Biology subjects

Jesse D Bloom

Publications and source records attributed to Jesse D Bloom.

6 recordsLinked to original sources

bioRxiv2016

Accurate measurement of the effects of all amino-acid mutations to influenza hemagglutinin

Influenza genes evolve mostly via point mutations, and so knowing the effect of every amino-acid mutation provides information about evolutionary paths available to the virus. We previously used high-throughput mutagenesis and deep sequencing to estimate the effects of all mutations to an H1 influenza hemagglutinin on viral replication in cell culture (Thyagarajan and Bloom, 2014); however, these measurements suffered from sub-stantial noise. Here we describe advances that greatly improve the accuracy and reproducibility of our measurements. The largest improvements come from using a helper virus to reduce bottlenecks when generating viruses from plasmids. Our measurements confirm that antigenic sites on the globular head of hemagglutinin are highly tolerant of mutations. However, other regions - including stalk epitopes targeted by broadly neutralizing antibodies - have a limited capacity to evolve. The ability to accurately measure the effects of all influenza mutations should enhance efforts to understand and predict viral evolution.

Microbiology↗

bioRxiv2016

Identification of positive selection in genes is greatly improved by using experimentally informed site-specific models

Sites of biologically interesting selection are identified by comparing observed evolutionary patterns to those expected under a null model for evolution in the absence of such selection. For protein-coding genes, the most common null model is that nonsynonymous and synonymous mutations fix at equal rates; this unrealistic model has limited power to detect interesting selection. I describe a new approach that uses a null model based on high-throughput measurements of a genes site-specific amino-acid preferences. This null model makes it possible to identify diversifying selection for amino-acid change and differential selection for mutations to unexpected amino acids. I show that this approach identifies sites of adaptive substitutions in four genes (lactamase, Gal4, influenza nucleoprotein, and influenza hemagglutinin) far better than traditional methods. As rapid increases in biological data enable increasingly nuanced descriptions of the constraints on individual sites, approaches like the one here can greatly improve our ability to identify biologically interesting selection.

Evolutionary Biology↗

bioRxiv2015

Software for the analysis and visualization of deep mutational scanning data

BackgroundDeep mutational scanning is a technique to estimate the impacts of mutations on a gene by using deep sequencing to count mutations in a library of variants before and after imposing a functional selection. The impacts of mutations must be inferred from changes in their counts after selection.\n\nResultsI describe a software package, dms_tools, to infer the impacts of mutations from deep mutational scanning data using a likelihood-based treatment of the mutation counts. I show that dms_tools yields more accurate inferences on simulated data than simply calculating ratios of counts pre-and post-selection. Using dms_tools, one can infer the preference of each site for each amino acid given a single selection pressure, or assess the extent to which these preferences change under different selection pressures. The preferences and their changes can be intuitively visualized with sequence-logo-style plots created using an extension to weblogo.\n\nConclusionsdms_tools implements a statistically principled approach for the analysis and subsequent visualization of deep mutational scanning data.

Bioinformatics↗

bioRxiv2014

The inherent mutational tolerance and antigenic evolvability of influenza hemagglutinin

Influenza is notable for its evolutionary capacity to escape immunity targeting the viral hemagglutinin. We used deep mutational scanning to examine the extent to which a high inherent mutational tolerance contributes to this antigenic evolvability. We created mutant viruses that incorporate most of the {approx} 104 amino-acid mutations to hemagglutinin from A/WSN/1933 (H1N1) influenza. After passaging these viruses in tissue culture to select for functional variants, we used deep sequencing to quantify mutation frequencies before and after selection. These data enable us to infer the preference for each amino acid at each site in hemagglutinin. These inferences are consistent with existing knowledge about the proteins structure and function, and can be used to create a model that describes hemagglutinins evolution far better than existing phylogenetic models. We show that hemagglutinin has a high inherent tolerance for mutations at antigenic sites, suggesting that this is one factor contributing to influenzas antigenic evolution.

Microbiology↗

bioRxiv2014

An experimentally informed evolutionary model improves phylogenetic fit to divergent lactamase homologs

Phylogenetic analyses of molecular data require a quantitative model for how sequences evolve. Traditionally, the details of the site-specific selection that governs sequence evolution are not known a priori, making it challenging to create evolutionary models that adequately capture the heterogeneity of selection at different sites. However, recent advances in high-throughput experiments have made it possible to quantify the effects of all single mutations on gene function. I have previously shown that such high-throughput experiments can be combined with knowledge of underlying mutation rates to create a parameter-free evolutionary model that describes the phylogeny of influenza nucleoprotein far better than commonly used existing models. Here I extend this work by showing that published experimental data on TEM-1 beta-lactamase (Firnberg et al, 2014) can be combined with a few mutation rate parameters to create an evolutionary model that describes beta-lactamase phylogenies much than most common existing models. This experimentally informed evolutionary model is superior even for homologs that are substantially diverged (about 35% divergence at the protein level) from the TEM-1 parent that was the subject of the experimental study. These results suggest that experimental measurements can inform phylogenetic evolutionary models that are applicable to homologs that span a substantial range of sequence divergence.

Evolutionary Biology↗

bioRxiv2014

An experimentally determined evolutionary model dramatically improves phylogenetic fit

All modern approaches to molecular phylogenetics require a quantitative model for how genes evolve. Unfortunately, existing evolutionary models do not realistically represent the site-heterogeneous selection that governs actual sequence change. Attempts to remedy this problem have involved augmenting these models with a burgeoning number of free parameters. Here I demonstrate an alternative: experimental determination of a parameter-free evolutionary model via mutagenesis, functional selection, and deep sequencing. Using this strategy, I create an evolutionary model for influenza nucleoprotein that describes the gene phylogeny far better than existing models with dozens or even hundreds of free parameters. Emerging high-throughput experimental strategies such as the one employed here provide fundamentally new information that has the potential to transform the sensitivity of phylogenetic and genetic analyses.

Evolutionary Biology↗