Integrated time-series multi-omics reveals temporal reprogramming and inter-organ divergence in diabetic cardiorenal syndrome
4 affiliations
- 1Department of Endocrinology, The Second Affiliated Hospital of Zunyi Medical University
- 2Department of Biomedical Sciences, Faculty of Medicine, University of Macau
- 3Thyroid and Breast Surgery, the Second Affiliated Hospital of Zunyi Medical University
- 4Department of Endocrinology and Metabolism, Affiliated Hospital of Zunyi Medical University
Abstract
Diabetes induces progressive transcriptional reprogramming in the heart and kidney, yet their tissue-specific temporal dynamics and cellular mechanisms remain incompletely understood. In this study, we employed a longitudinal design with dense sampling intervals, performing time-series bulk RNA-seq on heart and kidney tissues across six bi-weekly intervals from Control, high-fat diet (HF), diabetes (DM), and insulin-treated (INS) mice (n=38 total samples). To cross-validate and resolve cell-type-specific variations, these data were integrated with a public single-cell RNA-seq dataset (16 mouse kidneys) spanning Control, diabetic kidney disease (DKD), and SGLT2 inhibitor (SGLT2i) treatment groups. Principal component analysis and differential expression sequencing revealed a more pronounced, earlier transcriptional divergence in the kidney than in the heart under diabetic conditions. Using maSigPro regression analysis, we identified 74 core time-dependent genes in the kidney; hierarchical clustering showed that the major dynamic module was enriched in immunometabolic and inflammatory pathways. Single-cell dissection localized Enpp2 upregulation within injured proximal tubule cells (Inj.PT) of the DKD group, while MHC class I molecules (H2-K1 and H2-D1) were suppressed in immune cells. UpSet plot analysis demonstrated an overlap of these dynamic hub genes between bulk and single-cell cohorts. While insulin reversed systemic metabolic and early-phase immune aberrations in the bulk cohort, the single-cell dataset—derived from an independent SGLT2i-treated cohort—provided cellular resolution for key dynamic genes (Enpp2, H2-K1, H2-D1) and suggested that SGLT2i may similarly suppress Enpp2 in injured proximal tubule cells. These parallel observations from two distinct therapeutic interventions are hypothesis-generating and warrant direct comparative studies. These discovery-phase findings were validated by independent qPCR and immunohistochemical analyses. Taken together, this exploratory longitudinal analysis identifies tissue-specific temporal transcriptional reprogramming in diabetic heart and kidney, with evidence of partial modulation by insulin treatment.
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BibTeX
@article{gao2026integrated,
title = {{Integrated time-series multi-omics reveals temporal reprogramming and inter-organ divergence in diabetic cardiorenal syndrome}},
author = {Shan-shan Gao and Guoyue Wan and Qi Wang and Jing An and Qingmei Yang and Chuyu Liu and Yulan Cai},
journal = {Frontiers in Endocrinology},
year = {2026},
month = oct,
doi = {10.3389/fendo.2026.1950040},
url = {https://www.frontiersin.org/articles/10.3389/fendo.2026.1950040/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrated time-series multi-omics reveals temporal reprogramming and inter-organ divergence in diabetic cardiorenal syndrome
AU - Shan-shan Gao
AU - Guoyue Wan
AU - Qi Wang
AU - Jing An
AU - Qingmei Yang
AU - Chuyu Liu
AU - Yulan Cai
PY - 2026
DA - 2026/10/20
JO - Frontiers in Endocrinology
DO - 10.3389/fendo.2026.1950040
UR - https://www.frontiersin.org/articles/10.3389/fendo.2026.1950040/full
AB - Diabetes induces progressive transcriptional reprogramming in the heart and kidney, yet their tissue-specific temporal dynamics and cellular mechanisms remain incompletely understood. In this study, we employed a longitudinal design with dense sampling intervals, performing time-series bulk RNA-seq on heart and kidney tissues across six bi-weekly intervals from Control, high-fat diet (HF), diabetes (DM), and insulin-treated (INS) mice (n=38 total samples). To cross-validate and resolve cell-type-specific variations, these data were integrated with a public single-cell RNA-seq dataset (16 mouse kidneys) spanning Control, diabetic kidney disease (DKD), and SGLT2 inhibitor (SGLT2i) treatment groups. Principal component analysis and differential expression sequencing revealed a more pronounced, earlier transcriptional divergence in the kidney than in the heart under diabetic conditions. Using maSigPro regression analysis, we identified 74 core time-dependent genes in the kidney; hierarchical clustering showed that the major dynamic module was enriched in immunometabolic and inflammatory pathways. Single-cell dissection localized Enpp2 upregulation within injured proximal tubule cells (Inj.PT) of the DKD group, while MHC class I molecules (H2-K1 and H2-D1) were suppressed in immune cells. UpSet plot analysis demonstrated an overlap of these dynamic hub genes between bulk and single-cell cohorts. While insulin reversed systemic metabolic and early-phase immune aberrations in the bulk cohort, the single-cell dataset—derived from an independent SGLT2i-treated cohort—provided cellular resolution for key dynamic genes (Enpp2, H2-K1, H2-D1) and suggested that SGLT2i may similarly suppress Enpp2 in injured proximal tubule cells. These parallel observations from two distinct therapeutic interventions are hypothesis-generating and warrant direct comparative studies. These discovery-phase findings were validated by independent qPCR and immunohistochemical analyses. Taken together, this exploratory longitudinal analysis identifies tissue-specific temporal transcriptional reprogramming in diabetic heart and kidney, with evidence of partial modulation by insulin treatment.
SN - 1664-2392
ER -