Biology subjects

Liu, Y. C.

Publications and source records attributed to Liu, Y. C..

4 recordsLinked to original sources

bioRxiv2026

Interpretable machine learning coupled to gene regulatory networks uncovers subcircuits underlying cell fate decisions

Gene regulatory networks (GRNs) model causal linkages that control cell fate decisions and differentiation transitions. Prioritizing regulatory subnetworks underlying cell state differences is of critical importance, but current methods including those reliant on topological metrics introduce circularity as the metrics prioritizing TFs are computed from the same networks whose assumptions they inherit. Separately, interpretable machine learning methods can identify latent factors (LFs) that discriminate cellular states with formal statistical guarantees but do not model regulatory linkages. Here, we present FOCAL (Factor-Outcome Coupling for Assessment of Linkages), a paradigm to prioritize regulatory subnetworks by coupling state-specific and dynamic GRNs with outcome-supervised LFs learned using interpretable machine learning without reference to network topology. This shifts GRN focus from macroscopic TF nodes to state-specific and dynamic TF-gene linkages. In B and T cells, FOCAL identified GIFs (GRNs coupled to Interpretable latent Factors), prioritized regulatory subnetworks underlying established states as well as transient regulatory episodes preceding them. By coupling LFs learnt from perturbation experiments of lineage-defining TFs, FOCAL identified transcriptional predisposition to alternative fates within progenitor cell populations before overt differentiation. This uncovered a novel NFATC2-IRF8 interplay in activated B cells, that was validated by in-vitro and in-vivo genetic perturbations. The two transcription factors act cooperatively to restrain extrafollicular plasmablast differentiation and promote germinal center B cell fate.

systems biology↗

bioRxiv2026

Metabolic disease-relevant stimuli unmask context-dependent genetic regulation of cardiometabolic loci in human adipocytes

Genome-wide association studies (GWAS) have identified thousands of loci associated with cardiometabolic disease, yet translating these associations into regulatory mechanisms, effector genes, and cellular programs remains a major challenge. A key limitation is that genetic effects are often highly context dependent, varying across cell states and environmental conditions that are difficult to model at scale. Here, we leverage CellGenBank, a population-scale biobank of primary human adipose-derived mesenchymal stem cells (AMSCs), to implement a multi-donor cell village in vitro system to map cardiometabolic disease genetic variation across adipocyte differentiation and metabolic stress conditions. We pooled AMSCs from 118 donors into multiplexed villages, differentiated them toward adipocytes, and profiled chromatin accessibility and gene expression using single-nucleus multiome sequencing under four disease-relevant conditions: basal, elevated free fatty acids, low glucose, and hypoxia. By combining with genetic demultiplexing, we quantify how regulatory element activity, gene expression, and higher-order cellular programs are modulated by both genotype and environmental context. Across conditions, we identify widespread context-specific cis-regulatory effects, including expression and chromatin accessibility quantitative trait loci that are masked in baseline states. Genetic effects frequently converge on coordinated transcriptional programs linked to lipid metabolism, insulin responsiveness, and stress adaptation, enabling the identification of cellular program QTLs that bridge variants, genes, and disease-relevant phenotypes. Integration with cardiometabolic GWAS reveals enhanced colocalization in condition- and state-resolved analyses, highlighting the importance of modeling environmental context to resolve disease mechanisms. Together, our study establishes large-scale adipocyte cell villages as a powerful and generalizable framework to map the context-dependent regulatory architecture of cardiometabolic disease and provides a resource linking human genetic variation to adipocyte cellular programs.

genomics↗

bioRxiv2026

KLRG1 identifies circulating cytotoxic CD4 T cells with selective anti-tumor function in human cancer

Given the critical role of CD4 T cells in anti-tumor immunity, strategies to harness these cells for cancer immunotherapy are gaining increasing interest. Historically overshadowed by CD8 T cells, cytotoxic CD4 T cells can directly kill MHC class II-expressing tumor cells. However, the defining molecular signature and the mechanisms underlying their cytolytic activity remain poorly understood, particularly in cancer patients. Here, using ex vivo single-cell transcriptomic and spatial analyses of CD4 T cells from paired blood and tumor samples of melanoma patients, we identified Killer Cell Lectin-Like Receptor G1 (KLRG1) as a defining surface marker of cytotoxic CD4 T cells. The CD4+ KLRG1+ T cell subset was notably enriched among circulating cells compared with tumor-infiltrating populations, which were instead enriched in T follicular helper (Tfh) states. Functionally, KLRG1+ CD4 T cells expressed elevated levels of cytotoxic genes and exhibited superior tumor-killing capacity compared with their KLRG1- counterparts. We demonstrated that their cytotoxicity is granulysin-dependent, as confirmed by CRISPR/Cas9-mediated gene deletion. Mechanistically, CD4 T cells spared MHC class II+ cells lacking the KLRG1 ligands CD324 and CD325, such as professional antigen-presenting cells (APCs), indicating that cytotoxicity was selectively directed towards tumor cells while preserving immune cells. Finally, by investigating how the tumor microenvironment may impair CD4 T cell cytotoxicity, we showed that tumor-derived factors, including interleukin-6 (IL-6), are key drivers promoting the transition of cytotoxic CD4 T cells toward a Tfh phenotype. In summary, our findings define KLRG1 as a defining cell surface marker of cytotoxic CD4 T cells in cancer patients, as well as a key regulator that protects MHC class II+ APCs. Moreover, targeting the IL-6 signalling pathway may enhance CD4 T cell anti-tumor cytotoxicity, offering new avenues for cancer immunotherapy.

immunology↗

bioRxiv2021

BMSC-Derived Exosomal MiRNAs Can Modulate Bone Restoration in Diabetic Rats with Femoral Defects

The exosomal miRNAs of BMSCs participate in hyperglycemia induced poor healing of bone defects. Here, we demonstrate that exosomes derived from BMSCs harvested from diabetes mellitus(DM) rats suppressed bone formation when administered to normal rats with bone defects. Using high-throughput sequencing analysis of microRNA molecules, high miR-140-3p levels were expressed in exosomes released by N-BMSCs. Using TargetScan software and luciferase activity assays, plxnb1 was identified as the downstream molecular target of exosomal miR-140-3p that regulated osteogenesis. Transplantation of exosomes that overexpressed miR-140-3p into DM rats promoted the restoration of bone defects. Furthermore, miR-140-3p significantly promoted the differentiation of DM-BMSCs into osteoblasts and inhibited the expression p-RohA and p-ROCK in the plexin B1 signaling pathway. Taken together, these observations suggest that DM decreases the levels of exosomal miR-140-3p, which impedes bone formation and the differentiation of BMSCs. MiR-140-3p may represent a potential therapeutic target for DM related to abnormal bone metabolism.

cell biology↗