The MafA-target gene PPP1R1A regulates GLP1R-mediated amplification of glucose-stimulated insulin secretion in β-cells

Cataldo, Luis Rodrigo; Vishnu, Neelanjan; Singh, Tania; Bertonnier-Brouty, Ludivine; Bsharat, Sara; Luan, Cheng; Renström, Erik; Prasad, Rashmi B.; Fex, Malin; Mulder, Hindrik; Artner, Isabella

doi:10.1016/j.metabol.2021.154734

Cited by 21 publications

(21 citation statements)

References 47 publications

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“…For example, Ppp1r1a has almost all transcription activity in β cells (Fig. 3e), which was verified by previous studies [13, 7]. The Rnf130 and 1500009L16Rik genes similarly show significant branching trends that are accurately modeled by the branching ODE (Fig.…”

Section: Resultssupporting

confidence: 87%

“…u 0 (y) = u init y is a stem cell type u(t 0 (y), par(y)) otherwise (6) s 0 (y) = s init y is a stem cell type s(t 0 (y), par(y)) otherwise (7) Here, par(•) refers to the parent vertex in the transition graph. Note that τ is redefined as τ := t − t 0 (y) for each cell type y, i.e.…”

Section: Branching Odementioning

confidence: 99%

“…We assume that the cell time has already been inferred from VeloVAE. Thus, the branching ODE is a regression model with an analytical form shown in equation ( 6), (7). To train the model, we find the cell-type-specific rate parameters θ that maximize the Gaussian likelihood, which is equivalent to minimizing a Mahalanobis distance:…”

Section: Branching Odementioning

confidence: 99%

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Bayesian Inference of RNA Velocity from Multi-Lineage Single-Cell Data

Blaauw

Welch

2022

Preprint

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Experimental approaches for measuring single-cell gene expression can observe each cell at only one time point, requiring computational approaches for reconstructing the dynamics of gene expression during cell fate transitions. RNA velocity is a promising computational approach for this problem, but existing inference methods fail to capture key aspects of real data, limiting their utility. To address these limitations, we developed VeloVAE, a Bayesian model for RNA velocity inference. VeloVAE uses variational Bayesian inference to estimate the posterior distribution of latent time, latent cell state, and kinetic rate parameters for each cell. Our approach addresses key limitations of previous methods by inferring a global time and cell state value for each cell; explicitly modeling the emergence of multiple cell types; incorporating prior information such as time point labels; using scalable minibatch optimization; and quantifying parameter uncertainty. We show that VeloVAE significantly outperforms previous approaches in terms of data fit and accuracy of inferred differentiation directions. VeloVAE can also capture qualitative features of expression dynamics neglected by previous methods, including late induction, early repression, transcriptional boosts, and bifurcations. These improvements allow VeloVAE to accurately model gene expression dynamics in complex biological systems, including hematopoiesis, induced pluripotent stem cell reprogramming, neurogenesis, and organogenesis. We find that the latent time automatically inferred using all cells can even outperform pseudotime inferred using manually chosen cell subsets and root cells. We further use the inferred parameters to construct cell type transition graphs and fit branching differential equation models that describe the effects of cell type bifurcations on kinetic rate parameters.

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Section: Resultssupporting

confidence: 87%

Section: Branching Odementioning

confidence: 99%

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Bayesian Inference of RNA Velocity from Multi-Lineage Single-Cell Data

Blaauw

Welch

2022

Preprint

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“…We and others have previously demonstrated that MAFA and MAFB regulate genes required for glucose-stimulated insulin secretion (GSIS) and are reduced in human T2D islets. 18,19,20,27,28 However, the MAF-regulated genes required for insulin granule exocytosis in human β cells have been poorly investigated. Identification of MAFA…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies have demonstrated that mutations in the MAFA gene are causing mature onset diabetes of the young 26 further highlighting the importance of MAFA in human β‐cell function. We and others have previously demonstrated that MAFA and MAFB regulate genes required for glucose‐stimulated insulin secretion (GSIS) and are reduced in human T2D islets 18,19,20,27,28 . However, the MAF‐regulated genes required for insulin granule exocytosis in human β cells have been poorly investigated.…”

Section: Introductionmentioning

confidence: 99%

MAFA and MAFB regulate exocytosis‐related genes in human β‐cells

et al. 2022

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Beta‐Cell Tipe1 Orchestrates Insulin Secretion and Cell Proliferation by Promoting Gαs/cAMP Signaling via USP5

Ding,

Sun,

Liang

et al. 2024

Advanced Science

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Inadequate β‐cell mass and insulin secretion are essential for the development of type 2 diabetes (T2D). TNF‐α‐induced protein 8‐like 1 (Tipe1) plays a crucial role in multiple diseases, however, a specific role in T2D pathogenesis remains largely unexplored. Herein, Tipe1 as a key regulator in T2D, contributing to the maintenance of β cell homeostasis is identified. The results show that the β‐cell‐specific knockout of Tipe1 (termed Ins2‐Tipe1BKO) aggravated diabetic phenotypes in db/db mice or in mice with high‐fat diet‐induced diabetes. Notably, Tipe1 improves β cell mass and function, a process that depends on Gαs, the α subunit of the G‐stimulating protein. Mechanistically, Tipe1 inhibited the K48‐linked ubiquitination degradation of Gαs by recruiting the deubiquitinase USP5. Consequently, Gαs or cAMP agonists almost completely restored the dysfunction of β cells observed in Ins2‐Tipe1BKO mice. The findings characterize Tipe1 as a regulator of β cell function through the Gαs/cAMP pathway, suggesting that Tipe1 may emerge as a novel target for T2D intervention.

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The MafA-target gene PPP1R1A regulates GLP1R-mediated amplification of glucose-stimulated insulin secretion in β-cells

Abstract: The MafA-target gene PPP1R1A regulates GLP1R-mediated amplification of glucose-stimulated insulin secretion in β-cells.

Cited by 21 publications

References 47 publications

Bayesian Inference of RNA Velocity from Multi-Lineage Single-Cell Data

Bayesian Inference of RNA Velocity from Multi-Lineage Single-Cell Data

MAFA and MAFB regulate exocytosis‐related genes in human β‐cells

Beta‐Cell Tipe1 Orchestrates Insulin Secretion and Cell Proliferation by Promoting Gαs/cAMP Signaling via USP5

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