Biology subjects

Bloom, J. D.

Publications and source records attributed to Bloom, J. D..

117 records · Page 7Linked to original sources

bioRxiv2018

Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments

In single-cell RNA-sequencing, it is important to know the frequency at which the sequenced transcriptomes actually derive from multiple cells. A common method to estimate this multiplet frequency is to mix two different types of cells (e.g., human and mouse), and then determine how often the transcriptomes contain transcripts from both cell types. When the two cell types are mixed in equal proportion, the calculation of the multiplet frequency from the frequency of mixed transcriptomes is straightforward. But surprisingly, there are no published descriptions of how to calculate the multiplet frequency in the general case when the cell types are mixed unequally. Here I derive equations to analytically calculate the multiplet frequency from the numbers of observed pure and mixed transcriptomes when two cell types are mixed in arbitrary proportions, under the assumption that the loading of cells into droplets or wells is Poisson.

genomics↗

bioRxiv2017

Mapping mutational effects along the evolutionary landscape of HIV envelope

The immediate evolutionary space accessible to HIV is largely determined by how single amino-acid mutations affect fitness. These mutational effects can shift as the virus evolves. However, the prevalence of such shifts in mutational effects remains unclear. Here we quantify the effects on viral growth of all amino-acid mutations to two HIV envelope (Env) proteins that differ at >100 residues. Most mutations similarly affect both Envs, but the amino-acid preferences of a minority of sites have clearly shifted. These shifted sites usually prefer a specific amino acid in one Env, but tolerate many amino acids in the other. Surprisingly, shifts are only slightly enriched at sites that have substituted between the Envs -- and many occur at residues that do not even contact substitutions. Therefore, long-range epistasis can unpredictably shift Env's mutational tolerance during HIV evolution, although the amino-acid preferences of most sites are conserved between moderately diverged viral strains.

microbiology↗

bioRxiv2017

Cooperating H3N2 influenza virus variants are not detectable in primary clinical samples

The high mutation rates of RNA viruses lead to rapid genetic diversification, which can enable cooperative interactions between variants in a viral population. We previously described two distinct variants of H3N2 influenza virus that cooperate in cell culture. These variants differ by a single mutation, D151G, in the neuraminidase protein. The D151G mutation reaches a stable frequency of about 50% when virus is passaged in cell culture. However, it is unclear whether selection for the cooperative benefits of D151G is a cell-culture phenomenon, or whether the mutation is also sometimes present at appreciable frequency in virus populations sampled directly from infected humans. Prior work has not detected D151G in unpassaged clinical samples, but these studies have used methods like Sanger sequencing and pyrosequencing that are relatively insensitive to low-frequency variation. We identified nine samples of human H3N2 influenza collected between 2013 to 2015 in which Sanger sequencing had detected a high frequency of the D151G mutation following one to three passages in cell culture. We deep-sequenced the unpassaged clinical samples to identify low-frequency viral variants. The frequency of D151G did not exceed the frequency of library preparation and sequencing errors in any of the sequenced samples. We conclude that passage in cell culture is primarily responsible for the frequent observations of D151G in recent H3N2 influenza strains.\n\nIMPORTANCEViruses mutate rapidly, and recent studies of RNA viruses have shown that related viral variants can sometimes cooperate to improve each others growth. We previously described two variants of H3N2 influenza virus that cooperate in cell culture. The mutation responsible for cooperation is often observed when human samples of influenza virus are grown in the lab before sequencing, but it is unclear whether the mutation also exists in human infections or is exclusively the result of lab passage. We identified nine human isolates of influenza that had developed the cooperating mutation after being grown in the lab, and performed highly sensitive deep-sequencing of the unpassaged clinical samples to determine whether the mutation existed in the original human infections. We found no evidence of the cooperating mutation in the unpassaged samples, suggesting that the cooperation primarily arises in laboratory conditions.

microbiology↗

bioRxiv2017

Quantifying the ease of viral escape from broad and narrow antibodies to influenza hemagglutinin

Influenza virus can completely escape most antibodies with single mutations. However, rare antibodies broadly neutralize many viral strains. It is unclear how easily influenza virus might escape such antibodies if it was under strong pressure to do so. Here we map all single amino-acid mutations that increase resistance to broad antibodies targeting an H1 hemagglutinin. Crucially, our approach not only identifies antigenic mutations but also quantifies their effect sizes. All antibodies select mutations, but the effect sizes vary widely. The virus can escape a broad antibody that targets residues in hemagglutinins receptor-binding site the same way it escapes narrow strain-specific antibodies: via single mutations with huge effects. In contrast, broad antibodies targeting hemagglutinins stalk only select mutations with small effects. Therefore, among the antibodies we have examined, breadth is an imperfect indicator of the potential for viral escape via single mutations. Broadly neutralizing antibodies targeting the H1 hemagglutinin stalk are quantifiably harder to escape than the other antibodies tested here.

microbiology↗

bioRxiv2017

Extreme heterogeneity of influenza virus infection in single cells

Viral infection can dramatically alter a cells transcriptome. However, these changes have mostly been studied by bulk measurements on many cells. Here we use single-cell mRNA sequencing to examine the transcriptional consequences of Influenza virus infection. We 1nd extremely wide cell-to-cell variation in production of viral gene transcripts - viral transcripts compose less than a percent of total mRNA in many infected cells, but a few cells derive over half their mRNA from virus. Some infected cells fail to express at least one viral gene, and this gene absence partially explains variation in viral transcriptional load. Despite variation in total viral load, the relative abundances of viral mRNAs are fairly consistent across infected cells. Activation of innate immune pathways is rare, but some cellular genes co-vary in abundance with the amount of viral mRNA. Overall, our results highlight the complexity of viral infection at the level of single cells.

genomics↗

bioRxiv2017

Expanding the Atlas of Functional Missense Variation for Human Genes

Although we now routinely sequence human genomes, we can confidently identify only a fraction of the sequence variants that have a functional impact. Here we developed a deep mutational scanning framework that produces exhaustive maps for human missense variants by combining random codon-mutagenesis and multiplexed functional variation assays with computational imputation and refinement. We applied this framework to four proteins corresponding to six human genes: UBE2I (encoding SUMO E2 conjugase), SUMO1 (small ubiquitin-like modifier), TPK1 (thiamin pyrophosphokinase), and CALM1/2/3 (three genes encoding the protein calmodulin). The resulting maps recapitulate known protein features, and confidently identify pathogenic variation. Assays potentially amenable to deep mutational scanning are already available for 57% of human disease genes, suggesting that DMS could ultimately map functional variation for all human disease genes.

molecular biology↗

bioRxiv2017

phydms: Software for phylogenetic analyses informed by deep mutational scanning

BackgroundThe evolution of protein-coding genes can be quantitatively modeled using phylogenetic methods. Recently, it has been shown that high-throughput experimental measurements of mutational effects made via deep mutational scanning can inform site-specific phylogenetic substitution models of gene evolution. However, there is currently no software tailored for such analyses.\n\nResultsWe describe software that efficiently performs phylogenetic analyses with substitution models informed by deep mutational scanning. This software, phydms, is [~]100-fold faster than existing programs that accommodate such substitution models. It can be used to compare the results of deep mutational scanning experiments to the selection on genes in nature. For instance, phydms enables rigorous comparison of how well different experiments on the same gene describe natural selection. It also enables the re-scaling of deep mutational scanning data to account for differences in the stringency of selection in the lab and nature. Finally, phydms can identify sites that are evolving differently in nature than expected from experiments in the lab.\n\nConclusionsThe phydms software makes it easy to use phylogenetic substitution models informed by deep mutational scanning experiments. As data from such experiments becomes increasingly widespread, phydms will facilitate quantitative comparison of the experimental results to the actual selection pressures shaping evolution in nature.

evolutionary biology↗

bioRxiv2017

Comprehensive mapping of HIV-1 escape from a broadly neutralizing antibody

Precisely defining how viral mutations affect HIVs sensitivity to antibodies is vital to the development and evaluation of vaccines and antibody immunotherapeutics. But despite great effort, a full map of escape mutants has not yet been delineated for even a single anti-HIV antibody. Here we describe a massively parallel experimental approach to quantify how all single amino-acid mutations to Envelope (Env) affect HIVs sensitivity to a neutralizing antibody. We applied this approach to PGT151 and identified novel sites of escape in addition to those previously defined by structural and functional studies, such as glycans at sites 611 and 637, residue 647, and sites in the fusion peptide. Evaluating the effect of each amino acid at each site lends insight into the biochemical basis of escape throughout the epitope. Thus, comprehensive mapping of HIV antibody escape gives a quantitative, mutation-level view of the ways that Env can evade neutralization.

microbiology↗

bioRxiv2016

Complete mapping of viral escape from neutralizing antibodies

Identifying viral mutations that confer escape from antibodies is crucial for understanding the interplay between immunity and viral evolution. Here we quantify how every amino-acid mutation to influenza hemagglutinin affects neutralization by monoclonal antibodies targeting several antigenic regions. Our approach involves creating all replication-competent protein variants of the virus, selecting these variants with antibody, and using deep sequencing to identify enriched mutations. These high-throughput measurements are predictive of the effects of individual mutations in traditional neutralization assays. At many residues, only some of the possible mutations escape from an antibody. For instance, at a single residue targeted by two different antibodies, we identify some mutations that escape both antibodies and other mutations that escape only one or the other. Therefore, our approach maps how viruses can escape antibodies with mutation-level sensitivity, and shows that only some mutations at antigenic residues actually alter antigenicity.

microbiology↗