Biology subjects

Sparta, B.

Publications and source records attributed to Sparta, B..

3 recordsLinked to original sources

bioRxiv2021

Continuous sensing of nutrients and growth factors by the mTORC1-TFEB axis

mTORC1 senses nutrient and growth factor status and phosphorylates downstream targets, including the transcription factor TFEB, to coordinate metabolic supply and demand. The molecular mechanisms of mTORC1 activation are thought to enforce a strict requirement for simultaneous amino acid and growth factor stimuli, but this model has not been evaluated with quantitative or single-cell methods. Here, we develop a series of fluorescent protein-TFEB fusions and investigate how combinations of stimuli jointly regulate signaling from mTORC1 to TFEB at the single-cell level. Live-cell imaging of individual cells revealed that mTORC1-TFEB signaling responds with graded changes to individual amino acid and growth factor inputs, rather than behaving as a logical "AND" gate. We find that mTORC1 inputs can be sequentially sensed, with responses that vary between mTORC1 substrates and are amplified by input from other kinases, including GSK3{beta}. In physiologically relevant concentrations of amino acids, we observe fluctuations in mTORC1-TFEB signaling that indicate continuous responsiveness to nutrient availability. Our results clarify how the molecular regulation of mTORC1 enables homeostatic processes at the cellular level and provide a more precise understanding of its behavior as an integrator of multiple inputs.

cell biology↗

bioRxiv2021

Binomial models uncover biological variation during feature selection of droplet-based single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) aims to characterize how variation in gene expression is distributed across cells in tissues and organisms. Yet, effective comprehension of these extremely high-dimensional datasets remains a critical barrier to progress in biological research. In standard analyses of scRNA-seq data, feature selection steps aim to reduce the dimensionality of the data by focusing on a subset of genes that are the most biologically variable across a set of cells. Ideally, these features provide the genes that are the most informative for partitioning groups of transcriptionally distinct cells, each representing a different cell type or identity. In this work, we propose a simple feature selection model where a binomial sampling process for each mRNA species produces a null model of technical variation. To compare our model to existing methods, we use scRNA-seq data where cell identities have been established a priori for each cell, and characterize whether different feature sets retain biologically varying genes, distort neighborhood structures, and allow popular clustering algorithms to partition groups of cells into their established classes. We find that our model of biological variation, which we term "Differentially Distributed Genes" or DDGs, outperforms existing methods, and enables dimensionality reduction without loss of critical structure within the data set.

bioinformatics↗

bioRxiv2019

Cell-to-cell variability in AMPK activation reveals autonomous cycles in cellular energy balance

Cellular metabolism can be reconfigured to balance nutrient processing (catabolism) and cellular demands (anabolism). However, the kinetics of reconfiguration within individual mammalian cells, and heterogeneity between cells, have remained unexplored. Using live-cell imaging, we investigate the kinetics of bioenergetic adaptation in individual cells. In response to acute inhibition of oxidative phosphorylation, AMPK substrates are phosphorylated bimodally, identifying cells in different underlying states of anabolism and catabolism. Manipulation of glycolysis, insulin/Akt signaling, or protein synthesis shifts the distribution of these states. Long-term lineage analysis confirms that this cellular energy balance cycles over time within individual cells, independently of the cell division cycle. We further demonstrate that AMPK inhibits the ERK and mTOR cell growth signaling pathways specifically when anabolism is in excess. Our results reveal dynamic fluctuations of energetic balance, establish distinct time scales of cellular energetic control, and open opportunities for more precise prediction and control of cellular metabolic functions.

cell biology↗