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Peer-reviewedCC BY 4.0International Journal of Molecular Sciences

Kinase Regulation Through Metabolic Control Points Directs Glucose Fate

  • Susmi Varghese1,2
  • Prathik Basthikoppa Shivamurthy1
  • Vinitha Ramanath Pai3
  • Sowmya Soman1
  • Poornima Ramesh1
  • Rajesh Raju1,2
  1. 1Centre for Integrative Omics Data Science (CIODS), Yenepoya (Deemed to be University), Mangalore 575018, Karnataka, India
  2. 2Centre for Systems Biology and Molecular Medicine (CSBMM), Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, Karnataka, India
  3. 3Department of Biochemistry, Yenepoya Medical College, Yenepoya (Deemed to be University), Mangalore 575018, Karnataka, India
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Abstract

Glucose metabolism is organized through interconnected metabolic pathways whose enzymes determine whether glucose is utilized for energy production, storage, or biosynthesis. Although protein kinases modulate metabolic enzymes through phosphorylation, how kinase signaling collectively interfaces with the glucose metabolic network remains unclear. In addition, many reported kinase–substrate relationships are derived from prediction-based resources and lack experimental validation. Here, we systematically mapped kinase associations for 144 enzymes across six glucose metabolic pathways by integrating kinase–substrate databases, quantitative phosphoproteomics from 154 human studies, and protein–protein interaction networks. We developed a multi-layer prioritization framework combining kinase–substrate annotations, phosphoproteomic co-regulation, and interaction-supported evidence to rank kinase–enzyme associations and reconstruct a phosphoproteomic-supported kinase–metabolism network. Prioritization reduced a large initial association space to a refined set of candidate kinase–enzyme associations while retaining broad coverage across glucose metabolism. The resulting network revealed an uneven distribution of kinase associations across metabolic pathways and enzymes, with the highest kinase association density observed at metabolic control points, particularly enzymes integrating branch-point and flux-control functions. Pathway-level analysis further showed that glycogen metabolism exhibited the greatest kinase association density despite comprising relatively few enzymes, while futile-cycle pairs displayed distinct patterns of shared and reaction-specific kinase associations. Network analysis identified a small subset of highly connected kinase hubs together with bridge proteins linking the kinase and metabolic enzyme layers, providing a systems-level view of kinase association patterns across glucose metabolism. Together, these findings indicate that kinase associations are not uniformly distributed across glucose metabolism but preferentially concentrate at metabolic control points that govern pathway routing and glucose allocation. This study provides a systems-level framework for prioritizing candidate kinase–enzyme associations and a resource for investigating signaling–metabolism interactions in metabolic disease and cancer.

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Cite

BibTeXRIS
BibTeX
@article{varghese2026kinase,
  title = {{Kinase Regulation Through Metabolic Control Points Directs Glucose Fate}},
  author = {Susmi Varghese and Prathik Basthikoppa Shivamurthy and Vinitha Ramanath Pai and Sowmya Soman and Poornima Ramesh and Rajesh Raju},
  journal = {International Journal of Molecular Sciences},
  year = {2026},
  month = oct,
  doi = {10.3390/ijms27219402},
  url = {https://www.mdpi.com/1422-0067/27/21/9402},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Kinase Regulation Through Metabolic Control Points Directs Glucose Fate
AU  - Susmi Varghese
AU  - Prathik Basthikoppa Shivamurthy
AU  - Vinitha Ramanath Pai
AU  - Sowmya Soman
AU  - Poornima Ramesh
AU  - Rajesh Raju
PY  - 2026
DA  - 2026/10/22
JO  - International Journal of Molecular Sciences
DO  - 10.3390/ijms27219402
UR  - https://www.mdpi.com/1422-0067/27/21/9402
AB  - Glucose metabolism is organized through interconnected metabolic pathways whose enzymes determine whether glucose is utilized for energy production, storage, or biosynthesis. Although protein kinases modulate metabolic enzymes through phosphorylation, how kinase signaling collectively interfaces with the glucose metabolic network remains unclear. In addition, many reported kinase–substrate relationships are derived from prediction-based resources and lack experimental validation. Here, we systematically mapped kinase associations for 144 enzymes across six glucose metabolic pathways by integrating kinase–substrate databases, quantitative phosphoproteomics from 154 human studies, and protein–protein interaction networks. We developed a multi-layer prioritization framework combining kinase–substrate annotations, phosphoproteomic co-regulation, and interaction-supported evidence to rank kinase–enzyme associations and reconstruct a phosphoproteomic-supported kinase–metabolism network. Prioritization reduced a large initial association space to a refined set of candidate kinase–enzyme associations while retaining broad coverage across glucose metabolism. The resulting network revealed an uneven distribution of kinase associations across metabolic pathways and enzymes, with the highest kinase association density observed at metabolic control points, particularly enzymes integrating branch-point and flux-control functions. Pathway-level analysis further showed that glycogen metabolism exhibited the greatest kinase association density despite comprising relatively few enzymes, while futile-cycle pairs displayed distinct patterns of shared and reaction-specific kinase associations. Network analysis identified a small subset of highly connected kinase hubs together with bridge proteins linking the kinase and metabolic enzyme layers, providing a systems-level view of kinase association patterns across glucose metabolism. Together, these findings indicate that kinase associations are not uniformly distributed across glucose metabolism but preferentially concentrate at metabolic control points that govern pathway routing and glucose allocation. This study provides a systems-level framework for prioritizing candidate kinase–enzyme associations and a resource for investigating signaling–metabolism interactions in metabolic disease and cancer.
SN  - 1422-0067
ER  -