Biology subjects

Gu, G.

Publications and source records attributed to Gu, G..

21 records · Page 2Linked to original sources

bioRxiv2019

Myt Transcription Factors prevent stress-response gene over-activation to enable postnatal pancreatic β cell proliferation and function

Although stress response maintains cell function and survival under adverse conditions, over-activation of late-stage stress-gene effectors causes dysfunction and death. Here we show that the Myelin Transcription Factors (Myt 1, 2, and 3 TFs) prevent this over-activation. Co-inactivating Myt TFs in mouse pancreatic progenitors compromised postnatal {beta}-cell function, proliferation, and survival, preceded by upregulation of late-stage stress-response genes Activating Transcription Factors (e.g., Atf4) and Heat Shock Proteins (Hsps). Myt1 binds the putative enhancers of Atf4 and Hsps, whose over-expression in mouse {beta} cells largely recapitulated the Myt mutant phenotypes. Moreover, Myt(MYT)-TF levels were upregulated in functional mouse and human {beta} cells by metabolic stress but downregulated in those of type 2 diabetic islets that display ATF4 and HSP over-activation. Lastly, human MYT knockdown caused stress-gene over-activation and death in Endo-{beta}H1 cells. These findings suggest that the Myt TFs restrict stress-response to physiologically tolerable levels in mice and human.

developmental biology↗

bioRxiv2019

Microtubules Regulate Spatial Localization and Availability of Insulin Granules in Pancreatic Beta Cells.

Two key prerequisites for glucose stimulated insulin secretion (GSIS) in Beta cells are the proximity of insulin granules to the plasma membrane and their anchoring or docking to the plasma membrane (PM). While recent evidence has indicated that both of these factors are altered in the context of diabetes, it is unclear what regulates localization of insulin and its interactions with the PM within single cells. Here we demonstrate that microtubule (MT) motor mediated transport dynamics have a critical role in regulating both factors. Super-resolution imaging shows that while the MT cytoskeleton resembles a random meshwork in the cells interior, MTs near the cells surface are preferentially aligned with the PM. Computational modeling demonstrates two consequences of this alignment. First, this structured MT network preferentially withdraws granules from the PM. Second, the binding and transport of insulin granules by MT motors prevents their stable anchoring to the PM. The MT cytoskeleton thus negatively regulates GSIS by both limiting the amount of insulin proximal to the PM and preventing/breaking interactions between the PM and the remaining nearby insulin. These results predict that altering MT structure in beta cells can be used to tune GSIS. Thus, our study points to a potential of an alternative therapeutic strategy for diabetes by targeting specific MT regulators.

cell biology↗

bioRxiv2018

Quantitative assessment of cell population diversity in single-cell landscapes

Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool for the systematic investigation of cellular diversity. As a number of computational tools have been developed to identify and visualize cell populations within a single scRNA-seq dataset, there is a need for methods to quantitatively and statistically define proportional shifts in cell population structures across datasets, such expansion or shrinkage, or emergence or disappearance of cell populations. Here we present sc-UniFrac, a framework to statistically quantify compositional diversity in cell populations between single-cell transcriptome landscapes. sc-UniFrac enables sensitive and robust quantification in simulated and experimental datasets in terms of both population identity and quantity. We have demonstrated the utility of sc-UniFrac in multiple applications, including assessment of biological and technical replicates, classification of tissue phenotypes, identification and definition of altered cell populations, and benchmarking batch correction tools. sc-UniFrac provides a framework for quantifying diversity or alterations in cell populations across conditions, and has broad utility for gaining insight on how cell populations respond to perturbations.

bioinformatics↗