Publications by authors named "H Benjamin Larman"

45 Publications

Markers of Polyfunctional SARS-CoV-2 Antibodies in Convalescent Plasma.

mBio 2021 04 20;12(2). Epub 2021 Apr 20.

Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, New Hampshire, USA

Convalescent plasma is a promising therapy for coronavirus disease 2019 (COVID-19), but the antibody characteristics that contribute to efficacy remain poorly understood. This study analyzed plasma samples from 126 eligible convalescent blood donors in addition to 15 naive individuals, as well as an additional 20 convalescent individuals as a validation cohort. Multiplexed Fc Array binding assays and functional antibody response assays were utilized to evaluate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody composition and activity. Donor convalescent plasma samples contained a range of antibody cell- and complement-mediated effector functions, indicating the diverse antiviral activity of humoral responses observed among recovered individuals. In addition to viral neutralization, convalescent plasma samples contained antibodies capable of mediating such Fc-dependent functions as complement activation, phagocytosis, and antibody-dependent cellular cytotoxicity against SARS-CoV-2. Plasma samples from a fraction of eligible donors exhibited high activity across all activities evaluated. These polyfunctional plasma samples could be identified with high accuracy with even single Fc Array features, whose correlation with polyfunctional activity was confirmed in the validation cohort. Collectively, these results expand understanding of the diversity of antibody-mediated antiviral functions associated with convalescent plasma, and the polyfunctional antiviral functions suggest that it could retain activity even when its neutralizing capacity is reduced by mutations in variant SARS-CoV-2. Convalescent plasma has been deployed globally as a treatment for COVID-19, but efficacy has been mixed. Better understanding of the antibody characteristics that may contribute to its antiviral effects is important for this intervention as well as offer insights into correlates of vaccine-mediated protection. Here, a survey of convalescent plasma activities, including antibody neutralization and diverse effector functions, was used to define plasma samples with broad activity profiles. These polyfunctional plasma samples could be reliably identified in multiple cohorts by multiplex assay, presenting a widely deployable screening test for plasma selection and investigation of vaccine-elicited responses.
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http://dx.doi.org/10.1128/mBio.00765-21DOI Listing
April 2021

Author Correction: DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires.

Nat Commun 2021 Apr 13;12(1):2309. Epub 2021 Apr 13.

Bloomberg Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

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http://dx.doi.org/10.1038/s41467-021-22667-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044181PMC
April 2021

DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires.

Nat Commun 2021 03 11;12(1):1605. Epub 2021 Mar 11.

Bloomberg Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system and allows for modeling its sequence determinants of antigenicity. We present DeepTCR, a suite of unsupervised and supervised deep learning methods able to model highly complex TCR sequencing data by learning a joint representation of a TCR by its CDR3 sequences and V/D/J gene usage. We demonstrate the utility of deep learning to provide an improved 'featurization' of the TCR across multiple human and murine datasets, including improved classification of antigen-specific TCRs and extraction of antigen-specific TCRs from noisy single-cell RNA-Seq and T-cell culture-based assays. Our results highlight the flexibility and capacity for deep neural networks to extract meaningful information from complex immunogenomic data for both descriptive and predictive purposes.
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http://dx.doi.org/10.1038/s41467-021-21879-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7952906PMC
March 2021

Neutralizing IFNL3 Autoantibodies in Severe COVID-19 Identified Using Molecular Indexing of Proteins by Self-Assembly.

bioRxiv 2021 Mar 3. Epub 2021 Mar 3.

Unbiased antibody profiling can identify the targets of an immune reaction. A number of likely pathogenic autoreactive antibodies have been associated with life-threatening SARS-CoV-2 infection; yet, many additional autoantibodies likely remain unknown. Here we present Molecular Indexing of Proteins by Self Assembly (MIPSA), a technique that produces ORFeome-scale libraries of proteins covalently coupled to uniquely identifying DNA barcodes for analysis by sequencing. We used MIPSA to profile circulating autoantibodies from 55 patients with severe COVID-19 against 11,076 DNA-barcoded proteins of the human ORFeome library. MIPSA identified previously known autoreactivities, and also detected undescribed neutralizing interferon lambda 3 (IFN-λ3) autoantibodies. At-risk individuals with anti-IFN-λ3 antibodies may benefit from interferon supplementation therapies, such as those currently undergoing clinical evaluation.

One-sentence Summary: Molecular Indexing of Proteins by Self Assembly (MIPSA) identifies neutralizing IFNL3 autoantibodies in patients with severe COVID-19.
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http://dx.doi.org/10.1101/2021.03.02.432977DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7941622PMC
March 2021

A top scoring pairs classifier for recent HIV infections.

Stat Med 2021 Mar 3. Epub 2021 Mar 3.

Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.

Accurate incidence estimation of HIV infection from cross-sectional biomarker data is crucial for monitoring the epidemic and determining the impact of HIV prevention interventions. A key feature of cross-sectional incidence testing methods is the mean window period, defined as the average duration that infected individuals are classified as recently infected. Two assays available for cross-sectional incidence estimation, the BED capture immunoassay, and the Limiting Antigen (LAg) Avidity assay, measure a general characteristic of antibody response; performance of these assays can be affected and biased by factors such as viral suppression, resulting in sample misclassification and overestimation of HIV incidence. As availability and use of antiretroviral treatment increase worldwide, algorithms that do not include HIV viral load and are not impacted by viral suppression are needed for cross-sectional HIV incidence estimation. Using a phage display system to quantify antibody binding to over 3300 HIV peptides, we present a classifier based on top scoring peptide pairs that identifies recent infections using HIV antibody responses alone. Based on plasma samples from individuals with known dates of seroconversion, we estimated the mean window period for our classifier to be 217 days (95% confidence interval 183 to 257 days), compared to the estimated mean window period for the LAg-Avidity protocol of 106 days (76 to 146 days). Moreover, each of the four peptide pairs correctly classified more of the recent samples than the LAg-Avidity assay alone at the same classification accuracy for non-recent samples.
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http://dx.doi.org/10.1002/sim.8920DOI Listing
March 2021