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Quantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels.

ABSTRACT: Identifying the factors that shape protein expression variability in complex multi-cellular organisms has primarily focused on promoter architecture and regulation of single-cell expression in cis. However, this targeted approach has to date been unable to identify major regulators of cell-to-cell gene expression variability in humans. To address this, we have combined single-cell protein expression measurements in the human immune system using flow cytometry with a quantitative genetics analysis. For the majority of proteins whose variability in expression has a heritable component, we find that genetic variants act in trans, with notably fewer variants acting in cis. Furthermore, we highlight using Mendelian Randomization that these variability-Quantitative Trait Loci might be driven by the cis regulation of upstream genes. This indicates that natural selection may balance the impact of gene regulation in cis with downstream impacts on expression variability in trans.

SUBMITTER: Morgan MD

PROVIDER: S-EPMC7094872 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Quantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels.

Morgan Michael D MD Patin Etienne E Jagla Bernd B Hasan Milena M Quintana-Murci Lluís L Marioni John C JC

PLoS genetics 20200313 3

Identifying the factors that shape protein expression variability in complex multi-cellular organisms has primarily focused on promoter architecture and regulation of single-cell expression in cis. However, this targeted approach has to date been unable to identify major regulators of cell-to-cell gene expression variability in humans. To address this, we have combined single-cell protein expression measurements in the human immune system using flow cytometry with a quantitative genetics analysi ...[more]

PMID: 32168362

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Project description:Pyruvate oxidase encoded by spxB is a major virulence factor in the human respiratory pathogen Streptococcus pneumoniae. During aerobic growth, SpxB synthesizes large amounts of H2O2 and acetyl phosphate, which can serve as a phosphoryl group donor to response regulators and be converted to ATP. SpxB is the main source of the millimolar concentrations of H2O2 produced and tolerated by pneumococcus, despite its lack of a catalase. We report here the first cis- and trans-acting regulatory elements for spxB transcription. These elements were identified in a genetic screen, similar to those used previously for phase variants, for spontaneous mutations that caused colonies of virulent serotype 2 strain D39 to change from a transparent to an opaque appearance. Six of the seven opaque colonies recovered (frequency of 3 x 10-5) were impaired for SpxB function. Modeling suggested that two mutations changed amino acids in SpxB required for FAD cofactor or subunit binding. One mutation deleted a cis-acting adjacent direct repeat required for optimal spxB transcription. The other three independent mutations created the same frameshift near the start of a trans-acting regulatory gene designated as spxR. The SpxR protein contains helix-turn-helix, CBS, and HotDog domains implicated in DNA, adenosine, and CoA compound binding, respectively, consistent with the idea that SpxR positively regulates spxB transcript amount in response to energy and metabolic state rather than oxidative state. Finally, microarray analyses of a null spxB or a spxR mutant revealed the presence of a new oxidative stress response in pneumococcus and unexpectedly demonstrated that SpxR strongly positively regulates the transcript amount of the strH exoglycosidase gene, which like spxB, has been implicated in host colonization. Keywords: genetic modification Bacterial strains were grown exponentially in rich (BHI) media at 37C and an atmosphere of 5% CO2, and were processed as described in the related Sample records. Samples were collected from three independent biological replicates and included one dye swap. Data were normalized using the Lowess (subgrid) method without background subtraction.

Dataset Information

Quantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels.

Publications

Quantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels.

Similar Datasets

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