bioRxiv Science⌕ Search

Biology subjects

Greenbaum, J.

Publications and source records attributed to Greenbaum, J..

10 recordsLinked to original sources

Identification of pre-existing ubiquitous neoantigen-reactive tumor-infiltrating T-cells in a patient with metastatic pancreatic neuroendocrine tumor

BackgroundMutation-derived neoantigens, typically identified in primary tumors, are emerging therapeutic targets for personalized cancer vaccines and adoptive T-cell therapies. However, clinical efficacy of neoantigen-directed therapies in patients with metastatic disease remains limited, partly due to inter-site genetic heterogeneity. We investigated whether ubiquitous neoantigens-derived from mutations shared across all tumor sites-could provide more effective, durable targets, particularly in patients undergoing resection of metastatic lesions. MethodsWhole-exome and RNA sequencing were performed on 14 tumor samples (primary and 13 synchronous nodal metastases) from a treatment-naive patient with pancreatic neuroendocrine tumor (PNET). Ubiquitous mutations were identified bioinformatically, and their immunogenicity assessed using in-vitro stimulation of autologous peripheral blood mononuclear cells followed by IFN-{gamma} ELISpot assay. Neoantigen-specific T-cell clonotypes were further identified by HLA-tetramer staining and single-cell RNA/TCR sequencing. Neoantigen-reactive clonotypes identified in peripheral blood were tracked across multiple metastatic sites using bulk TCR{beta} repertoire sequencing. ResultsAmong 1,195 non-synonymous mutations detected, eight were shared across all 14 tumor sites. Of these, one encoded a neoantigen that elicited a reproducible IFN-{gamma} ELISpot response in peripheral blood, confirming its immunogenicity. Further, we identified the corresponding neoantigen-reactive TCR clonotypes in blood. Comparison with bulk TCR{beta} repertoires from eight metastatic sites showed that these clonotypes were present in every site analyzed, with evidence of local clonal expansion. ConclusionThis study provides direct evidence that a single ubiquitous mutation-derived neoantigen can generate systemic T-cell responses and clonotype expansion across multiple metastatic sites in a TMB-low, TIL-low tumor. Our findings support incorporating mutation-sharing status across metastases as a key criterion for neoantigen selection in cancer vaccines and adoptive T-cell therapies. This approach could inform the design of neoantigen-directed immunotherapies in metastatic PNET and potentially other metastatic solid tumors. What is already known on this topicNeoantigen-directed therapies, such as personalized cancer vaccines or adoptive T-cell transfer, can induce anti-tumor responses but have shown limited success in metastatic disease. One major barrier is genetic heterogeneity between tumor sites, suggesting that targeting ubiquitous mutations-those shared across all tumor sites-may improve the efficacy of such therapies. What this study addsIn one patient with metastatic pancreatic neuroendocrine tumor involving 13 lymph nodes, we identified eight ubiquitous mutations, one of which generated a detectable neoantigen-specific T-cell response in blood. The corresponding T-cell clonotypes were found across all metastatic sites analyzed and showed evidence of clonal expansion, providing direct evidence of systemic and local recognition of a shared neoantigen in a TMB-low/TIL-low cancer. How this study might affect research, practice or policyThese findings support incorporating mutation sharing across metastases as a key criterion in neoantigen selection for cancer vaccines and adoptive T-cell therapies. This strategy could enhance the relevance and durability of neoantigen- directed approaches in patients with metastatic disease.

immunology↗

Curation at Scale with EPITOME: Extraction Pipeline for Immunological Texts and Open-Source Multimodal Enquiry

The Immune Epitope Database (IEDB, iedb.org) has manually curated epitope data from over 26,000 publications across two decades. With PubMed adding [~]5,000 articles daily, traditional curation methods face scalability challenges. Given the multimodality of data contained in scientific papers, we have sought to build an open-source vision language model (VLM)-based tool that human curators can use to speed up and automate biological data curation. Here we present a multimodal document ingestion and Question-Answering (QnA) pipeline that ties traditional Optical Character Recognition (OCR) and text matching with Vision-Language Model (VLM) capabilities. The system, which we call EPITOME, implements three-stage processing: regex-based epitope and MHC molecule identification, visual element extraction from PDFs, and contextual indexing that links peptide sequences, MHC molecules, and assays to their locations across text, tables, and figures. This indexing is used to supply context for further VLM QnA. Our preliminary results from EPITOME demonstrate promising zero-shot performance of open-source VLMs that suggest promise for accelerating biocuration through a curator-in-the-loop process, with our evaluation identifying strategic points where curator-in-the-loop intervention can enhance overall system accuracy.

bioinformatics↗

Enrichment of a CD4-CD8- NK-like cytotoxic Vδ1/3 T cell subset in tuberculosis disease

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading cause of global morbidity and mortality. Although gamma-delta ({gamma}{delta}) T cells have increasingly been suggested to contribute to the TB immune response, quantitative and qualitative differences in this immune cell compartment between healthy and TB diseased individuals are not well-characterized. In this study, we used single-cell RNA sequencing to provide a high-resolution characterization of CD4-CD8- {gamma}{delta} T cells in peripheral blood across healthy Mtb-non-sensitized, healthy Mtb-sensitized, and TB disease pre-/post-treatment cohorts. We found upregulation of an activated and cytotoxic gene signature in {gamma}{delta} T cells of TB disease compared to both healthy cohorts. Strikingly, these differences persisted through one year following diagnosis of TB disease (corresponding to six months after completion of anti-TB therapy). We found that these transcriptomic differences were largely mediated by an NK-like cytotoxic V{delta}1 and V{delta}3 subset that was enriched in TB disease, with a unique V{delta}3 TCR gene usage. Our findings suggest long-lasting changes in the CD4-CD8- {gamma}{delta} T cell compartment and highlight V{delta}3 cells, a previously underappreciated {gamma}{delta} T cell subset, as potentially important in TB.

immunology↗

Peptide Driven Identification of TCRs (PDI-TCR) reveals dynamics and phenotypes of CD4 T cells in tuberculosis

Assigning antigen specificity to T cell receptor (TCR) sequences is challenging due to the TCR repertoires diversity and the complexity of TCR:antigen recognition. We developed the Peptide-Driven Identification of TCRs (PDI-TCR) assay that combines in vitro expansion of cells with peptide pools, bulk TCR sequencing, and statistical analysis to identify antigen-specific TCRs from human blood. A key feature of PDI-TCR is the ability to distinguish true antigen-specific TCR clonotypes from TCRs associated with unspecific bystander activation by comparing responses to non-overlapping peptide pools. We applied PDI-TCR to Tuberculosis (TB) patients, sampling blood at diagnosis and throughout treatment, and Mycobacterium tuberculosis (Mtb)-sensitized healthy individuals (IGRA+). We identified hundreds of Mtb-specific TCRs, as well as unspecific TCRs, and characterized their phenotype in each cohort by single-cell RNA sequencing ex vivo. Mtb-specific T cells were highly diverse, with short-lived effector phenotypes only present in TB at diagnosis, while memory phenotypes were maintained through treatment. In contrast, unspecific expanded T cells were more clonally restricted, had a cytotoxic phenotype, and were maintained throughout treatment. This showcases PDI-TCR as a powerful tool for identifying antigen-specific TCRs, which enables direct ex vivo identification and monitoring of antigen-specific T cells.

immunology↗

Multi-omics analyses of drug repurposing reveal Acebutolol and Amiloride for osteoporosis treatment

Osteoporosis is a metabolic bone disease that occurs during aging, characterized by low bone mineral density (BMD) and a high risk of trauma fracture. While current pharmacological interventions provide symptomatic benefits, they are unsatisfactory and have major side effects. In this study, we used multi-omics data and drug similarity to construct osteoporosis driver signaling networks (ODSN) and drug functional networks (DFN), respectively. By integrating ODSN and DFN with treatment transcriptional responses, we observed 8 drugs that demonstrated strong targeting effects on ODSN. Mendelian Randomization analysis determines the causal effect on BMD using cis-eQTLs of the drug targets and BMD GWAS data. The findings suggested Acebutolol and Amiloride may increase BMD, while Acenocoumarol, Aminocaproic acid and Armodafinil may enhance bone loss. Zebrafish experiments experimentally showed Acebutolol hydrochloride and Amiloride hydrochloride had significant protective effects on osteoporosis in zebrafish embryos induced by Dexamethasone. Also, Acenocoumarol reduced bone mineralization compared with the control group. The findings suggest that the hypertension drugs Acebutolol and Amiloride warrant further investigation in functional mechanistic experiments to evaluate their effectiveness for osteoporosis treatments.

systems biology↗

Transcriptomes and metabolism define mouse and human MAIT cell heterogeneity

Mucosal-associated invariant T (MAIT) cells are a subpopulation of T lymphocytes that respond to microbial metabolites. We performed single-cell RNA sequencing and metabolic analyses of MAIT cell subsets in thymus and peripheral tissues from mice and humans to define the heterogeneity and developmental pathway of these innate-like lymphocytes. We show that the predominant mouse subset, which produces IL-17 (MAIT17), and the subset that produces IFN{gamma} (MAIT1), have greatly different transcriptomes and metabolic states in the thymus and periphery. A splenic MAIT subset has a transcriptome similar to circulating lymphocytes, and in mice these also are found in recent thymic emigrants, suggesting partially mature cells emigrate from the thymus. Human MAIT cells are predominantly MAIT1 cells, but have a different metabolism from their mouse counterparts with increased fatty acid uptake and storage. Although mouse and human subsets are similar in thymus, in the periphery they diverge, likely reflecting environmental influences.

immunology↗

Combined assessment of MHC binding and antigen expression improves T cell epitope predictions

MHC class I antigen processing consists of multiple steps that result in the presentation of MHC bound peptides that can be recognized as T cell epitopes. Many of the pathway steps can be predicted using computational methods, but one is often neglected: mRNA expression of the epitope source proteins. In this study, we improve epitope prediction by taking into account both peptide-MHC binding affinities and expression levels of the peptides source protein. Specifically, we utilized biophysical principles and existing MHC binding prediction tools in concert with RNA expression to derive a function that estimates the likelihood of a peptide being presented on a given MHC class I molecule. Our combined model of Antigen eXpression based Epitope Likelihood-Function (AXEL-F) outperformed predictions based only on binding or based only on antigen expression for discriminating eluted ligands from random background peptides as well as in predicting neoantigens that are recognized by T cells. We also showed that in cases where cancer patient-specific RNA-Seq data is not available, cancer-type matched expression data from TCGA can be used to accurately estimate patient-specific gene expression. Using AXEL-F together with TGCA expression data we were able to more accurately predict neoantigens that are recognized by T cells. The method is available in the IEDB Analysis Resource and free to use for the academic community. Significance statementEpitope prediction tools have been used to call epitopes in viruses and other pathogens for almost 30 years, and more recently, to call cancer neoantigens. Several such tools have been developed, however most of them ignore the mRNA expression of the epitope source proteins. In the present study, we have, to our knowledge for the first time, developed a biophysically motivated model to combine peptide-MHC binding and abundance of the peptides source protein to improve epitope predictions. Our novel tool AXEL-F is freely available on the IEDB and presents a clear opportunity for predicting and selecting epitopes more efficiently.

bioinformatics↗

Transcriptome and Chromatin Landscape of iNKT cells are Shaped by Subset Differentiation and Antigen Exposure

Invariant natural killer T cells (iNKT cells) differentiate into thymic and peripheral NKT1, NKT2 and NKT17 subsets. We determined if the gene programs associated with these thymic subsets were maintained in peripheral sites, the influence of tissue location, and if there were large-scale changes after antigen exposure. RNA-seq and ATAC-seq analyses showed that iNKT cells in any subset were similar, regardless of tissue location. Lung iNKT cell subsets possessed the most distinct location-specific features, shared with other innate lymphocytes in the lung, possibly consistent with increased activation. After antigenic stimulation, iNKT cells underwent chromatin and transcription changes leading to two populations: one similar to follicular helper T cells and the other like NK or effector cells. Phenotypic analysis indicated these changes were observed long-term, suggesting that iNKT cells gene programs are not fixed, but they are capable of chromatin remodeling after antigen to give rise to several new subsets.

immunology↗

A systematic dissection of human primary osteoblasts in vivo at single-cell resolution

Osteoblasts are multifunctional bone cells, which play essential roles in bone formation, angiogenesis regulation, as well as maintenance of hematopoiesis. Although both in vivo and in vitro studies on mice have identified several potential osteoblast subtypes based on their different transition stages or biological responses to external stimuli, the categorization of primary osteoblast subtypes in vivo in humans has not yet been achieved. Here, we used single-cell RNA sequencing (scRNA-seq) to perform a systematic cellular taxonomy dissection of freshly isolated human osteoblasts. Based on the gene expression patterns and cell lineage reconstruction, we identified three distinct cell clusters including preosteoblasts, mature osteoblasts, and an undetermined rare osteoblast subpopulation. This novel subtype was mainly characterized by the nuclear receptor subfamily 4 group A member 1 and 2 (NR4A1 and NR4A2), and its existence was confirmed by immunofluorescence staining. Trajectory inference analysis suggested that the undetermined cluster, together with the preosteoblasts, are involved in the regulation of osteoblastogenesis and also give rise to mature osteoblasts. Investigation of the biological processes and signaling pathways enriched in each subpopulation revealed that in addition to bone formation, preosteoblasts and undetermined osteoblasts may also regulate both angiogenesis and hemopoiesis. Finally, we demonstrated that there are systematic differences between the transcriptional profiles of human osteoblasts in vivo and mouse osteoblasts both in vivo and in vitro, highlighting the necessity for studying bone physiological processes in humans rather than solely relying on mouse models. Our findings provide novel insights into the cellular heterogeneity and potential biological functions of human primary osteoblasts at the single-cell level, which is an important and necessary step to further dissect the biological roles of osteoblasts in bone metabolism under various (patho-) physiological conditions.

bioinformatics↗

Single-cell RNA sequencing deconvolutes the in vivo heterogeneity of human bone marrow-derived mesenchymal stem cells

Bone marrow-derived mesenchymal stem cells (BM-MSCs) are multipotent stromal cells, which have a critical role in the maintenance of skeletal tissues such as bone, cartilage, and the fat found in bone marrow. In addition to providing microenvironmental support for hematopoietic processes, BM-MSCs can differentiate into various mesodermal lineages including osteoblast/osteocyte, chondrocyte, and adipocyte cells that are crucial for bone metabolism. While BM-MSCs have high cell-to-cell heterogeneity in gene expression, the cell subtypes that contribute to this heterogeneity in vivo in humans have not been characterized. To investigate the transcriptional diversity of BM-MSCs, we applied single-cell RNA sequencing (scRNA-seq) on freshly isolated CD271+ BM-derived mononuclear cells (BM-MNCs) from two human subjects. We successfully identified LEPRhiCD45low BM-MSCs within the CD271+ BM-MNC population, and further codified the BM-MSCs into distinct subpopulations corresponding to the osteogenic, chondrogenic, and adipogenic differentiation trajectories, as well as terminal-stage quiescent cells. Biological functional annotations of transcriptomes suggest that osteoblast precursors may induce angiogenesis coupled with osteogenesis, and chondrocyte precursors may have the potential to differentiate into myocytes. We discovered transcripts for several cluster of differentiation (CD) markers that were highly expressed (e.g., CD167b, CD91, CD130 and CD118) or absent (e.g., CD74, CD217, CD148 and CD68) in BM-MSCs and could be novel markers for human BM-MSC purification. This study is the first systematic in vivo dissection of human BM-MSCs cell subtypes at the single-cell resolution, revealing insight into the extent of their cellular heterogeneity and bone homeostasis.

cell biology↗