling Relationship ◽  
Exponential Distance

The structure of the adult brain is the result of complex physical mechanisms acting through development. These physical processes, acting in threedimensional space, mean that the brain’s spatial embedding plays a key role in its organization, including the gradient-like patterning of gene expression that encodes the molecular underpinning of functional specialization. However, we do not yet understand how the dramatic changes in brain shape and size that occur in early development influence the brain’s transcriptional architecture. Here we investigate the spatial embedding of transcriptional patterns of over 1800 genes across seven time points through mousebrain development using data from the Allen Developing Mouse Brain Atlas. We find that transcriptional similarity decreases exponentially with separation distance across all developmental time points, with a correlation length scale that follows a powerlaw scaling relationship with a linear dimension of brain size. This scaling suggests that the mouse brain achieves a characteristic balance between local molecular similarity (homogeneous gene expression within a specialized brain area) and longer-range diversity (between functionally specialized brain areas) throughout its development. Extrapolating this mouse developmental scaling relationship to the human cortex yields a prediction consistent with the value measured from microarray data. We introduce a simple model of brain growth as spatially autocorrelated gene-expression gradients that expand through development, which captures key features of the mouse developmental data. Complementing the well-known exponential distance rule for structural connectivity, our findings characterize an analogous exponential distance rule for transcriptional gradients that scales across mouse brain development, providing new understanding of spatial constraints on the brain’s molecular patterning.

scholarly journals Identification of Multiple Hub Genes and Pathways in Hepatocellular Carcinoma: A Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Junwei Liu ◽  
Fang Han ◽  
Jianyi Ding ◽  
Xiaodong Liang ◽  
Jie Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Parameters ◽  
Functional Study ◽  
Ppi Network ◽  
Hub Genes ◽  
Qrt Pcr

Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system, and its early asymptomatic characteristic increases the difficulty of diagnosis and treatment. This study is aimed at obtaining some novel biomarkers with diagnostic and prognostic meaning and may find out potential therapeutic targets for HCC. We screen differentially expressed genes (DEGs) from the HCC gene expression profile GSE14520 using GEO2R. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted by using the clusterProfiler software while a protein-protein interaction (PPI) network was performed based on the STRING database. Then, prognosis analysis of hub genes was conducted using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to further verify the expression of hub genes and explore the correlation between gene expression and clinicopathological parameters. A total of 1053 DEGs were captured, containing 497 upregulated genes and 556 downregulated genes. GO and KEGG analysis indicated that the downregulated DEGs were mainly enriched in the fatty acid catabolic process while upregulated DEGs were primarily enriched in the cell cycle. Simultaneously, ten hub genes (CYP3A4, UGT1A6, AOX1, UGT1A4, UGT2B15, CDK1, CCNB1, MAD2L1, CCNB2, and CDC20) were identified by the PPI network. Five prognosis-related hub genes (CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20) were uncovered by the survival analysis based on TCGA database. The ten hub genes were further validated by qRT-PCR using samples obtained from our hospital. The prognosis-related hub genes such as CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20 could be considered potential diagnosis biomarkers and prognosis targets for HCC. We also use Oncomine for further verification, and we found CCNB1, CCNB2, CDK1, and CYP3A4 which were highly expressed in HCC. Meanwhile, CCNB1, CCNB2, and CDK1 are highly expressed in almost all cancer types, which may play an important role in cancer. Still, further functional study should be conducted to explore the underlying mechanism and biological effect in the near future.

scholarly journals Gene Expression Profile Analysis in Epilepsy by Using the Partial Least Squares Method

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Dong Wang ◽  
Xixiao Song ◽  
Yan Wang ◽  
Xia Li ◽  
Shanshan Jia ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
Least Squares Method ◽  
Profile Analysis ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Gene Expression Omnibus ◽  
Differentially Expressed

Purpose. Epilepsy is a common chronic neurological disorder. We aim to investigate the underlying mechanism of epilepsy with partial least squares- (PLS-) based gene expression analysis, which is more sensitive than routine variance/regression analysis.Methods. Two microarray data sets were downloaded from the Gene Expression Omnibus (GEO) database. PLS analysis was used to identify differentially expressed genes. Gene ontology and network analysis were also implemented.Results. A total of 752 genes were identified to be differentially expressed, including 575 depressed and 177 overexpressed genes in patients. For GO enrichment analysis, except for processes related to the nervous system, we also identified overrepresentation of dysregulated genes in angiogenesis. Network analysis revealed two hub genes,CUL3andEP300, which may serve as potential targets in further therapeutic studies.Conclusion. Our results here may provide new understanding for the underlying mechanisms of epilepsy pathogenesis and will offer potential targets for producing new treatments.

scholarly journals The Key Gene Expression Patterns and Prognostic Factors in Malignant Transformation from Enchondroma to Chondrosarcoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Junqing Wu ◽  
Yue Huang ◽  
Chengxuan Yu ◽  
Xia Li ◽  
Limengmeng Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Malignant Transformation ◽  
Epithelial Mesenchymal Transition ◽  
Expression Patterns ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Underlying Mechanism ◽  
Gene Interaction Network ◽  
Mesenchymal Transition ◽  
Cell Immunity

Enchondroma (EC) is a common benign bone tumor. It has the risk of malignant transformation to Chondrosarcoma (CS). However, the underlying mechanism is unclear. The gene expression profile of EC and CS was obtained from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using GEO2R. We conducted the enrichment analysis and constructed the gene interaction network using the DEGs. We found that the epithelial-mesenchymal transition (EMT) and the VEGFA-VEGF2R signaling pathway were more active in CS. The CD8+ T cell immunity was enhanced in CS I. We believed that four genes (MFAP2, GOLM1, STMN1, and HN1) were poor predictors of prognosis, while two genes (CAB39L and GAB2) indicated a good prognosis. We have revealed the mechanism in the tumor progression and identified the key genes that predicted the prognosis. This study provided new ideas for the diagnosis and treatment of EC and CS.

A Method of Pathway Enrichment Analysis Based Gene Expression Variability

2013 ◽  
Vol 40 (12) ◽  
pp. 1256
Author(s):  
XiaoDong JIA ◽  
XiuJie CHEN ◽  
Xin WU ◽  
JianKai XU ◽  
FuJian TAN ◽  
...  
Keyword(s):  
Gene Expression ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Expression Variability ◽  
Pathway Enrichment

scholarly journals Identification of potential biomarkers for abdominal pain in IBS patients by bioinformatics approach

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhongyuan Lin ◽  
Yimin Wang ◽  
Shiqing Lin ◽  
Decheng Liu ◽  
Guohui Mo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Irritable Bowel Syndrome ◽  
Abdominal Pain ◽  
Gastrointestinal Disease ◽  
Rectal Mucosa ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Irritable Bowel ◽  
Potential Biomarkers

Abstract Background Irritable bowel syndrome (IBS) is the most common functional gastrointestinal disease characterized by chronic abdominal discomfort and pain. The mechanisms of abdominal pain, as a relevant symptom, in IBS are still unclear. We aimed to explore the key genes and neurobiological changes specially involved in abdominal pain in IBS. Methods Gene expression data (GSE36701) was downloaded from Gene Expression Omnibus database. Fifty-three rectal mucosa samples from 27 irritable bowel syndrome with diarrhea (IBS-D) patients and 40 samples from 21 healthy volunteers as controls were included. Differentially expressed genes (DEGs) between two groups were identified using the GEO2R online tool. Functional enrichment analysis of DEGs was performed on the DAVID database. Then a protein–protein interaction network was constructed and visualized using STRING database and Cytoscape. Results The microarray analysis demonstrated a subset of genes (CCKBR, CCL13, ACPP, BDKRB2, GRPR, SLC1A2, NPFF, P2RX4, TRPA1, CCKBR, TLX2, MRGPRX3, PAX2, CXCR1) specially involved in pain transmission. Among these genes, we identified GRPR, NPFF and TRPA1 genes as potential biomarkers for irritating abdominal pain of IBS patients. Conclusions Overexpression of certain pain-related genes (GRPR, NPFF and TRPA1) may contribute to chronic visceral hypersensitivity, therefore be partly responsible for recurrent abdominal pain or discomfort in IBS patients. Several synapses modification and biological process of psychological distress may be risk factors of IBS.

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