Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study

Predicting gene expression from genotyped data is valuable for studying inaccessible tissues such as the brain. Herein we present eGenScore, a polygenic/poly-variation method, and compare it with PrediXcan, a method based on regularized linear regression using elastic nets. While both methods have the same purpose of predicting gene expression based on genotype, they carry important methodological differences. We compared the performance of expression quantitative trait loci (eQTL) models to predict gene expression in the frontal cortex, comparing across these frameworks (eGenScore vs. PrediXcan) and training datasets (BrainEAC, which is brain-specific, vs. GTEx, which has data across multiple tissues). In addition to internal five-fold cross-validation, we externally validated the gene expression models using the CommonMind Consortium database. Our results showed that (1) PrediXcan outperforms eGenScore regardless of the training database used; and (2) when using PrediXcan, the performance of the eQTL models in frontal cortex is higher when trained with GTEx than with BrainEAC.

Feedback autoregulation of gene expression always amplifies noise in timing of threshold crossing

Many cellular events occur when their corresponding regulatory proteins attain critical thresholds. Can cells schedule such events with precision by controlling the dynamics of the proteins? We investigate this question by considering a simple gene expression model that consists of switching the gene between OFF and ON states and degradation of the protein. Because feedback regulation is a pervasive method of control in biological systems, we analyzed three feedback mechanisms (protein regulates either its own transcription, or the rate at which gene turns ON, or the rate at which gene turns OFF) for their abilities to suppress noise in timing. We show that in the limiting case where the protein does not degrade, feedbacks always amplify noise in event timing.

Protein noise and distribution in a two-stage gene-expression model extended by an mRNA inactivation loop

Chemical reaction networks involving molecular species at low copy numbers lead to stochasticity in protein levels in gene expression at the single-cell level. Mathematical modelling of this stochastic phenomenon enables us to elucidate the underlying molecular mechanisms quantitatively. Here we present a two-stage stochastic gene expression model that extends the standard model by an mRNA inactivation loop. The extended model exhibits smaller protein noise than the original two-stage model. Interestingly, the fractional reduction of noise is a non-monotonous function of protein stability, and can be substantial especially if the inactivated mRNA is stable. We complement the noise study by an extensive mathematical analysis of the joint steady-state distribution of active and inactive mRNA and protein species. We determine its generating function and derive a recursive formula for the protein distribution. The results of the analytical formula are cross-validated by kinetic Monte-Carlo simulation.

Division stochasticity can be transmitted to protein expression through chromosome replication

Stochastic fluctuations (noise) are a fundamental characteristic of protein production. Some sources of this stochasticity are still under debate. In this work, we explore how these fluctuations can originate from the stochasticity on division events. To do that, we consider the classical gene expression model with chromosome replication following the known Helmstetter & Cooper model. This model predicts intervals of the cell cycle where bacteria can have more than one copy of a particular gene. Considering the transcription rate as proportional to the number of chromosomes and division based on a continuous rate model, we explore how stochasticity in division or equivalently in cell size, could be transmitted to gene expression. Our simulations suggest that division can be an important source of such fluctuations only if chromosomes are replicating, otherwise, this noise is not well transmitted. This effect happens even if replication is deterministic. This work can be helpful for understanding cell cycle dynamics and their interplay with phenotypic variability.