scholarly journals Novel approach to quantitative spatial gene expression uncovers genetic stochasticity in the developing Drosophila eye

2017 ◽  
Author(s):  
Sammi Ali ◽  
Sarah A. Signor ◽  
Konstantin Kozlov ◽  
Sergey V. Nuzhdin
Keyword(s):  
Gene Expression ◽  
Complex Disease ◽  
Hybridization Chain Reaction ◽  
Morphogenetic Furrow ◽  
Regulatory Relationship ◽  
Disease Phenotypes ◽  
Novel Approach ◽  
Eye Disc ◽  
Genetic Perturbations ◽  
Minimal Effort

AbstractRobustness in development allows for the accumulation of neutral genetically based variation in expression, and here will be termed ‘genetic stochasticity‘. This largely neutral variation is potentially important for both evolution and complex disease phenotypes. However, it has generally only been investigated as variation exhibited in the response to large genetic perturbations. In addition, work on variation in gene expression has similarly generally been limited to being spatial, or quantitative, but because of technical restrictions not both. Here we bridge these gaps by investigating replicated quantitative spatial gene expression using rigorous statistical models, in different genotypes, sexes, and species (Drosophila melanogaster and D. simulans). Using this type of quantitative approach with developmental data allows for effective comparison among conditions, including health versus disease. We apply this approach to the morphogenetic furrow, a wave of differentiation that sweeps across the developing eye disc. Within the morphogenetic furrow, we focus on four conserved morphogens, hairy, atonal, hedgehog, and Delta. Hybridization chain reaction quantitatively measures spatial gene expression, co-staining for all four genes simultaneously and with minimal effort. We find considerable variation in the spatial expression pattern of these genes in the eye between species, genotypes, and sexes. We also find that there has been evolution of the regulatory relationship between these genes. Lastly, we show that the spatial interrelationships of these genes evolved between species in the morphogenetic furrow. This is essentially the first ‘population genetics of development’ as we are able to evaluate wild type differences in spatial and quantitative gene expression at the level of genotype, species and sex.

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.

Targeting microRNAs involved in human diseases: A novel approach for modification of gene expression and drug development

2011 ◽  
Vol 82 (10) ◽  
pp. 1416-1429 ◽  
Author(s):  
Roberto Gambari ◽  
Enrica Fabbri ◽  
Monica Borgatti ◽  
Ilaria Lampronti ◽  
Alessia Finotti ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Development ◽  
Human Diseases ◽  
Novel Approach

scholarly journals dE2F2-Independent Rescue of Proliferation in Cells Lacking an Activator dE2F1

2007 ◽  
Vol 27 (24) ◽  
pp. 8561-8570 ◽  
Author(s):  
Aaron M. Ambrus ◽  
Brandon N. Nicolay ◽  
Vanya I. Rasheva ◽  
Richard J. Suckling ◽  
Maxim V. Frolov
Keyword(s):  
Cell Cycle ◽  
Morphogenetic Furrow ◽  
Independent Manner ◽  
Dead Box ◽  
Cycle Arrest ◽  
Eye Disc ◽  
Severe Reduction ◽  
Functional Inactivation ◽  
Mutant Cells ◽  
Dependent Mechanism

ABSTRACT In Drosophila melanogaster, the loss of activator de2f1 leads to a severe reduction in cell proliferation and repression of E2F targets. To date, the only known way to rescue the proliferation block in de2f1 mutants was through the inactivation of dE2F2. This suggests that dE2F2 provides a major contribution to the de2f1 mutant phenotype. Here, we report that in mosaic animals, in addition to de2f2, the loss of a DEAD box protein Belle (Bel) also rescues proliferation of de2f1 mutant cells. Surprisingly, the rescue occurs in a dE2F2-independent manner since the loss of Bel does not relieve dE2F2-mediated repression. In the eye disc, bel mutant cells fail to undergo a G1 arrest in the morphogenetic furrow, delay photoreceptor recruitment and differentiation, and show a reduction of the transcription factor Ci155. The down-regulation of Ci155 is important since it is sufficient to partially rescue proliferation of de2f1 mutant cells. Thus, mutation of bel relieves the dE2F2-mediated cell cycle arrest in de2f1 mutant cells through a novel Ci155-dependent mechanism without functional inactivation of the dE2F2 repressor.

scholarly journals Analysis of chromatin organization and gene expression in T cells identifies functional genes for rheumatoid arthritis

2019 ◽  
Author(s):  
Jing Yang ◽  
Amanda McGovern ◽  
Paul Martin ◽  
Kate Duffus ◽  
Xiangyu Ge ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gene Expression ◽  
T Cells ◽  
Complex Disease ◽  
Target Genes ◽  
Disease Risk ◽  
Association Studies ◽  
Dna Interaction ◽  
Genome Wide Association Studies ◽  
Causal Genes

AbstractGenome-wide association studies have identified genetic variation contributing to complex disease risk. However, assigning causal genes and mechanisms has been more challenging because disease-associated variants are often found in distal regulatory regions with cell-type specific behaviours. Here, we collect ATAC-seq, Hi-C, Capture Hi-C and nuclear RNA-seq data in stimulated CD4+ T-cells over 24 hours, to identify functional enhancers regulating gene expression. We characterise changes in DNA interaction and activity dynamics that correlate with changes gene expression, and find that the strongest correlations are observed within 200 kb of promoters. Using rheumatoid arthritis as an example of T-cell mediated disease, we demonstrate interactions of expression quantitative trait loci with target genes, and confirm assigned genes or show complex interactions for 20% of disease associated loci, including FOXO1, which we confirm using CRISPR/Cas9.

scholarly journals Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types

2017 ◽  
Author(s):  
Hilary K. Finucane ◽  
Yakir A. Reshef ◽  
Verneri Anttila ◽  
Kamil Slowikowski ◽  
Alexander Gusev ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Complex Disease ◽  
Genome Wide Association Study ◽  
Ex Vivo ◽  
Cell Types ◽  
Inhibitory Neurons ◽  
Biliary Cirrhosis ◽  
Expression Data ◽  
Specific Expression

ABSTRACTGenetics can provide a systematic approach to discovering the tissues and cell types relevant for a complex disease or trait. Identifying these tissues and cell types is critical for following up on non-coding allelic function, developing ex-vivo models, and identifying therapeutic targets. Here, we analyze gene expression data from several sources, including the GTEx and PsychENCODE consortia, together with genome-wide association study (GWAS) summary statistics for 48 diseases and traits with an average sample size of 169,331, to identify disease-relevant tissues and cell types. We develop and apply an approach that uses stratified LD score regression to test whether disease heritability is enriched in regions surrounding genes with the highest specific expression in a given tissue. We detect tissue-specific enrichments at FDR < 5% for 34 diseases and traits across a broad range of tissues that recapitulate known biology. In our analysis of traits with observed central nervous system enrichment, we detect an enrichment of neurons over other brain cell types for several brain-related traits, enrichment of inhibitory over excitatory neurons for bipolar disorder but excitatory over inhibitory neurons for schizophrenia and body mass index, and enrichments in the cortex for schizophrenia and in the striatum for migraine. In our analysis of traits with observed immunological enrichment, we identify enrichments of T cells for asthma and eczema, B cells for primary biliary cirrhosis, and myeloid cells for Alzheimer's disease, which we validated with independent chromatin data. Our results demonstrate that our polygenic approach is a powerful way to leverage gene expression data for interpreting GWAS signal.

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