Biology subjects

Zhou, Z.

Publications and source records attributed to Zhou, Z..

At least 109 records · Page 6Linked to original sources

bioRxiv2025

A Shared Adenocarcinoma Transcriptomic Program Enables Prediction of Therapeutics Applicable Across Tissues

BackgroundAdenocarcinomas are malignancies arising from glandular epithelial cells or secretory tissue, and account for most cancer-related mortalities worldwide, despite advances in treatment. New treatments are often developed in the context of the organ from which the tumor originates. However, there exists shared biology across glandular epithelium at the molecular level regardless of organ system, and there has been a shift towards the molecular classification of tumors. Defining and targeting pan-adenocarcinoma specific molecular features may facilitate the development of treatments applicable across adenocarcinomas, expediting drug discovery efforts and translation to the clinical setting. MethodsWe performed an integrated transcriptomics analysis of adenocarcinomas originating from different organ systems. RNA sequencing expression profiles from lung adenocarcinoma (LUAD), stomach adenocarcinoma (STAD), and colorectal adenocarcinoma (COAD) with matched normal tissue from The Cancer Genome Atlas (TCGA) was used to discover a pan-adenocarcinoma specific transcriptomic module. A standardized DESeq2 based pipeline was used to test for differentially expressed genes (DEGs) between each adenocarcinoma and its matched normal tissues, followed by cross-cancer comparisons to identify a consensus transcriptional module. Enrichment analysis was performed to reveal biological pathways associated with the consensus module, with a particular focus on those involved in oncogenesis. Prognostic significance of the consensus transcriptional module was evaluated using survival modeling, and the module was tested against in vitro transcriptomic drug perturbation signatures using Connectivity Map (cMAP) analysis to identify candidate drugs targeting adenocarcinomas, regardless of organ system of origin. ResultsDespite the diversity of adenocarcinomas tested, there existed a significant and large overlap of the genes dysregulated across tested tumors. A consensus transcriptomic module was defined, and it predicted patient prognosis in each of the three adenocarcinomas. Leveraging the top shared biomarkers through cMAP analysis, we identified 36 FDA-approved drugs that are capable of reversing the shared malignant transcriptional module towards the normal state. Among the FDA-approved drugs were the EGFR and ALK inhibitors gefitinib and crizotinib, both currently used for treating LUAD, providing validation the pipeline could discover efficacious drugs. Taken together, we developed an approach for organ system independent cancer biomarkers and drug discovery, and leveraged it to identify drug candidates for expanded use in adenocarcinomas. The FDA-approved drugs identified through this pipeline serve as candidates to repurpose as pan-adenocarcinoma anti-cancer therapeutics, reducing both time and cost for drug development. ConclusionOur research provided insights into the common molecular mechanisms across multiple adenocarcinomas and unveiled potential drug candidates for future therapeutic testing.

cancer biology↗

bioRxiv2025

Enterococcus faecalis delivers Obg GTPase via extracellular vesicles to instigate mTOR activity and promote HCC tumorigenesis

Hepatocellular carcinoma (HCC) is a malignant tumor that has been associated with dysbiosis of the gut microbiota. However, how the gut microbiota plays an oncogenic role in HCC remains largely unknown. Here, we show that Enterococcus faecalis (E. faecalis) is highly enriched in liver tumor tissues and is positively correlated with pathogenesis of HCC. E. faecalis promotes liver cancer cell proliferation, protein translation, cell migration and tumorigenesis. Mechanistically, we found that EF-derived extracellular vesicles (EF-EVs) deliver EF-Obg GTPase to activate host mTOR, thereby promoting liver cancer progression. Intriguingly, the EF-Obg protein exerts its influence on the mTOR pathway via a Ras-like G domain involved in GTP binding. EF-Obg exhibits a striking homology with the G1 site of the G domain of Rheb, a key positive regulator of mTOR. Obg gene is critical for E. faecalis to activate mTOR to promote hepatocarcinogenesis based on an engineered obg knockdown strain by CRISPR interference experiment. Clinically, abundant EF-Obg protein expression is correlated with enhanced activation of mTOR, leading to poor overall survival in HCC patients. Significantly, treatment of mTOR inhibitor Everolimus confers effectiveness in EF-colonized liver cancer orthotopic model, suggesting that Everolimus therapeutic approach can be effective for liver cancer patients with enriched E. faecalis. Taken together, we provide mechanistic and functional evidence to verify a direct causal relationship between tumor-resident E. faecalis enrichment and liver carcinogenesis, revealing that EF-Obg functions as a previously unidentified cross-kingdom activator of mTOR to promote liver tumorigenesis.

cell biology↗

bioRxiv2025

Prolonged oscillating kisspeptin neuron activity underlies the preovulatory luteinizing hormone surge in mice

The population of kisspeptin neurons located in the rostral periventricular area of the third ventricle (RP3V) is thought to have a key role in generating the GnRH surge that triggers ovulation. Using a modified GCaMP fibre photometry procedure, we have been able to record the in vivo population activity of RP3VKISS neurons across the estrous cycle of female mice. A marked increase in GCaMP activity was detected beginning on the afternoon of proestrus that lasted in total for 13{+/-}1 hours. This was comprised of slow baseline oscillations with a period of 91{+/-}4 min and associated with high frequency rapid transients. Very little oscillating baseline or transient activity was detected at other stages of the estrous cycle. Concurrent blood sampling showed that the peak of the LH surge occurred 3.5{+/-}1.1 h after the first baseline RP3VKISS neuron baseline oscillation on the afternoon of proestrus. The time of onset of RP3VKISS neuron oscillations varied between mice and across subsequent proestrous stages in the same mice. To assess the impact of estradiol on RP3VKISS neuron activity, mice were ovariectomized and given an incremental estradiol replacement regimen. Minimal patterned GCaMP activity was found in OVX mice, and this was not changed acutely by any of the estradiol treatments. However, on the afternoon of the expected LH surge, the same oscillating baseline activity with associated transients occurred for 7.1{+/-}0.5 h. These observations reveal an unexpected prolonged oscillatory pattern of RP3VKISS neuron activity that is dependent on estrogen and underlies the preovulatory LH surge as well as potentially other facets of reproductive behavior.

neuroscience↗

bioRxiv2025

Hypoxia-induced metastatic heterogeneity in pancreatic cancer

In most solid tumors, hypoxia constitutes a defining microenvironmental feature that reprograms malignant cells into a highly metastatic state by driving cellular plasticity and exacerbating chromosomal instability (CIN). However, the mechanisms by which cancer cells concurrently co-opt these elements of hypoxic adaptation to promote metastasis remains poorly understood. Here, we report that hypoxia promotes metastasis by suppressing the JmjC-containing histone lysine demethylase Kdm8. CRISPR/Cas9-mediated targeting of Kdm8 in a Kras;Trp53-driven mouse model of pancreatic ductal adenocarcinoma (PDA) robustly rewires the malignant cell transcriptomic programs, leading to a profound loss of the epithelial morphology and widespread metastatic disease. In PDA patients, a high KDM8-induced gene signature is associated with reduced metastatic burden and better survival in advanced disease. Notably, Kdm8 suppression in normoxia recapitulates key aspects of the global epigenetic and transcriptomic rewiring, mitotic spindle defects, and CIN induced by hypoxia. Moreover, disruption of Kdm8s demethylase activity phenocopies Kdm8 loss, whereas expression of hypermorphic Kdm8 variants resistant to hypoxic suppression markedly reduces metastasis beyond the levels achieved by the wildtype protein. Through the suppression of Kdm8 demethylase function, hypoxia unleashes a potent metastatic program by simultaneously advancing cellular plasticity and CIN.

cell biology↗

bioRxiv2025

IsoPairFinder: A tool for biochemical pathway discovery using stable isotope tracing metabolomics

SummaryThe functional annotation of microbial genes lags far behind genome sequencing, leaving critical gaps in our knowledge of metabolic pathways. While integrating genetic manipulation with stable isotope tracing (SIT) metabolomics holds promise for pathway discovery, existing tools lack specialized capabilities for gene perturbation experiments. To address this need, we developed IsoPairFinder, a computational tool that identifies pathway intermediates by analyzing paired unlabeled (12C) and isotope-labeled (13C) metabolomics data from gene-edited microbes. By prioritizing substrate-specific feature pairs, IsoPairFinder efficiently prioritizes biologically relevant intermediates. Implemented as an open-source R package and integrated into the GNPS2 ecosystem, IsoPairFinder provides an accessible platform for the research community to accelerate novel pathway discovery and validation. Availability and implementationIsoPairFinder is freely available as an open-source R package (https://github.com/DoddLab/IsoPairFinder) and as a web-based workflow in the GNPS2 ecosystem (https://gnps2.org/workflowinput?workflowname=isopairfinder_nextflow_workflow). Comprehensive tutorials are provided at https://doddlab.github.io/IsoPairFinder_Tutorials. These implementations support both command-line and graphical analysis workflows. Contactddodd2@stanford.edu Supplementary informationSupplementary information is available at Bioinformatics online.

bioinformatics↗

bioRxiv2025

ProStab: Prediction of protein stability change upon mutations by protein language and inverse folding models

Predicting protein stability change upon mutation is critical for protein engineering, yet remains limited by the modeling assumptions of physics-based methods and the generalization bottlenecks of data-driven approaches. We present ProStab, a deep learning framework that integrates sequence- and structure-based information, including the mutation-aware sequence embeddings from protein language models and the geometric features extracted via an inverse folding model. Trained on the large-scale Megascale dataset, ProStab demonstrates strong performance across diverse test sets and robust generalization across distribution shifts between the training and test sets. In head-to-head comparisons, ProStab outperforms all state-of-the-art methods with consistently higher Spearman correlation and precision. To evaluate its practical utility, we experimentally validated ProStab-predicted mutations on the model enzyme transaminase. Among the 16 successfully expressed variants, 4 exhibited improved thermal stability. Remarkably, the 1st top-ranked predicted mutation yielded the highest observed enzymatic activity, retaining three-fold that of the wild type after 10 minutes at 40 {degrees}C. To facilitate broader application, a publicly accessible web server has been developed. We envisage that ProStab provides a scalable and accurate platform for intelligent protein stability design.

bioinformatics↗

bioRxiv2025

Tracking the Evolutionary Patterns of RNA Modifications from Bryophyte to Flowering Plants by Mass Spectrometry

RNA modifications are critical for the regulation of gene expression. Comprehensive profiling of RNA modifications is a prerequisite for unravelling their identities and functions. The plant hormone auxin plays pivotal roles in regulating plant growth, development, and environmental responses. In contrast to the extensive studies in animals, there is a paucity of knowledge regarding the existence and functions of RNA modifications in plants. This is due to the low abundance of modified ribonucleosides and the lack of accurate analytical methods. This study employs a state-of-the-art hydrophilic interaction liquid chromatography-tandem-mass spectrometry (HILIC-MS/MS) platform to achieve the sensitive detection of 12 nucleosides at the pM to nM level. Utilizing this platform, we were able to identify N4,2-O-dimethylcytidine (m4Cm) in plants by LC-MS/MS for the first time, which provided direct evidence for the widespread of this modification in plants. Moreover, a comprehensive investigation was undertaken to examine the 12 types of RNA modifications and their response patterns to auxin in a range of prototype plants, spanning from bryophyte (Marchantia polymorpha) to flowering plants (Arabidopsis thaliana, Oryza sativa, and Zea mays). The evolutionary patterns of the 12 RNA modifications were depicted from bryophyte to flowering plants, and their potential functions in plant growth, development, and auxin responses were revealed.

biochemistry↗

bioRxiv2025

Dachsous-Fat Signaling Shapes the Drosophila Wing through Mechanical Forces

Proper organ shape is critical for function. The Drosophila wing normally adopts an elongated shape, but mutations in the Dachsous-Fat pathway result in rounder wings. The mechanism by which this occurs has remained unclear. Here, we show that Ds-Fat signaling shapes the wing during the larval stage, rather than during pupal development when morphogenetic rearrangements transform the developing wing disc into the adult wing. We further find that Ds-Fat alters tissue wide stresses in the wing disc, and genetic manipulations that reduce cytoskeletal tension result in rounder wings, whereas increasing cytoskeletal tension produces more elongated wings. Reduced tension is also associated with less oriented growth during development. Notably, increased cytoskeletal tension partially rescues the rounder shape caused by ds knockdown. These results reveal a previously unrecognized mechanism by which Ds-Fat signaling determines wing shape, involving regulation of tissue tension to orient growth and shape the wing primordia during larval development.

developmental biology↗

bioRxiv2025

NAB2-STAT6 Fusion Proteins Drive Nuclear Condensate Formation and Transcriptional Reprogramming in Solitary Fibrous Tumors

Solitary fibrous tumor (SFT) is a rare and aggressive sarcoma driven by NAB2-STAT6 gene fusions, yet effective targeted therapies remain unavailable. Here, we report that the NAB2ex4-STAT6ex2 fusion variant forms nuclear condensates via liquid-liquid phase separation (LLPS) in engineered fibroblast models and primary SFT cells. These condensates co-localize with BRD4S and EGR1, key transcriptional regulators, and are functionally active, driving widespread transcriptional reprogramming. Treatment with Mithramycin A, a compound that disrupts EGR1-DNA interactions, dissolves NAB2-STAT6 condensates and reverses their aberrant gene expression and chromatin binding signatures. Our findings uncover a previously unrecognized role for NAB2-STAT6 in condensate-mediated oncogenic signaling and provide a mechanistic rationale for condensate-targeted therapy in SFT.

cancer biology↗

bioRxiv2025

Integrative Analysis of the Mouse Cecal Microbiome Across Diet, Age, and Metabolic State in the Diverse BXD Population

BackgroundThe gut microbiota adapts to and shapes the hosts metabolic state through affecting circulating metabolites and consequent gene regulatory networks, resulting in systemic influences in diverse organs via connections such as the gut-liver axis. Numerous variables such as diet, age, and host genetics modulate the composition of the gut microbiome, but their interactions and specific associative and mechanistic links to host molecular phenotypes remain incompletely unannotated. Integrated multi-omics approaches in genetically diverse populations offer an opportunity to dissect these interactions and identify predictive microbial signatures for host phenotypes, such as body weight, and molecular associations with gene expression pathways in gut and liver. ResultsWe sequenced, aligned, and integrated the cecal metagenome, metatranscriptome, and host transcriptome from 232 mice across 175 distinct cohorts according to a low fat chow diet (CD) or a high-fat diet (HF), four adult ages (between roughly 180 to 730 days of age), and 43 distinct genotypes (inbred BXD strains). Genetics and diet exerted the strongest influence on microbiota abundance and activity, followed by age. HF feeding significantly reduced diversity across all ages and all genotypes, altering >300 species. Machine learning models based on microbial profiles reliably predicted body weight within dietary group (AUC = 0.84 for CD, 0.79 for HF) and chronological age (AUC = 0.84), with model performance rising of age prediction rising to 0.95 when integrating top microbial features with liver proteomics. Network analyses of expression data revealed links between genes, pathways, and specific microbes, including a negative association between cecal Ido1 expression and short-chain fatty acid (SCFA)-producing Lachnospiraceae, suggesting dietary fat may modulate host tryptophan metabolism through microbiota shifts. ConclusionsWhole metagenome and metatranscriptome sequencing approaches have massively expanded the landscape of microbiome analysis compared to earlier short-read 16S analyses. The resulting datasets quantify hundreds of uniquely identifiable microbes, which can be used to create sets of highly predictive microbial biomarkers for aging and obesity. When trained on controlled mouse populations, these results demonstrate that microbiome profiling can achieve high predictive capacity (AUC = 0.95 with multi-omics integration) for complex readouts such as age and body weight (AUC = 0.84), even considering genetic and dietary variation, establishing a framework for biomarker development. While at present many bacteria are still functionally unannotated at the species level, multi-omics approaches -- including gene expression from the host tissues -- provide insights into the functional associations of specific taxa in the microbiome.

microbiology↗

bioRxiv2025

Population-level super-pangenome reveals genome evolution and empowers precision breeding in watermelon

Pangenomes are increasingly critical for harnessing crop genetic diversity, yet their resolution and utility are often limited by insufficient sampling of high-quality genome assemblies. Here, we report a population-level watermelon super-pangenome constructed from 138 reference-grade assemblies, including 135 newly generated near-gapless genomes representing all seven watermelon species. The super-pangenome captures approximately one million structural variants (SVs), enabling accurate variant genotyping across ~900 watermelon accessions and substantially expanding variant discovery both across and within species. Broader sampling within the pangenome provides insights into genome evolution among watermelon species and sheds light on the origin of cultivated watermelon. SV-inclusive genome-wide association studies enhance trait mapping resolution and identify a copy number variation upstream of ClFCI1 that regulates flesh color intensity in a dosage-dependent manner. Leveraging this comprehensive variation map, we developed high-accuracy genomic prediction models for 18 agronomic traits. Together, our findings and genomic resources establish a foundational framework for dissecting complex traits and accelerating precision breeding in watermelon, while offering a valuable model for SV-resolved pangenomics in crop species.

plant biology↗

bioRxiv2025

TMEM63 proteins act as mechanically-activated cholesterol modulated lipid scramblases contributing to membrane mechano-resilience

OSCA/TMEM63 mechanosensitive ion channels play critical physiological roles in plants and animals. These channels bear structural homology to the dual functional TMEM16 family, and OSCA1.2 was recently shown to form a lipid-lined ion conduction pathway in the open state. This raised the question of whether members of the OSCA/TMEM63 family may also function as mechanically activated lipid scramblases. Using a combination of in vitro and cellular assays with computational techniques, we show that phospholipids can be translocated through the open pores of OSCA1.1/1.2/2.2 and TMEM63A/B proteins, suggesting a dual ion channel and lipid scramblase function for members of this protein family. We characterize the effects of mutating key groove lining residues demonstrating that different residues form bottlenecks for lipids and ions respectively and show that cholesterol inhibits lipid scrambling by stabilizing the closed state and slowing translocation through the open pore. We show that lipid scrambling in TMEM63 proteins can be activated by mechanical forces in the membrane, making these the first identified mechanically activated lipid scramblases. Finally, we demonstrate that this activity is important for the mechanically induced morphological remodeling of biological membranes and the resilience of cells to high mechanical forces.

biophysics↗

bioRxiv2025

From Family Relationship to Regulation: Parent-Child Amygdala Activation Similarity is Linked to Prefrontal Recruitment and Youth Emotional Adaptation

Extensive research highlights the importance of parent-child similarity at psychological, behavioral, and physiological levels in fostering childrens adaptation. While emerging studies have begun to explore neural similarity, little is known about the factors that influence parent- child neural similarity during adolescence or how such similarity may support youth emotional adaptation when coping with heightened uncertainty. The current study investigated the relations among family relationship quality, parent-youth neural similarity, and youth emotional adaptation using task-based fMRI. With 50 participants (25 parent-youth dyads; parent: Mage = 43.88{+/-}7.42 years, 72% female; youths: Mage = 11.16{+/-}2.85 years, 44% female), we found that higher parent-youth neural similarity in the amygdala activation patterns during emotionally uncertain situations was associated with better youths emotional adaptation, as indicated by lower levels of anxiety and depression and higher resilience. This neural similarity mediated the beneficial effects of positive family relationships on youths emotional adaptation. Additionally, amygdala activation similarity was associated with amygdala to dorsolateral prefrontal cortex (dlPFC) connectivity during uncertainty in youth, revealed by generalized psychophysiological interaction (gPPI) analyses. This amygdala-dlPFC connectivity, in turn, was linked to better emotional adaptation in youth. Importantly, these findings were specific to uncertainty-related bottom-up processing, rather than effortful top-down regulation processing or responses to certain aversive stimuli. These findings offer new insight into how relational and neural factors jointly shape youth emotional adaptation, and suggest that parent-child neural similarity in the amygdala may serve as a mechanism for supporting implicit emotion regulation, helping youth cope with uncertain situations. Significance StatementDuring adolescence, youth begin to expand their social worlds beyond the family, yet family relationships continue to shape how they navigate emotionally complex environments. How do these relationships influence youth brain function and emotional well-being? Using task-based fMRI, we found that greater parent-child neural similarity in the amygdala during emotionally uncertain situations is associated with youths lower anxiety and depression and greater resilience. This amygdala neural similarity was also linked to prefrontal engagement in youth during early arousal stage, supporting adaptive implicit regulation under uncertainty. These findings suggest that neural alignment between parents and youths may serve as a key mechanism by which positive family relationships promote emotional adaptation, highlighting the enduring role of family context in adolescent brain development.

neuroscience↗

bioRxiv2025

Functional screening of TCR-like antibodies for targeted cancer immunotherapy

Although hybridomas and display technologies have long been used in antibody discovery, identifying TCR-like antibodies that recognize major histocompatibility (MHC)-presented neoantigens remains an inefficient process. Here, we present a functional screening approach for antibody discovery based on T cell-specific functionality. By constructing Synthetic T cell receptor and Antigen Receptor (STAR)-T cells expressing antibody libraries and co-culturing them with target cells, we observed distinct antigen-specific STAR endocytosis and T cell activation. These two indicators are combined into an E-A functional index, which we demonstrate as an effective screening strategy for identifying antibodies with strict antigen sensitivity and specificity. Using this E-A functional screening method, we successfully discovered antibodies or nanobodies targeting cell-surface tumor antigens and neoantigens presented by human leukocyte antigen (HLA). These antibodies exhibited potent anti-tumor efficacy both in vitro and in vivo. Moreover, converting these antibodies into chimeric antigen receptors (CARs) and bispecific antibodies also induced T cell activation and tumor killing. In summary, our study establishes a robust functional screening strategy for identifying antibodies or TCR-like nanobodies targeting tumor-specific neoantigens, offering significant potential for advancing targeted cancer therapies.

immunology↗

bioRxiv2025

A gradient of complementary learning systems emerges through meta-learning

Long-term learning and memory in the primate brain rely on a series of hierarchically organized subsystems extending from early sensory neocortical areas to the hippocampus. The components differ in their representational attributes and plasticity, with evidence for sparser, more decorrelated activity and faster plasticity in regions higher up in the hierarchy. How and why did the brain arrive at this organization? We explore the principles that allow such an organization to emerge by simulating a hierarchy of learning subsystems in artificial neural networks (ANNs) using a meta-learning approach. As ANNs optimized weights for a series of tasks, they concurrently meta-learned layer-wise plasticity and sparsity parameters. This approach enhanced the computational efficiency of ANNs, promoting hidden activation sparsity while benefitting task performance. Meta-learning also gave rise to a brain-like hierarchical organization, with higher layers displaying faster plasticity and a sparser, more pattern-separated neural code than lower layers. Early layers peaked early in their plasticity and stabilized, whereas higher layers continued to develop and maintained elevated plasticity over time, mirroring empirical developmental trajectories. Moreover, when trained on dual tasks imposing competing demands for item discrimination and categorization, ANNs with parallel pathways developed distinct representational and plasticity profiles, convergent with the distinct properties observed empirically across intra-hippocampal pathways. These results suggest that the macroscale organization and development of heterogeneous learning subsystems in the brain may emerge in part from optimizing biological variables that govern plasticity and sparsity.

neuroscience↗

bioRxiv2025

Controlling treatment toxicity in ovarian cancer to prime the patient for tumor extinction therapy

High-grade serous ovarian cancer (HGSOC) remains a major clinical challenge. In particular among those patients with homologous recombination (HR)-proficient tumors (>50%), most eventually succumb to their disease due to high recurrence rates, acquired resistance, and cumulative toxicity. This report summarizes work from the 12th IMO Workshop in which we explored an alternative "extinction therapy" strategy for frontline treatment of HGSOC. Inspired by ecological principles, this multi-strike approach aims to eradicate tumors not through a singular "magic bullet" but through a series of therapies after standard frontline treatment when the tumor is still, and perhaps most, vulnerable. We present a framework leveraging mathematical modeling (MM) to develop personalized multi-strike protocols for HGSOC. Key contributions include: 1) An "IMOme" score using liquid biopsy data to assess patient-specific hematopoietic toxicity risk, guiding the timing and selection of subsequent therapies, 2) MM strategies to design effective lowdose combinations of targeted agents to achieve synthetic lethality while managing toxicity, and 3) A MM framework to analyze the interplay between chemotherapy, gut microbiome toxicity, and immunotherapy, demonstrating how mitigating microbiome damage could enhance immune response. Overall, the computational approaches presented herein aim to support the design of personalized, multi-strike regimens in the frontline setting that proactively target tumor extinction while managing toxicity, ultimately seeking to deliver cures for patients with HGSOC.

cancer biology↗

bioRxiv2025

Domain-General Brain Networks Support Language Development

Understanding the neural basis of verbal intelligence across development requires disentangling the contributions of domain-general and language-selective brain systems. Although language is often considered a domain-specific function, complex language tasks also engage domain-general networks, such as the Default-Mode (DM) and Multiple-Demand (MD) systems. Yet how these systems contribute to the maturation of verbal competence remains poorly understood. Here, we examined this question using gray matter volume measures in an accelerated longitudinal dataset of children and adolescents from Beijing (N = 170), using the Verbal Comprehension Index (VCI) from the Wechsler Intelligence Scale as a benchmark for verbal abilities. We observed that individual differences in VCI were more strongly associated with structural maturation of domain-general networks (DM and MD) than with the language-selective network, and that these effects varied with age. Targeted validation in an independent cohort from Chongqing (N = 150) confirmed significant contributions of domain-general networks in adolescence (13-15 years), highlighting the robustness of these developmental effects. These findings suggest that domain-general cortical systems play a critical and previously underappreciated role in the emergence of verbal intelligence during adolescence, with implications for understanding how large-scale brain networks support the development of abstract verbal reasoning.

neuroscience↗

bioRxiv2025

CAT-APP: Contamination Analysis and Tempering--An Automated Online Platform for Plasma Proteomics with Data Rescuing Capabilities

Plasma and serum proteome profiling have become central to biomarker discovery for medical research. The advent of high-throughput plasma proteomics pipelines has enabled the generation of massive datasets. However, biomarker discovery is frequently compromised by sample contamination, primarily from erythrocytes, platelets, and coagulation factors. Such contamination can lead to the false identification of contaminants as biomarkers in over 50% of studies or obscure genuine biomarkers due to contamination-induced variability. Currently, no tools are available to salvage such compromised data, and the default approach is often to discard the affected samples. To address this gap, we introduce CAT-APP (Contamination Analysis and Tempering-An Automated Online Platform for Plasma Proteomics). CAT-APP tackles contamination through three key modules: multi-dimensional contamination assessment and adaptive contamination indexing, mathematical model-based contamination tempering, and data recovery evaluation with visualization. We applied CAT-APP to 28 independent plasma proteomics datasets and found that 82% exhibited contamination issues. CAT-APP markedly improved data quality by removing false-positive biomarkers introduced by contamination and restoring true biological signals that were previously obscured in representative datasets. To promote widespread adoption, we provide CAT-APP as an freely accessible, user-friendly, and robust web platform at https://www.bloodecosystem.com/tools/CAT-APP/.

bioinformatics↗