Biology subjects

Chen, S.

Publications and source records attributed to Chen, S..

At least 181 records · Page 10Linked to original sources

bioRxiv2025

Microbiome-host interactions drive a trade-off between sleep quality and lifespan in Drosophila

Understanding the interactions between various aging processes and the resulting heterogeneity in aging is crucial for promoting healthy aging. Here, we provide evidence that heterogeneity in microbiome and host interactions contributes to diversifying aging phenotypes in sleep, gut integrity, and longevity in Drosophila. Aged flies exhibiting sleep fragmentation preserve gut integrity, accompanied by a shift in microbiota composition, particularly an increase in Acinetobacter junii. A. junii induces sleep fragmentation via its metabolite, urocanic acid, through serotonin receptor-dependent dopamine upregulation. In parallel, A. junii exploits the host response to promote its growth, leading to lifespan extension, which is recapitulated by genetically modified Escherichia coli, suggesting a trade-off between sleep quality and lifespan. Our study demonstrates a systematic mechanism underlying aging heterogeneity, suggesting interventions through bacterial supplements.

animal behavior and cognition↗

bioRxiv2025

Fully computational design of PAM-relaxed Staphylococcus aureus Cas9 with expanded targeting capability

CRISPR-Cas9 nucleases have transformed genome engineering, yet their application is often constrained by protospacer-adjacent motif (PAM) requirements. Staphylococcus aureus Cas9 (SaCas9) is particularly attractive for in vivo applications due to its compact size; however, its NNGRRT PAM limits targetable genomic sites. Here, we report KRH, a SaCas9 variant designed entirely from the wild-type enzyme through a fully computational point-mutation design workflow, UniDesign, without additional experimental optimization. As expected, KRH efficiently recognizes an expanded NNNRRT PAM and exhibits substantially enhanced editing efficiency at non-canonical PAM sites, with improvements of up to 116-fold over the wild type. KRH achieves genome- and base-editing efficiencies comparable to, or exceeding, those of the well-known evolution-derived KKH variant. Computational modeling by UniDesign provides a mechanistic explanation for the PAM relaxation observed in both KRH and KKH, with structural and energetic analyses revealing that KRH relaxes PAM specificity by fine-tuning the balance between sequence-specific interactions with PAM bases and nonspecific contacts with the DNA backbone. Beyond its practical utility, KRH demonstrates that computational design can identify a minimal set of mutations sufficient to remodel the PAM interface while preserving high nuclease activity. This approach recapitulates--and in some cases surpasses--the performance of evolution-derived variants, offering a scalable strategy for high-throughput Cas9 engineering. Overall, these results establish KRH as a blueprint for rationally engineered, PAM-relaxed nucleases and underscores the power of computational design to accelerate next-generation genome editing.

bioengineering↗

bioRxiv2025

Monocyte and Lymphoid Lineages Share a Common Precursor Distinct from Neutrophils in Hematopoiesis

During lineage commitment in hematopoiesis, hematopoietic stem cells (HSCs) differentiate into distinct lymphoid and myeloid lineages. Within the myeloid lineages, granulocytes and monocytes have been considered to originate from granulocyte-macrophage progenitors (GMPs) or common myeloid progenitor (CMPs). Single-cell sequencing has refined this hematopoietic hierarchy, revealing that neutrophils and basophils diverge early during commitment. Although current hierarchy models propose that neutrophils and monocytes arise from common neutrophil-macrophage progenitor (NMP) or GMP, their early differentiation trajectories remain inconsistent, leaving the origin relationship between neutrophils and monocytes unclear. This study aimed to clarify the developmental origin of neutrophils and monocytes during hematopoiesis. Leveraging a previously established single-cell atlas of hematopoietic progenitor cells (HPCs), we refined a trilineage hierarchy model, revealing the direct commitment of neutrophils from early multipotent progenitors (MPCs) and their divergence from lymphoid lineages. HOPX was identified as a key regulator during lymphoid lineage commitment. Using HOPX-based lineage tracing, we reconstructed the differentiation trajectory of neutrophils and monocytes. Lineage-tracing results revealed that HOPX-derived tdTomato positive cells contribute extensively to lymphoid lineages and monocytes, but not to neutrophils or erythroid cells. Ex vivo primary macrophage cultures derived from bone marrow cells yielded consistent findings. Our results indicate that monocytes are committed from same precursor with lymphoid lineages, and diverge from neutrophils. Moreover, we propose that classical common lymphoid progenitors (CLPs), redefined as lymphoid-monocyte progenitors (LMPs), rather than GMPs, constitute the likely ancestral source of monocyte lineage. These findings challenge the dogma of hematopoietic hierarchy and open new avenues for understanding lineage specification. Future studies involving conditional knockout of HOPX will provide further mechanistic validation of these conclusions.

immunology↗

bioRxiv2025

Resolution of collided ribosomes by ZNF598 is required for adipocyte energy dissipation

Cellular adaptation to stress evoked by high energy demand requires tight coordination between transcriptional programs, protein synthesis, and organelle function. In many physiological contexts, metabolic remodeling depends on increased mitochondrial capacity and activity, placing high demands on the translational machinery. Beige and brown adipocytes, which undergo rapid mitochondrial expansion and activation in response to physiological cues, provide a powerful model to study how cells adapt their translational output to meet increased metabolic demand. How translational fidelity is maintained, and stress-associated conflicts in protein synthesis are resolved during such adaptive processes, remains incompletely understood. Here, we identify ZNF598 as a key factor that resolves translation stress caused by ribosome collisions during metabolic adaptation of adipocytes. We show that diverse physiological stimuli, including hormonal signaling, environmental challenges, and dietary changes, induce ZNF598 and associated enzymes in metabolically active cells. ZNF598 is required for efficient mitochondrial biogenesis and function, supporting adaptive increases in respiration and energy expenditure. Loss of ZNF598 compromises these adaptive responses and leads to metabolic dysfunction, whereas enhancement of ZNF598 activity improves cellular and organismal metabolic flexibility. Together, these findings indicate that distal steps of the translational machinery are a rate-limiting factor for adaptation to increased energy demand.

cell biology↗

bioRxiv2025

In-cell cryo-electron tomography reveals differential effects of type I and type II kinase inhibitors on LRRK2 filament formation and microtubule association

Mutations in Leucine-Rich Repeat Kinase 2 (LRRK2) are a leading contributor to developing familial and idiopathic Parkinsons disease (PD). Most PD-causing LRRK2 mutations increase the kinase activity, leading to increased phosphorylation of Rab GTPases, disrupting vesicular trafficking, cytoskeletal dynamics, and autophagy. Under homeostatic conditions, the bulk of WT and PD-mutant LRRK2 is found in the cytosol. However, exogenously expressed LRRK2 can form microtubule-associated filaments that have been shown to affect molecular transport along microtubules in vitro. While the physiological relevance of microtubule binding has not been established yet, inhibitors being designed and tested as therapeutics have been shown to either promote or prevent filament formation of LRRK2. In this study, we examine the localization and resulting molecular organization of hyperactive LRRK2-I2020T, a common PD mutant, in cells treated with type I (MLi-2) or type II (GZD-824) kinase inhibitors. Treatment with a type I kinase inhibitor results in extensive LRRK2-I2020T decoration around microtubules and microtubule bundling. Stabilization of LRRK2-I2020T filaments by type I inhibitor treatment allowed us to build a full-length closed-kinase model of LRRK2-I2020T in its cellular environment. Conversely, treatment with a type II inhibitor resulted in minimal microtubule decoration by LRRK2-I2020T compared to Type I inhibitor treated cells. This study provides a structural framework for understanding how type I and type II kinase inhibitors differentially modulate LRRK2 filament formation, demonstrating that type I inhibitor treatment promotes a distinct filament architecture, whereas such assemblies are not observed with type II inhibitors.

biophysics↗

bioRxiv2025

scProtoTransformer: Scalable Reference Mapping Across Molecules, Cells and Donors

The rapid accumulation of single-cell data has made it possible to comprehensively characterize biological systems at molecular, cellular, and donor levels. However, scalable reference mapping across different resolutions remains a major challenge in current research. Here, we propose scProtoTransformer, a prototype-based Transformer architecture designed to achieve scalable reference mapping across molecular, cell, and donor levels. scProtoTransformer introduces a knowledge-guided prototype tokenizer that projects gene expression into biologically interpretable pathway prototypes, effectively reducing numerical batch effects while preserving biological semantic patterns. Furthermore, by leveraging knowledge distilled from the foundation model and a dynamic supervised fine-tuning strategy, scProtoTransformer achieves robust biological representations with reduced pre-training requirements. Benchmark experiments across molecular, cell, and donor-level reference mapping demonstrate that scProtoTransformer delivers competitive or even superior performance compared with state-of-the-art approaches, while providing interpretability through biologically prototypes. Together, these results establish scProtoTransformer as a unified framework for scalable reference mapping, laying the foundation for systematic understanding from genes to individuals.

bioinformatics↗

bioRxiv2025

Harmine Selectively Drives Human Beta Cell Differentiation and Function Via Protein Kinase A Pathways

Harmine and other small molecule inhibitors of the kinase DYRK1A induce human beta cells to replicate and regenerate in vitro and in vivo, and are effective at reversing diabetes in animal models. In addition to its beta cell proliferative and regenerative effects, harmine also induces expression of transcription factors and other genes involved in beta cell differentiation and function, exemplified by PDX1, MAFA, NKX6.1, GLP1R, PCSK1 and many others. Harmine also rapidly enhances glucose-stimulated insulin secretion in vitro and in vivo, reversing diabetes within days in diabetic mice transplanted with a marginal mass of human islets. We had assumed that this pro-differentiation or pro-function effect was a common feature of all DYRK1A inhibitors, and was mediated by DYRK1A inhibition. Thus, DYRK1A is a primary target (we refer to it as Target 1) for harmine. Here, to our surprise, we report that the pro-differentiation effect is not a generalized action for all small molecule DYRK1A inhibitors and does not result from DYRK1A interference or genetic silencing. Instead, the prodifferentiation effect is restricted to a small select subset of DYRK1A inhibitors (harmine, 2-2c and 5-IT). Remarkably, the pro-differentiation effect results from the unique ability of these drugs to activate protein kinase A (PKA), a dual mechanism that distinguishes this class from other DYRK1A inhibitors. The beneficial effects of harmine on PKA appear to be indirect, driven by an as yet unidentified, Target 2 in the PKA pathway. These findings make it clear that all DYRK1A inhibitors are not interchangeable, and that those that drive both proliferation and differentiation/function will likely be preferable in human clinical therapeutic settings. They also provide a novel target or roadmap for enhancing human beta cell differentiation in Type 1 and Type 2 diabetes. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/692818v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@bd015corg.highwire.dtl.DTLVardef@a46f9borg.highwire.dtl.DTLVardef@19b4f65org.highwire.dtl.DTLVardef@179aa28_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2025

The long noncoding RNA Peanut (Gm11454) promotes neurogenesis and rod photoreceptor differentiation during postnatal retinal development.

Long noncoding RNAs (lncRNAs) display pervasive expression and function in the developing nervous system. Temporal profiling of gene expression in the retina has demonstrated differential expression of lncRNAs throughout development; however, determinations of lncRNA function during retinal development remain limited. In this study, we identify numerous lncRNAs with dynamic temporal expression and characterize the function of the lncRNA Gm11454, which we have named Peanut. Using overexpression of Peanut in mice retinas, we determine that Peanut promotes rod photoreceptor fate and neurogenesis of retinal progenitor cells (RPCs) via inhibition of Notch signaling and by regulating expression of neighboring gene Tox2. A novel Peanut knockout mouse model demonstrates that Peanut is required for proper visual function and photoreceptor gene expression. Finally, we determined that Peanut is necessary for proper cell cycle progression and neurogenesis. Our results characterize the function of a novel lncRNA as a regulator of RPC neurogenesis and differentiation and support the importance of lncRNAs in the developing retina.

developmental biology↗

bioRxiv2025

3-Phenylpropionic acid, a microbiota-derived polyphenol metabolite linked to hippuric acid, ameliorates colitis as a colon-enriched PPAR-γ agonist

Polyphenols are promising therapeutics for Crohns disease (CD), yet their bioavailability is largely dependent on the gut microbiome. The bioactive metabolites, metabolic pathways, and anti-inflammatory mechanisms underlying their effects remain poorly defined. We identified hippuric acid (HIPA) as a biomarker of polyphenol-microbiome interaction. HIPA is a meta-organismal metabolite produced through a cross-organ pathway that involves synthesis from gut microbial polyphenol metabolism via 3-phenylpropionic acid (3-PPA), followed by renal clearance. We found that CD is characterised by impaired polyphenol metabolism, resulting in deficient colonic 3-PPA and reduced serum hippuric acid relative to healthy individuals. Furthermore, we demonstrate that 3-PPA is a colon-enriched, mild PPAR-{gamma} agonist that ameliorates colitis in murine model. Systemic bioinformatic profiling of metagenomic data suggested the existence of undiscovered 3-PPA producers. Through targeted microbial incubation with polyphenols, we identified the probiotic Bifidobacterium breve and a lab isolate Escherichia coli FAH as novel 3-PPA producers. Collectively, our findings establish 3-PPA as a diet-dependent, colon-produced, mild PPAR-{gamma} agonist with the potential to minimise adverse effects associated with traditional PPAR-{gamma} agonists. This work establishes a foundation for developing microbiota-targeted, polyphenol-based therapeutic strategies for CD patients.

microbiology↗

bioRxiv2025

Overexpression of TPM4 promotes cancer progression through EMT in anaplastic thyroid cancer

Anaplastic thyroid cancer (ATC) is an undifferentiated tumour originating from thyroid follicular epithelium and represents the most malignant subtype of thyroid cancer. However, the mechanisms underlying ATC development are not fully understood. This study aimed to investigate the mechanisms driving ATC invasion and metastasis to provide novel insights into its aggressive behavior. Through an integrated bioinformatics analysis of the GEO database, we identified Tropomyosin 4 (TPM4) as a differentially expressed gene in ATC. Bioinformatic correlation and pathway enrichment analyses suggested that TPM4 expression is potentially linked to tumor cell invasion and lymph node metastasis. We showed that TPM4 was highly expressed in both ATC tissues and cell lines, and its expression levels were positively correlated with enhanced cell migration and invasion. Mechanistic studies indicated that TPM4 likely enhances tumour cell migration and invasion by facilitating cytoskeletal remodeling and promoting the epithelial-mesenchymal transition (EMT). Our findings suggest that TPM4 represents a promising diagnostic and therapeutic biomarker for ATC.

cancer biology↗

bioRxiv2025

Melody: Decoding the Sequence Determinants of Locus-Specific DNA Methylation Across Human Tissues

DNA methylation is a fundamental epigenetic modification that plays crucial roles in transcriptional regulation, cellular differentiation, and genome stability. However, how locus-specific DNA methylation is determined by intrinsic DNA sequence remains poorly understood. Here, we introduce Melody, a deep learning framework that predicts DNA methylation from 10-kb genomic sequences, enabling the integration of both local and long-range sequence signals. Across 39 human tissues, Melody accurately predicts methylation profiles and consistently outperforms existing state-of- the-art methods in whole-chromosome, hypomethylated-region, and cell-type-specific benchmarks. Melody also generalizes to methylation quantitative trait locus (meQTL) effect prediction and identifies regulatory sequence motifs associated with methylation variability. To extend prediction beyond profiled tissues, we further develop Melody-G, which incorporates single-cell RNA-seq foundation model embeddings to infer methylation states in previously unseen cell types directly from transcriptomic data. Together, Melody provides a unified framework for linking genomic sequence and cellular state to DNA methylation and offers new insights into the regulatory logic governing the human methylome.

bioinformatics↗

bioRxiv2025

Neural dynamics outside task-coding dimensions drive decision trajectories through transient amplification

Linking neural activity to behavior typically involves identifying activity subspaces that encode task information, such as stimuli, memory, and choices. However, it is unclear whether activity in these "coding dimensions" drives behavior or merely reflects the underlying computations. Neural activity in other "residual dimensions" is typically ignored. We developed a recurrent neural network that explicitly models interactions between coding and residual subspaces. Applied to multi-regional Neuropixels recordings from mice performing a delayed movement task, the model reveals that perturbations of residual dimensions reliably alter behavior, whereas perturbations of the choice dimension, which encodes the animals decision, are less effective. These effects arise because residual dimensions drive amplification across an intermediate number ([~]10) of dimensions, before the dynamics settle into discrete attractors corresponding to choice. Our findings show that neural activity previously considered task-irrelevant can have critical roles in driving behavior.

neuroscience↗

bioRxiv2025

The 9p21.3 Coronary Artery Disease Risk Locus Modulates Vascular Cell-State Transitions via Enhancer-Driven Regulation of MTAP

The 9p21.3 locus is the strongest genetic association with coronary artery disease (CAD), yet its causal mechanisms remain unresolved. We map the regulatory architecture of 9p21.3 in disease-relevant vascular cells, identifying 12 enhancers within the CAD risk haplotype that respond dynamically to inflammatory and metabolic stress in fibroblasts and smooth muscle cells. These activated states are enriched for CAD heritability, implicating stress-responsive vascular wall cells in disease pathophysiology. Dense CRISPRi tiling integrated with fine-mapping and genomic constraint across >500,000 individuals nominates MTAP as the effector gene, with rs1537371 as a likely causal variant. Perturbation and multi-modal analyses show that MTAP loss induces pro-fibrotic and angiogenic programs and sensitizes vascular cells to TGF-{beta}-driven pathological transitions. Our findings reveal a vascular-specific enhancer network through which noncoding variation at 9p21.3 modulates CAD risk via MTAP--a previously unrecognized regulator of vascular remodeling located 269 kb from the risk haplotype. HighlightsO_LI9p21.3 CAD risk locus harbors 12 functional enhancers active in vascular wall cell types C_LIO_LICAD heritability is enriched in stressed vascular fibroblasts and smooth muscle cells C_LIO_LIHigh-resolution CRISPRi-MAC-seq maps 9p21.3 enhancer-gene and SNP-gene interactions C_LIO_LIMTAP identified as a causal effector gene regulated by 9p21.3 fine-mapped and constrained variants C_LIO_LIMTAP modulates TGF-{beta}-driven pathological vascular cell transitions C_LI

genomics↗

bioRxiv2025

Brain-wide Genome Editing via STEP-RNPs for Treatment of Angelman Syndrome

Brain-wide genome-editing remains a major hurdle for the treatment of neurogenetic disorders. Here, we report a non-viral, non-nanoparticle, chemical modification-based method, called Stimuli-Responsive Traceless Engineering Platform (STEP) that achieves highly efficient and brain-wide genome-editing of neurons in mice. Using cholesterol-based STEP as a lead, we show that a single administration of STEP-ribonucleoproteins (RNPs) results in functional genetic rescue with significant improvements across a battery of neurobehavioral domains in the Angelman syndrome (AS) mouse model. No significant off-target events or general toxicity effects are observed. STEP-RNPs are also highly efficient at editing human neurons and cortical brain organoids differentiated from AS patient-derived iPSCs. scRNA-seq analysis confirms functional genetic rescue via reactivation of Ube3a/UBE3A expression in human and mouse STEP-RNP-treated neuronal cells. Genome editing via STEP-RNPs has broad applications and the potential to treat many other neurogenetic disorders.

genetics↗

bioRxiv2025

METTL3-mediated SOCS3 m6A Modification in Preserving CD4+ T Cell Homeostasis in HIV Infected Long-term Non-progressors

The maintenance of normal CD4+ T cell levels in human immunodeficiency virus (HIV) infected long-term non-progressors (LTNPs) remains elusive. N6-methyladenosine (m6A) regulates RNA metabolism and immune function, but its role in LTNP pathogenesis is unelucidated. This study integrated in vivo analyses of peripheral blood mononuclear cells (PBMCs) from LTNPs, typical progressors (TPs), ART-treated patients, and healthy controls with in vitro experiments using the MT2-HIV-1B model to explored the m6A regulatory role in LTNPs. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) identified uniquely elevated m6A modification abundance and METTL3 expression in LTNPs vs. TPs. Differential m6A and mRNA analysis bighlighted enrichment of innate immunity/inflammation pathways, particularly the SOCS/IL/JAK axis. Further experiments confirmed that METTL3 mediates m6A modification of SOCS3, which suppresses JAK2/STAT4 phosphorylation to modulate IL-12/IFN-{gamma}/IL-4 expression. These findings uncover a novel m6A-METTL3-SOCS3 regulatory axis underlying HIV long-term non-progression, explaining preserved CD4+ T cell homeostasis in LTNPs. IMPOTANCE LTNPs represent a unique subset of HIV-infected individuals who naturally maintain normal CD4 T cell levels and slow disease progression, offering a valuable model to dissect host protective mechanisms against HIV. mA modification has emerged as a pivotal regulator roles in HIV replication and T cell activation, yet its contribution to LTNP biology has remained unclear. This novel mechanism provides a potential explanation for the maintenance of normal CD4+ T cell levels in LTNPs. Our findings shed light on the molecular basis of HIV-1 long-term non-progression and offered crucial insights for developing functional cure for AIDS and new host immune strategies.

immunology↗

bioRxiv2025

Language may be all omics needs: Harmonizing multimodal data for omics understanding with CellHermes

Decoding cellular systems requires integrating diverse omics data, yet most models are trained from scratch on a single modality, restricting generalization. Here we present CellHermes, a biological language model that leverages pretrained large language models (LLMs) to integrate multimodal forms of omics data, such as transcriptomic profile and PPI network, through natural language for better omics understanding. By reformulating these datasets into question-answer pairs, CellHermes emulates multiple self-supervised learning paradigms within a single, universal, language-based framework with LORA fine-tuning on existing natural language-LLM, achieving comparable or even better performance compared to current single-cell foundation models. Within this framework, CellHermes functions as an encoder, a predictor and an explainer for supporting a range of downstream tasks. For encoder, CellHermes encodes biological entities with embeddings that enable accurate network discovery and cross-dataset generalization; For predictor, CellHermes unifies heterogeneous downstream tasks by translating them into natural language question-answers pairs, allowing a single model to perform multi-task prediction by instruction fine-tuning; For explainer, CellHermes explains molecular mechanisms by combining attention analysis with text-based reasoning, leveraging the interpretability and reasoning capabilities of LLMs. We evaluated the ability of CellHermes as an encoder on representing genes and cells using 5 gene-level downstream tasks and 5 diverse single-cell datasets across different tissues compared with other single cell foundation models. We also evaluated CellHermes as a predictor on BioUniBench, a new benchmark of 10 tasks across 7 databases for LLMs, including perturbation response, cell fitness estimation, gene-disease association, and cell type identification. All benchmarks suggest CellHermes can unify multiple biological tasks within one model without compromising performance. By applying CellHermes as an explainer for a melanoma patients dataset, it can uncover potential key genes on tumor-reactive T cells. Together, CellHermes establishes natural language as a unifying medium for omics, offering a foundation that, in the future, potentially enables a more integrated and interpretable research loop of biological representation, prediction, and interpretability.

bioinformatics↗

bioRxiv2025

High-Concordance Validation of Droplet Digital PCR and Next-Generation Sequencing for EGFR Mutation Detection Across Diverse Biospecimens in a Large-scale NSCLC Cohort Study

BackgroundsNext-generation sequencing (NGS) and droplet digital PCR (ddPCR) are both established methods for detecting EGFR mutations in non-small cell lung cancer (NSCLC). However, comprehensive validation of their concordance in mutation detection and variant allele frequency (VAF) quantification across heterogeneous sample types remains limited. Inconsistent results from different sample types (e.g., cfDNA, FFPE) pose a significant challenge to clinical decisions. This is particularly critical for advanced NSCLC patients, who often rely on liquid biopsy, yet the concordance between liquid and tissue-based testing lacks validation in large-scale studies. Another issue in clinical practice is that many tumor samples are limited in quantity, making it difficult to meet testing requirements. Therefore, it is worth exploring whether pre-capture NGS libraries can serve as substitutes for original DNA. MethodsIn this study, we first developed three assays to detect EGFR L858R, exon 19 deletions (Ex19del) and T790M, respectively using ddPCR platform. Their Limit of Detection (LOD) could reach 0.01% at 100 ng of input DNA. Subsequently, we conducted a large retrospective clinical study to systematically compare the detection performance of ddPCR and NGS across three mutation types using approximately 1,000 EGFR-positive samples, including cell-free DNA (cfDNA), pre-capture NGS libraries of cfDNA (cfDNA-prePCR), FFPE-derived DNA (ffpeDNA), pre-capture NGS libraries of FFPE-derived DNA (ffpeDNA-prePCR), fresh tumor tissue DNA (ttDNA), pre-capture NGS libraries of ttDNA (ttDNA-prePCR), pleural effusion supernatants DNA (peDNA), and pre-capture NGS libraries of peDNA (peDNA-prePCR). They were analyzed for detection concordance and VAF correlation. Especially, we made comparisons of the tumor DNA, including ctDNA and tumor tissue DNA, with their paired pre-capture NGS library. ResultsKey findings demonstrated excellent overall agreement between NGS and ddPCR. The mutation detection concordance rates were 98.72% (overall), with subtype-specific rates of 98.93% (L858R), 99.23% (Ex19del), and 97.14% (T790M). VAF measurements between ddPCR and NGS showed exceptional correlation (Pearsons r = 0.975, P<0.001). Notably, pre-capture NGS libraries showed remarkable VAF concordance with their source materials (0.993 for cfDNA libraries vs cfDNA; 0.998 for tumor tissue libraries vs tumor DNA with EGFR L858R; 0.991 for tumor tissue libraries vs tumor DNA with EGFR Ex19del). ConclusionsNGS and ddPCR demonstrate high concordance in EGFR mutation detection and VAF quantification, supporting their complementary roles in clinical testing. Using pre-capture libraries as an alternative to source samples can avoid repeat biopsies and enables subsequent testing for patients with inadequate FFPE sample quantity. These findings establish an evidence base for integrated diagnostic paradigms leveraging NGSs multiplexing power and ddPCRs sensitivity.

cancer biology↗

bioRxiv2025

Bronsted-basic small molecules activate GTP hydrolysis in Ras Q61 mutants

The RAS oncogenes (KRAS, HRAS, NRAS) are among the most frequently mutated genes in human cancer, affecting over three million patients annually. Therapeutic development has largely focused on inhibitors for KRAS codon 12 mutations (G12C/D/V/S/R) which are key drivers in lung, colorectal, and pancreatic cancers. In contrast, mutant-selective inhibitors for Q61 variants remain elusive. A common mechanistic feature of G12 and Q61 mutants is the impaired hydrolysis of GTP, which traps Ras in its active, signaling-competent state. We envisioned that an alternative therapeutic strategy - reactivation of GTP hydrolysis - could address this shared oncogenic mechanism. Here we report small molecules that accelerate GTP hydrolysis in K-Ras Q61 mutants. These compounds compensate for the loss of the catalytic residue Gln61 by introducing a general base into the active site, selectively enhancing hydrolysis of K-Ras Q61X (X = H, L, K, R) mutants by up to 20-fold while sparing the wild-type protein. In mutant cancer cell lines, these compounds reduce GTP-bound Ras levels and suppress downstream signaling. We show that the chemical design principles are generalizable to other Ras isoforms. This work establishes a mechanistic foundation for small-molecule "GTPase activators" and offers a new paradigm for targeting Ras-driven cancers.

cancer biology↗