Biology subjects

SUI, J.

Publications and source records attributed to SUI, J..

4 recordsLinked to original sources

bioRxiv2025

B7-H4 Binds Galectin-9 Glycosylation-Dependently and Attenuates Galectin-9-Mediated CD28/AKT Activation and T Cell Death

B7-H4, a member of the B7 family, is broadly expressed on various cancer cells and has been implicated in negative immune regulation, particularly in suppressing anti-tumor immunity. However, the receptor for B7-H4 and the molecular mechanisms underlying its immunoinhibitory effects remain poorly understood. In this study, using peritoneal immune cells from mice adoptively transferred with OVA-expressing tumor cells and OVA-specific OT-1 T cells, we identified Galectin-9 (Gal-9) as a binding partner for B7-H4 and elucidated its role in modulating T cell responses through this interaction. We demonstrated that glycosylation in the IgC domain of B7-H4 is required for its binding to Gal-9; while the N-terminal carbohydrate recognition domain (N-CRD) of Gal-9, including the R65 residue in the N-CRD, is essential for this interaction. Additionally, we found that other B7 family members (B7.1, B7.2, B7-H2, and B7-DC) and immune cell surface receptors (CD28, 2B4, CD226, and SLAMF1) also bind to Gal-9 at comparable levels to B7-H4 and T cell immunoglobulin mucin receptor 3 (TIM-3). In vitro functional assays revealed that B7-H4 inhibits Gal-9-induced activation of CD28 downstream signaling and reduces Gal-9-mediated T cell death. In vivo, Gal-9 deficiency in mice resulted in a significant reduction in the proportion of splenic CD4+ T cells, whereas B7-H4 deficiency exhibited no observable phenotype. Furthermore, B7-H4 and Gal-9 double-knockout mice displayed no additional phenotype differences compared to Gal-9 single-knockout mice. Notably, tumor growth following tumor cell challenge was unaffected in all three knockout models (Gal-9 single-, B7-H4 single-, or double-knockout). Collectively, these findings suggest that B7-H4, other B7 family members, Gal-9, and T cell surface immune receptors form a complex regulatory network that modulates T cell activity and anti-tumor responses, although no single member exerts a major effect. This study provides a detailed molecular characterization of the interaction between B7-H4 and Gal-9 and identifies other previously unknown Gal-9 binding partners, offering valuable insights into the intricate regulatory network involving these molecules.

immunology↗

bioRxiv2022

Canonical and Replicable Multi-Scale Intrinsic Connectivity Networks in 100k+ Resting-State fMRI Datasets

Resting-state functional magnetic resonance imaging (rsfMRI) has shown considerable promise for improving our understanding of brain function and characterizing various mental and cognitive states in the healthy and disordered brain. However, the lack of accurate and precise estimations of comparable functional patterns across datasets, individuals, and ever-changing brain states in a way that captures both individual variation and inter-subject correspondence limits the clinical utility of rsfMRI and its application to single-subject analyses. We posit that using reliable network templates and advanced group-informed network estimation approaches to accurately and precisely obtain individualized (dynamic) networks that retain cross-subject correspondence while maintaining subject-specific information is one potential solution to overcome the aforementioned barrier when considering cross-study comparability, independence of subject-level estimates, the limited data available in single studies, and the low signal-to-noise ratio (SNR) of rsfMRI. Toward this goal, we first obtained a reliable and replicable network template. We combined rsfMRI data of over 100k individuals across private and public datasets and selected around 58k that meet quality control (QC) criteria. We then applied multi-model-order independent component analysis (ICA) and subsampling to obtain reliable canonical intrinsic connectivity networks (ICNs) across multiple spatial scales. The selected ICNs (i.e., network templates) were also successfully replicated by independently analyzing the data that did not pass the QC criteria, highlighting the robustness of our adaptive template to data quality. We next studied the feasibility of estimating the corresponding subject-specific ICNs using a multivariate-spatially constrained ICA as an example of group-informed network estimation approaches. The results highlight that several factors, including ICNs themselves, data length, and spatial resolution, play key roles in successfully estimating the ICNs at the subject level. Large-scale ICNs, in general, require less data to achieve a specific level of spatial similarity with their templates (as well as within- and between-subject spatial similarity). Moreover, increasing data length can reduce an ICNs subject-level specificity, suggesting longer scans might not always be desirable. We also show spatial smoothing can alter results, and the positive linear relationship we observed between data length and spatial smoothness (we posit that it is at least partially due to averaging over intrinsic dynamics or individual variation) indicates the importance of considering this factor in studies such as those focused on optimizing data length. Finally, the consistency in the spatial similarity between ICNs estimated using the full-length of data and subset of it across different data lengths may suggest that the lower within-subject spatial similarity in shorter data lengths is not necessarily only defined by lower reliability in ICN estimates; rather, it can also be an indication of brain dynamics (i.e., different subsets of data may reflect different ICN dynamics), and as we increase the data length, the result approaches the average (also known as static) ICN pattern, and therefore loses its distinctiveness.

neuroscience↗

bioRxiv2020

Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises

The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move towards a translational neuroscience era. Regression-based multivariate models (hereafter "predictive modeling") provide a powerful and widely-used approach to predict human behavior with neuroimaging features. These studies maintain a focus on decoding individual differences in a continuously behavioral phenotype from neuroimaging data, opening up an exciting opportunity to describe the human brain at the single-subject level. In this survey, we provide an overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade. We first review regression-based approaches and highlight connectome-based predictive modeling (CPM), which has grown in popularity in recent years. Next, we systematically describe recent representative studies using these tools in the context of cognitive function, symptom severity, personality traits and emotion processing. Finally, we highlight a few challenges related to combining multimodal data, longitudinal prediction, external validations and the employment of deep learning methods that have emerged from our review of the existing literature, as well as present some promising and challenging future directions.

neuroscience↗

bioRxiv2019

Structural brain architectures match intrinsic functional networks and vary across domains: A study from 15000+ individuals

Brain structural networks have been shown to consistently organize in functionally meaningful architectures covering the entire brain. However, to what extent brain structural architectures match the intrinsic functional networks in different functional domains remains under explored. In this study, based on independent component analysis, we revealed 45 pairs of structural-functional (S-F) component maps, distributing across 9 functional domains, in both a discovery cohort (n=6005) and a replication cohort (UK Biobank, n=9214), providing a well-match multimodal spatial map template for public use. Further network module analysis suggested that unimodal cortical areas (e.g. somatomotor and visual networks) indicate higher S-F coherence, while heteromodal association cortices, especially the frontoparietal network (FPN), exhibit more S-F divergence. Collectively, these results suggest that the expanding and maturing brain association cortex demonstrates a higher degree of changes compared to unimodal cortex, which may lead to higher inter-individual variability and lower S-F coherence.

neuroscience↗