Biology subjects

Shi, C.

Publications and source records attributed to Shi, C..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Atomic structure and dynamics of the mechanosensitive channel MscL from E. coli by cryo-EM and solid-state NMR

Mechanosensitive channels are central to cellular responses to membrane tension, yet the structural basis of their gating remains incompletely understood. Here, we determine the structures of wild-type and G22S mutants of MscL from E. coli (EcMscL) by cryo-EM in peptide-based lipid nanodiscs and complement them with solid-state NMR measurements in liposomes to capture their dynamics in a native-like membrane environment. The cryo-EM structures reveal a closed conformation, whereas analysis of the low-threshold G22S mutant by NMR uncovers widespread conformational changes in both cytoplasmic and periplasmic regions. These data indicate enhanced dynamics and conformational heterogeneity in the mutant, revealing the early transitions from the closed towards the open state. Together, our results establish a synergistic framework integrating cryo-EM and NMR to resolve both structure and dynamics of mechanosensitive channels, and identify lipid-protein interactions as key determinants of MscL gating and mechanosensitivity. Our study further provides a quantitative benchmark for computational investigations of mechanogating and lays the foundation for the rational design of channels with tunable gating kinetics. TeaserBy integrating cryo-EM and solid-state NMR, we reveal how lipid-coupled dynamics prime MscL for opening, capturing the earliest transitions from closed to active states.

biophysics↗

bioRxiv2026

A unified spatial transcriptome profiling of ten mouse organs

Spatial transcriptomics has enabled numerous deep learning models in this area, and training them requires large amounts of high-quality data, especially expression matrices paired with histological images. Here, we present a unified spatial transcriptomic dataset generated using the Stereo-seq platform, covering 10 mouse organs--including brain, kidney, lung, thymus, large intestine, skin, spleen, ovary, testis, and uterus--encompassing 23 tissue sections generated from 21 chips, each with matched ssDNA or H&E staining images. The dataset comprises single-cell-resolution (cell-bin) or square bin-50 (25 {micro}m x 25 {micro}m) expression matrices for each sample, accompanied by corresponding cell type annotations. Annotation robustness was further supported by concordance across different sections of the same tissue and corroboration with canonical marker gene expression patterns. Finally, we compared the characteristics of the cell-bin and bin-50 expression matrices and demonstrated the advantages of cell-bin resolution for cell annotation. This dataset provides a standardized resource for spatial transcriptomics method development, benchmarking, and multimodal analysis.

bioinformatics↗

bioRxiv2026

Alternative splicing expands the functional portfolio of a plant virus to control the viral cycle

Viruses frequently have limited coding space, yet efficiently manipulate complex hosts by deploying intricate, often poorly understood strategies. Geminiviruses, devastating plant pathogens with small single-stranded (ss) DNA genomes, replicate in the nucleus aided by the viral replication-associated protein (Rep), the most conserved protein within this family and related ssDNA viruses. Rep initiates viral DNA replication and represses its own promoter to regulate the infection cycle. The molecular mechanisms enabling this dual functionality are so far unknown. Here, we show that the geminivirus tomato yellow leaf curl virus (TYLCV) exploits the host spliceosome to produce novel Rep splice variants. Splicing generates Rep isoforms lacking the central oligomerization domain that cannot initiate replication but strongly repress the Rep promoter. Conversely, Rep mutants deficient in splicing promote replication but fail to repress transcription. Reduced Rep splicing interferes with the viral gene expression hierarchy and decreases infectivity. Our findings therefore reveal a previously unrecognized viral strategy in which alternative splicing produces functionally specialized viral protein isoforms, providing a mechanistic explanation for the dual role of Rep in viral replication and gene regulation. Splicing events with potentially similar functional consequences in related viruses suggest that this strategy may have convergently evolved across diverse viral lineages infecting different domains of life.

plant biology↗

bioRxiv2026

Mechanical Stiffening Promotes Growth, Invasion, and Mitogen-activated protein kinase kinase (MEK) Inhibitor Resistance in 3D Plexiform Neurofibroma Cultures

Plexiform neurofibromas with neurofibromatosis type I (pNF1s) are benign peripheral nerve sheath tumors caused by NF1 loss, leading to dysregulated RAS/mitogen-activated protein kinase (MAPK) signaling. While mitogen-activated protein kinase kinase (MEK) inhibitors, selumetinib and mirdametinib, can reduce tumor volume, surgical resection remains the primary treatment for immediate debulking and symptom relief. Complete removal is often limited by tumor infiltration along nerve plexuses, and residual tumors may undergo postsurgical tissue remodeling, producing localized regions of stiffened extracellular matrix (ECM). The impact of ECM stiffness on pNF1 progression and drug response is unknown. Using patient-derived pNF1 tumor cells cultured in 3D hydrogels with defined stiffness (1.5 kPa soft, 7 kPa stiff), we found that stiff ECM promoted spread morphology, increased growth, and progressive intracellular softening. Stiff ECM reduced lysyl oxidase (LOX) expression, reflecting mechanoadaptive ECM remodeling, and upregulated P-glycoprotein, leading to decreased sensitivity to selumetinib. These results provide the first evidence that ECM stiffening, such as that arising from postsurgical remodeling, directly drives pNF1 progression and therapeutic resistance. Our findings highlight mechanobiology as a key regulator of tumor behavior and support targeting ECM mechanics to improve clinical outcomes in NF1 patients.

cancer biology↗

bioRxiv2025

STEAP4+ neutrophils: a promising circulating biomarker for lung metastasis

As the leading contributor to cancer-related mortality, metastasis often evades detection until advanced stages, underscoring the critical need for early predictive biomarkers. Neutrophils are naturally circulating sentinels that have been implicated in pioneering pre-metastatic niche formation in distant organs. Nevertheless, their heterogeneity within evolving metastatic microenvironments remains poorly characterized. Here, through dynamically delineating neutrophil subsets via single-cell transcriptomics, we identified a conserved feature of lung metastasis in breast cancer and other cancer models. Among the core genes, a cell surface marker, STEAP4, was selected and validated to indicate lung metastases at both RNA and protein levels. In both humans and mice, these STEAP4+ neutrophils were detectable not only in lung metastatases but also in the circulation. Clinically, the abundance of circulating STEAP4+ neutrophils robustly discriminates patients with lung metastasis from those with localized primary tumor. Our findings uncover STEAP4+ neutrophils as a promising biomarker and lay the groundwork for a non-invasive, blood-based diagnostic strategy for metastatic disease.

cancer biology↗

bioRxiv2025

Assessment of Visual Function in Mice Using Light/Dark Box and Multi-Feature Machine Learning

The light/dark box test can be used to assess visual function in rodents based on their spontaneous behavior in response to light. Commonly used assay relies on a single behavioral metric, dwell time in the light or dark compartment, which may be influenced by factors other than vision, leading to unreliable assessment results. To overcome this, we developed a multi-feature machine learning paradigm by extracting multiple mouse behavioral metrics, standardizing them as features to train machine learning models, thereby achieving reliable and automated vision assessment. We systematically compared the classification performance of single-metric versus multi-feature machine learning approaches in sighted and blind mice, using wild-type and rhodopsin-deficient mice, with a subset further subjected to double optic nerve crush. We found that the multi-feature method can improve classification performance and exhibit great robustness to different experimental settings. Additionally, we further improved model performance by applying feature importance analysis and constructing an optimized feature subset. These findings suggest that the reliability of commonly used single dwell time measure for vision assessment could become unreliable, as shown in our experiment, probably because in addition to vision other factors also impact dwell time. Our study demonstrated an improved assessment method based on a combination of multiple behavior features through machine learning. Author SummaryAssessing visual function in mice is essential for studying eye diseases and drug development. The light/dark box test evaluates visual function by measuring the spontaneous behavioral response of mice to light, providing a training-free behavioral approach that helps simplify the assessment process and improve research efficiency. However, traditional light/dark box tests rely on a single behavioral metric, dwell time in the light or dark compartment, to assess visual function, which may be influenced by factors other than vision, such as anxiety and exploratory behavior, leading to limited reliability of assessment results. Here, we demonstrate that integrating multiple behavioral features through machine learning can improve the reliability and stability of vision assessment. By automatically tracking and analyzing various behavioral metrics of mice, such as movement patterns, speed, and spatial preferences, the proposed method can more reliably distinguish between sighted and blind mice. Furthermore, the method demonstrates stable performance across different experimental settings, showing good applicability. This automated, reliable, and easily generalizable method can provide a convenient and efficient means for visual assessment in preclinical research, facilitating vision disease research and drug development.

animal behavior and cognition↗

bioRxiv2025

CellBin:a generalist framework to process spatial omics data to cell level

Spatial omics has rapidly expanded with increasingly diverse imaging modalities, molecular targets, and chip sizes. However, no general framework currently exists to construct cell level matrices that are robust across platforms and omics types. Here we present CellBin, a universal and scalable frame-work that unifies image stitching, cell segmentation, and spot-to-cell mapping for multiple spatial omics technologies. CellBin integrates a multi-field weighted stitching algorithm for large-area images, a family of U-Net-based models trained across diverse staining modalities, and an optimized computational architecture for high-throughput processing. Across five technological platforms and three omics data types, CellBin achieves robust segmentation and accurate single-cell matrix construction, consistently outperforming seven state-of-the-art methods in F1-score, cell size precision, and annotation accuracy. By providing a generalizable, cross-platform solution, CellBin bridges multiple spatial omics, enabling unified, high-resolution cell level analyses across technologies.

bioinformatics↗

bioRxiv2025

Macrophages display spatiotemporal specificity in intrahepatic cholangiocarcinoma and drive tumour progression via OSM and THBS1

Tumor-associated macrophages (TAMs) are abundant in intrahepatic cholangiocarcinoma (ICC) and correlate with poor prognosis, but their non-immunosuppressive roles remain unclear. This study aimed to define TAM transcriptional heterogeneity and map their interactions with ICC cells that promote malignancy beyond immune suppression. Single-cell and spatial transcriptome analysis with mouse and human ICC tissues revealed a TAM subpopulation, designated M{varphi}_VEGFA, that emerged as the major macrophage population from the intermediate stage of ICC development onward. Data from Gene Expression Omnibus (GEO) and spatial transcriptome indicate that M{varphi}_VEGFA was associated with poor prognosis and spatially localized to the ICC cells. Cell co-culture and cell-cell communication analyses revealed that M{varphi}_VEGFA promoted ICC cell proliferation via secreted OSM and downstream OSMR-STAT3-CCND1 axis, and enhanced ICC cell invasion through THBS1 and downstream CD47-mTOR/DBN1 signaling. Critically, multiplex immunofluorescence with human ICC tissue microarray indicated that OSM and THBS1 were co-elevated in human ICCs, and dual blockade of both targets inhibited ICC progression in mouse models. This study uncovers the direct and critical roles of M{varphi}_VEGFA in driving ICC progression with paracrine signaling and further highlights the potential of targeting macrophages in ICC treatment.

cancer biology↗

bioRxiv2025

Pervasive splicing in a plant DNA virus

RNA splicing is considered an oddity in plant viruses; here, unexpectedly, we find it to be prevalent in the geminivirus tomato yellow leaf curl virus. Transcriptome analysis revealed eight splicing events generating novel protein isoforms, six of which were validated and two shown to depend on the plant spliceosome. Splicing impairment reduced viral accumulation and symptom severity, demonstrating that splicing contributes to infectivity, potentially by expanding the viral proteome, and suggesting that plant virus transcriptomes need to be reassessed.

plant biology↗

bioRxiv2025

CSRefiner: A lightweight framework for fine-tuning cell segmentation models with small datasets

AO_SCPLOWBSTRACTC_SCPLOWRecent advances in spatial omics technologies have enabled transcriptome profiling at subcellular resolution. By performing cell segmentation on nuclear or membrane staining images, researchers can acquire single cell level spatial gene expression data, which in turn enables subsequent biological interpretation. Although deep learning-based segmentation models achieve high overall accuracy, their performance remains suboptimal for whole-tissue analysis, particularly in ensuring consistent segmentation accuracy across diverse cell populations. Existing fine-tuning approaches often require extensive retraining or are tailored to specific model architectures, limiting their adaptability and scalability in practical settings. To address these challenges, we present CSRefiner, a lightweight and efficient fine-tuning framework for precise whole-tissue single-cell spatial expression analysis. Our approach incorporates support for fine-tuning widely uaed segmentation models in the field of spatial omics, including recent published model Cellpose-SAM, while achieving high accuracy with very limited annotated data. This study demonstrates CSRefiners superior performance across various staining types and its compatibility with multiple mainstream models. Combining operational simplicity with robust accuracy, our framework offers a practical solution for real-world spatial transcriptomics applications.

bioinformatics↗

bioRxiv2025

A novel rat spatial transitive inference paradigm for investigating memory schema and deliberation

Inferential reasoning is a vital cognitive ability that enables animals to navigate novel situations by leveraging existing relational knowledge of memory schema, with hypothesized roles of prefrontal cortical - hippocampal circuits. Transitive inference (TI) tasks test the ability of subjects to infer relationships within a value hierarchy (e.g., A>B>C>D>E) after being trained only on adjacent premise pairs (e.g., A-B, B-C, etc.). In rodents, current TI paradigms are primarily based on odor-cues and have several limitations that preclude investigation of physiological mechanisms underlying schemas and deliberation. To address these challenges, we developed a novel, automated spatial TI task for rats using a radial maze with maze arms as premise elements and a dedicated deliberation zone. Most rats successfully learned the premise pairs over training. Further, animals demonstrated rapid, successful inference (test pair B>D and control pair A>E) within a single test session, with higher initial accuracy than comparable premise pairs, indicating the use of schema-based inference. We also investigated vicarious trial and errors (VTE), a behavioral correlate of spatial deliberation. VTE behavior was elevated on choice trajectories early in learning, when novel premise pairs were introduced, and generally for incorrect trials, corresponding to the hypothesized association of VTEs with uncertainty. Further, rats also exhibited elevated VTE behavior with high variability during inference testing, with individual variability suggestive of varying strengths of schema usage. Our findings demonstrate the feasibility of a rodent spatial TI task that provides new insights into the behavioral correlates of schemas and deliberation for inferential reasoning.

animal behavior and cognition↗

bioRxiv2025

Atomistic Mechanism of Calcium-Mediated Inward Rectification of the MthK Potassium Channel by Solid-State NMR and MD Simulations

Inward rectification is a fundamental but poorly understood phenomenon in potassium channel physiology. Despite its physiological importance, the exact mechanism has remained elusive. In this work, we uncover a previously unrecognized calcium-mediated gating mechanism in the MthK potassium channel that sheds new light on this essential process. By combining state-of-the-art proton-detected solid-state NMR spectroscopy with atomistic molecular dynamics simulations, we reveal that divalent calcium ions bind to a novel site just below the selectivity filter, physically obstructing the outward flow of potassium ions whereas inward flow is still possible - analogous to a molecular ball check valve. Secondly, the binding of Ca2+ to the newly identified site leads to stabilization of the selectivity filter and allows us to directly observe ion-ion interactions in the filter. These results offer direct experimental support for the long-debated "direct knock-on" mechanism, in which potassium ions move through the filter, without water co-transport.

biophysics↗

bioRxiv2025

A machine-learning-guided hydrogen-bonded organic framework for long-term, ultrasound-triggered pain therapy

Effective treatment of chronic pain remains hindered by the lack of drug delivery systems that simultaneously achieve long-term stability, high spatial precision, and non-invasiveness1,2,3. Here, we utilize a programmable, ultrasound-responsive drug delivery platform based on hydrogen-bonded organic framework (HOF) nanoparticles, enabling on-demand anesthetic release with long-term and durable analgesic efficacy. A machine learning (ML)-guided screening pipeline was developed to evaluate about 250 FDA-approved drugs, spanning both hydrophilic and lipophilic agents, and identified bupivacaine (lipophilic) and lidocaine hydrochloride (hydrophilic) as optimal candidates. These agents were efficiently encapsulated into HOF nanoparticles via diffusion and double-solvent methods. Ultrasound-triggered drug release in vitro transiently suppressed calcium signaling in adeno-associated virus (AAV)-transfected neurons. In vivo, ultrasound-activated release of bupivacaine or lidocaine HCl at the sciatic nerve site significantly elevated mechanical nociceptive thresholds for up to seven days, reduced autotomy (self-mutilaion) behavior, and improved motor function in a rat model of chronic pain. Notably, the machine learning-identified top candidates not only exhibited high loading efficiency, but also demonstrated superior therapeutic outcomes in vivo, establishing a direct link between computational prediction and biological efficacy. This non-invasive, programmable HOF-based system provides a clinically translatable platform for on-demand, spatiotemporally precise pain management.

bioengineering↗

bioRxiv2025

Construction of recombinant Bacillus subtilis spores surface-displaying PRRSV ORF1b, GP5, M, and N proteins in tandem and evaluation of their immunogenicity in mice

Porcine reproductive and respiratory syndrome virus (PRRSV), a single-stranded RNA virus, is a highly contagious pathogen that causes severe reproductive and respiratory disorders in pigs, leading to significant economic losses in the swine industry worldwide. However, current commercial vaccines provide only limited protection against circulating PRRSV strains, highlighting the urgent need for novel vaccine strategies. B. Bacillus subtilis, a well-characterized probiotic, has emerged as a promising platform for mucosal vaccine delivery due to its safety profile and ability to induce robust immune responses. In this study, we screened B cell and T cell linear epitopes from PRRSV-ORF1b, GP5, M, and N proteins to construct a recombinant tandem antigen (ER), which was displayed on the surface of B. subtilis 168 spores. The resulting recombinant strain, designated B. subtilis BE, was evaluated for immunogenicity in mice via oral administration. The results showed that Oral immunization with B. subtilis BE significantly elevated antigen-specific secretory IgA levels in intestinal contents and IgG levels in serum, indicating potent mucosal and systemic humoral responses. Moreover, B. subtilis BE induced measurable neutralizing antibody titers and enhanced cellular immunity, as evidenced by increased frequencies of CD3+ and CD8+ T cells in mesenteric lymph nodes and upregulation of IFN-, TNF-, and IFN-{gamma} expression in intestinal tissues. Collectively, these findings demonstrate that B. subtilis BE elicits both humoral and cellular immune responses and may serve as a novel oral vaccine candidate against PRRSV, offering a promising alternative for controlling the ongoing epidemic.

immunology↗

bioRxiv2025

Continental-scale integration of soil metagenomes and metabolomes reveals ubiquitous microbial capacity for recalcitrant carbon decomposition.

Soil organic matter (SOM) decomposition by microorganisms represents one of the largest uncertainties in predicting terrestrial carbon-atmosphere feedbacks. Yet we lack systematic understanding of the microbial taxa that drive decomposition of chemically distinct carbon pools and of the metabolic pathways they employ across environmental gradients. Here, we address this critical gap using the first continental-scale soil dataset pairing shotgun metagenomes with high-resolution molecular characterization of SOM chemistry. We use data from 47 standardized soil cores - selected using microbial respiration rates from 106 soils across the United States - to assemble 0.76 terabases (Tbs) of prokaryotic metagenome-assembled genomes (828 MAGs) and identify 66,727 distinct SOM molecules. Integrating these datasets revealed widespread microbial potential for depolymerizing chemically-recalcitrant carbon compounds previously considered stable. Notably, we uncover complementary metabolic specialization between genera affiliated with two abundant bacterial orders, Rhizobiales and Chthoniobacterales, and an archaeal order, Nitrososphaerales. This metabolic partitioning is consistent across soil depths and activity levels, suggesting coordinated decomposition of chemically-complex carbon through distinct but complementary biochemical strategies. The metabolic potential for depolymerization of chemically-recalcitrant compounds is supported by the abundance of these molecules across the soils, as indicated by Fourier-Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS), and by flux balance analysis of metabolic models. Our continental-scale integration of microbial genotypes with their chemical substrates reveals that a substantial fraction of SOM that is considered to be relatively stable harbors decomposition potential that current Earth system models fail to capture. These findings provide mechanistic insight for incorporating microbial metabolic pathways into carbon cycle predictions and highlight an underappreciated vulnerability of soil carbon to environmental variability.

microbiology↗

bioRxiv2025

Multiplexed Neuromodulatory-Type-Annotated EM-Reconstruction of Larval Zebrafish

Diverse neuromodulatory systems confer functional flexibility upon the hardwired sensorimotor pathways of the brain1-6. Among these, the evolutionarily conserved locus coeruleus (LC)-norepinephrinergic (NE) system integrates and broadcasts information globally7-9. While previous studies have primarily focused on its axonal outputs and effects on neural processing, the organizational logic of its synaptic inputs, enabling it to sense global brain states and in turn shape its activity dynamics for appropriate neuromodulation10,11, remains poorly explored. To address this, we mapped the synaptic input architecture of individual LC-NE neurons by establishing Fish-X, a whole-brain microscale reconstruction of larval zebrafish with neuron-type annotations. This dataset encompasses the retina, brain and anterior spinal cord, capturing >240,000 cells and >25 million synapses. Monoaminergic (including NE, dopaminergic, and serotonergic), hypocretinergic, and glycinergic neurons were resolved by multiplexed subcellular APEX2 labeling, while glutamatergic and GABAergic identities were inferred via morphology comparison with a zebrafish mesoscopic atlas12,13. Compared with other neuronal populations, LC-NE neurons display distinct perisomatic features and high dendritic indegrees. Reconstruction of near-complete dendritic inputs to individual LC-NE neurons reveals their broad yet sparse synaptic convergence across the brain. These synaptic inputs are not randomly distributed but instead organized according to sensory/motor modality, excitatory/inhibitory identity, and synaptic strength. Individual LC-NE neurons share common inputs, a feature conserved within and across monoaminergic systems, suggesting a co-innervation mechanism for coordinated neuromodulation. Thus, our study uncovers multi-level principles governing the spatial organization of LC-NE neurons broad-yet-sparse inputs and provides a pivotal resource for deciphering the microscale architecture of neuromodulatory systems.

neuroscience↗

bioRxiv2025

Global profiling of the proteome and acetylome in mice with abdominal aortic aneurysms

BackgroundAbdominal Aortic Aneurysm (AAA) is a life-threatening vascular condition characterized by a progressive dilation of the abdominal aorta and a high risk of rupture. Currently, no effective pharmacological therapies are available, and treatment relies mainly on surgical intervention. The lack of drug targets is largely due to limited understanding of the molecular mechanisms underlying AAA development. This study aims to identify potential therapeutic targets by performing comprehensive proteomic and acetylomic analyses in a mouse model of AAA. MethodsWe conducted global proteomic and acetylomic profiling on abdominal aortic tissues collected from an AAA mouse model. A total of 7,858 proteins and 1,790 acetylated proteins encompassing 4,581 acetylation sites were quantified. Bioinformatics analyses were performed to investigate the regulatory networks and biological pathways associated with these proteins and post-translational modifications. ResultsIntegrated analysis revealed that histone-related proteins were significantly enriched in pathways co-regulated by the proteome and acetylome. These pathways are influenced by histone acetyltransferases and deacetylases, implicating histone acetylation in AAA-related inflammatory and immune processes. Notably, the deacetylases Sirt2 and Sirt5 were identified as potential regulators of neutrophil extracellular trap (NET) formation through suppression of histone acetylation. ConclusionsThis study provides the first comprehensive proteomic and acetylomic dataset for abdominal aortic tissues in a mouse model of AAA, substantially expanding the mouse acetylation database. The results highlight the central role of histone modifications in immune dysregulation and inflammation, and propose Sirt2 and Sirt5 as candidate therapeutic targets that may inhibit NET formation and slow AAA progression. These findings suggest new avenues for drug development targeting the sirtuin family, although further experimental validation and cross-species studies are needed to confirm translational relevance. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/657536v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@1c397a3org.highwire.dtl.DTLVardef@188180org.highwire.dtl.DTLVardef@de1a59org.highwire.dtl.DTLVardef@193e740_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Competing Subclones and Fitness Diversity Shape Tumor Evolution Across Cancer Types

Intratumor heterogeneity arises from ongoing somatic evolution complicating cancer diagnosis, prognosis, and treatment. Here we present TEATIME (estimating evolutionary events through single-timepoint sequencing), a novel computational framework that models tumors as mixtures of two competing cell populations: an ancestral clone with baseline fitness and a derived subclone with elevated fitness. Using cross-sectional bulk sequencing data, TEATIME estimates mutation rates, timing of subclone emergence, relative fitness, and number of generations of growth. To quantify intratumor fitness asymmetries, we introduce a novel metric--fitness diversity--which captures the imbalance between competing cell populations and serves as a measure of functional intratumor heterogeneity. Applying TEATIME to 33 tumor types from The Cancer Genome Atlas, we revealed divergent as well as convergent evolutionary patterns. Notably, we found that immune-hot microenvironments constraint subclonal expansion and limit fitness diversity. Moreover, we detected temporal dependencies in mutation acquisition, where early driver mutations in ancestral clones epistatically shape the fitness landscape, predisposing specific subclones to selective advantages. These findings underscore the importance of intratumor competition and tumor-microenvironment interactions in shaping evolutionary trajectories, driving intratumor heterogeneity. Lastly, we demonstrate that TEATIME-derived evolutionary parameters and fitness diversity offer novel prognostic insights across multiple cancer types.

bioinformatics↗