Biology subjects

Hao, S. P.

Publications and source records attributed to Hao, S. P..

4 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↗

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↗

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↗

bioRxiv2023

A harmonized public resource of deeply sequenced diverse human genomes

Underrepresented populations are often excluded from genomic studies due in part to a lack of resources supporting their analyses. The 1000 Genomes Project (1kGP) and Human Genome Diversity Project (HGDP), which have recently been sequenced to high coverage, are valuable genomic resources because of the global diversity they capture and their open data sharing policies. Here, we harmonized a high quality set of 4,094 whole genomes from HGDP and 1kGP with data from the Genome Aggregation Database (gnomAD) and identified over 153 million high-quality SNVs, indels, and SVs. We performed a detailed ancestry analysis of this cohort, characterizing population structure and patterns of admixture across populations, analyzing site frequency spectra, and measuring variant counts at global and subcontinental levels. We also demonstrate substantial added value from this dataset compared to the prior versions of the component resources, typically combined via liftover and variant intersection; for example, we catalog millions of new genetic variants, mostly rare, compared to previous releases. In addition to unrestricted individual-level public release, we provide detailed tutorials for conducting many of the most common quality control steps and analyses with these data in a scalable cloud-computing environment and publicly release this new phased joint callset for use as a haplotype resource in phasing and imputation pipelines. This jointly called reference panel will serve as a key resource to support research of diverse ancestry populations.

genomics↗