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Biology subjects

Goldman, J. D.

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

8 recordsLinked to original sources

EBV Reprograms B Cells in an Autoimmune-Like Fashion in Patients with COVID-19

Epstein-Barr virus (EBV) reprograms B cells in autoimmune disease. Reprogrammed EBV+ B cells activate nearby B and CD4+ T cells, via upregulated antigen presentation and costimulatory machinery, to drive autoimmune pathology. EBV reactivation is a known correlate of long COVID, which is a heterogeneous condition that can bear similarities to autoimmune disease. However, the mechanisms underpinning this association remain unresolved. We report on EBV metabolically reprogrammed B cells in patients with COVID-19. We find EBV+ B cells provide stimulatory signals to bystander B and CD4+ T cells. SARS-CoV-2 infected participants exhibiting elevated fractions of EBV+ B cells present, at convalescence, with dysregulated lipid profiles, increased autoantibody titers, and post-acute symptomology likely reflective of this metabolic reprogramming and cell-cell interactions. Enrichment of our EBV+ B cell signatures seen in patients with COVID-19 is similar in patients with lupus and multiple sclerosis suggesting a potentially shared pathway of EBV-driven dysfunction across diseases.

immunology↗

Whole-Proteome Screening and Multi-Modal Profiling of Antigen-Specific CD4+ T Cells at Single-Cell Resolution

Systematic whole-proteome screening and comprehensive profiling of antigen-specific CD4+ T cells are crucial for advancing our understanding of CD4+ T cell immunity, yet such efforts remain technically challenging. Here, we present a high-throughput platform that employs large-scale class II single-chain trimer libraries to detect antigen-specific CD4+ T cells, while simultaneously profiling their antigen specificity, TCR/{beta} sequences, MHC restriction, whole transcriptomes, and patient/timepoint origins at single-cell resolution. We benchmarked SCTs against conventional pMHCs and validated the SCT library-based approach in direct ex vivo identification of antigen-specific CD4+ T cells in healthy donors. We then applied the platform to screen the entire SARS-CoV-2 receptor-binding domain in a longitudinal patient cohort, identifying 2,188 antigen-specific CD4+ T cells and revealing key features that define antigen immunogenicity. Extending to cancer, we performed whole-proteome screening of HPV-16 E6/E7 for TCR repertoire profiling in a precancerous cohort, uncovering functional heterogeneity of HPV-specific TCRs. By integrating high-throughput antigen screening with high-dimensional, multi-modal cellular characterization, our approach offers an unprecedented window into CD4+ T cell immunity across diverse disease contexts and empowers the development of new therapies.

bioengineering↗

APMAT analysis reveals the association between CD8 T cell receptors, cognate antigen, and T cell phenotype and persistence

Elucidating the relationships between a class I peptide antigen, a CD8 T cell receptor (TCR) specific to that antigen, and the T cell phenotype that emerges following antigen stimulation, remains a mostly unsolved problem, largely due to the lack of large data sets that can be mined to resolve such relationships. Here, we describe Antigen-TCR Pairing and Multiomic Analysis of T-cells (APMAT), an integrated experimental-computational framework designed for the high-throughput capture and analysis of CD8 T cells, with paired antigen, TCR sequence, and single-cell transcriptome. Starting with 951 putative antigens representing a comprehensive survey of the SARS-CoV-2 viral proteome, we utilize APMAT for the capture and single cell analysis of CD8 T cells from 62 HLA A*02:01 COVID-19 participants. We leverage this unique, comprehensive dataset to integrate with peptide antigen properties, TCR CDR3 sequences, and T cell phenotypes to show that distinct physicochemical features of the antigen-TCR pairs strongly associate with both T cell phenotype and T cell persistence. This analysis suggests that CD8+ T cell phenotype following antigen stimulation is at least partially deterministic, rather than the result of stochastic biological properties.

immunology↗

Association between COVID-19 Disease Severity and T Cell Receptor Repertoire

During the COVID-19 pandemic, while most infected individuals experienced mild to moderate symptoms, a significant subset developed severe illness. A clinical test distinguishing between mild and severe cases could inform effective treatment strategies. Toward the latter stages of the pandemic, it became evident that vaccination or prior infection cannot entirely prevent reinfection. However, they are crucial in reducing the risk of severe disease by inducing T-cell memory. T cell receptors (TCRs), which can be obtained from human blood, serve as valuable biomarkers for monitoring T cell responses to SARS-CoV-2 infection. In this study, we investigated the associations between TCR metrics and COVID-19 severity and found significant associations. Furthermore, such associations could depend on the subset of TCRs used (e.g., TCRs from CD8+ T or CD4+ T cells) and when the TCRs were collected.

bioinformatics↗

Prediction of HLA genotypes from single-cell transcriptome data

The human leukocyte antigen (HLA) locus plays a central role in adaptive immune function and has significant clinical implications for tissue transplant compatibility and allelic disease associations. Studies using bulk-cell RNA sequencing have demonstrated that HLA transcription may be regulated in an allele-specific manner and single-cell RNA sequencing (scRNA-seq) has the potential to better characterize these expression patterns. However, quantification of allele-specific expression (ASE) for HLA loci requires sample-specific reference genotyping due to extensive polymorphism. While genotype prediction from bulk RNA sequencing is well described, the feasibility of predicting HLA genotypes directly from single-cell data is unknown. Here we evaluate and expand upon several computational HLA genotyping tools by comparing predictions from human single-cell data to gold-standard, molecular genotyping. The highest 2-field accuracy averaged across all loci was 76% by arcasHLA and increased to 86% using a composite model of multiple genotyping tools. We also developed a highly accurate model (AUC 0.93) for predicting HLA-DRB345 copy number in order to improve genotyping accuracy of the HLA-DRB locus. Genotyping accuracy improved with read depth and was reproducible at repeat sampling. Using a metanalytic approach, we also show that HLA genotypes from PHLAT and OptiType can generate ASE ratios that are highly correlated (R2 = 0.8 and 0.94, respectively) with those derived from gold-standard genotyping. Highlights- Benchmarking of HLA genotype prediction accuracy from 5- and 3-based single-cell RNA-seq data compared to molecular HLA genotyping - Quantification of transcript coverage by 5- and 3-based single cell sequencing methods - Accurate prediction of complex HLA-DRB345 copy numbers using supervised learning - Balancing accuracy and performance through composite HLA genotyping - Meta-analytic approach to summarizing allele-specific expression of HLA genotypes at single-cell level

bioinformatics↗

Disease diagnostics using machine learning of immune receptors

Clinical diagnosis typically incorporates physical examination, patient history, and various laboratory tests and imaging studies, but makes limited use of the human systems own record of antigen exposures encoded by receptors on B cells and T cells. We analyzed immune receptor datasets from 593 individuals to develop MAchine Learning for Immunological Diagnosis (Mal-ID), an interpretive framework to screen for multiple illnesses simultaneously or precisely test for one condition. This approach detects specific infections, autoimmune disorders, vaccine responses, and disease severity differences. Human-interpretable features of the model recapitulate known immune responses to SARS-CoV-2, Influenza, and HIV, highlight antigen-specific receptors, and reveal distinct characteristics of Systemic Lupus Erythematosus and Type-1 Diabetes autoreactivity. This analysis framework has broad potential for scientific and clinical interpretation of human immune responses.

immunology↗

Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8+ and CD4+ T cells, and cytotoxic CD4+ T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

immunology↗

Tumor-specific changes in Kaposi sarcoma-associated herpesvirus genomes in Ugandan adults with Kaposi sarcoma

Intra-host evolved tumor virus variants have provided insights into the risk, pathogenesis and treatment responses of associated cancers. However, the intra-host variability of Kaposi sarcoma-associated herpesvirus (KSHV), the etiologic agent of Kaposi sarcoma (KS), has not been explored at the whole viral genome level. An accurate and detailed description of KSHV intra-host diversity in whole KSHV genomes from matching tumors and oral swabs from Ugandan adults with HIV-associated KS was obtained by deep, short read sequencing, using duplex unique molecular identifiers (dUMI) - random double-stranded oligonucleotides that barcode individual DNA molecules before library amplification. This allowed suppression of PCR and sequencing errors down to [~]10-9/base. KSHV genomes were assembled de novo, and identified rearrangements were confirmed by PCR. 131-kb KSHV genome sequences, excluding major repeat regions and averaging 2.3 x 104 reads/base, were successfully obtained from 23 specimens from 9 individuals, including 7 tumor-oral pairs. Sampling more than 100 viral genomes in at least one specimen per individual showed that KSHV genomes were virtually homogeneous within samples and within individuals at the point mutational level. Heterogeneity, if present, was due to point mutations and genomic rearrangements in tumors. In 2 individuals, the same mutations were found in distinct KS tumors. The K8.1 gene was inactivated in tumors from 3 individuals, and all KSHV genomic aberrations retained the region surrounding the first major internal repeat (IR1). These findings suggest that lytic gene alterations may contribute to KS tumorigenesis or persistence. Author summaryKaposi sarcoma (KS) is a leading cancer in sub-Saharan Africa and in those with HIV co-infection. Infection by Kaposi sarcoma-associated herpesvirus (KSHV) is necessary for KS, yet why only few KSHV infections develop into KS is largely unknown. While strain differences or mutations in other tumor viruses are known to affect the risk and progression of their associated cancers, whether KSHV genetic variation is important to the natural history of KS is unclear. Most studies of KSHV diversity have characterized only [~]4% of its 165-kb genome and may have been impacted by PCR or cloning artifacts. Here, we performed highly sensitive, single-molecule sequencing of whole KSHV genomes in paired KS tumors and oral swabs from 9 individuals with KS. We found that KSHV genomes were virtually identical within individuals, with no evidence of quasispecies formation nor multistrain infection. However, KSHV genome aberrations and inactivating mutations appeared to be a common, tumor-associated phenomenon, with some mutations shared by distinct tumors within an individual. Certain regions of the KSHV genome featured prominently among tumor-associated mutations, suggesting that they are important contributors to the pathogenesis or persistence of KS.

microbiology↗