Biology subjects

Tomic, A.

Publications and source records attributed to Tomic, A..

8 recordsLinked to original sources

bioRxiv2026

Learned Immune Architectures of Durable Antibody Responses Across Vaccines

Vaccination is one of the most effective public health interventions. However, vaccine efficacy varies widely among individuals, as immunity arises from complex interplay between genetic, pathogen, and immunological factors. To date, most systems vaccinology studies have remained pathogen-specific, precluding the discovery of potential shared immune architectures underlying durable antibody responses. To address this gap, we leveraged transcriptomic data from 1,032 participants receiving influenza, hepatitis B, or yellow fever vaccines to develop an interpretable machine learning framework for comparative analysis across diverse vaccine platforms. Pathogen-specific models using Blood Transcriptional Module-based feature aggregation accurately predicted high antibody responders and consistently outperformed gene-level models. Distinct predictive immune architectures identified across vaccines were further resolved for dominant hierarchical immune programs using surrogate decision trees. This approach identified the dominant decision boundaries underlying each vaccine model, highlighting leukocyte migration and Th2 differentiation in Hepatitis B, CD4+ T cells, M2 macrophages, and c-MYC signaling in Influenza, and B-cell receptor signaling with B-cell developmental pathways in Yellow Fever. Cross-pathogen concordance analyses further identified four shared transcriptional modules, suggesting partially conserved immune architectures across diverse vaccines. Together, these findings provide new insights into the immune mechanistic underpinnings of durable vaccine responses across vaccines and provide an interpretable framework for comparative systems vaccinology that may guide the rational design of next-generation vaccines. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=77 SRC="FIGDIR/small/737303v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1646f50org.highwire.dtl.DTLVardef@c816eforg.highwire.dtl.DTLVardef@59b19eorg.highwire.dtl.DTLVardef@9c39cc_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2026

Spike Antibody Fc Drives Protection from SARS-CoV-2 Challenge in Macaques

A definitive correlate of protection (CoP) for SARS-CoV-2 has yet to be formally established. Previously, using data from a series of non-human primate vaccine challenge studies, we reported that neutralising antibodies (NAbs) are the strongest candidate for clinical protection against COVID-19 and that spike binding antibody is the strongest candidate CoP for viral burden post-challenge. In this study, we further characterised the protective binding antibody profile by analysing spike antibody-dependent complement deposition, Fc{gamma}R binding, isotype and antibody glycosylation. Using the machine learning platform SIMON, we demonstrate that antibody-dependent complement deposition (ADCD) and Fc{gamma}R binding are strong candidate co-correlates for each of the post-challenge outcomes; viral load and lung pathology. We found that spike antibody sialylation closely followed by Fc{gamma}R2A, was the spike antibody feature with the strongest negative correlation with histopathology score. Spike antibody ADCD, Fc{gamma}R binding, isotype and glycosylation significantly differed by immunisation regimen and sex, which demonstrates the heterogeneity of immune mechanisms induced by different immunisation platforms. We conclude that spike binding antibody, with the protective functional characteristics described herein, is a candidate CoP that captures both protection from severe clinical disease and protection against a high viral burden. These findings should be taken into consideration for future SARS-CoV-2 vaccine development.

immunology↗

bioRxiv2025

Innate antiviral readiness drives the expansion of protective T stem cell memory against influenza

The development of T-cell-based influenza vaccines relies on eliciting broad CD8+ T-cell immunity, wherein T stem cell-like memory (TSCM) cells serve as the ultimate long-lived reservoir for immune memory, thereby unlocking the potential for durable protection against viral drift and shift. However, the specific immunological cues that drive the robust expansion and functional preservation of this self-renewing, multipotent subset remain unknown. Here, utilizing multi-omic systems immunology in a pediatric cohort immunized with live attenuated influenza vaccine, we identified the determinants governing the expansion of influenza virus-reactive TSCM cells. We show that a pre-existing state of innate antiviral readiness, defined by a plasmacytoid dendritic cell-associated type I interferon signature, is the requisite condition for a robust TSCM expansion. Mechanistically, this baseline innate state enhances antigen priming and enforces a qualitative divergence in T-cell fate, driving responders toward a functionally poised, Th1-dominant phenotype while non-responders default to a dysfunctional, hyper-proliferative state. To determine the clinical relevance of this cellular subset, we analyzed an independent controlled human influenza challenge study. This validation revealed a critical functional division of labor in host defense: whereas pre-existing antibodies primarily mitigated symptom severity, the baseline frequency of influenza virus-reactive TSCM cells was the strongest predictor of rapid viral load clearance. These findings establish that the expansion of durable cellular memory is not stochastic but is predetermined by the innate cytokine environment, providing a predictive biomarker for patient stratification and a validated target for adjuvants designed to expand the TSCM reservoir deliberately.

immunology↗

bioRxiv2025

Integrative Mapping of Pre-existing Immune Landscapes for Vaccine Response Prediction

Predicting individual vaccine responses remains a significant challenge due to the complexity and variability of immune processes. To address this gap, we developed immunaut, an open-source, data-driven framework implemented as an R package specifically designed for all systems vaccinologists seeking to analyze and predict immunological outcomes across diverse vaccination settings. Leveraging one of the most comprehensive live attenuated influenza vaccine (LAIV) datasets to date - 244 Gambian children enrolled in a phase 4 immunogenicity study - immunaut integrates humoral, mucosal, cellular, transcriptomic, and microbiological parameters collected before and after vaccination, providing an unprecedentedly holistic view of LAIV-induced immunity. Through advanced dimensionality reduction, clustering, and predictive modeling, immunaut identifies distinct immunophenotypic responder profiles and their underlying baseline determinants. In this study, immunaut delineated three immunophenotypes: (1) CD8 T-cell responders, marked by strong baseline mucosal immunity and extensive prior influenza virus exposure that boosts memory CD8 T-cell responses, without generating influenza virus-specific antibody responses; (2) Mucosal responders, characterized by pre-existing systemic influenza A virus immunity (specifically to H3N2) and stable epithelial integrity, leading to potent mucosal IgA expansions and subsequent seroconversion to influenza B virus; and (3) Systemic, broad influenza A virus responders, who start with relatively naive immunity and leverage greater initial viral replication to drive broad systemic antibody responses against multiple influenza A virus variants beyond those included in the LAIV vaccine. By integrating pathway-level analysis, model-derived contribution scores, and hierarchical decision rules, immunaut elucidates how distinct immunological landscapes shape each response trajectory and how key baseline features, including pre-existing immunity, mucosal preparedness, and cellular support, dictate vaccine outcomes. Collectively, these findings emphasize the power of integrative, predictive frameworks to advance precision vaccinology, and highlight immunaut as a versatile, community-available resource for optimizing immunization strategies across diverse populations and vaccine platforms. One-Sentence SummaryUsing one of the most comprehensive LAIV datasets compiled to date, immunaut, an integrative machine learning framework, identifies distinct immunophenotypic responder groups shaped by baseline immune landscapes, advancing precision vaccinology and guiding more effective, personalized immunization strategies. Graphical abstractImmunaut, an automated framework for mapping and predicting vaccine response immunotypes. Step 1 outlines the identification of vaccine response outcomes using pre- and post-vaccination data integration across immune features, including antibodies, flu-specific T-cells, and immunophenotyping at mucosal and systemic sites. Clustering methods define the vaccine response landscape, stability, and validation through t-SNE-based visualization. Step 2 leverages an automated machine learning modeling approach, to enhance the accuracy and interpretability of vaccine response predictions, enabling stratification and targeted intervention strategies for personalized vaccine immunogenicity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/634302v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@62276corg.highwire.dtl.DTLVardef@a5f25org.highwire.dtl.DTLVardef@eca34borg.highwire.dtl.DTLVardef@1d4999b_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

Genular: An Integrated Platform for Defining Cellular Identity and Function through Single-Cell Gene Expression and Multi-Domain Biological Data

Accurately defining cellular identity and function is essential for advancing immunology, understanding disease mechanisms, and developing targeted therapies. However, current bioinformatics tools are limited in their ability to integrate and analyze the vast and diverse single-cell RNA sequencing (scRNA-seq) datasets available, hindering the comprehensive capture of cellular heterogeneity and the identification of subtle genetic changes across immune states, differentiation pathways, and tissue contexts. To overcome these challenges, we introduce genular, an open-source platform that unifies gene expression data analysis across diverse cell types by integrating scRNA-seq data with extensive genomic and proteomic information from 16 databases, including NCBI Gene, Human Protein Atlas, STRING, and UniProt. genular aggregates data from more than 2,893 scRNA-seq experiments, encompassing over 74.5 million unique cells across various tissues and conditions. A key feature of genular is calculating a cell marker score for each gene, enabling the quantification of gene expression across cells to derive unique profiles specific to cell types, states, and lineages. Using genular, we differentiate T cell memory states, map differentiation profiles by tracking gene expression changes as monocytes mature into macrophages and lymphoid progenitor cells develop into T cells, and capture tissue-specific reprogramming of macrophages, revealing distinct gene expression profiles that enable specialized functions in different tissues. By integrating scRNA-seq data with multi-domain biological information and employing advanced statistical methodologies, genular provides a scalable platform that accurately defines cellular identities, functional states, and differentiation pathways. This comprehensive approach facilitates breakthroughs in immunology, gene regulation, cellular differentiation, and disease research, enabling a deeper understanding of immune cell functions and their roles in health and disease.

bioinformatics↗

bioRxiv2020

SIMON: open-source knowledge discovery platform

Data analysis and knowledge discovery has become more and more important in biology and medicine with the increasing complexity of the biological datasets, but necessarily sophisticated programming skills and in-depth understanding of algorithms needed pose barriers to most biologists and clinicians to perform such research. We have developed a modular open-source software SIMON to facilitate the application of 180+ state-of-the-art machine learning algorithms to high-dimensional biomedical data. With an easy to use graphical user interface, standardized pipelines, automated approach for machine learning and other statistical analysis methods, SIMON helps to identify optimal algorithms and provides a resource that empowers non-technical and technical researchers to identify crucial patterns in biomedical data.

systems biology↗

bioRxiv2019

The FluPRINT dataset: A multidimensional analysis of the influenza vaccine imprint on the immune system

Recent advances in machine learning have allowed identification of molecular and cellular factors that underly successful antibody responses to influenza vaccines. Results of these studies have revealed the high level of complexity necessary to establish influenza immunity, and many different cellular and molecular components involved. However, identified correlates of protection fail to account for the majority of vaccinated cases across ages, cohorts, and influenza seasons. Major challenges arise from small sample sizes and from analysis of only one aspect of the biology such by using transcriptome data. The objective of the current study is to create a unified database, entitled FluPRINT, to enable a large-scale study exploring novel cellular and molecular underpinnings of successful immunity to influenza vaccines. Over 3,000 parameters were considered, including serological responses to influenza strains, serum cytokines, cell subset phenotypes, and cytokine stimulations. FluPRINT, thus facilitates application of machine learning algorithms for data mining. The data are publicly available and represent a resource to uncover new markers and mechanisms that drive successful influenza vaccination.

systems biology↗

bioRxiv2019

SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses

Machine learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive models. However, available approaches have difficulty coping with incomplete datasets which is often the case when combining studies. Additionally, there are hundreds of algorithms available and no simple way to find the optimal one. Here, we developed Sequential Iterative Modelling "OverNight" or SIMON, an automated machine learning system that compares results from 128 different algorithms and is particularly suitable for datasets containing many missing values. We applied SIMON to data from five clinical studies of seasonal influenza vaccination. The results reveal previously unrecognized CD4+ and CD8+ T cell subsets strongly associated with a robust antibody response to influenza antigens. These results demonstrate that SIMON can greatly speed up the choice of analysis modalities. Hence, it is a highly useful approach for data-driven hypothesis generation from disparate clinical datasets. Our strategy could be used to gain biological insight from ever-expanding heterogeneous datasets that are publicly available.

immunology↗