Project description:Gene expression variation results from numerous sources including genetic, environmental, life stage, and even the environment experienced by previous generations. While the importance of each has been demonstrated in diverse organisms, their relative contributions remain understudied because few investigations have simultaneously determined each within a single experiment. Here we quantified genome-wide gene expression traits in Drosophila, quantified the contribution of multiple different sources of trait variation and determined the molecular mechanisms underlying observed variation. Our results show that there is a clear hierarchy in our data with genome and developmental stage contributing on average considerably more than current and finally previous generation environmental effects. We also determined the role of cis and trans-regulatory changes across different sources of trait variation, highlighting their importance in adaptation and environmental responses and showing unexpectedly that transgenerational effects herein were predominantly associated with changes in trans-regulation.
Project description:In our study, we developed an alternative next-generation sequencing dataset processing strategy to identify genetic variation in backcross mouse models. We used this approach to define SJL genetic content in B6.SJL-CD45.1 mice from different vendor sources.
Project description:We carried out a parallel nested experiment performed simultaneously on RNA-Seq and microarrays that systematically split variation into four stages (treatment, biological variation, library preparation, and chip/lane noise), allowing a separation and comparison of the sources of variation in a well-controlled cellular system, Saccharomyces cerevisiae.
Project description:Copy number variations (CNVs) can create new genes, change gene dosage, reshape gene structures, and modify elements regulating gene expression. As with all types of genetic variation, CNVs may influence phenotypic variation and gene expression. CNVs are thus considered major sources of genetic variation. Little is known, however, about their contribution to genetic variation in rice. To detect CNVs, we used a set of NimbleGen whole-genome comparative genomic hybridization arrays containing 715,851 oligonucleotide probes with a median probe spacing of 500 bp. We compiled a high-resolution map of CNVs in the rice genome, showing 641 CNVs between the genomes of the rice cultivars ‘Nipponbare’ (from O. sativa ssp. japonica) and ‘Guang-lu-ai 4’ (from O. sativa ssp. indica). These CNVs contain some known genes. They are linked to variation among rice varieties, and are likely to contribute to subspecific characteristics.
Project description:In our study, we developed an alternative next-generation sequencing dataset processing strategy to identify genetic variation in backcross mouse models. We used this approach to define SJL genetic content in B6.SJL-CD45.1 mice from different vendor sources.
2019-06-24 | GSE129575 | GEO
Project description:Sources of variation in gastrointestinal tract microbes in Anna's Hummingbirds
Project description:Inter-individual variation in gene regulation has been shown to be heritable and it is quite often associated with differences in disease susceptibility between individuals. While many human studies focused on mapping associations between genetic and gene regulatory variation, much less attention has been paid to the evolutionary processes that shape the observed differences in gene regulation between individuals in humans or any other primate. To begin addressing this gap, we performed a comparative expression quantitative trait loci (eQTL) mapping study in humans and chimpanzees, using gene expression data from primary heart samples. While expression variability in both species is strongly determined by non-genetic sources, such as cell type heterogeneity, we found evidence that the degree of inter-individual variation in gene regulation is generally conserved in humans and chimpanzees. In particular, we found a significant overlap of genes associated with eQTLs in the two species. We conclude that humans and chimpanzees share sources common determinants of gene expression variability, including genetically regulated constraints from stabilizing and diversifying selection pressures.
Project description:Mesenchymal stromal cells (MSCs) can be obtained from several sources and the significant differences in their properties, makes it crucial to investigate the differentiation potential of MSCs from different sources to determine the optimal source of MSCs. We investigated if this biological heterogeneity in MSCs from different sources results in different mechanisms for their differentiation. In this study, we compared the gene expression patterns of phenotypically defined MSCs derived from three ontogenically different sources: Embryonic stem cells (hES-MSCs), Fetal limb (Flb-MSCs) and Bone Marrow (BM-MSCs). Differentially expressed genes between differentiated cells and undifferentiated controls were compared across the three MSC sources. We found minimal overlap in differential gene expression (5-16%) among the three sources. Flb-MSCs were similar to BM-MSCs based on differential gene expression patterns. Pathway analysis of the differentially expressed genes using Ingenuity Pathway Analysis (IPA) revealed a large variation in the canonical pathways leading to MSC differentiation. The similar canonical pathways among the three sources were lineage specific. The Flb-MSCs showed maximum overlap of canonical pathways with the BM-MSCs, indicating that the Flb-MSCs is an intermediate source between the less specialised hES-MSC source and the more specialised BM-MSC source. The source specific pathways prove that MSCs from the three ontogenically different sources use different biological pathways to obtain similar differentiation outcomes. Thus our study advocates the understanding of biological pathways to obtain optimal sources of MSCs for various clinical applications.
Project description:Mesenchymal stromal cells (MSCs) can be obtained from several sources and the significant differences in their properties, makes it crucial to investigate the differentiation potential of MSCs from different sources to determine the optimal source of MSCs. We investigated if this biological heterogeneity in MSCs from different sources results in different mechanisms for their differentiation. In this study, we compared the gene expression patterns of phenotypically defined MSCs derived from three ontogenically different sources: Embryonic stem cells (hES-MSCs), Fetal limb (Flb-MSCs) and Bone Marrow (BM-MSCs). Differentially expressed genes between differentiated cells and undifferentiated controls were compared across the three MSC sources. We found minimal overlap in differential gene expression (5-16%) among the three sources. Flb-MSCs were similar to BM-MSCs based on differential gene expression patterns. Pathway analysis of the differentially expressed genes using Ingenuity Pathway Analysis (IPA) revealed a large variation in the canonical pathways leading to MSC differentiation. The similar canonical pathways among the three sources were lineage specific. The Flb-MSCs showed maximum overlap of canonical pathways with the BM-MSCs, indicating that the Flb-MSCs is an intermediate source between the less specialised hES-MSC source and the more specialised BM-MSC source. The source specific pathways prove that MSCs from the three ontogenically different sources use different biological pathways to obtain similar differentiation outcomes. Thus our study advocates the understanding of biological pathways to obtain optimal sources of MSCs for various clinical applications.