Hibernation in bears involves a suite of metabolical and physiological changes, including the onset of insulin resistance, that are driven in part by sweeping changes in gene expression in multiple tissues. Feeding bears glucose during hibernation partially restores active season physiological phenotypes, including partial resensitization to insulin, but the molecular mechanisms underlying this transition remain poorly understood. Here, we analyze tissue-level gene expression in adipose, liver, and muscle to identify genes that respond to midhibernation glucose feeding and thus potentially drive postfeeding metabolical and physiological shifts. We show that midhibernation feeding stimulates differential expression in all analyzed tissues of hibernating bears and that a subset of these genes responds specifically by shifting expression toward levels typical of the active season. Inferences of upstream regulatory molecules potentially driving these postfeeding responses implicate peroxisome proliferator-activated receptor gamma (PPARG) and other known regulators of insulin sensitivity, providing new insight into high-level regulatory mechanisms involved in shifting metabolic phenotypes between hibernation and active states.
more »
« less
Temporal Analysis of Gene Expression and Isoform Switching in Brown Bears ( Ursus arctos )
Hibernation in brown bears is an annual process involving multiple physiologically distinct seasons—hibernation, active, and hyperphagia. While recent studies have characterized broad patterns of differential gene regulation and isoform usage between hibernation and active seasons, patterns of gene and isoform expression during hyperphagia remain relatively poorly understood. The hyperphagia stage occurs between active and hibernation seasons and involves the accumulation of large fat reserves in preparation for hibernation. Here, we use time-series analyses of gene expression and isoform usage to interrogate transcriptomic regulation associated with all three seasons. We identify a large number of genes with significant differential isoform usage (DIU) across seasons and show that these patterns of isoform usage are largely tissue-specific. We also show that DIU and differential gene-level expression responses are generally non-overlapping, with only a small subset of multi-isoform genes showing evidence of both gene-level expression changes and changes in isoform usage across seasons. Additionally, we investigate nuanced regulation of candidate genes involved in the insulin signaling pathway and find evidence of hyperphagia-specific gene expression and isoform regulation that may enhance fat accumulation during hyperphagia. Our findings highlight the value of using temporal analyses of both gene- and isoform-level gene expression when interrogating complex physiological phenotypes and provide new insight into the mechanisms underlying seasonal changes in bear physiology.
more » « less- Award ID(s):
- 2138649
- NSF-PAR ID:
- 10388568
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Integrative And Comparative Biology
- Volume:
- 62
- Issue:
- 6
- ISSN:
- 1540-7063
- Format(s):
- Medium: X Size: p. 1802-1811
- Size(s):
- ["p. 1802-1811"]
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract Early lineage diversification is central to understand what mutational events drive species divergence. Particularly, gene misregulation in interspecific hybrids can inform about what genes and pathways underlie hybrid dysfunction. In
Drosophila hybrids, how regulatory evolution impacts different reproductive tissues remains understudied. Here, we generate a new genome assembly and annotation inDrosophila willistoni and analyse the patterns of transcriptome divergence between two allopatrically evolvedD. willistoni subspecies, their male sterile and female fertile hybrid progeny across testis, male accessory gland, and ovary. Patterns of transcriptome divergence and modes of regulatory evolution were tissue‐specific. Despite no indication for cell‐type differences in hybrid testis, this tissue exhibited the largest magnitude of expression differentiation between subspecies and between parentals and hybrids. No evidence for anomalous dosage compensation in hybrid male tissues was detected nor was a differential role for the neo‐ and the ancestral arms of theD. willistoni X chromosome. Compared to the autosomes, theX chromosome appeared enriched for transgressively expressed genes in testis despite being the least differentiated in expression between subspecies. Evidence for fine genome clustering of transgressively expressed genes suggests a role of chromatin structure on hybrid gene misregulation. Lastly, transgressively expressed genes in the testis of the sterile male progeny were enriched for GO terms not typically associated with sperm function, instead hinting at anomalous development of the reproductive tissue. Our thorough tissue‐level portrait of transcriptome differentiation between recently divergedD. willistoni subspecies and their hybrids provides a more nuanced view of early regulatory changes during speciation. -
INTRODUCTION Diverse phenotypes, including large brains relative to body size, group living, and vocal learning ability, have evolved multiple times throughout mammalian history. These shared phenotypes may have arisen repeatedly by means of common mechanisms discernible through genome comparisons. RATIONALE Protein-coding sequence differences have failed to fully explain the evolution of multiple mammalian phenotypes. This suggests that these phenotypes have evolved at least in part through changes in gene expression, meaning that their differences across species may be caused by differences in genome sequence at enhancer regions that control gene expression in specific tissues and cell types. Yet the enhancers involved in phenotype evolution are largely unknown. Sequence conservation–based approaches for identifying such enhancers are limited because enhancer activity can be conserved even when the individual nucleotides within the sequence are poorly conserved. This is due to an overwhelming number of cases where nucleotides turn over at a high rate, but a similar combination of transcription factor binding sites and other sequence features can be maintained across millions of years of evolution, allowing the function of the enhancer to be conserved in a particular cell type or tissue. Experimentally measuring the function of orthologous enhancers across dozens of species is currently infeasible, but new machine learning methods make it possible to make reliable sequence-based predictions of enhancer function across species in specific tissues and cell types. RESULTS To overcome the limits of studying individual nucleotides, we developed the Tissue-Aware Conservation Inference Toolkit (TACIT). Rather than measuring the extent to which individual nucleotides are conserved across a region, TACIT uses machine learning to test whether the function of a given part of the genome is likely to be conserved. More specifically, convolutional neural networks learn the tissue- or cell type–specific regulatory code connecting genome sequence to enhancer activity using candidate enhancers identified from only a few species. This approach allows us to accurately associate differences between species in tissue or cell type–specific enhancer activity with genome sequence differences at enhancer orthologs. We then connect these predictions of enhancer function to phenotypes across hundreds of mammals in a way that accounts for species’ phylogenetic relatedness. We applied TACIT to identify candidate enhancers from motor cortex and parvalbumin neuron open chromatin data that are associated with brain size relative to body size, solitary living, and vocal learning across 222 mammals. Our results include the identification of multiple candidate enhancers associated with brain size relative to body size, several of which are located in linear or three-dimensional proximity to genes whose protein-coding mutations have been implicated in microcephaly or macrocephaly in humans. We also identified candidate enhancers associated with the evolution of solitary living near a gene implicated in separation anxiety and other enhancers associated with the evolution of vocal learning ability. We obtained distinct results for bulk motor cortex and parvalbumin neurons, demonstrating the value in applying TACIT to both bulk tissue and specific minority cell type populations. To facilitate future analyses of our results and applications of TACIT, we released predicted enhancer activity of >400,000 candidate enhancers in each of 222 mammals and their associations with the phenotypes we investigated. CONCLUSION TACIT leverages predicted enhancer activity conservation rather than nucleotide-level conservation to connect genetic sequence differences between species to phenotypes across large numbers of mammals. TACIT can be applied to any phenotype with enhancer activity data available from at least a few species in a relevant tissue or cell type and a whole-genome alignment available across dozens of species with substantial phenotypic variation. Although we developed TACIT for transcriptional enhancers, it could also be applied to genomic regions involved in other components of gene regulation, such as promoters and splicing enhancers and silencers. As the number of sequenced genomes grows, machine learning approaches such as TACIT have the potential to help make sense of how conservation of, or changes in, subtle genome patterns can help explain phenotype evolution. Tissue-Aware Conservation Inference Toolkit (TACIT) associates genetic differences between species with phenotypes. TACIT works by generating open chromatin data from a few species in a tissue related to a phenotype, using the sequences underlying open and closed chromatin regions to train a machine learning model for predicting tissue-specific open chromatin and associating open chromatin predictions across dozens of mammals with the phenotype. [Species silhouettes are from PhyloPic]more » « less
-
more » « less
Abstract Motivation The analysis of spatially resolved transcriptome enables the understanding of the spatial interactions between the cellular environment and transcriptional regulation. In particular, the characterization of the gene–gene co-expression at distinct spatial locations or cell types in the tissue enables delineation of spatial co-regulatory patterns as opposed to standard differential single gene analyses. To enhance the ability and potential of spatial transcriptomics technologies to drive biological discovery, we develop a statistical framework to detect gene co-expression patterns in a spatially structured tissue consisting of different clusters in the form of cell classes or tissue domains.
Results We develop SpaceX (spatially dependent gene co-expression network), a Bayesian methodology to identify both shared and cluster-specific co-expression network across genes. SpaceX uses an over-dispersed spatial Poisson model coupled with a high-dimensional factor model which is based on a dimension reduction technique for computational efficiency. We show via simulations, accuracy gains in co-expression network estimation and structure by accounting for (increasing) spatial correlation and appropriate noise distributions. In-depth analysis of two spatial transcriptomics datasets in mouse hypothalamus and human breast cancer using SpaceX, detected multiple hub genes which are related to cognitive abilities for the hypothalamus data and multiple cancer genes (e.g. collagen family) from the tumor region for the breast cancer data.
Availability and implementation The SpaceX R-package is available at github.com/bayesrx/SpaceX.
Supplementary information Supplementary data are available at Bioinformatics online.
-
Abstract Background KDM6A is a demethylase encoded by a gene with female-biased expression due to escape from X inactivation. Its main role is to facilitate gene expression through removal of the repressive H3K27me3 mark, with evidence of some additional histone demethylase-independent functions. KDM6A mutations have been implicated in congenital disorders such as Kabuki Syndrome, as well as in sex differences in cancer. Methods Kdm6a was knocked out using CRISPR/Cas9 gene editing in F1 male and female mouse embryonic stem cells (ES) derived from reciprocal crosses between C57BL6 x Mus castaneus . Diploid and allelic RNA-seq analyses were done to compare gene expression between wild-type and Kdm6a knockout (KO) clones. The effects of Kdm6a KO on sex-biased gene expression were investigated by comparing gene expression between male and female ES cells. Changes in H3K27me3 enrichment and chromatin accessibility at promoter regions of genes with expression changes were characterized by ChIP-seq and ATAC-seq followed by diploid and allelic analyses. Results We report that Kdm6a KO in male and female embryonic stem (ES) cells derived from F1 hybrid mice cause extensive gene dysregulation, disruption of sex biases, and specific parental allele effects. Among the dysregulated genes are candidate genes that may explain abnormal developmental features of Kabuki syndrome caused by KDM6A mutations in human. Strikingly, Kdm6a knockouts result in a decrease in sex-biased expression and in preferential downregulation of the maternal alleles of a number of genes. Most promoters of dysregulated genes show concordant epigenetic changes including gain of H3K27me3 and loss of chromatin accessibility, but there was less concordance when considering allelic changes. Conclusions Our study reveals new sex-related roles of KDM6A in the regulation of developmental genes, the maintenance of sex-biased gene expression, and the differential expression of parental alleles.more » « less
