Biology subjects

Chen, O. Y.

Publications and source records attributed to Chen, O. Y..

6 recordsLinked to original sources

bioRxiv2025

Targeting PD-1+ T-cells with Chimeric Antigen Receptors to reduce the HIV Reservoir

The unique ability of chimeric antigen receptor (CAR) T-cells to infiltrate tissues is revolutionizing our perspectives for tackling severe-refractory and otherwise untreatable diseases. In HIV, CAR-T-cells have been designed to target viral biomarkers, with limited success so far. Here, we investigated the possibility of redirecting CAR-T-cells against a cellular biomarker of the HIV reservoir, PD-1. We designed two second-generation 4-1BB-CARs using the scFv of either a blocking (bPD1-CAR) or a nonblocking (nbPD1-CAR) anti-PD-1 monoclonal antibody. The CAR avidity modulated T-cell sensitivity, trogocytosis, and effector functions, independently of the PD-1 signalling domain. Both anti-PD-1 CAR T-cells could persist for 70 days in HIV-infected humanized mice, correlating with viral protection and a disruption of the lymphoid architecture in the white pulp of the spleen. Altogether, our results open new strategic avenues for reducing the HIV reservoir as we demonstrate the feasibility of depleting specific T-cell subpopulations. SummaryT cells can be redirected against cellular rather than viral-specific biomarkers to reduce the HIV reservoir.

immunology↗

bioRxiv2024

Follicular Bcl6 reactivity is associated with a unique immune landscape and spatial transcriptome in COVID-19

The regulation of follicular (F) and germinal center (GC) immune reactivity in human lymph nodes (LNs), particularly during the early stages of viral infections, remains poorly understood. We have analyzed lung-draining lymph nodes (LD-LNs) from COVID-19 autopsies using multiplex imaging and spatial transcriptomics to examine the immune landscape and with respect to the aging. We identified three subgroups of Reactive Follicles (RFs) based on Bcl6 prevalence, RF-Bcl6no/low, RF-Bcl6int and RF-Bcl6high. RF-Bcl6high tissues express a distinct B/TFH immune landscape associated with increased prevalence of proliferating B- and TFH-cell subsets. Comparison between LD-LNs and matched subdiaphragmatic LNs revealed a disconnected Bcl6 reactivity between the two anatomical sites. LD-LNs Bcl6 reactivity was associated with a distinct spatial transcriptomic profile. TH1-associated genes/pathways (e.g. CXCR3, STAT5, TNF signaling) were significantly upregulated in RF-Bcl6no/low tissues while the RF-Bcl6high tissues exhibited significant upregulation of GC-promoting genes/pathways (e.g. CXCL13, B cell receptor signaling). Despite the similar prevalence, the in-situ transcriptome profiling indicates a higher monocyte/macrophage functionality in "Aged" compared to "Young" follicles from donors with comparable Bcl6 reactivity. Our findings reveal a heterogeneous F/GC landscape in COVID-19 LD-LNs and highlight specific molecular targets and pathways that could regulate human F/GC immune dynamics during early viral infections.

immunology↗

bioRxiv2024

Tensor Kernel Learning for Classification of Alzheimer's Conditions using Multimodal Data

Early, timely, and accurate assessment of Alzheimers disease (AD), particularly at its earlier stage - mild cognitive impairment (MCI) -, is central to detecting, managing, and potentially treating the disease. The biological underpinnings of AD, however, is multifaceted, from genetic variations, abnormal protein accumulation, to irregular brain functions and structure. A joint analysis of these data, therefore, may offer potentially new insights about AD-related biomarkers and AD prediction. But such explorations must confront the complexity of these data: heterogeneity, multimodality and high-dimensionality. Here, to address these challenges, we propose a new machine-learning method, namely the tensor kernel learning (TKL), leveraging tensor methods and kernel learning, to enhance AD assessment by enhancing multi-modal data integration. More specifically, TKL first uses CP/PARAFAC decomposition and graph diffusion to fuse multiple kernels learned from four complementary data modalities (MRI, PET, CSF, and SNP data). We then used a supervised kernel for a kernel SVM classifier to identify potential patients. To evaluate the effectiveness of TKL, we apply it to data from the Alzheimers Disease Neuroimaging Initiative (ADNI; the sample size n = 331 subjects), including cognitively normal individuals (CN), MCI subjects, and AD patients. TKL improves AD classification performance in both linear and nonlinear combinations, achieving accuracies of 91.31% for CN vs. AD, 81.45% for CN vs. MCI, and 78.27% for AD vs. MCI, compared to 85.48%, 70.89%, and 73.51% using the best single modality. Additionally, TKL reveals clearer, more structured patterns in the data, enhancing interpretation and understanding of the relationships among different modalities. Detailed implementation details of our method can be found at: https://github.com/thanhvd18/Tensor-Kernel-Learning-matlab.

neuroscience↗

bioRxiv2024

Residual Partial Least Squares Learning: Brain Cortical Thickness Simultaneously Predicts Eight Non-pairwise-correlated Behavioural and Disease Outcomes in Alzheimer's Disease

Alzheimers Disease (AD) is the leading cause of dementia, affecting brain structure, function, cognition, and behaviour. While previous studies have linked brain regions to univariate outcomes (e.g., disease status), the relationship between brain-wide changes and multiple disease and behavioural outcomes of AD is still not well understood. Here, we propose Residual Partial Least Squares (re-PLS) Learning, an explainable and generalisable framework that models high-dimensional brain features and multivariate outcomes, accounting for confounders. Using re-PLS, we map the many-to-many pathways between cortical thickness and multivariate AD outcomes; identify neural biomarkers that simultaneously predict multiple outcomes; control for confounding variables; conduct longitudinal AD prediction; and perform cross-cohort AD prediction. To evaluate its efficacy, we first carry out within-cohort cross-subject validation using ADNI data, and further examine its reproducibility via between-cohort cross-validation using ADNI and OASIS data. Together, our results unveil brain regions jointly but differentially predictive of distinctive cognitive-behavioural scores in AD.

neuroscience↗

bioRxiv2018

The Biological Basis of Mathematical Beauty

Through our past studies of the neurobiology of beauty, we have come to divide aesthetic experiences into two broad categories: biological and artifactual. The aesthetic experience of biological beauty is dictated by inherited brain concepts, which are resistant to change even in spite of extensive experience. The experience of artifactual beauty on the other hand is determined by post-natally acquired concepts, which are modifiable throughout life by exposure to different experiences (Zeki, 2009). Hence, in terms of aesthetic rating, biological beauty (in which we include the experience of beautiful faces or human bodies) is characterized by less variability between individuals belonging to different ethnic origins and cultural backgrounds or the same individual at different times. Artifactual beauty (in which we include the aesthetic experience of human artifacts such as buildings and cars) is characterized by greater variability between individuals belonging to different ethnic and cultural groupings and by the same individual at different times. In this paper, we present results to show that the experience of mathematical beauty (Zeki et al 2014), even though it constitutes an extreme example of beauty that is dependent upon (mathematical) culture and learning, belongs to the biological category and obeys one of its characteristics, namely a lesser variability in terms of the aesthetic ratings given to mathematical formulae experienced as beautiful.

neuroscience↗

bioRxiv2016

The Bayesian-Laplacian Brain

We outline what we believe could be an improvement in future discussions of the brain acting as a Bayesian-Laplacian system. We do so by distinguishing between two broad classes of priors on which the brains inferential systems operate: in one category are biological priors ({beta} priors) and in the other artifactual ones ( priors). We argue that {beta} priors, of which colour categories and faces are good examples, are inherited or acquired very rapidly after birth, are highly or relatively resistant to change through experience, and are common to all humans. The consequence is that the probability of posteriors generated from {beta} priors having universal assent and agreement is high. By contrast, priors, of which man-made objects are examples, are acquired post-natally and modified at various stages throughout post-natal life; they are much more accommodating of, and hospitable to, new experiences. Consequently, posteriors generated from them are less likely to find universal assent. Taken together, in addition to the more limited capacity of experiment and experience to alter the {beta} priors compared to priors, another cardinal distinction between the two is that the probability of posteriors generated from {beta} priors having universal agreement is greater than that for priors. The two categories are not, however, always totally distinct and can merge into one another to varying extents, resulting in posteriors that draw upon both categories.

neuroscience↗