Journal article
Leveraging inter-individual transcriptional correlation structure to infer discrete signaling mechanisms across metabolic tissues
eLife, Vol.12, 88863
01/15/2024
DOI: 10.7554/eLife.88863
PMID: 38224289
Abstract
Inter-organ communication is a vital process to maintain physiologic homeostasis, and its dysregulation contributes to many human diseases. Given that circulating bioactive factors are stable in serum, occur naturally, and are easily assayed from blood, they present obvious focal molecules for therapeutic intervention and biomarker development. Recently, studies have shown that secreted proteins mediating inter-tissue signaling could be identified by 'brute force' surveys of all genes within RNA-sequencing measures across tissues within a population. Expanding on this intuition, we reasoned that parallel strategies could be used to understand how individual genes mediate signaling across metabolic tissues through correlative analyses of gene variation between individuals. Thus, comparison of quantitative levels of gene expression relationships between organs in a population could aid in understanding cross-organ signaling. Here, we surveyed gene-gene correlation structure across 18 metabolic tissues in 310 human individuals and 7 tissues in 103 diverse strains of mice fed a normal chow or high-fat/high-sucrose (HFHS) diet. Variation of genes such as
and
showed enrichments which recapitulate experimental observations. Further, similar analyses were applied to explore both within-tissue signaling mechanisms (liver
) and genes encoding enzymes producing metabolites (adipose
), where inter-individual correlation structure aligned with known roles for these critical metabolic pathways. Examination of sex hormone receptor correlations in mice highlighted the difference of tissue-specific variation in relationships with metabolic traits. We refer to this resource as
ene-derived correlations across tissues (GD-CAT) where all tools and data are built into a web portal enabling users to perform these analyses without a single line of code (gdcat.org). This resource enables querying of any gene in any tissue to find correlated patterns of genes, cell types, pathways, and network architectures across metabolic organs.
Details
- Title: Subtitle
- Leveraging inter-individual transcriptional correlation structure to infer discrete signaling mechanisms across metabolic tissues
- Creators
- Mingqi Zhou - University of California, IrvineIan Tamburini - University of California, IrvineCassandra Van - University of California, IrvineJeffrey Molendijk - The University of MelbourneChristy M Nguyen - University of California, IrvineIvan Yao-Yi Chang - University of California, IrvineCasey Johnson - University of California, IrvineLeandro M Velez - University of California, IrvineYoungseo Cheon - University of California, IrvineReichelle Yeo - Translational Research InstituteHosung Bae - University of California, IrvineJohnny Le - University of California, IrvineNatalie Larson - University of California, IrvineRon Pulido - University of California, IrvineCarlos H V Nascimento-Filho - University of California, IrvineCholsoon Jang - University of California, IrvineIvan Marazzi - University of California, IrvineJamie Justice - Geriatric Research Education and Clinical CenterNicholas Pannunzio - University of California, IrvineAndrea L Hevener - University of California, Los AngelesLauren Sparks - Translational Research InstituteErin E Kershaw - Pittsburg State UniversityDequina Nicholas - University of California, IrvineBenjamin L Parker - The University of MelbourneSelma Masri - University of California, IrvineMarcus M Seldin - University of California, Irvine
- Resource Type
- Journal article
- Publication Details
- eLife, Vol.12, 88863
- DOI
- 10.7554/eLife.88863
- PMID
- 38224289
- ISSN
- 2050-084X
- eISSN
- 2050-084X
- Grant note
- U24 DK097771 / NIDDK NIH HHS DK063491 / NIH HHS R01 DK109724 / NIDDK NIH HHS CA266042 / NIH HHS DK109724 / NIH HHS R01 DK117850 / NIDDK NIH HHS U54 DK120342 / NIDDK NIH HHS R37 CA266042 / NCI NIH HHS F31 DK134173 / NIDDK NIH HHS F31DK134173-01A1 / NIH HHS
- Language
- English
- Date published
- 01/15/2024
- Academic Unit
- Internal Medicine
- Record Identifier
- 9985217010302771
Metrics
1 Record Views