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1. A multi-tissue gene expression dataset for hibernating brown bears

   https://doi.org/10.1186/s12863-023-01136-3  

   Perry, Blair W. ; Saxton, Michael W. ; Jansen, Heiko T. ; Quackenbush, Corey R. ; Evans Hutzenbiler, Brandon D. ; Robbins, Charles T. ; Kelley, Joanna L. ; Cornejo, Omar E. ( June 2023 , BMC Genomic Data)

   Complex physiological adaptations often involve the coordination of molecular responses across multiple tissues. Establishing transcriptomic resources for non-traditional model organisms with phenotypes of interest can provide a foundation for understanding the genomic basis of these phenotypes, and the degree to which these resemble, or contrast, those of traditional model organisms. Here, we present a one-of-a-kind gene expression dataset generated from multiple tissues of two hibernating brown bears (Ursus arctos).

   This dataset is comprised of 26 samples collected from 13 tissues of two hibernating brown bears. These samples were collected opportunistically and are typically not possible to attain, resulting in a highly unique and valuable gene expression dataset. In combination with previously published datasets, this new transcriptomic resource will facilitate detailed investigation of hibernation physiology in bears, and the potential to translate aspects of this biology to treat human disease.

    

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2. Feeding during hibernation shifts gene expression toward active season levels in brown bears (Ursus arctos)

   Perry, Blair W. ; McDonald, Anna L. ; Trojahn, Shawn ; Saxton, Michael W. ; Vincent, Ellery P. ; Lowry, Courtney ; Evans Hutzenbiler, Brandon D. ; Cornejo, Omar E. ; Robbins, Charles T. ; Jansen, Heiko T. ; et al ( August 2023 , Physiological genomics)

   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. 

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3. Temporal Analysis of Gene Expression and Isoform Switching in Brown Bears ( Ursus arctos )

   https://doi.org/10.1093/icb/icac093  

   Perry, Blair W. ; Armstrong, Ellie E. ; Robbins, Charles T. ; Jansen, Heiko T. ; Kelley, Joanna L. ( June 2022 , Integrative And Comparative Biology)

   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.

    

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4. Accurate flux predictions using tissue-specific gene expression in plant metabolic modeling

   https://doi.org/10.1093/bioinformatics/btad186  

   Kaste, Joshua A ; Shachar-Hill, Yair ( May 2023 , Bioinformatics)

   Birol, Inanc (Ed.)

   Abstract Motivation The accurate prediction of complex phenotypes such as metabolic fluxes in living systems is a grand challenge for systems biology and central to efficiently identifying biotechnological interventions that can address pressing industrial needs. The application of gene expression data to improve the accuracy of metabolic flux predictions using mechanistic modeling methods such as flux balance analysis (FBA) has not been previously demonstrated in multi-tissue systems, despite their biotechnological importance. We hypothesized that a method for generating metabolic flux predictions informed by relative expression levels between tissues would improve prediction accuracy. Results Relative gene expression levels derived from multiple transcriptomic and proteomic datasets were integrated into FBA predictions of a multi-tissue, diel model of Arabidopsis thaliana’s central metabolism. This integration dramatically improved the agreement of flux predictions with experimentally based flux maps from 13C metabolic flux analysis compared with a standard parsimonious FBA approach. Disagreement between FBA predictions and MFA flux maps was measured using weighted averaged percent error values, and for parsimonious FBA this was169%–180% for high light conditions and 94%–103% for low light conditions, depending on the gene expression dataset used. This fell to 10%-13% and 9%-11% upon incorporating expression data into the modeling process, which also substantially altered the predicted carbon and energy economy of the plant. Availability and implementation Code and data generated as part of this study are available from https://github.com/Gibberella/ArabidopsisGeneExpressionWeights. 

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5. Neurogenomic landscape associated with status‐dependent cooperative behaviour

   https://doi.org/10.1111/mec.17327  

   Bolton, Peri E. ; Ryder, T. Brandt ; Dakin, Roslyn ; Houtz, Jennifer L. ; Moore, Ignacio T. ; Balakrishnan, Christopher N. ; Horton, Brent M. ( March 2024 , Molecular Ecology)

   The neurogenomic mechanisms mediating male–male reproductive cooperative behaviours remain unknown. We leveraged extensive transcriptomic and behavioural data on a neotropical bird species (Pipra filicauda) that performs cooperative courtship displays to understand these mechanisms. In this species, the cooperative display is modulated by testosterone, which promotes cooperation in non‐territorial birds, but suppresses cooperation in territory holders. We sought to understand the neurogenomic underpinnings of three related traits: social status, cooperative display behaviour and testosterone phenotype. To do this, we profiled gene expression in 10 brain nuclei spanning the social decision‐making network (SDMN), and two key endocrine tissues that regulate social behaviour. We associated gene expression with each bird's behavioural and endocrine profile derived from 3 years of repeated measures taken from free‐living birds in the Ecuadorian Amazon. We found distinct landscapes of constitutive gene expression were associated with social status, testosterone phenotype and cooperation, reflecting the modular organization and engagement of neuroendocrine tissues. Sex‐steroid and neuropeptide signalling appeared to be important in mediating status‐specific relationships between testosterone and cooperation, suggesting shared regulatory mechanisms with male aggressive and sexual behaviours. We also identified differentially regulated genes involved in cellular activity and synaptic potentiation, suggesting multiple mechanisms underpin these genomic states. Finally, we identified SDMN‐wide gene expression differences between territorial and floater males that could form the basis of ‘status‐specific’ neurophysiological phenotypes, potentially mediated by testosterone and growth hormone. Overall, our findings provide new, systems‐level insights into the mechanisms of cooperative behaviour and suggest that differences in neurogenomic state are the basis for individual differences in social behaviour.

    

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