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Deep mutational scanning reveals functional constraints and antibody-escape potential of Lassa virus glycoprotein complex

Carr, Caleb R. ; Crawford, Katharine H.D. ; Murphy, Michael ; [et al.]

Elsevier BV ; 2024

In: Immunity ( 2024-7)

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In: Immunity, Elsevier BV, ( 2024-7)

Type of Medium: Online Resource

ISSN: 1074-7613

URL: Article

DOI: 10.1016/j.immuni.2024.06.013

RVK:

XA 48754.8

Language: English

Publisher: Elsevier BV

Publication Date: 2024

detail.hit.zdb_id: 2001966-X

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Predicting the Landscape of Recombination Using Deep Learning

Adrion, Jeffrey R ; Galloway, Jared G ; Kern, Andrew D ; [et al.]

Oxford University Press (OUP) ; 2020

In: Molecular Biology and Evolution Vol. 37, No. 6 ( 2020-06-01), p. 1790-1808

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In: Molecular Biology and Evolution, Oxford University Press (OUP), Vol. 37, No. 6 ( 2020-06-01), p. 1790-1808

Abstract: Accurately inferring the genome-wide landscape of recombination rates in natural populations is a central aim in genomics, as patterns of linkage influence everything from genetic mapping to understanding evolutionary history. Here, we describe recombination landscape estimation using recurrent neural networks (ReLERNN), a deep learning method for estimating a genome-wide recombination map that is accurate even with small numbers of pooled or individually sequenced genomes. Rather than use summaries of linkage disequilibrium as its input, ReLERNN takes columns from a genotype alignment, which are then modeled as a sequence across the genome using a recurrent neural network. We demonstrate that ReLERNN improves accuracy and reduces bias relative to existing methods and maintains high accuracy in the face of demographic model misspecification, missing genotype calls, and genome inaccessibility. We apply ReLERNN to natural populations of African Drosophila melanogaster and show that genome-wide recombination landscapes, although largely correlated among populations, exhibit important population-specific differences. Lastly, we connect the inferred patterns of recombination with the frequencies of major inversions segregating in natural Drosophila populations.

Type of Medium: Online Resource

ISSN: 0737-4038 , 1537-1719

URL: Article

DOI: 10.1093/molbev/msaa038

Language: English

Publisher: Oxford University Press (OUP)

Publication Date: 2020

detail.hit.zdb_id: 2024221-9

SSG: 12

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Efficient ancestry and mutation simulation with msprime 1.0

Baumdicker, Franz ; Bisschop, Gertjan ; Goldstein, Daniel ; [et al.]

Oxford University Press (OUP) ; 2022

In: Genetics Vol. 220, No. 3 ( 2022-03-03)

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In: Genetics, Oxford University Press (OUP), Vol. 220, No. 3 ( 2022-03-03)

Abstract: Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.

Type of Medium: Online Resource

ISSN: 1943-2631

URL: Article

DOI: 10.1093/genetics/iyab229

Language: English

Publisher: Oxford University Press (OUP)

Publication Date: 2022

detail.hit.zdb_id: 1477228-0

SSG: 12

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Comprehensive characterization of the antibody responses to SARS-CoV-2 Spike protein finds additional vaccine-induced epitopes beyond those for mild infection

Garrett, Meghan E ; Galloway, Jared G ; Wolf, Caitlin ; [et al.]

eLife Sciences Publications, Ltd ; 2022

In: eLife Vol. 11 ( 2022-01-24)

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In: eLife, eLife Sciences Publications, Ltd, Vol. 11 ( 2022-01-24)

Abstract: When SARS-CoV-2 – the virus that causes COVID-19 – infects our bodies, our immune system reacts by producing small molecules called antibodies that stick to a part of the virus called the spike protein. Vaccines are thought to work by triggering the production of similar antibodies without causing disease. Some of the most effective antibodies against SARS-CoV-2 bind a specific area of the spike protein called the ‘receptor binding domain’ or RBD. When SARS-CoV-2 evolves it creates a challenge for our immune system: mutations, which are changes in the virus’s genetic code, can alter the shape of its spike protein, meaning that existing antibodies may no longer bind to it as effectively. This lowers the protection offered by past infection or vaccination, which makes it harder to tackle the pandemic. As it stands, it is not clear which mutations to the virus’s genetic code can affect antibody binding, especially to portions outside the RBD. To complicate things further, the antibodies people produce in response to mild infection, severe infection, and vaccination, while somewhat overlapping, exhibit some differences. Studying these differences could help minimize emergence of mutations that allow the virus to ‘escape’ the antibody response. A phage display library is a laboratory technique in which phages (viruses that infect bacteria) are used as a ‘repository’ for DNA fragments that code for a specific protein. The phages can then produce the protein (or fragments of it), and if the protein fragments bind to a target, it can be easily detected. Garrett, Galloway et al. exploited this technique to study how different portions of the SARS-CoV-2 spike protein were bound by antibodies. They made a phage library in which each phage encoded a portion of the spike protein with different mutations, and then exposed the different versions of the protein to antibodies from people who had experienced prior infection, vaccination, or both. The experiment showed that antibodies produced during severe infection or after vaccination bound to similar parts of the spike protein, while antibodies from people who had experienced mild infection targeted fewer areas. Garrett, Galloway et al. also found that mutations that affected the binding of antibodies produced after vaccination were more consistent than mutations that interfered with antibodies produced during infection. While these results show which mutations are most likely to help the virus escape existing antibodies, this does not mean that the virus will necessarily evolve in that direction. Indeed, some of the mutations may be impossible for the virus to acquire because they interfere with the virus’s ability to spread. Further studies could focus on revealing which of the mutations detected by Garrett, Galloway et al. are most likely to occur, to guide vaccine development in that direction. To help with this, Garrett, Galloway et al. have made the data accessible to other scientists and the public using a web tool.

Type of Medium: Online Resource

ISSN: 2050-084X

URL: Article

DOI: 10.7554/eLife.73490

Language: English

Publisher: eLife Sciences Publications, Ltd

Publication Date: 2022

detail.hit.zdb_id: 2687154-3

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Detailed analysis of antibody responses to SARS-CoV-2 vaccination and infection in macaques

Willcox, Alexandra C. ; Sung, Kevin ; Garrett, Meghan E. ; [et al.]

Public Library of Science (PLoS) ; 2022

In: PLOS Pathogens Vol. 18, No. 4 ( 2022-4-11), p. e1010155-

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In: PLOS Pathogens, Public Library of Science (PLoS), Vol. 18, No. 4 ( 2022-4-11), p. e1010155-

Abstract: Macaques are a commonly used model for studying immunity to human viruses, including for studies of SARS-CoV-2 infection and vaccination. However, it is unknown whether macaque antibody responses resemble the response in humans. To answer this question, we employed a phage-based deep mutational scanning approach (Phage-DMS) to compare which linear epitopes are targeted on the SARS-CoV-2 Spike protein in convalescent humans, convalescent (re-infected) rhesus macaques, mRNA-vaccinated humans, and repRNA-vaccinated pigtail macaques. We also used Phage-DMS to determine antibody escape pathways within each epitope, enabling a granular comparison of antibody binding specificities at the locus level. Overall, we identified some common epitope targets in both macaques and humans, including in the fusion peptide (FP) and stem helix-heptad repeat 2 (SH-H) regions. Differences between groups included a response to epitopes in the N-terminal domain (NTD) and C-terminal domain (CTD) in vaccinated humans but not vaccinated macaques, as well as recognition of a CTD epitope and epitopes flanking the FP in convalescent macaques but not convalescent humans. There was also considerable variability in the escape pathways among individuals within each group. Sera from convalescent macaques showed the least variability in escape overall and converged on a common response with vaccinated humans in the SH-H epitope region, suggesting highly similar antibodies were elicited. Collectively, these findings suggest that the antibody response to SARS-CoV-2 in macaques shares many features with humans, but with substantial differences in the recognition of certain epitopes and considerable individual variability in antibody escape profiles, suggesting a diverse repertoire of antibodies that can respond to major epitopes in both humans and macaques. Differences in macaque species and exposure type may also contribute to these findings.

Type of Medium: Online Resource

ISSN: 1553-7374

URL: Article

DOI: 10.1371/journal.ppat.1010155

DOI: 10.1371/journal.ppat.1010155.g001

DOI: 10.1371/journal.ppat.1010155.g002

DOI: 10.1371/journal.ppat.1010155.g003

DOI: 10.1371/journal.ppat.1010155.g004

DOI: 10.1371/journal.ppat.1010155.g005

DOI: 10.1371/journal.ppat.1010155.t001

DOI: 10.1371/journal.ppat.1010155.s001

DOI: 10.1371/journal.ppat.1010155.s002

DOI: 10.1371/journal.ppat.1010155.s003

DOI: 10.1371/journal.ppat.1010155.s004

DOI: 10.1371/journal.ppat.1010155.s005

DOI: 10.1371/journal.ppat.1010155.s006

DOI: 10.1371/journal.ppat.1010155.s007

DOI: 10.1371/journal.ppat.1010155.s008

DOI: 10.1371/journal.ppat.1010155.s009

DOI: 10.1371/journal.ppat.1010155.s010

Language: English

Publisher: Public Library of Science (PLoS)

Publication Date: 2022

detail.hit.zdb_id: 2205412-1

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Automatic Differentiation is no Panacea for Phylogenetic Gradient Computation

Fourment, Mathieu ; Swanepoel, Christiaan J ; Galloway, Jared G ; [et al.]

Oxford University Press (OUP) ; 2023

In: Genome Biology and Evolution Vol. 15, No. 6 ( 2023-06-01)

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In: Genome Biology and Evolution, Oxford University Press (OUP), Vol. 15, No. 6 ( 2023-06-01)

Abstract: Gradients of probabilistic model likelihoods with respect to their parameters are essential for modern computational statistics and machine learning. These calculations are readily available for arbitrary models via “automatic differentiation” implemented in general-purpose machine-learning libraries such as TensorFlow and PyTorch. Although these libraries are highly optimized, it is not clear if their general-purpose nature will limit their algorithmic complexity or implementation speed for the phylogenetic case compared to phylogenetics-specific code. In this paper, we compare six gradient implementations of the phylogenetic likelihood functions, in isolation and also as part of a variational inference procedure. We find that although automatic differentiation can scale approximately linearly in tree size, it is much slower than the carefully implemented gradient calculation for tree likelihood and ratio transformation operations. We conclude that a mixed approach combining phylogenetic libraries with machine learning libraries will provide the optimal combination of speed and model flexibility moving forward.

Type of Medium: Online Resource

ISSN: 1759-6653

URL: Article

DOI: 10.1093/gbe/evad099

Language: English

Publisher: Oxford University Press (OUP)

Publication Date: 2023

detail.hit.zdb_id: 2495328-3

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A community-maintained standard library of population genetic models

Adrion, Jeffrey R ; Cole, Christopher B ; Dukler, Noah ; [et al.]

eLife Sciences Publications, Ltd ; 2020

In: eLife Vol. 9 ( 2020-06-23)

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In: eLife, eLife Sciences Publications, Ltd, Vol. 9 ( 2020-06-23)

Abstract: The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here, we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

Type of Medium: Online Resource

ISSN: 2050-084X

URL: Article

DOI: 10.7554/eLife.54967

Language: English

Publisher: eLife Sciences Publications, Ltd

Publication Date: 2020

detail.hit.zdb_id: 2687154-3

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phippery : a software suite for PhIP-Seq data analysis

Galloway, Jared G ; Sung, Kevin ; Minot, Samuel S ; [et al.]

Oxford University Press (OUP) ; 2023

In: Bioinformatics Vol. 39, No. 10 ( 2023-10-03)

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In: Bioinformatics, Oxford University Press (OUP), Vol. 39, No. 10 ( 2023-10-03)

Abstract: We present the phippery software suite for analyzing data from phage display methods that use immunoprecipitation and deep sequencing to capture antibody binding to peptides, often referred to as PhIP-Seq. It has three main components that can be used separately or in conjunction: (i) a Nextflow pipeline, phip-flow, to process raw sequencing data into a compact, multidimensional dataset format and allows for end-to-end automation of reproducible workflows. (ii) a Python API, phippery, which provides interfaces for tasks such as count normalization, enrichment calculation, multidimensional scaling, and more, and (iii) a Streamlit application, phip-viz, as an interactive interface for visualizing the data as a heatmap in a flexible manner. Availability and implementation All software packages are publicly available under the MIT License. The phip-flow pipeline: https://github.com/matsengrp/phip-flow. The phippery library: https://github.com/matsengrp/phippery. The phip-viz Streamlit application: https://github.com/matsengrp/phip-viz.

Type of Medium: Online Resource

ISSN: 1367-4811

URL: Article

DOI: 10.1093/bioinformatics/btad583

Language: English

Publisher: Oxford University Press (OUP)

Publication Date: 2023

detail.hit.zdb_id: 1468345-3

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Self-expanding Transcatheter vs Surgical Aortic Valve Replacement in Intermediate-Risk Patients : 5-Year Outcomes of the SURTAVI Randomized Clinical Trial

Van Mieghem, Nicolas M. ; Deeb, G. Michael ; Søndergaard, Lars ; [et al.]

American Medical Association (AMA) ; 2022

In: JAMA Cardiology Vol. 7, No. 10 ( 2022-10-01), p. 1000-

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In: JAMA Cardiology, American Medical Association (AMA), Vol. 7, No. 10 ( 2022-10-01), p. 1000-

Abstract: In patients with severe aortic valve stenosis at intermediate surgical risk, transcatheter aortic valve replacement (TAVR) with a self-expanding supra-annular valve was noninferior to surgery for all-cause mortality or disabling stroke at 2 years. Comparisons of longer-term clinical and hemodynamic outcomes in these patients are limited. Objective To report prespecified secondary 5-year outcomes from the Symptomatic Aortic Stenosis in Intermediate Risk Subjects Who Need Aortic Valve Replacement (SURTAVI) randomized clinical trial. Design, Setting, and Participants SURTAVI is a prospective randomized, unblinded clinical trial. Randomization was stratified by investigational site and need for revascularization determined by the local heart teams. Patients with severe aortic valve stenosis deemed to be at intermediate risk of 30-day surgical mortality were enrolled at 87 centers from June 19, 2012, to June 30, 2016, in Europe and North America. Analysis took place between August and October 2021. Intervention Patients were randomized to TAVR with a self-expanding, supra-annular transcatheter or a surgical bioprosthesis. Main Outcomes and Measures The prespecified secondary end points of death or disabling stroke and other adverse events and hemodynamic findings at 5 years. An independent clinical event committee adjudicated all serious adverse events and an independent echocardiographic core laboratory evaluated all echocardiograms at 5 years. Results A total of 1660 individuals underwent an attempted TAVR (n = 864) or surgical (n = 796) procedure. The mean (SD) age was 79.8 (6.2) years, 724 (43.6%) were female, and the mean (SD) Society of Thoracic Surgery Predicted Risk of Mortality score was 4.5% (1.6%). At 5 years, the rates of death or disabling stroke were similar (TAVR, 31.3% vs surgery, 30.8%; hazard ratio, 1.02 [95% CI, 0.85-1.22]; P = .85). Transprosthetic gradients remained lower (mean [SD], 8.6 [5.5] mm Hg vs 11.2 [6.0] mm Hg; P & amp;lt; .001) and aortic valve areas were higher (mean [SD], 2.2 [0.7] cm 2 vs 1.8 [0.6] cm 2 ; P & amp;lt; .001) with TAVR vs surgery. More patients had moderate/severe paravalvular leak with TAVR than surgery (11 [3.0%] vs 2 [0.7%] ; risk difference, 2.37% [95% CI, 0.17%- 4.85%]; P = .05). New pacemaker implantation rates were higher for TAVR than surgery at 5 years (289 [39.1%] vs 94 [15.1%] ; hazard ratio, 3.30 [95% CI, 2.61-4.17]; log-rank P & amp;lt; .001), as were valve reintervention rates (27 [3.5%] vs 11 [1.9%] ; hazard ratio, 2.21 [95% CI, 1.10-4.45]; log-rank P = .02), although between 2 and 5 years only 6 patients who underwent TAVR and 7 who underwent surgery required a reintervention. Conclusions and Relevance Among intermediate-risk patients with symptomatic severe aortic stenosis, major clinical outcomes at 5 years were similar for TAVR and surgery. TAVR was associated with superior hemodynamic valve performance but also with more paravalvular leak and valve reinterventions.

Type of Medium: Online Resource

ISSN: 2380-6583

URL: Article

DOI: 10.1001/jamacardio.2022.2695

Language: English

Publisher: American Medical Association (AMA)

Publication Date: 2022

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A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk

Redondo, Maria J. ; Geyer, Susan ; Steck, Andrea K. ; [et al.]

American Diabetes Association ; 2018

In: Diabetes Care Vol. 41, No. 9 ( 2018-09-01), p. 1887-1894

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In: Diabetes Care, American Diabetes Association, Vol. 41, No. 9 ( 2018-09-01), p. 1887-1894

Abstract: We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals. RESEARCH DESIGN AND METHODS We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients’ relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2–51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial–Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables. RESULTS Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06–1.6; P = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS & gt;0.295, 95% CI 1.47–3.51; P = 0.0002). CONCLUSIONS The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.

Type of Medium: Online Resource

ISSN: 0149-5992 , 1935-5548

URL: Article

DOI: 10.2337/dc18-0087

Language: English

Publisher: American Diabetes Association

Publication Date: 2018

detail.hit.zdb_id: 1490520-6

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