scholarly journals Evaluation of animal model congruence to human depression based on large-scale gene expression patterns of the CNS

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Stephen C. Gammie
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Animal Models ◽  
Large Scale ◽  
Mitochondrial Gene ◽  
Expression Patterns ◽  
Conditional Knockout ◽  
Gene Expression Patterns ◽  
Male And Female ◽  
Wide Range

AbstractDepression is a complex mental health disorder that is difficult to study. A wide range of animal models exist and for many of these data on large-scale gene expression patterns in the CNS are available. The goal of this study was to evaluate how well animal models match human depression by evaluating congruence and discordance of large-scale gene expression patterns in the CNS between almost 300 animal models and a portrait of human depression created from male and female datasets. Multiple approaches were used, including a hypergeometric based scoring system that rewards common gene expression patterns (e.g., up-up or down-down in both model and human depression), but penalizes opposing gene expression patterns. RRHO heat maps, Uniform Manifold Approximation Plot (UMAP), and machine learning were used to evaluate matching of models to depression. The top ranked model was a histone deacetylase (HDAC2) conditional knockout in forebrain neurons. Also highly ranked were various models for Alzheimer’s, including APPsa knock-in (2nd overall), APP knockout, and an APP/PS1 humanized double mutant. Other top models were the mitochondrial gene HTRA2 knockout (that is lethal in adulthood), a modified acetylcholinesterase, a Huntington’s disease model, and the CRTC1 knockout. Over 30 stress related models were evaluated and while some matched highly with depression, others did not. In most of the top models, a consistent dysregulation of MAP kinase pathway was identified and the genes NR4A1, BDNF, ARC, EGR2, and PDE7B were consistently downregulated as in humans with depression. Separate male and female portraits of depression were also evaluated to identify potential sex specific depression matches with models. Individual human depression datasets were also evaluated to allow for comparisons across the same brain regions. Heatmap, UMAP, and machine learning results supported the hypergeometric ranking findings. Together, this study provides new insights into how large-scale gene expression patterns may be similarly dysregulated in some animals models and humans with depression that may provide new avenues for understanding and treating depression.

scholarly journals Gene Expression Patterns Underlying the Reinstatement of Plasticity in the Adult Visual System

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ettore Tiraboschi ◽  
Ramon Guirado ◽  
Dario Greco ◽  
Petri Auvinen ◽  
Jose Fernando Maya-Vetencourt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Visual Cortex ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Monocular Deprivation ◽  
Adult Brain ◽  
Gene Expression Patterns ◽  
Postnatal Life ◽  
Critical Periods

The nervous system is highly sensitive to experience during early postnatal life, but this phase of heightened plasticity decreases with age. Recent studies have demonstrated that developmental-like plasticity can be reactivated in the visual cortex of adult animals through environmental or pharmacological manipulations. These findings provide a unique opportunity to study the cellular and molecular mechanisms of adult plasticity. Here we used the monocular deprivation paradigm to investigate large-scale gene expression patterns underlying the reinstatement of plasticity produced by fluoxetine in the adult rat visual cortex. We found changes, confirmed with RT-PCRs, in gene expression in different biological themes, such as chromatin structure remodelling, transcription factors, molecules involved in synaptic plasticity, extracellular matrix, and excitatory and inhibitory neurotransmission. Our findings reveal a key role for several molecules such as the metalloproteases Mmp2 and Mmp9 or the glycoprotein Reelin and open up new insights into the mechanisms underlying the reopening of the critical periods in the adult brain.

scholarly journals A Large Scale Genetic Analysis of c-Myc-regulated Gene Expression Patterns

2003 ◽  
Vol 278 (14) ◽  
pp. 12563-12573 ◽  
Author(s):  
Brenda C. O'Connell ◽  
Ann F. Cheung ◽  
Carl P. Simkevich ◽  
Wanny Tam ◽  
Xiaojia Ren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Genetic Analysis ◽  
Large Scale ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Regulated Gene Expression

scholarly journals Evolution of gene expression across species and specialized zooids in Siphonophora

2021 ◽  
Author(s):  
Catriona Munro ◽  
Felipe Zapata ◽  
Mark Howison ◽  
Stefan Siebert ◽  
Casey W Dunn
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Developmental Stages ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Gene Trees ◽  
Species Trees ◽  
Examine Gene Expression ◽  
Specialized Tissue ◽  
Species Specific

Background: Siphonophores are complex colonial animals, consisting of asexually-produced bodies (called zooids) that are functionally specialized for specific tasks, including feeding, swimming, and sexual reproduction. Though this extreme functional specialization has captivated biologists for generations, its genomic underpinnings remain unknown. We use RNA-seq to investigate gene expression patterns in five zooids and one specialized tissue (pneumatophore) across seven siphonophore species. Analyses of gene expression across species present several challenges, including identification of comparable expression changes on gene trees with complex histories of speciation, duplication, and loss. Here, we conduct three analyses of expression. First, we examine gene expression within species. Then, we conduct classical analyses examining expression patterns between species. Lastly, we introduce Speciation Branch Filtering, which allows us to examine the evolution of expression in a phylogenetic framework. Results: Within and across species, we identified hundreds of zooid-specific and species-specific genes, as well as a number of putative transcription factors showing differential expression in particular zooids and developmental stages. We found that gene expression patterns tended to be largely consistent in zooids with the same function across species, but also some large lineage-specific shifts in gene expression. Conclusions: Our findings show that patterns of gene expression have the potential to define zooids in colonial organisms. We also show that traditional analyses of the evolution of gene expression focus on the tips of gene phylogenies, identifying large-scale expression patterns that are zooid or species variable. The new explicit phylogenetic approach we propose here focuses on branches (not tips) offering a deeper evolutionary perspective into specific changes in gene expression within zooids along all branches of the gene (and species) trees.

scholarly journals Network-guided supervised learning on gene expression using a graph convolutional neural network

2021 ◽  
Author(s):  
Chayaporn Suphavilai ◽  
Hatairat Yingtaweesittikul
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Protein Interaction ◽  
Expression Patterns ◽  
Interaction Network ◽  
Gene Expression Patterns ◽  
Learning Models ◽  
Machine Learning Models

Background: Transcriptomic profiles have become crucial information in understanding diseases and improving treatments. While dysregulated gene sets are identified via pathway analysis, various machine learning models have been proposed for predicting phenotypes such as disease type and drug response based on gene expression patterns. However, these models still lack interpretability, as well as the ability to integrate prior knowledge from a protein-protein interaction network. Results: We propose Grandline, a graph convolutional neural network that can integrate gene expression data and structure of the protein interaction network to predict a specific phenotype. Transforming the interaction network into a spectral domain enables convolution of neighbouring genes and pinpointing high-impact subnetworks, which allow better interpretability of deep learning models. Grandline achieves high phenotype prediction accuracy (67-85% in 8 use cases), comparable to state-of-the-art machine learning models while requiring a smaller number of parameters, allowing it to learn complex but interpretable gene expression patterns from biological datasets. Conclusion: To improve the interpretability of phenotype prediction based on gene expression patterns, we developed Grandline using graph convolutional neural network technique to integrate protein interaction information. We focus on improving the ability to learn nonlinear relationships between gene expression patterns and a given phenotype and incorporation of prior knowledge, which are the main challenges of machine learning models for biological datasets. The graph convolution allows us to aggregate information from relevant genes and reduces the number of trainable parameters, facilitating model training for a small-sized biological dataset.

scholarly journals Conservation of expression regulation throughout the animal kingdom

2014 ◽  
Author(s):  
Michael Kuhn ◽  
Andreas Beyer
Keyword(s):  
Gene Expression ◽  
Related Species ◽  
Expression Patterns ◽  
Gene Families ◽  
Tissue Expression ◽  
Rate Of Change ◽  
Gene Expression Patterns ◽  
Transfer Of Knowledge ◽  
Tissue Specific ◽  
Wide Range

Following the increase in available sequenced genomes, tissue-specific transcriptomes are being determined for a rapidly growing number of highly diverse species. Traditionally, only the transcriptomes of related species with equivalent tissues have been compared. Such an analysis is much more challenging over larger evolutionary distances when complementary tissues cannot readily be defined. Here, we present a method for the cross-species mapping of tissue-specific and developmental gene expression patterns across a wide range of animals, including many non-model species. Our approach maps gene expression patterns between species without requiring the definition of homologous tissues. With the help of this mapping, gene expression patterns can be compared even across distantly related species. In our survey of 36 datasets across 27 species, we detected conserved expression programs on all taxonomic levels, both within animals and between the animals and their closest unicellular relatives, the choanoflagellates. We found that the rate of change in tissue expression patterns is a property of gene families. Our findings open new avenues of study for the comparison and transfer of knowledge between different species.

Gene Regulation: A Genomic Perspective

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. SCI-10-SCI-10
Author(s):  
John Stamatoyannopoulos
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Conflicts Of Interest ◽  
Expression Patterns ◽  
Regulatory Elements ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Regulatory Regions ◽  
Regulatory Pathways ◽  
Wide Range

Abstract Regulatory elements control the anatomic and cellular contexts, timing, and magnitude of gene expression patterns. Under the ENCODE and Roadmap Epigenomics Projects, human regulatory DNA has been mapped using a variety of approaches in over 300 cell and tissue types and developmental states. Collectively, the human genome encodes several million regulatory elements, most of which are located at some distance from promoters. The vast majority of these elements exhibit exquisite cell-and lineage-selective activation patterns, providing novel insights into the coordination of gene expression patterns. Genomic footprinting is a new and powerful technology that enables simultaneous profiling of the occupancy of hundreds of sequence-specific transcription factors within regulatory regions. These profiles in turn enable construction of transcription factor regulatory networks that are providing new insights into how cell-and lineage-specific gene expression programs arise. Hundreds of genetic variants associated with a wide range of hematological traits and disorders localize within regulatory regions. Many such variants disrupt specific transcription factor-DNA interactions, exposing pathophysiologically relevant transcriptional regulatory pathways. Disclosures: No relevant conflicts of interest to declare.

Sperm Entry into the Egg Cell Induces the Progression of Karyogamy in Rice Zygotes

2019 ◽  
Vol 60 (8) ◽  
pp. 1656-1665 ◽  
Author(s):  
Yukinosuke Ohnishi ◽  
Iwao Kokubu ◽  
Tetsu Kinoshita ◽  
Takashi Okamoto
Keyword(s):  
Gene Expression ◽  
Sperm Cell ◽  
Nuclear Fusion ◽  
Expression Patterns ◽  
Egg Cell ◽  
Gene Expression Patterns ◽  
Early Developmental Stage ◽  
Male And Female ◽  
Sperm Entry

Abstract Karyogamy is a prerequisite event for plant embryogenesis, in which dynamic changes in nuclear architecture and the establishment of appropriate gene expression patterns must occur. However, the precise role of the male and female gametes in the progression of karyogamy still remains elusive. Here, we show that the sperm cell possesses the unique property to drive steady and swift nuclear fusion. When we fertilized egg cells with sperm cells in vitro, the immediate fusion of the male and female nuclei in the zygote progressed. This rapid nuclear fusion did not occur when two egg cells were artificially fused. However, the nuclear fusion of two egg nuclei could be accelerated by additional sperm entry or the exogenous application of calcium, suggesting that possible increase of cytosolic Ca2+ level via sperm entry into the egg cell efficiently can facilitate karyogamy. In contrast to zygotes, the egg–egg fusion cells failed to proliferate beyond an early developmental stage. Our transcriptional analyses also revealed the rapid activation of zygotic genes in zygotes, whereas there was no expression in fused cells without the male contribution. Thus, the male sperm cell has the ability to cause immediate karyogamy and to establish appropriate gene expression patterns in the zygote.

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