Biology subjects

Tan, Z. C.

Publications and source records attributed to Tan, Z. C..

8 recordsLinked to original sources

bioRxiv2026

Spatial patterns of APOBEC mutagenesis in the tumour microenvironment of Asian breast cancer

A germline deletion of the coding region at the APOBEC3B (A3B) gene that is common in Asian and Oceanic populations has been found to be correlated with increased APOBEC mutagenesis, immune activation, and tumour heterogeneity in breast cancer. The mechanism by which A3B deletion and APOBEC mutagenesis induces immune activation in Asian breast cancers is still unclear, but has been thought to involve increased neoantigen burden. In this study, we investigated the mechanisms by which A3B deletion and APOBEC mutagenesis may lead to immune activation using Stereo-seq spatial transcriptomics of Malaysian triple-negative breast cancer (TNBC) tumours with and without the A3B deletion to examine the spatial expression patterns of APOBEC3A/B as well the spatial patterns of APOBEC-associated mutations and neoantigens. Unexpectedly, we found only limited associations between A3B deletion, APOBEC-associated neoantigens, and immune activation. Instead, we found strong spatial associations between mitochondrial APOBEC-associations mutations, the cGAS-STING pathway, and the NF-{kappa}B pathway. Our study thus supports a new model whereby APOBEC-mediated activation of the immune system does not occur via neoantigen presentation, but instead occurs via mitochondrial hypermutation that drives cGAS-STING activation of the NF-{kappa}B pathway. Our study may provide new insights into the spatial landscape of Asian breast tumours and the effect of germline A3B deletion and APOBEC mutagenesis on the tumour immune microenvironment.

cancer biology↗

bioRxiv2025

Reconstructing developmental and disease progression with sample-level embeddings

Single-cell genomics is transformative for characterizing cellular heterogeneity, but many translational questions require comparing samples rather than individual cells. Typical analyses compare "case" and "control" groups, ignoring sample-level variation within them. Here we present scSLIDE, a framework that transforms each samples single-cell data into a compact profile describing where its cells fall in high-dimensional space. By comparing these density profiles, scSLIDE calculates embeddings to characterization variation across samples. Applied to COVID-19 infection, Alzheimers disease, and zebrafish embryogenesis, we show that scSLIDE can be used to cluster patients, reconstruct sample-level disease trajectories, and identify coordinated cellular programs across samples. We discover independent axes of infection and severity, a molecular disease progression that aligns with pathology-based estimates of neurodegeneration, and embryonic "pseudostages" varying across and within timepoints. In each case, we demonstrate how case-control analyses collapse rich biological variation into binary labels, and how sample-level embedding represents a powerful analytical alternative.

genomics↗

bioRxiv2025

Tensor-Based Integration of Time-Series Measurements Reveals Relationships Between Underlying Disease, Early Injury, and CD4+ Polarization in Liver Transplantation

For patients with end-stage liver failure, liver transplantation (LT) remains the standard-of-care, though five-year allograft survival rates remain below 80%. Liver ischemia reperfusion injuries (LIRI) arise during transplant and contribute to allograft dysfunction. While many drivers of LIRI have been well-characterized, the relationships between LIRI and later immunological signatures remain poorly understood, possibly because of limited integrated studies examining immunological signatures across the LT process. Furthermore, the role of underlying disease in LT remains poorly understood as it is unknown if later immunological signatures are universal or etiology dependent. To better examine etiology- and LIRI-driven immunological mechanisms across the LT lifetime, we collected cytokine and liver function test (LFT) measurements from LT donors and recipients at multiple pre- and post-operative timepoints. By developing a tensor-based factorization method to integrate these longitudinal measurements, we reduced the datasets to four immunological signatures that manifested across cytokines, LFTs, and time. Correlative analyses revealed associations between these integrated immunological signatures with etiology and LIRI. Later integration of allograft survival into this factorization method reduced these four signatures to two that were predictive of five-year allograft survival. Examination of these signatures prognostic of allograft survival found that CD4+ hyperpolarization towards Th1 or Th2 subtypes impaired growth factor expression and led to increased allograft loss risk. Clinical correlates revealed that donor and recipient age alongside etiology and donor liver health modulate this balance of CD4+ polarization in LT. Collectively, these results link immunological mechanisms to etiology and LIRI offering immediately actionable clinical insights alongside etiology- and donor-specific therapeutic targets for improving LT outcomes.

immunology↗

bioRxiv2024

Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization

Tensor factorization is a dimensionality reduction method applied to multidimensional arrays. These methods are useful for identifying patterns within a variety of biomedical datasets due to their ability to preserve the organizational structure of experiments and therefore aid in generating meaningful insights. However, missing data in the datasets being analyzed can impose challenges. Tensor factorization can be performed with some level of missing data and reconstruct a complete tensor. However, while tensor methods may impute these missing values, the choice of fitting algorithm may influence the fidelity of these imputations. Previous approaches, based on alternating least squares with prefilled values or direct optimization, suffer from introduced bias or slow computational performance. In this study, we propose that censored least squares can better handle missing values with data structured in tensor form. We ran censored least squares on four different biological datasets and compared its performance against alternating least squares with prefilled values and direct optimization. We used the error of imputation and the ability to infer masked values to benchmark their missing data performance. Censored least squares appeared best suited for the analysis of high-dimensional biological data by accuracy and convergence metrics across several studies.

systems biology↗

bioRxiv2024

Multivalent binding model quantifies antibody species from systems serology

Systems serology aims to broadly profile the antigen binding, Fc biophysical features, immune receptor engagement, and effector functions of antibodies. This experimental approach excels at identifying antibody functional features that are relevant to a particular disease. However, a crucial limitation of this approach is its incomplete description of what structural features of the antibodies are responsible for the observed immune receptor engagement and effector functions. Knowing these antibody features is important for both understanding how effector responses are naturally controlled through antibody Fc structure and designing antibody therapies with specific effector profiles. Here, we address this limitation by modeling the molecular interactions occurring in these assays and using this model to infer quantities of specific antibody species among the antibodies being profiled. We used several validation strategies to show that the model accurately infers antibody properties. We then applied the model to infer previously unavailable antibody fucosylation information from existing systems serology data. Using this capability, we find that COVID-19 vaccine efficacy is associated with the induction of afucosylated spike protein-targeting IgG. Our results also question an existing assumption that controllers of HIV exhibit gp120-targeting IgG that are less fucosylated than those of progressors. Additionally, we confirm that afucosylated IgG is associated with membrane-associated antigens for COVID-19 and HIV, and present new evidence indicating that this relationship is specific to the host cell membrane. Finally, we use the model to identify redundant assay measurements and subsets of information-rich measurements from which they can be inferred. In total, our modeling approach provides a quantitative framework for the reasoning typically applied in these studies, improving the ability to draw mechanistic conclusions from these data. Author summaryAntibodies play an important role in our immune response by binding to pathogens and engaging other immune cells to eliminate threats. Mounting evidence points toward the importance of which immune cells are being engaged by antibodies in determining the effectiveness of an immune response. While sophisticated experimental methods such as systems serology have been developed to broadly profile the engagement activities of antibodies, determining the presence of antibody structural features relevant to immune engagement remains challenging. Our study addresses this gap by developing a computational model that interprets data from systems serology, allowing us to infer detailed, engagement-relevant structural information about antibodies that are difficult to measure directly. We applied our approach to existing data from COVID-19 and HIV studies, revealing new insights into how antibody structure relates to vaccine efficacy and disease progression. For instance, we found that COVID-19 vaccine effectiveness is linked to the production of certain antibodies lacking a sugar residue called fucose. Our model also helps identify which measurements in systems serology are most informative, potentially streamlining future studies. This work enhances our ability to understand antibody function in disease and may guide the development of more effective antibody-based therapies. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/602296v1_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@16e8b3forg.highwire.dtl.DTLVardef@77ffe3org.highwire.dtl.DTLVardef@40d0f2org.highwire.dtl.DTLVardef@155fd8b_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Mixed IgG Fc immune complexes exhibit blended binding profiles and refine FcR affinity estimates

Immunoglobulin (Ig)G antibodies coordinate immune effector responses by selectively binding to target antigens and then interacting with various effector cells via the Fc{gamma} receptors. The Fc domain of IgG can promote or inhibit distinct effector responses across several different immune cell types through variation based on subclass and Fc domain glycosylation. Extensive characterization of these interactions has revealed how the inclusion of certain Fc subclasses or glycans results in distinct immune responses. During an immune response, however, IgG is produced with mixtures of Fc domain properties, so antigen-IgG immune complexes are likely to almost always be comprised of a combination of Fc forms. Whether and how this mixed composition influences immune effector responses has not been examined. Here, we measured Fc{gamma} receptor binding to immune complexes of mixed Fc domain composition. We found that the binding properties of the mixed-composition immune complexes fell along a continuum between those of the corresponding pure cases. Binding quantitatively matched a mechanistic binding model, except for several low-affinity interactions mostly involving IgG2. We found that the affinities of these interactions are different than previously reported, and that the binding model could be used to provide refined estimates of these affinities. Finally, we demonstrated that the binding model can predict effector-cell elicited platelet depletion in humanized mice, with the model inferring the relevant effector cell populations. Contrary to the previous view in which IgG2 poorly engages with effector populations, we observe appreciable binding through avidity, but insufficient amounts to observe immune effector responses. Overall, this work demonstrates a quantitative framework for reasoning about effector response regulation arising from IgG of mixed Fc composition. Summary pointsO_LIThe binding behavior of mixed Fc immune complexes is a blend of the binding properties for each constituent IgG species. C_LIO_LIAn equilibrium, multivalent binding model can be generalized to incorporate immune complexes of mixed Fc composition. C_LIO_LIParticularly for low-affinity IgG-Fc{gamma} receptor interactions, immune complexes provide better estimates of affinities. C_LIO_LIThe Fc{gamma}R binding model predicts effector-elicited cell clearance in humanized mice. C_LI

immunology↗

bioRxiv2022

Cytokine-expression patterns reveal coordinated immunological programs associated with persistent MRSA bacteremia

Methicillin-resistant Staphylococcus aureus (MRSA) bacteremia is a common, life-threatening infection that imposes up to 30% mortality even when appropriate therapy is used. Despite in vitro efficacy, antibiotics often fail to resolve the infection in vivo, resulting in persistent MRSA bacteremia. Recently, several genetic, epigenetic, and proteomic correlates of persistent outcomes have been identified. However, the extent to which single variables or composite patterns operate as independent predictors of outcome or reflect shared underlying mechanisms of persistence is unknown. To explore this question, we employed a tensor-based integration of host transcriptional and proteomic data across a well-characterized cohort of patients with persistent and resolving MRSA bacteremia outcomes. Tensor-based data integration yielded high correlative accuracy with persistence and revealed immunologic signatures shared across both the transcriptomic and proteomic datasets. We find that elevated proliferation of mature granulocytes associates with resolving bacteremia outcomes. In contrast, patients with persistent bacteremia heterogeneously exhibit correlates of granulocyte dysfunction or immature granulocyte proliferation. Collectively, these results suggest that transcriptional and proteomic correlates of persistent versus resolving bacteremia outcomes are complex and may not be disclosed by conventional modeling. However, a tensor-based integration approach can help to reveal consensus molecular mechanisms in an interpretable manner. Significance StatementWhile antibacterial therapies effectively resolve MRSA in vitro, these treatments often fail to clear MRSA bacteremia in vivo, suggesting that host-pathogen interactions are essential to persistent MRSA bacteremia. Recent studies have identified genetic, transcriptomic, and proteomic determinants of MRSA persistence. These determinants independently, however, provide insufficient mechanistic insight and it is unclear if they indicate unique or overlapping persistence mechanisms. Here, we use tensor-based decomposition to jointly analyze cytokine and transcriptomic measurements from patients with MRSA bacteremia. Results indicate that persistence mechanisms integrated across biological modalities reflect diverging mechanisms of persistent bacteremia. Ultimately, these results may help to identify future therapeutic targets for treating persistent MRSA bacteremia.

immunology↗

bioRxiv2021

A general model of multivalent binding with ligands of heterotypic subunits and multiple surface receptors

Multivalent cell surface receptor binding is a ubiquitous biological phenomenon with functional and therapeutic significance. Predicting the amount of ligand binding for a cell remains an important question in computational biology as it can provide great insight into cell-to-cell communication and rational drug design toward specific targets. In this study, we extend a mechanistic, two-step multivalent binding model. This model predicts the behavior of a mixture of different multivalent ligand complexes binding to cells expressing various types of receptors. It accounts for the combinatorially large number of interactions between multiple ligands and receptors, optionally allowing a mixture of complexes with different valencies and complexes that contain heterogeneous ligand units. We derive the macroscopic predictions and demonstrate how this model enables large-scale predictions on mixture binding and the binding space of a ligand. This model thus provides an elegant and computationally efficient framework for analyzing multivalent binding.

systems biology↗