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      Pair bonding and disruption impact lung transcriptome in monogamous Peromyscus californicus

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          Abstract

          Social interactions affect physiological and pathological processes, yet their direct impact in peripheral tissues remains elusive. Recently we showed that disruption of pair bonds in monogamous Peromyscus californicus promotes lung tumorigenesis, pointing to a direct effect of bonding status in the periphery (Naderi et al., 2021). Here we show that lung transcriptomes of tumor-free Peromyscus are altered in a manner that depends on pair bonding and superseding the impact of genetic relevance between siblings. Pathways affected involve response to hypoxia and heart development. These effects are consistent with the profile of the serum proteome of bonded and bond-disrupted Peromyscus and were extended to lung cancer cells cultured in vitro, with sera from animals that differ in bonding experiences. In this setting, the species’ origin of serum (deer mouse vs FBS) is the most potent discriminator of RNA expression profiles, followed by bonding status. By analyzing the transcriptomes of lung cancer cells exposed to deer mouse sera, an expression signature was developed that discriminates cells according to the history of social interactions and possesses prognostic significance when applied to primary human lung cancers. The results suggest that present and past social experiences modulate the expression profile of peripheral tissues such as the lungs, in a manner that impacts physiological processes and may affect disease outcomes. Furthermore, they show that besides the direct effects of the hormones that regulate bonding behavior, physiological changes influencing oxygen metabolism may contribute to the adverse effects of bond disruption.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12864-023-09873-6.

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            Marital status and survival in patients with cancer.

            To examine the impact of marital status on stage at diagnosis, use of definitive therapy, and cancer-specific mortality among each of the 10 leading causes of cancer-related death in the United States.
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              Monogamy in Mammals

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                Author and article information

                Contributors
                kiarish@cop.sc.edu
                Journal
                BMC Genomics
                BMC Genomics
                BMC Genomics
                BioMed Central (London )
                1471-2164
                19 December 2023
                19 December 2023
                2023
                : 24
                : 789
                Affiliations
                [1 ]Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, ( https://ror.org/02b6qw903) Columbia, SC USA
                [2 ]Department of Mathematics and Computer Sciences, Claflin University, ( https://ror.org/052rx6v10) Orangeburg, SC USA
                [3 ]Department of Biology, Claflin University, ( https://ror.org/052rx6v10) Orangeburg, SC USA
                [4 ]Peromyscus Genetic Stock Center, University of South Carolina, ( https://ror.org/02b6qw903) Columbia, SC USA
                Article
                9873
                10.1186/s12864-023-09873-6
                10729396
                38114920
                161fbe0d-0ac9-41c5-98f3-eb7d1e6a9f47
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 17 April 2023
                : 5 December 2023
                Funding
                Funded by: NSF
                Award ID: OIA-1736150
                Award ID: OIA-1736150
                Award ID: OIA-1736150
                Award ID: OIA-1736150
                Award ID: OIA-1736150
                Award ID: OIA-1736150
                Award Recipient :
                Funded by: NIH-NIGMS
                Award ID: P20GM109091
                Award Recipient :
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2023

                Genetics
                deer mice,outbred,transcriptome,expression signature,peromyscus,monogamous,cancer
                Genetics
                deer mice, outbred, transcriptome, expression signature, peromyscus, monogamous, cancer

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