Biology subjects

Lehner, B.

Publications and source records attributed to Lehner, B..

5 recordsLinked to original sources

bioRxiv2018

Empirical noise-mean fitness landscapes and the evolution of gene expression

The effects of cell-to-cell variation (noise) in gene expression have proven difficult to quantify, in part due to the mechanistic coupling of noise to mean expression. To independently evaluate the effects of changes in expression mean and noise we determined the fitness landscapes in mean-noise expression space for 33 genes in yeast. The landscapes can be decomposed into two principal topologies: the fitness effects of protein shortage and surplus. For most genes, the fitness impact of sustained (mean) and short-lived (noise) deviations away from the expression optimum are linked and of similar magnitude. Sensitivity to both protein shortage and surplus creates a fitness landscape in which an epistatic ratchet uncouples the evolution of noise from mean expression, promoting noise minimization. These results demonstrate that noise is detrimental for many genes and reveal non-trivial consequences of mean-noise-fitness topologies for the evolution of gene expression systems.\n\nHighlights- Expression fitness landscapes in mean-noise space allow quantitative independent assessment of the effects of noise and mean expression on fitness for 33 genes\n- Landscapes are described by a combination of just two principal topologies: fitness defects due protein shortage, or due to protein surplus\n- Direct evidence that high expression noise is detrimental to fitness for 50% of genes\n- Mean expression and noise have equivalent impact on organismal fitness\n- Landscapes created by sensitivities to protein shortage and surplus facilitate independent evolution of gene expression noise via an epistatic ratchet

systems biology↗

bioRxiv2018

Single cell expression analysis uncouples transdifferentiation and reprogramming

Many somatic cell types are plastic, having the capacity to convert into other specialized cells (transdifferentiation)(1) or into induced pluripotent stem cells (iPSCs, reprogramming)(2) in response to transcription factor over-expression. To explore what makes a cell plastic and whether these different cell conversion processes are coupled, we exposed bone marrow derived pre-B cells to two different transcription factor overexpression protocols that efficiently convert them either into macrophages or iPSCs and monitored the two processes over time using single cell gene expression analysis. We found that even in these highly efficient cell fate conversion systems, cells differ in both their speed and path of transdifferentiation and reprogramming. This heterogeneity originatesin two starting pre-B cell subpopulations,large pre-BII and the small pre-BII cells they normally differentiate into. The large cells transdifferentiate slowly but exhibit a high efficiency of iPSC reprogramming. In contrast, the small cells transdifferentiate rapidly but are highly resistant to reprogramming. Moreover, the large B cells induce a stronger transient granulocyte/macrophage progenitor (GMP)-like state, while the small B cells undergo a more direct conversion to the macrophage fate. The large cells are cycling and exhibit high Myc activity whereas the small cells are Myc low and mostly quiescent. The observed heterogeneity of the two cell conversion processes can therefore be traced to two closely related cell types in the starting population that exhibit different types of plasticity. These data show that a somatic cells propensity for either transdifferentiation and reprogramming can be uncoupled.\n\nOne sentence summarySingle cell transcriptomics of cell conversions

developmental biology↗

bioRxiv2018

Single cell functional genomics reveals the importance of mitochondria in cell-to-cell variation in proliferation, drug resistance and mutation outcome

Mutations frequently have outcomes that differ across individuals, even when these individuals are genetically identical and share a common environment. Moreover, individual microbial and mammalian cells can vary substantially in their proliferation rates, stress tolerance, and drug resistance, with important implications for the treatment of infections and cancer. To investigate the causes of cell-to-cell variation in proliferation, we developed a high-throughput automated microscopy assay and used it to quantify the impact of deleting >1,500 genes in yeast. Mutations affecting mitochondria were particularly variable in their outcome. In both mutant and wild-type cells mitochondria state - but not content - varied substantially across individual cells and predicted cell-to-cell variation in proliferation, mutation outcome, stress tolerance, and resistance to a clinically used anti-fungal drug. These results suggest an important role for cell-to-cell variation in the state of an organelle in single cell phenotypic variation.

genomics↗

bioRxiv2018

Determining protein structures using genetics

Determining the three dimensional structures of macromolecules is a major goal of biological research because of the close relationship between structure and function. Structure determination usually relies on physical techniques including x-ray crystallography, NMR spectroscopy and cryo-electron microscopy. Here we present a method that allows the high-resolution three-dimensional structure of a biological macromolecule to be determined only from measurements of the activity of mutant variants of the molecule. This genetic approach to structure determination relies on the quantification of genetic interactions (epistasis) between mutations and the discrimination of direct from indirect interactions. This provides a new experimental strategy for structure determination, with the potential to reveal functional and in vivo structural conformations at low cost and high throughput.

systems biology↗

bioRxiv2017

Noise control is a primary function of microRNAs and post-transcriptional regulation

microRNAs are pervasive post-transcriptional regulators of protein-coding genes in multicellular organisms. Two fundamentally different models have been proposed for the function of microRNAs in gene regulation. In the first model, microRNAs act as repressors, reducing protein concentrations by accelerating mRNA decay and inhibiting translation. In the second model, in contrast, the role of microRNAs is not to reduce protein concentrations per se but to reduce fluctuations in these concentrations. Here we present genome-wide evidence that mammalian microRNAs frequently function as noise controllers rather than repressors. Moreover, we show that post-transcriptional noise control has been widely adopted across species from bacteria to animals, with microRNAs specifically employed to reduce noise in regulatory and context-specific processes in animals. Our results substantiate the detrimental nature of expression noise, reveal a universal strategy to control it, and suggest that microRNAs represent an evolutionary innovation for adaptive noise control in animals.\n\nHighlightsO_LIGenome-wide evidence that microRNAs function as noise controllers for genes with context-specific functions\nC_LIO_LIPost-transcriptional noise control is universal from bacteria to animals\nC_LIO_LIAnimals have evolved noise control for regulatory and context-specific processes\nC_LI

genetics↗