Biology subjects

Han, N.

Publications and source records attributed to Han, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Non-exonic read content reorganises lncRNA co-expression networks in renal and other cancers

Co-expression networks are used to find prognostic lncRNA programmes, but lncRNA quantification depends on unprocessed RNA in the library. In renal cancer (1,062 patients), the leading lncRNA axis tracked the non-exonic (non-feature) read fraction, a routine alignment metric, at Spearman rho 0.90. Without adjustment, most of the lncRNA network merged into one module. Adjustment preserved agreement with single-cell tumour-normal differences. Under rules fixed with the renal results known, 9 of 25 TCGA cancer types had a lncRNA-specific axis and 4 had network collapse (8 and 2 of 23 non-renal types). Intronic lncRNAs tracked the metric more than intergenic ones in all 25, and the metric was not consistently prognostic. All five renal modules that survived adjustment kept their direction in an external cohort, two nominally significant and none at a 5% false discovery rate. Three localised to malignant or epithelial cells. Together they added no discrimination over TNM and grade.

bioinformatics↗

bioRxiv2026

Malignant and stromal partial EMT programmes and cetuximab outcome in head and neck cancer

Partial epithelial-to-mesenchymal transition (pEMT) was defined in single malignant cells of head and neck squamous cell carcinoma but is usually scored in bulk tumours. In three single-cell datasets, its laminin-332 and hemidesmosome genes were expressed by malignant cells (the malignant arm) and its fibrillar collagens and vimentin by fibroblasts (the stromal arm). In bulk tumours the stromal arm dominated co-expression, so standard pEMT scores mainly measure stroma. We recovered the malignant arm with a classifier trained on single cells and a 12-gene core. In 40 patients treated with platinum and cetuximab, both malignant measures were higher in those with long progression-free survival (area under the curve 0.78 and 0.82), and the stromal arm was not. The malignant arm was inseparable from the Basal subtype, whose score did as well or better in these patients (0.93) and in two xenograft panels. Bulk pEMT scores should be read by cell of origin.

cancer biology↗

bioRxiv2026

Filling the Missing Puzzle: Integrating Interaction and Evolutionary Information to Enhance Protein Fitness Prediction in Inverse Folding

Protein inverse folding, which infers compatible amino acid sequences from a given backbone structure, is central to rational protein design. Current methods rely largely on sequence recovery rate as the evaluation metric, emphasizing structural compatibility but seldom addressing function. Yet functional activity depends on the proper network of non-covalent interactions-protein-ligand contacts being a prime example. We argue that function-oriented protein design is governed by three complementary constraints: structural, interaction, and evolutionary, none of which are handled in a unified manner by existing pretrained models. To bridge this gap, we introduce zPolaron, a graph neural network built on protein-ligand complexes for functional protein design. zPolaron represents residues and ligand atoms as a heterogeneous graph and jointly encodes structural, interaction, and evolutionary information by fusing physical-interaction-enhanced graph representations with ESM-2 embeddings. Across multiple fitness prediction benchmarks, zPolaron outperforms all baselines, with gains evident across structural, interaction, and evolutionary dimensions. Wet-lab experiments further confirm its utility in functional enzyme screening and design: the optimized CalA6-D205A-S160G mutant achieves a 17.84-fold improvement in activity, while the CalB-I121M mutant yields a 1.91-fold increase. Together, these results show that integrating interaction and evolutionary information fills the missing puzzle of inverse folding, enhancing protein fitness prediction beyond structural compatibility. zPolaron is available at https://github.com/zelixirSH/zPolaron.

bioinformatics↗

bioRxiv2026

An amyloid-β/APP regulatory layer in human brown adipocyte recruitment identifies repurposable drugs for thermogenic fat expansion

Brown adipose tissue (BAT) increases energy expenditure but is scarce in adults, yet most strategies focus on activating existing thermogenic adipocytes rather than recruiting new ones. We integrate human single-nucleus transcriptomics with protein-interaction and knowledge-graph analyses to identify stage-specific, druggable programmes governing brown adipogenesis. This pipeline resolved four ordered transcriptional states and three transitions, revealing an amyloid-{beta}/APP regulatory layer and nominating APP as a central signalling hub. Most prioritised compounds altered differentiation or metabolic phenotype, several substantially. Phenserine (APP-lowering) and sacubitril (neprilysin inhibitor) promoted differentiation through distinct mechanisms and stages and could be combined sequentially for benefit. Phenserine increased differentiation across three human models and, in mice, expanded BAT mass and increased thermogenic capacity. Together, these findings reveal targetable control points in human brown adipocyte recruitment, nominate repurposable drugs for BAT expansion, and establish a generalisable framework coupling single-cell transcriptomics with network-based drug repurposing across staged biological systems.

cell biology↗

bioRxiv2026

Towards Quantitative In Vitro Modelling of Focused Ultrasound-mediated Blood-Brain Barrier Opening using an Ultrasound-transparent Organ-on-Chip Device

Gas-bubble-enhanced focused ultrasound (FUSGB) is advancing clinically across a myriad of applications, including targeted drug delivery through transient blood-brain barrier opening (BBBO). Its broader translation requires a better understanding of the bioeffects that enable reproducible enhancement of barrier permeability while avoiding vascular injury, underscoring the need to clarify the relationship between acoustic exposure, bubble activity, and the biological response central to treatment optimization. These interactions are difficult to isolate in vivo, and acoustic reflections, geometry, and field distortion in conventional cultureware hinder quantitative in vitro studies. Herein, we present a theoretically and experimentally characterized ultrasound-transparent organ-on-chip device with >99.9% US transparency that enables quantitative in vitro modeling of the FUSGB procedure and study of BBBO. Furthermore, submicron bubbles (sMB, ~800nm) were fabricated and characterized. Acoustic dose-dependent subharmonic and broadband cavitation dose curves (SCD and BCD), as well as temporal responses, were studied. A 3-day accelerated Caco--2 surrogate barrier with a high TEER (~1200xcm2) was used to demonstrate US dose-dependent targeted barrier opening, visualized using an in vitro Evans Blue assay. Together, this study and its findings establish a foundation for quantitative in vitro modeling of FUS BBBO, as well as other US interactions in microphysiological systems, supporting systematic evaluation of procedural regimens, large-parameter-space optimization, acoustic safety window, and exposure conditions for brain drug delivery, and additionally enabling mechanistic insight into FUSGB BBBO.

bioengineering↗

bioRxiv2026

BRIDGE-AD reveals Alzheimer's disease effectors through interpretable large-scale omics integration

The growing landscape of Alzheimer's disease (AD) datasets creates opportunities to integrate heterogeneous evidence and systematically discover disease effectors. We present BRIDGE-AD, an interpretable network medicine framework that transforms multimodal data into a unified, disease-specific gene representation for AD effector prioritisation. We integrated more than 30 datasets and curated resources spanning omics, functional, genetic and prior disease knowledge layers. BRIDGE-AD outperformed recently published pretrained and modality-specific gene embeddings in recovering AD-associated genes and produced a genome-wide resource of candidate AD effectors. Established and newly prioritised effectors formed 19 functional clusters, revealing a global molecular landscape of AD biology. BRIDGE-AD supported an SPP1-centred cross-compartment hypothesis and nominated SCARB2, a poorly characterised candidate, for functional validation. SCARB2 rewired lysosomal, lipid-handling and autophagic programmes in microglia, whereas disrupted SCARB2 glycosylation in AD implicated altered SCARB2 processing and function. The accompanying website, https://explore-bridgead.com, enables users to trace the curated evidence and generate mechanistic hypotheses.

bioinformatics↗

bioRxiv2026

Carcinogen-induced preinvasive human squamous lung cancer is characterised by a novel atypical basal cell state and presents opportunities for chemoprevention

There are no adequate carcinogen-induced models of the earliest stages of human squamous lung cancer (LUSC), limiting the development of effective chemoprevention strategies. We developed a novel human model of early LUSC by exposing differentiated primary human airway cells to a chemical carcinogen known to cause murine LUSC. Carcinogen exposure induced a phenotype recapitulating the earliest stages of human LUSC and associated with the emergence of a novel population of atypical basal cells (ABCs). ABCs express key cancer hallmark markers but without evidence of either common LUSC-associated mutations or an emergent major clonal/subclonal population. We established the clinical relevance of this model by demonstrating enrichment of the ABC expression profile in independent clinical precancer specimens. We then show that capivasertib, a clinically approved kinase inhibitor, prevented the emergence of the abnormal phenotype and the accumulation of cells with an active DNA damage response. Further, capivasertib reversed the established phenotype; and remained effective when nebulised in vitro, mimicking therapeutic inhalation. Together, these findings establish a tractable human model of carcinogen-induced early LUSC and provide a proof-of-principle for repurposing targeted therapies for the prevention of lung cancer.

cancer biology↗

bioRxiv2026

Single-cell foundation models reveal context-sensitive cancer programmes under subtype shift

Single-cell foundation models (scFMs) have shown promise as transferable representations of cellular state, but recent zero-shot evaluations suggest that they do not consistently outperform simpler baselines. We asked whether this apparent limitation reflects an intrinsic weakness of scFMs or instead the difficulty of using them without task-specific adaptation. To test this, we fine-tuned two widely used scFMs, Geneformer and scGPT, on common tumour subtypes from renal, lung, and breast cancer, and compared them with a LightGBM baseline on within-domain validation cohorts and on out-of-domain rarer, unseen cancer subtypes. Across all three organs, the models achieved near-perfect within-domain discrimination (AUROC 0.98-1.00), but differences emerged under subtype shift. On chromophobe RCC, scGPT and Geneformer achieved AUROC 0.88 and 0.92 respectively versus 0.64 for LightGBM; on SCLC, Geneformer reached 1.00 versus 0.82 for LightGBM; and on TNBC, scGPT achieved 0.80 versus 0.49 for LightGBM. To determine whether this generalisation reflected meaningful adaptation rather than arbitrary feature drift, we applied Integrated Gradients, an interpretability technique, to the fine-tuned scFMs and SHAP to LightGBM. LightGBM showed highly stable gene-importance rankings across datasets, whereas the foundation models were substantially more context-sensitive. However, this flexibility was not random: all models converged on a shared within-domain core, while scFMs acquired larger rare-subtype-specific gene sets and pathway programmes during transfer. Pathway enrichment further supported the biological relevance of these attributed genes. Together, these results suggest that fine-tuned scFMs can bridge clinically relevant domain shifts in cancer single-cell analysis and that interpretability provides a practical route to distinguishing biologically grounded adaptation from rigid reuse of training-era rules.

bioinformatics↗

bioRxiv2026

Quantitative Optimization of Sensitivity and Specificity in Targeted and Whole-Exome Sequencing Using Reference-Standard DNA Mixtures

BackgroundWe previously developed a benchmarking strategy using mixtures of homozygote and heterozygote DNAs as reference standards to simultaneously assess sensitivity and false positive (FP) error rates in targeted next-generation sequencing (T-NGS) and whole-exome sequencing (WES), revealing substantial variability across commercial platforms. However, optimal analytic conditions for clinical application remain undefined. MethodsWe systematically evaluated multiple sequencing kits and bioinformatics pipelines across various variant allele fraction (VAF) thresholds to identify conditions that maximize both sensitivity and specificity. Recurrent error-prone alleles were defined and filtered to enhance specificity. ResultsOptimal performance was achieved using the DRAGEN pipeline with recurrent FP allele filtering. For T-NGS, a 1% VAF cutoff yielded a 95% detection threshold of 2.99% and 1.21 FPs per megabase (FP/Mb); for WES, a 2% cutoff yielded a 95% threshold of 5.02% and 1.15 FP/Mb. These settings improved sensitivity >3-fold and reduced FP rates >96% versus suboptimal pipelines. Notably, VAF thresholds flattened sensitivity differences across platforms, obscuring key performance disparities--challenging assumptions that T-NGS is inherently more sensitive than WES. In-house and conventional pipelines undercalled up to 10% of true variants. Restricting reporting of 1-4% VAF variants to [~]1,000 predefined actionable sites enabled recovery of clinically relevant mutations while reducing FP risk >99%. ConclusionsThis study provides a quantitative framework for optimizing NGS performance. Our findings support actionable strategies to improve diagnostic accuracy in clinical genomics through tailored pipeline selection, VAF thresholding, and artifact filtering.

bioinformatics↗

bioRxiv2025

Compensatory tRNA Modification by DUS3L Confers Resistance to METTL1 Loss in Oesophageal Cancer

tRNA N-methylguanosine (mG46), installed by the METTL1/WDR4 methyltransferase, stabilizes the tRNA pool supporting high proliferative rates. Accordingly, many cancer cell lines are vulnerable to METTL1 loss, underscoring its potential as a therapeutic target. We identify a subset of cell lines insensitive to METTL1 depletion and use oesophageal lines as a model to explore the mechanism. The METTL1-resistant adenocarcinoma line OE33 preserves tRNA balance upon mG46 deficiency by elevating dihydrouridylation at position 47 (D47). Depletion of DUS-L enzymes that catalyse D16/D17/D20/D47 on tRNAs sensitises OE33 to METTL1 loss. Concomitant loss of dihydrouridylation and mG46 destabilizes the canonical L-shaped tRNA structure, disrupts coaxial helix stacking and anticodon-loop presentation, and reduces cellular fitness. Notably, METTL1-sensitive cell lines lack this compensatory pathway due to the deleterious effects of increasing their intrinsically low DUS3L levels. These findings reveal a DUS-L-mediated buffering mechanism that supports tRNA homeostasis in METTL1-resistant cancers and highlight DUS3L abundance as a potential biomarker for predicting METTL1 sensitivity. Moreover, they suggest that combined targeting of DUS-L and METTL1 may provide a therapeutic strategy for overcoming resistance in cancers, exemplified in cell lines such as OE33. HighlightsO_LIThe oesophagus cancer line OE33 shows no proliferative defect upon METTL1/WDR4 loss, despite complete lack of m7G46 on its tRNA. C_LIO_LIOE33 coordinates codon usage and abundance of cognate tRNAs in the absence of METTL1/WDR4; the METTL1/WDR4 sensitive line, OE21, fails to do so. C_LIO_LIDepleting dihydrouridine synthases (DUS-L) renders OE33 sensitive to METTL1/WDR4 loss. C_LIO_LIOE33 responds to lack of METTL1/WDR4 activity by upregulating DUS3L and its activity (D47) on tRNA; the sensitive line OE21 fails to do so. C_LIO_LIMETTL1-sensitive lines have intrinsically low DUS3L and cannot sustain higher levels. C_LIO_LIDUS3L reveals as a potential biomarker for METTL1 sensitivity. C_LI

biochemistry↗

bioRxiv2025

Towards an AI-based knowledge assistant for goat farmers based on Retrieval-Augmented Generation

Large language models (LLMs) are increasingly being recognised as valuable knowledge communication tools in many industries. However, their application in livestock farming remains limited, being constrained by several factors not least the availability, diversity and complexity of knowledge sources. This study introduces an intelligent knowledge assistant system designed to support health management in farmed goats. Leveraging the Retrieval-Augmented Generation (RAG), two structured knowledge processing methods - table textualization and decision-tree textualization - were proposed to enhance large language models (LLMs) understanding of heterogeneous data formats. Based on these methods, a domain-specific goat farming knowledge base was established to improve LLMs capacity for cross-scenario generalization. The knowledge base spans five key domains: Disease Prevention and Treatment, Nutrition Management, Rearing Management, Goat Milk Management, and Basic Farming Knowledge. Additionally, an online search module is integrated to enable real-time retrieval of up-to-date information. To evaluate system performance, six ablation experiments were conducted to examine the contribution of each component. The results demonstrated that heterogeneous knowledge fusion method achieved the best results, with mean accuracies of 87.90% on the validation set and 84.22% on the test set. Across the text-based, table-based, decision-tree based Q&A tasks, accuracy consistently exceeded 85%, validating the effectiveness of structured knowledge fusion within a modular design. Error analysis identified omission as the predominant error category, highlighting opportunities to further improve retrieval coverage and context integration. In conclusion, the results highlight the robustness and reliability of the proposed system for practical applications in goat farming.

bioengineering↗

bioRxiv2025

A network medicine framework for multi-modal data integration in therapeutic target discovery

The high cost and attrition rate of drug development underscore the need for more effective strategies for therapeutic target discovery. Here, we present a network medicine-based machine learning framework that integrates single-cell transcriptomics, bulk multi-omic profiles, genome-wide CRISPR perturbation screens, and protein-protein interaction networks to systematically prioritise disease-specific targets. Applied to clear cell renal cell carcinoma, the framework successfully recovered established targets and predicted five therapeutic candidates, with subsequent in vitro validation demonstrating that among these, ENO2 inhibition had the strongest anti-tumour effect, followed by LRRK2, a repurposing candidate with phase III Parkinsons disease inhibitors. The proposed approach advances target discovery by moving beyond single-feature, single-modality heuristics to a scalable, machine learning-driven strategy that is generalisable across diseases.

bioinformatics↗

bioRxiv2025

Multimodal gene and targeted drug therapy for chronic myelogenous leukemia: Computational target analysis and therapeutic validation

Developing an efficient and safe therapy necessitates a mechanistic understanding of the complex underlying pathology and manipulation of the multiple pathways at the molecular and genetic level. Network-based simulation of chronic myeloid leukemia (CML), a relatively well-understood cancer model, revealed the dynamics of simultaneously expressing pro-apoptotic BIM and silencing pro-survival MCL-1 in combination with the BCR-ABL-targeted tyrosine kinase inhibitor dasatinib. Viral/nonviral chimeric nanoparticles (ChNPs) composed of a BIM-expressing adeno-associated virus (AAV) core and a degradable polymeric shell that encapsulates MCL-1 siRNA (BIM/MCL-1 ChNPs) synergistically and selectively killed BCR-ABL+ CML cells in combination with dasatinib. In a mouse CML model, the BIM/MCL-1 ChNPs and dasatinib combination therapy suppressed proliferation of BCR-ABL+ hematopoietic cells and prevented leukemic infiltration of organs. The synergistic anti-leukemic effect was further pronounced in an acute phase model of the disease. This study investigated a strategy of developing a versatile and tunable multimodal therapy assisted by a computational toolset that analyzes the molecular foundation of a disease and predicts therapeutic response. The interdisciplinary approach developed and validated in this study can be used in discovering new therapies for cancer and other diseases. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=182 SRC="FIGDIR/small/656072v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@1e3632borg.highwire.dtl.DTLVardef@1839f12org.highwire.dtl.DTLVardef@4060c0org.highwire.dtl.DTLVardef@8ff0de_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO summarizing the study design and approach. A schematic representation of the integrated in silico and in vivo pipeline utilized in the study. The workflow begins with in silico simulations, including Boolean network modeling and protein-protein interaction (PPI) network analyses, leading to target discovery, optimized nanoparticle design, and validation in pathological contexts. This was followed by therapeutic efficacy assessments of BIM/MCL-1 ChNPs and their combination with dasatinib in BCR-ABL+ leukemia models in vitro and in vivo. C_FIG

cancer biology↗

bioRxiv2025

A systems-level machine learning approach uncovers therapeutic targets in clear cell renal cell carcinoma

We present a generalisable, interpretable machine learning framework for therapeutic target discovery using single-cell transcriptomics, protein interaction networks, and drug proximity analysis. The pipeline integrates feature selection via gradient boosting classifiers, systems-level network inference, and in silico drug repurposing, enabling the identification of actionable targets with cellular specificity. As a proof of concept, we apply the method to clear cell renal cell carcinoma (ccRCC), an aggressive kidney cancer with limited treatment options. The model identifies 96 tumour-intrinsic genes, refines them to 16 targets through CRISPR screens and biological curation, and prioritises FDA-approved compounds via network-based proximity scoring. Several novel therapeutic mechanisms - including ABL1, CDK4/6, and JAK inhibition - emerge from this analysis, with predicted compounds showing superior efficacy to standard-of-care drugs across multiple ccRCC cell lines. Beyond ccRCC, this framework offers a scalable strategy for drug discovery across diverse diseases, combining machine learning interpretability with systems biology to accelerate therapeutic development.

systems biology↗

bioRxiv2025

Utilising multi-modal data-driven network analysis to identify monotherapy and combinational therapy targets in SOX2-dependent squamous cell lung cancer

Drug discovery requires a deep understanding of disease mechanisms, making the integration and analysis of diverse, multi-modal data types essential. These data types, spanning omics types, disease- associated, and biological pathway information, often originate from disparate sources and must be combined to uncover critical insights. We have developed iPANDDA (in-silico Pipeline for Agnostic Network-based Drug Discovery Analysis), a computational pipeline that integrates multi-modal data through a network-based approach to predict candidate drug target proteins for specific diseases. We applied iPANDDA to lung squamous cell carcinoma (LUSC), a subtype of non-small cell lung cancer (NSCLC) that accounts for approximately 25% of all lung cancer cases globally. Despite advances in cancer therapeutics, targeted therapies specifically approved for LUSC remain lacking, exacerbated by the shortage of robust models for studying LUSC carcinogenesis and therapeutic responses. The SOX2 gene, amplified in about 50% of LUSC patients, plays a critical role in driving and maintaining the cancer phenotype. Using iPANDDA, we identified relevant therapeutic targets for SOX2-dependent LUSC. Selected candidate drug targets were validated in vitro using cell-based models. We conducted target inhibition studies in both SOX2-dependent and non-SOX2-dependent cell lines, evaluating the effects of inhibition and knockout through cell viability assays. Our findings confirmed key monotherapy and combination therapy targets for SOX2-focused models. Specifically, we validated the AKT and mTOR complexes as promising therapeutic targets for LUSC. Additionally, we identified potential pathways for developing novel combination therapies targeting SOX2-dependent LUSC. iPANDDA offers a robust approach to refining and focusing therapeutic strategies for diseases with unmet clinical needs.

bioinformatics↗

bioRxiv2024

Dissecting compounded hepatocyte injury in a model of MASLD progression from human induced pluripotent stem cells

Drug discovery for multifactorial diseases like metabolic dysfunction-associated steatotic liver disease (MASLD) remains challenging due to inadequate models and untargeted drug screenings. We combined stem-cell-based modeling with computational drug predictions identifying flavin pathways as therapeutic targets in MASLD. For disease stage-specific discovery, we established a MASLD testing model, compounding metabolic triggers to intensify mitochondrial crisis. In vitro injuries included adipo- and myokines, immune cell co-culture, and genomic risk factors. Benchmarking experiments revealed similarities with advanced human MASLD. To query therapeutic compounds, protein-protein-interaction networks, weighted gene co-expression, and knowledge graph-based analyses independently predicted flavin adenine dinucleotide (FAD) as an anti-MASLD factor. Dysregulated flavoproteomes in vitro and in vivo-in pediatric and adult MASLD patients- supported our flavin network-focused strategy. We established therapeutic FAD concentrations to mitigate metabolic injury and fibro-inflammation in human multicellular liver organoids and other assays. We enhanced therapeutic FAD effects through genetic mitochondrial biogenic augmentation and identified orally available flavo-active compounds--including Aspirin--restoring mitochondrial respiration. Our study demonstrates how integrating stem cell-derived disease modeling with computed drug predictions can expedite therapeutic discovery.

cell biology↗

bioRxiv2024

Glutamine availability regulates cDC subsets in tissue

AbstractProliferating tumor cells take up glutamine for anabolic processes engendering glutamine deficiency in the tumor microenvironment. How this might impact immune cells is not well understood. Using multiple mouse models of soft tissue sarcomas, glutamine antagonists, as well as genetic and pharmacological inhibition of glutamine utilization, we found that the number and frequency of conventional dendritic cells (cDC) is dependent on microenvironmental glutamine levels. cDCs comprise two distinct subsets - cDC1 and cDC2, with the former subset playing a critical role in antigen cross-presentation and tumor immunity. While both subsets show dependence on Glutamine, cDC1s are particularly sensitive. Notably, glutamine antagonism did not reduce the frequency of DC precursors but decreased proliferation and survival of cDC1s. Further studies suggest a role of the nutrient sensing mTOR signaling pathway in this process. Taken together, these findings uncover glutamine dependence of cDC1s that is coopted by tumors to escape immune responses. One Sentence SummaryType 1 conventional dendritic cells require glutamine to maintain their number in non-lymphoid tissue. SignificanceImmune evasion is a key hallmark of cancer; however, the underlying pathways are diverse, tumor-specific and not fully elucidated. Many tumor cells avidly import glutamine to support their anabolic needs, creating a glutamine-deficient tumor microenvironment (TME). Herein, using mouse models of soft tissue sarcomas, we show that glutamine depletion in TME leads to reduced type 1 conventional dendritic cells - a cell type that is critical for adaptive immune responses. This work is a paradigm for how tumor cell metabolism can regulate anti-tumor immune responses and will be foundational to future efforts targeting glutamine metabolism for cancer immunotherapy.

immunology↗

bioRxiv2024

LLPS REDIFINE allows the biophysical characterization of multicomponent condensates without tags or labels

Liquid-liquid phase separation (LLPS) phenomenon plays a vital role in multiple cell biology processes, providing a mechanism to concentrate biomolecules and promote cellular reactions locally. Despite its significance in biology, there is a lack of conventional techniques suitable for studying biphasic samples in their biologically relevant form. Here, we present a label-free and non-invasive approach to characterize protein, RNA and water in biomolecular condensates termed LLPS REstricted DIFusion of INvisible speciEs (REDIFINE). Relying on diffusion NMR measurements, REDIFINE exploits the exchange dynamics between the condensed and dispersed phases to allow the determination of not only diffusion constants in both phases but also the fractions of the species, the average radius of the condensed droplets and the exchange rate between the phases. We can also access the concentration of proteins in both phases. Observing proteins, RNAs, water, and even small molecules, REDIFINE analysis allows a rapid biophysical characterization of multicomponent condensates which is important to understand their functional roles. In comparing multiple systems, REDIFINE reveals that folded RNA-binding proteins form smaller and more dynamic droplets compared to the disordered ones. In addition, REDIFINE proved to be valuable beyond LLPS for the determination of binding constants in soluble protein-RNA without the need for titration.

biophysics↗