Biology subjects

Lehner, B.

Publications and source records attributed to Lehner, B..

49 records · Page 3Linked to original sources

bioRxiv2021

Global mapping of the energetic and allosteric landscapes of protein binding domains

Allosteric communication between distant sites in proteins is central to nearly all biological regulation but still poorly characterised for most proteins, limiting conceptual understanding, biological engineering and allosteric drug development. Typically only a few allosteric sites are known in model proteins, but theoretical, evolutionary and some experimental studies suggest they may be much more widely distributed. An important reason why allostery remains poorly characterised is the lack of methods to systematically quantify long-range communication in diverse proteins. Here we address this shortcoming by developing a method that uses deep mutational scanning to comprehensively map the allosteric landscapes of protein interaction domains. The key concept of the approach is the use of multidimensional mutagenesis: mutational effects are quantified for multiple molecular phenotypes--here binding and protein abundance--and in multiple genetic backgrounds. This is an efficient experimental design that allows the underlying causal biophysical effects of mutations to be accurately inferred en masse by fitting thermodynamic models using neural networks. We apply the approach to two of the most common human protein interaction domains, an SH3 domain and a PDZ domain, to produce the first global atlases of allosteric mutations for any proteins. Allosteric mutations are widely dispersed with extensive long-range tuning of binding affinity and a large mutational target space of network-altering edgetic variants. Mutations are more likely to be allosteric closer to binding interfaces, at Glycines in secondary structure elements and at particular sites including a chain of residues connecting to an opposite surface in the PDZ domain. This general approach of quantifying mutational effects for multiple molecular phenotypes and in multiple genetic backgrounds should allow the energetic and allosteric landscapes of many proteins to be rapidly and comprehensively mapped.

biophysics↗

bioRxiv2020

The genetic landscape for amyloid beta fibril nucleation accurately discriminates familial Alzheimer's disease mutations

Amyloid fibrils are associated with many human diseases but how mutations alter the propensity of proteins to form fibrils has not been comprehensively investigated and is not well understood. Alzheimers Disease (AD) is the most common form of dementia with amyloid plaques of the amyloid beta (A{beta}) peptide a pathological hallmark of the disease. Mutations in A{beta} also cause familial forms of AD (fAD). Here we use deep mutational scanning to quantify the effects of >14,000 mutations on the aggregation of A{beta}. The resulting genetic landscape reveals fundamental mechanistic insights into fibril nucleation, including the importance of charge and gatekeeper residues in the disordered region outside of the amyloid core in preventing nucleation. Strikingly, unlike computational predictors and previous measurements, the in vivo nucleation scores accurately identify all known dominant fAD mutations, validating this simple cell-based assay as highly relevant to the human genetic disease and suggesting accelerated fibril nucleation is the ultimate cause of fAD. Our results provide the first comprehensive map of how mutations alter the formation of any amyloid fibril and a validated resource for the interpretation of genetic variation in A{beta}. HighlightsO_LIFirst comprehensive map of how mutations alter the propensity of a protein to form amyloid fibrils. C_LIO_LICharge and gatekeeper residues in the disordered N-terminus of amyloid beta prevent fibril nucleation. C_LIO_LIRates of nucleation in a cell-based assay accurately identify the mutations that cause dominant familial Alzheimers disease. C_LIO_LIThe combination of deep mutational scanning and human genetics provides a general strategy to quantify the disease-relevance of in vitro and in vivo assays. C_LI

genomics↗

bioRxiv2020

Neuronal perception of the social environment intergenerationally controls germline development and generation time in C. elegans

An old and controversial question in biology is whether information perceived by the nervous system of an animal can cross the Weismann barrier to alter the phenotypes and fitness of their progeny. Here we show that such intergenerational transmission of sensory information occurs in the model organism, C. elegans, with a major effect on fitness. Specifically, that perception of social pheromones by chemosensory neurons controls the post-embryonic timing of development of one tissue - the germline - relative to others in an animals progeny. Neuronal perception of the social environment thus intergenerationally controls the generation time of this animal.

genetics↗

bioRxiv2020

Mutations primarily alter the inclusion of alternatively spliced exons

Genetic analyses and systematic mutagenesis have revealed that synonymous, non-synonymous and intronic mutations frequently alter the inclusion levels of alternatively spliced exons, consistent with the concept that altered splicing might be a common mechanism by which mutations cause disease. However, most exons expressed in any cell are highly-included in mature mRNAs. Here, by performing deep mutagenesis of highly-included exons and by analysing the association between genome sequence variation and exon inclusion across the transcriptome, we report that mutations only very rarely alter the inclusion of highly-included exons. This is true for both exonic and intronic mutations as well as for perturbations in trans. Therefore, mutations that affect splicing are not evenly distributed across primary transcripts but are focussed in and around alternatively spliced exons with intermediate inclusion levels. These results provide a resource for prioritising synonymous and other variants as disease-causing mutations.

genomics↗

bioRxiv2020

DiMSum: an error model and pipeline for analyzing deep mutational scanning data and diagnosing common experimental pathologies

Deep mutational scanning (DMS) enables multiplexed measurement of the effects of thousands of variants of proteins, RNAs and regulatory elements. Here, we present a customizable pipeline - DiMSum - that represents an end-to-end solution for obtaining variant fitness and error estimates from raw sequencing data. A key innovation of DiMSum is the use of an interpretable error model that captures the main sources of variability arising in DMS workflows, outperforming previous methods. DiMSum is available as an R/Bioconda package and provides summary reports to help researchers diagnose common DMS pathologies and take remedial steps in their analyses.

bioinformatics↗

bioRxiv2019

The mutational landscape of a prion-like domain

Specific insoluble protein aggregates are the hallmarks of many neurodegenerative diseases1-5. For example, cytoplasmic aggregates of the RNA-binding protein TDP-43 are observed in 97% of cases of Amyotrophic Lateral Sclerosis (ALS)6,7. However, it is still unclear for ALS and other diseases whether it is the insoluble aggregates or other forms of the mutated proteins that cause these diseases that are actually toxic to cells8-13. Here we address this question for TDP-43 by systematically mutating14 the protein and quantifying the effects on cellular toxicity. We generated >50,000 mutations in the intrinsically disordered prion-like domain (PRD) and observed that changes in hydrophobicity and aggregation potential are highly predictive of changes in toxicity. Surprisingly, however, increased hydrophobicity and cytoplasmic aggregation actually reduce cellular toxicity. Mutations have their strongest effects in a central region of the PRD, with variants that increase toxicity promoting the formation of more dynamic liquid-like condensates. The genetic interactions in double mutants reveal that specific structures exist in this unstructured region in vivo. Our results demonstrate that deep mutagenesis is a powerful approach for probing the sequence-function relationships of intrinsically disordered proteins as well as their in vivo structural conformations. Moreover, we show that aggregation of TDP-43 is not harmful but actually protects cells, most likely by titrating the protein away from a toxic liquid-like phase.

genetics↗

bioRxiv2019

Combinatorial genetic analysis of a regulatory network reveals the importance of higher order epistasis for gene deletion phenotypes

We lack an understanding of how the full range of genetic variants that occur in individuals can interact. To address this shortcoming, here we combine diverse mutations between genes in a model regulatory network, the galactose (GAL) switch of budding yeast. The effects of thousands of pairs of mutations fall into a limited number of phenotypic classes. While these effects are mostly predictable using simple rules that capture the stereotypical genetic interactions of the network, some double mutants have unexpected outcomes including constituting alternative functional switches. Each of these harmonious genetic combinations exhibits altered dependency on other regulatory genes. These cases illustrate how both pairwise and higher epistasis determines gene essentiality and how combinations of mutations rewire regulatory networks. Together, our results provide an overview of how broad spectra of mutations interact, how these interactions can be predicted, and how diverse genetic solutions can achieve wild-type phenotypic behavior.

genetics↗

bioRxiv2019

Changes in gene expression shift and switch genetic interactions

An important goal in disease genetics and evolutionary biology is to understand how mutations combine to alter phenotypes and fitness. Non-additive interactions between mutations occur extensively and change across conditions, cell types, and species, making genetic prediction a difficult challenge. To understand the reasons for this, we reduced the problem to a minimal system where we combined mutations in a single protein performing a single function (a transcriptional repressor inhibiting a target gene). Even in this minimal system, a change in gene expression altered both the strength and type of genetic interactions. These seemingly complicated changes could, however, be predicted by a mathematical model that propagates the effects of mutations on protein folding to the cellular phenotype. We show that similar changes will be observed for many genes. These results provide fundamental insights into genotype-phenotype maps and illustrate how changes in genetic interactions can be predicted using hierarchical mechanistic models.\n\nOne sentence SummaryDeep mutagenesis of the lambda repressor reveals that changes in gene expression will alter the strength and direction of genetic interactions between mutations in many genes.\n\nHighlightsO_LIDeep mutagenesis of the lambda repressor at two expression levels reveals extensive changes in mutational effects and genetic interactions\nC_LIO_LIGenetic interactions can switch from positive (suppressive) to negative (enhancing) as the expression of a gene changes\nC_LIO_LIA mathematical model that propagates the effects of mutations on protein folding to the cellular phenotype accurately predicts changes in mutational effects and interactions\nC_LIO_LIChanges in expression will alter mutational effects and interactions for many genes\nC_LIO_LIFor some genes, perfect mechanistic models will never be able to predict how mutations of known effect combine without measurements of intermediate phenotypes\nC_LI

genetics↗

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↗