Biology subjects

Cheng, X.

Publications and source records attributed to Cheng, X..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

The in vivo inhibitory function of the MHC-I alpha3 domain/CD8alpha interaction

The interaction between the Major Histocompatibility Complex Class I (MHC-I) 3 domain and CD8 has classically been viewed as a positive coreceptor interaction that stabilizes TCR signaling during antigen recognition. However, its physiological function in mature peripheral CD8+ T cells in vivo remains incompletely understood. Here, we identify the MHC-I 3 domain-CD8 interaction as a previously unrecognized inhibitory pathway that tonically restrains peripheral CD8+ T-cell activation and maintains T-cell tolerance in vivo. Antibody-mediated disruption of the MHC-I 3 domain-CD8 interaction induced spontaneous activation of peripheral CD8+ T cells without impairing their survival, lowered the threshold for antigen-induced activation, and enhanced responsiveness to cognate peptide stimulation. In peptide-induced OT-I T-cell anergy models, blockade of either H-2Db or H-2Kb 3 domain interactions with CD8 prevented the induction of anergy and restored responsiveness of previously anergic T cells. Notably, blockade of the H-2Kb 3 domain enhanced OT-I responses despite simultaneously disrupting the classical positive coreceptor interaction within the TCR-peptide-MHC complex, indicating that tonic inhibitory signaling mediated by the MHC-I 3 domain predominates under these conditions. Together, these findings redefine the classical MHC-I-CD8 interaction as a bidirectional pathway that not only supports antigen recognition but also imposes tonic inhibitory control over peripheral CD8+ T cells. These results identify the MHC-I 3 domain-CD8 axis as a potential target for reversing T-cell tolerance and enhancing antitumor or antiviral immunity.

immunology↗

bioRxiv2026

Chemogenetic Inhibition of the Cortical Amygdala Reduces Alcohol Intake and Restores Thalamic Connectivity in Dependent Female Mice

BackgroundAlcohol use disorder is a chronic relapsing condition characterized by excessive drinking and withdrawal symptoms. Alcohol dependence disrupts function across multiple brain regions, and recent evidence implicates the cortical amygdala (CoA) as a critical node in alcohol-related circuits. However, how CoA activity influences alcohol intake and brain-wide network function during withdrawal remains unclear. MethodsAlcohol dependence was induced using chronic intermittent ethanol vapor (CIE). In one cohort, electrophysiological activity of CoA neurons was assessed during withdrawal. In a second cohort, mice underwent CIE paired with two-bottle choice drinking, and inhibitory DREADDs (hM4Di) were used to suppress CoA activity during drinking and withdrawal while behavioral outcomes were measured. Brains were then collected for Fos immunolabeling and iDISCO+ based whole-brain activity mapping to determine how CoA inhibition during withdrawal altered network organization. ResultsRepeated CIE increased alcohol sensitivity in CoA neurons during withdrawal. Chemogenetic inhibition of the CoA reduced alcohol intake in dependent mice without affecting withdrawal-related behaviors. Whole-brain Fos mapping showed that CoA inhibition reduced activity within the CoA while enhancing functional connectivity across multiple brain regions, particularly in the isocortex, thalamus, and anterior hypothalamic nucleus. During withdrawal without CoA inhibition, thalamic regions exhibited negative connectivity, consistent with disrupted network function; CoA inhibition reversed this pattern, producing strongly positive thalamic and medial prefrontal cortex connectivity. ConclusionsThese findings demonstrate that alcohol dependence alters CoA sensitivity, alcohol dependence-induced drinking and brain-wide network organization during withdrawal. The CoA appears to selectively regulate withdrawal-associated alcohol drinking, and its inhibition may reduce intake by restoring thalamic and cortical connectivity. HighlightsO_LIThis study identifies the cortical amygdala as a previously underexplored brain region involved in alcohol-related behaviors. C_LIO_LIBy integrating chemogenetic inhibition with brain-wide network analysis, the study reveals candidate circuit connections through which the CoA may regulate alcohol dependence-related brain activity. C_LIO_LIThis study establishes the CoA as a potential driver of excessive alcohol drinking and alcohol-related network dysfunction. C_LI

neuroscience↗

bioRxiv2026

Bipartite DNA binding domain of transcription factor BCL11B binds clustered short DNA sequence motifs

B-cell leukemia/lymphoma 11B (BCL11B), despite its name, is a key regulator of T-cell development, specification, and T-cell malignancies. BCL11B contains a bipartite DNA binding domain composed of two C2H2 zinc finger arrays: low-affinity ZF2-3 and high affinity ZF4-6. These arrays function as homotypic modules that recognize similar six-nucleotide motifs, TG(O_SCPLOWNC_SCPLOW)CC(O_SCPLOWCC_SCPLOWO_SCPCAP/C_SCPCAPO_SCPLOWTC_SCPLOWO_SCPCAP/C_SCPCAPO_SCPLOWAC_SCPLOW), as seven of the eight DNA base-contacting residues are conserved between them. The most conserved interactions involve GG dinucleotides, contacted by arginine and lysine residues at key base-interacting positions in ZF3 and ZF5. The two ZF arrays are connected by a long [~]300-residue linker that provides flexibility in how the arrays engage DNA, allowing ZF2-3 and ZF4-6 binding to the same or opposite strands with variable orientation, spacing and positioning along the DNA. This extended linker is enriched in serine/threonine, acidic residues (aspartate/glutamate), and structural residues (glycine/proline), providing additional layers of transcriptional regulation possibly through post-translational modification, electrostatic modulation, and/or condensate formation. We also examined six missense mutations in base-interacting residues, that are associated with neurodevelopmental disorders. Substitutions replacing bulky, positively charged arginine or lysine with smaller or hydrophobic residues likely reduce DNA-binding affinity and/or specificity, whereas substitutions between asparagine and lysine may alter base recognition preferences.

biochemistry↗

bioRxiv2026

A motif for domain-specific analysis applets that are easy to learn, reuse, test, and to compose into pipelines: application to vision science

Scientific progress depends on the analysis of primary data, yet the small, domain-specific programs that perform most scientific analyses are typically poorly documented, narrowly tested, and difficult to reuse outside the lab that created them. General-purpose pipeline tools address the problem of running steps in order but do not enforce documentation, testing, or standardized outputs. We describe a motif for building domain-specific analysis applets, which we call calculators, that constrains developer choices in order to produce code that is readable, tested, and reusable almost as a byproduct of following the template. Calculators operate on a typed, searchable database of documents, eliminating the need to explicitly wire inputs and outputs together; instead, each calculator searches the database for documents it can operate on and adds its results as new typed documents. Calculators must provide documentation in a standard location, self-tests that can be run and inspected interactively, adjustable input parameters, a single well-defined output document type, and a default plotting method. Sets of calculators compose naturally into pipelines whose outputs satisfy FAIR principles at every stage. We demonstrate the motif by implementing calculators for common analyses in vision science, including orientation and direction selectivity, contrast tuning, spatial and temporal frequency tuning, speed tuning, and Hartley reverse correlation. These calculators have been used in published work and are in active use across collaborating laboratories. We discuss the design principles of the motif, its advantages and limitations, and its applicability to domain-specific computation across neuroscience and beyond. Significance StatementScientists often must write small programs to analyze their own data. These programs are usually poorly documented, lightly tested, and hard for other labs to reuse. Mistakes in this kind of code have even caused well-known papers to be retracted. We describe a simple pattern for writing these programs, which we call a calculator. The pattern requires the programmer to include clear documentation, built-in tests, adjustable settings, and a standard form of output. Calculators work by searching a shared database for data they know how to handle, so many calculators can be chained together into a pipeline without extra setup. We show how this works by building calculators for common visual neuroscience analyses that other labs are already using.

neuroscience↗

bioRxiv2026

Large Language Model Agent Enables Autonomous Machine Learning Model Building for Biomedicine

Machine learning accelerates biomedical discovery, but creating effective predictive models requires specialized human expertise and demanding manual effort. Researchers must iteratively design pipelines, select architectures, and debug code. This challenge is particularly severe in biomedicine because of the heterogeneous datasets, sparse annotations, and complex evaluation protocols that are common in the domain. We present AIDO.Builder, an agentic artificial intelligence system that fully automates the entire life-cycle of biomedical model development. Provided only with a natural language task description and a target metric, AIDO.Builder autonomously constructs executable training and evaluation pipelines. The system selects suitable modeling strategies, executes experiments, and uses automated feedback-loop to iteratively revise its own code, configurations, and training procedures. It flexibly adapts to new tasks by training specialized models de novo or by using pretrained foundation models to build predictive models through task-appropriate adaptation. We show that across diverse biomedical benchmarks, AIDO. Builder produces highly competitive solutions against human alternatives, while eliminating the manual iteration previously required for robust model development. By automating the translation of raw data into reliable AI models, AIDO.Builder demonstrates how AI itself can be used to accelerate AI for biomedical research.

bioinformatics↗

bioRxiv2026

Agent-Guided De Novo Design of Nanobody Binders Against a Novel Cancer Target

Therapeutic antibody discovery remains slow and resource-intensive, with traditional methods providing limited control over epitope selection. We present a workflow for de novo nanobody design applied to a novel Desmoplastic Small Round Cell Tumor target encompassing four stages: (1) epitope identification guided by our hotspot recommendation agent using physical chemistry-based structure and sequence analysis tools with two curated databases (IEDB, PFAM), (2) de novo nanobody generation using three independent methods (RFantibody, IgGM, mBER) across multiple predicted antigen structures and nanobody frameworks, (3) multi-metric scoring including structural metrics from folding models, and in silico binding affinity from our sequence-based predictor, (4) high-throughput yeast surface display (YSD) screening followed by surface plasmon resonance (SPR) characterization of the specific binders. We generated 288,000 nanobody designs spanning eight target epitope regions and three variable domains of heavy chain-only antibody (VHH) frameworks. Multi-objective Pareto filtering with our candidate selection agent yielded 100,000 candidates for YSD screening with fluorescence-activated cell sorting (FACS). Of 116 enriched candidates advanced to SPR characterization, 46/116 (39.7%) produced reliable kinetic fits with Rmax [≥] 30 RU, yielding KD values from 0.66 nM to 305 nM (median 31.7 nM). These results show that an agent-guided computational workflow can design nanomolar to sub-nanomolar nanobody binders against a novel target without experimental structure or prior antibody information.

bioinformatics↗

bioRxiv2026

Harnessing AI to Build Virtual Cells

A virtual cell is a world model of a cell: a computational system that predicts, simulates and programs cellular processes across modalities and scales. An important path toward this goal is to model how genetic and chemical perturbations give rise to transcriptional responses, a core capability for disease understanding and drug discovery. However, current approaches remain expert-intensive, relying on iterative manual model design, training and debugging over months. Here we present VCHarness, an autonomous AI system that constructs perturbation-response models by combining an AI coding agent with multimodal biological foundation models. The system explores large spaces of architectures and training pipelines with minimal human intervention, iteratively generating, evaluating and refining candidate models. Across multiple perturbation-response benchmarks, VCHarness identifies architectures that outperform expert-designed approaches while reducing development time from months to days. It further uncovers non-obvious architectural patterns associated with improved performance, indicating that automated search can extend beyond conventional design strategies. These results suggest a shift from manually engineered models toward autonomous systems for constructing components of virtual cell world models, enabling scalable and data-driven exploration of cellular systems.

bioinformatics↗

bioRxiv2026

ARID1A deficiency unleashes centromeric RNA transcription to drive chromosomal instability and boosts PKMYT1 inhibitor efficacy via RNA sensing

Cancer gene-associated mutations and molecular hallmarks of chromosomal instability (CIN) are unexpectedly common in histologically normal cells and tissues. These emerging findings challenge the binary distinction between "normal" and "cancerous" cells and suggest that early tumorigenesis may commence against a background of widespread yet largely tolerated genomic instability. However, it remains largely unexplored how a cancer gene-associated mutation can initiate the development of CIN-like states in non-malignant cells and drive tumor evolution. ARID1A, a chromatin remodeling factor, was identified as the most frequently mutated gene in both gastric normal epithelium and tumors. This distinctive molecular convergence presents an opportunity to elucidate the mechanisms by which a cancer-associated gene facilitates the initiation of early CIN phenotypes and develop effective antitumor strategies. In the present study, using primary human gastric organoids, we employed optical genome mapping (OGM) and live-imaging technologies to demonstrate that ARID1A depletion induced a wide spectrum of structural variants (SVs), copy number variants (CNVs), and chromosomal segregation errors, characteristic features of CIN at a very early stage of gastric tumorigenesis. Mechanistically, ARID1A bound centromere repetitive satellite DNA (satDNA) sequences. Its SWI/SNF-associated chromatin remodeling activity was required for suppressing satDNA transcription and the production of -SatRNA, through restricting RNAPII elongation. Consequently, ARID1A depletion led to overexpression of -SatRNA, and a higher incidence of sister chromatid exchange (SCE), a sensitive indicator of CIN. Importantly, the elevated -SatRNA expression in ARID1A-deficient cells further established a dual therapeutic vulnerability for G2/M checkpoint blockade, such as PKMYT1 inhibitor (PKMYTi), by concurrently aggregating CIN-induced cell death and activating self-dsRNA sensing-mediated innate immune response. Notably, PKMYTi markedly promoted -SatRNA expression, aberrant release of these self-derived dsRNAs into the cytosol and a robust activation of the RIG/MDA5-MAVS-depenent type-I interferon response in ARID1A-depleted cells. As expected, PKMYTi potentiated the efficacy of immunotherapy in ARID1A-deficient gastric tumors. Together, our findings reveal that ARID1A deficiency unleashes centromeric -SatRNA transcription, which sets the molecular stage for tumor evolution and targeted therapy by coordinately inducing CIN and self-dsRNA-induced innate immune responses.

cancer biology↗

bioRxiv2026

Mapping Slow Speckle Dynamics to Probe Cellular Metabolic Activity In Vivo using Laser Speckle Contrast Imaging

SignificanceLaser speckle contrast imaging (LSCI) is widely used to measure blood flow, but speckle fluctuations may also encode biologically meaningful dynamics beyond perfusion. Foundational studies in dynamic light scattering (DLS) and micro-optical coherence tomography (OCT) have also demonstrated that slow coherent signal fluctuations can arise from energy-dependent intracellular motion in in vitro and ex vivo systems. Building upon these advances, recent work has shown that LSCI has the potential to detect slow speckle dynamics (SSD) correlated with cellular dynamics in vivo. However, the biophysical mechanisms underlying SSD in intact brain tissues remain insufficiently validated. Establishing a mechanistic bridge from controlled ex vivo and in vitro conditions to in vivo brain measurements is critical for translating speckle-based imaging beyond perfusion measurements to enable label-free assessment of cellular and metabolic activity in disease models. AimThe objective of this study is to investigate the biophysical origin of the SSD in vivo and evaluate its sensitivity to intracellular metabolic activity in brain tissue. ApproachWe utilize an epi-illumination LSCI system to measure speckle contrast as a function of camera exposure time and extract characteristic decorrelation time constants. SSD was investigated in acute mouse brain slices, where blood flow is absent, to eliminate vascular confounds. Cellular metabolism was systematically modulated using 2-deoxyglucose and glucose. Complementary in vivo measurements were performed to reveal SSDs response to hyperoxia and normoxia after ischemic stroke. ResultsSSD signals persisted in acute brain slices in the absence of blood flow. Inhibition of glycolysis significantly reduced SSD, while restoration of metabolic substrates partially recovered the signal. In in vivo measurements, SSD increased during hyperoxia compared to normoxia after ischemic stroke, suggesting increased oxygen-supported cellular metabolic activity. ConclusionsThese results indicate that SSD is sensitive to energy-dependent cellular processes closely tied to metabolic activity. SSD represents a previously uncharacterized, label-free in vivo optical contrast that enables assessment of cellular metabolic activity as well as vascular dynamics. This work establishes a mechanistic foundation for using SSD as a general optical marker of cellular viability in in vivo measurements.

neuroscience↗

bioRxiv2026

Using Light to Establish Habits in Laboratory Mice

Background and aimsPerseverative behaviours are commonly assessed using operant paradigms in which rodents work for drugs or food under physiological deprivation, limiting translational relevance to some behavioural addictions. Here we validated an operant paradigm in which the acquired behaviour is driven neither by physiological needs nor hedonic responses. MethodsMice were trained to lever-press for green light. Exp.1 used a within-subjects design to examine lever discrimination and whether responding could be "satiated" by light preexposure. Exp.2 examined instrumental contingency using a between-subjects design, with light delivery equated between contingent and non-contingent groups. Exp.3 replaced green light with dim red light producing less retinal photoreceptor excitation but comparable heat to assess non-photic cues. Exp.4 examined whether green light could affect food seeking different motivational states. ResultsIn Exp.1, green light supported lever discrimination. Among high responders, the satiation effect was modest (<15% reduction) and did not deter lever pressing. In Exp.2, instrumental contingency promoted response acquisition whereas random light delivery did not. In Exp.3, dim red light failed to sustain behaviour, producing [~]50% response decrement. In Exp.4, light potentiated food seeking under ad libitum feeding. Discussion and conclusionsResponse-contingent light serves as a reward to establish operant responding, which cannot be explained by alerting effects or thermal cues. Our study bridges the gap between animal models and findings from humans that coloured light may exacerbate smartphone use and that light therapy may reshape reward circuits in individuals with Internet gaming disorder symptoms [Li et al. (2026) Advanced Science 13:e14044].

animal behavior and cognition↗

bioRxiv2026

Physics-informed multi-encoder adaptive optics enables rapid aberration correction for intravital microscopy of deep complex tissue

Tissue-induced optical aberrations fundamentally constrain intravital microscopy in deep or complex biological specimens. While adaptive optics (AO) can compensate for these aberrations, conventional AO methods are limited by either guide-star dependency or slow correction speeds. Here, we develop MeNet-AO, a multi-encoder network-based AO method that enables rapid, guide-star-free aberration correction. By integrating a noise-resilient, structure-independent feature extraction model with a physics-informed multi-encoder architecture, MeNet-AO jointly decodes multiple large-amplitude aberration modes from wavefront-modulated image pairs, achieving an effective balance between prediction accuracy and temporal efficiency. Validated in living organisms, MeNet-AO improves fluorescence imaging in zebrafish brain and eye, enhances neuronal calcium transients and direction selectivity in mouse visual cortex, and enables subcellular-resolution microglial calcium imaging through thinned-skull windows - revealing spatiotemporally heterogeneous signaling patterns previously obscured by skull aberration. The speed and robustness of MeNet-AO in low-signal and scattering conditions establish it as a versatile platform for dynamic subcellular imaging deep within native tissue environments.

bioengineering↗

bioRxiv2026

Targeting FGFR signaling overcomes therapeutic resistance and immune evasion in oncogenic PIK3CA-driven serous-like endometrial cancer

Serous endometrial cancer (SEC) is an aggressive subtype of endometrial cancer (EC) with poor prognosis and limited treatment options. Here, we developed a clinically relevant, immunocompetent serous-like mouse model incorporating oncogenic PIK3CA mutation, Trp53 loss, and MYC overexpression. Using this model together with human EC cell lines, patient-derived organoids (PDOs), xenografts, and patient datasets, we investigated mechanisms underlying resistance to PI3K-targeted therapy. Single-cell profiling reveals that FGFR1/2 upregulation associates with intrinsic resistance, whereas FGFR3 characterizes acquired resistance. Dual FGFR and PI3K inhibition produced superior tumor control compared with either agent alone. Mechanistically, FGFR signaling promotes immune evasion by downregulating MHC-I/HLA-mediated antigen presentation and enriching M2-type tumor-associated macrophages. FGFR inhibition reversed these changes and synergized with anti-PD-1 therapy to enhance antitumor immune responses and establish durable immune memory. Collectively, these findings identify FGFR signaling as a key driver of therapeutic resistance and immune escape in SEC and support FGFR-targeted combination strategies.

cancer biology↗

bioRxiv2026

Regulation of BCL11A DNA binding and expression in human erythrocyte precursor HUDEP-2 cells

BCL11A is a transcription factor crucial for neurodevelopment and hematopoiesis. It regulates the developmental switch from fetal hemoglobin (HbF) to adult hemoglobin and is a major therapeutic target for sickle cell disease and {beta}-thalassemia. BCL11A exists in multiple isoforms, including the L isoform (containing a single two-finger ZF2-3 DNA-binding domain) and the XL isoform (containing two arrays: the two-finger ZF2-3 and the three-finger ZF4-6). We used three approaches to investigate BCL11A functions. First, we examined DNA recognition by BCL11A, which preferentially binds the short 6-bp DNA motif TGNCCA. ZF4-6 recognizes all four variants of this motif with distinct strand-specific interactions: TGTCCA on the top strand, TG(A/C)CCA on the complementary strand, and the palindromic TGGCCA on either strand. ZF2-3 also binds TGTCCA from the top strand, featuring a unique thymine interaction by ZF2 Phe388. Motif multiplicity within BCL11A binding sites may promote BCL11A oligomerization by enabling multiple ZF arrays to engage DNA simultaneously. Second, we treated HUDEP-2 cells (which express adult hemoglobin) with inhibitors targeting three epigenetic silencing marks - DNA methylation, histone H3 lysine 9 methylation or H3 lysine 27 methylation. All treatments, individually or in combination, increased HbF expression to varying degrees. Notably, FTX6058 markedly reduced BCL11A transcription and translation (likely via effects on LIN28B), while EML741 caused a partial reduction. Third, we screened 213 pomalidomide- and lenalidomide-derived compounds and quantified proportions of HbF+ cells by flow cytometry. Effects of four compounds were analyzed by protein mass spectrometry. Although BCL11A levels themselves were unchanged, all four compounds selectively decreased levels of known pomalidomide targets, with consistently decreased levels of the zinc-finger proteins IKZF1 and ZFP91. Together, our studies clarify how BCL11A recognizes DNA, how its expression can be modulated epigenetically, and how small-molecule degraders influence its regulatory network, providing new avenues for HbF reactivation therapies.

biochemistry↗

bioRxiv2026

Downregulation of Satb1 is required to prevent autoimmunity by maintaining Tfh homeostasis

T follicular helper (Tfh) cells are a specialized subset of CD4 T cells that localize to germinal centers (GC), where they provide critical help to B cells through the delivery of IL-21 and other cytokines. Here, we demonstrate that the tight control of the chromatin remodeler Special AT-rich sequence-binding protein 1 (Satb1) is key for this process, as overexpression of Satb1 drives lymphoproliferation and expansion of the T cell and B cell compartments in secondary lymphoid organs. Specifically, Satb1 overexpression induces a pronounced shift towards Tfh cell differentiation and increased GC formation accompanied by an increase in non-classed switched GC B cells and auto-antibody secretion. These findings highlight the importance of the precise regulation of Satb1 in fine-tuning CD4 T cells and B cells responses and suggest a potential role for dysregulation of Satb1 in the pathogenesis of autoimmune disease such as systemic lupus erythematodes (SLE).

immunology↗

bioRxiv2026

Physics-Informed Neural Network for Mapping Vascular and Tissue Dynamics Using Laser Speckle Contrast Imaging

SignificanceQuantitatively mapping both cerebral blood flow and tissue dynamics from laser speckle contrast imaging (LSCI) is powerful for studying cerebral blood flow and neurovascular coupling, particularly in the context of stroke. Conventional multi-exposure fitting is slow and difficult to scale. Efficient, physically grounded methods are needed to extract both vascular and tissue dynamic biomarkers from LSCI data. AimTo develop and validate a physics-informed neural network (PINN) that quantitatively estimates fast (vascular) and slow (tissue-related) speckle decorrelation parameters directly from LSCI measurements without requiring ground-truth labels. ApproachWe developed a physics-informed neural network (PINN) to estimate fast (vascular) and slow (tissue-related) speckle decorrelation parameters directly from multi-exposure LSCI data without requiring ground-truth labels. The analytical LSCI model is embedded in the network loss function, enforcing consistency with speckle physics during training. The model operates in a self-supervised manner and performs pixel-wise inference across full-field images. The framework was evaluated using in vivo mouse stroke LSCI datasets. ResultsThe PINN accurately recovered fast decorrelation rates associated with cerebral blood flow and slower dynamics linked to tissue and cellular motion. The parameter maps closely match those from traditional nonlinear fitting, but at orders-of-magnitude higher speed, reducing analysis to under one second per image. It also generalizes to unseen subjects and remains robust under noise. ConclusionsOur approach establishes physics-informed learning as a practical framework for near real-time extraction of vascular and cellular biomarkers from LSCI, enabling longitudinal monitoring of stroke progression and potentially facilitating clinical translation.

bioengineering↗

bioRxiv2026

Compartment-specific core microbiomes in potato tubers are associated with plant health across genotypes, soils, and years

Harnessing plant microbiomes for sustainable agriculture requires understanding not only whether they can boost crop performance, but also how they assemble, persist, and support plant growth and health across environments. While we previously showed that seed tuber microbiomes can predict potato vigour using machine learning, it remained unclear how ecological processes shape tuber microbiome stability and functionality across host genotypes, tuber compartments, soil types, and years. Here, we analyzed the national-scale dataset of 240 field-collected potato seedlots, spanning six genotypes, two soil types, and two growing years, with a focus on the spatially distinct heel and eye compartments of the potato tuber. By profiling over 1,200 bacterial and fungal communities and linking microbiome composition to plant performance using leaf area as a health proxy, we show that plant genotype and tuber compartment are the strongest determinants of microbial diversity and composition. Compartment-specific enrichment of functional traits was observed, with organic compound conversion and nitrogen cycling dominant in the heel, and energy metabolism enriched in the eye. Using a macroecological abundance-occupancy framework, we identified a stable core microbiome of bacterial and fungal taxa that persisted across environments and years. Core members were more strongly associated with plant performance than non-core taxa, and pathogen-suppressive functions were spatially structured, with distinct protective taxa dominating in the heel versus the eye. Together, our findings demonstrate that tuber compartments act as selective microbial filters, shaping persistent microbiomes with specialized functions. By providing an ecological and functional framework for compartment-resolved, stable core microbiomes, this study complements our predictive modelling work and highlights persistent microbial partners as promising targets for improving potato resilience and productivity.

ecology↗

bioRxiv2025

Thioredoxin interacting protein (TXNIP), a redox regulator, mediates the RAPGEF3/4 signaling dependency in primary melanoma

Progression of cutaneous primary melanoma that arises from melanocytes leads to lethal metastatic disease. Molecular mechanisms that control the growth of primary melanoma in the skin and promote progression are not fully understood. Previously we showed that RAP guanine exchange factors EPAC1/2 (Exchange Protein Activated by cyclic AMP) promote the growth of primary melanoma and loss of dependency on EPACs is associated with metastatic progression. In this study, we show that EPACs are activated during malignant transformation of melanocytes, and chemical inhibition or genetic deletion of EPAC inhibits melanomagenesis in Braf/Pten mice. Low expression of EPAC mRNA and its effector RAP1-GTP protein in primary melanoma correlate with better recurrence-free survival. RNAseq analysis of matched primary and metastatic melanoma cells treated with an EPAC inhibitor showed that TXNIP, an important regulator of redox homeostasis, is a downstream effector of EPAC signaling. We also show that EPACs promote melanoma growth by regulating redox homeostasis and mitochondrial ROS through activation of mechanistic target of rapamycin complex 1 (mTORC1) that stabilizes hypoxia-inducible factor 1-alpha (HIF-1), a transcriptional activator of redox regulator TXNIP and glycolytic enzymes. Our data suggest that targeting mechanisms that melanoma cells employ to bypass EPAC dependency is a potential therapeutic approach. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/696903v2_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@1967afaorg.highwire.dtl.DTLVardef@63b907org.highwire.dtl.DTLVardef@1089da4org.highwire.dtl.DTLVardef@a5b2ef_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Identification of Vector Organisms and Their Carried Pathogens by Metagenomic Sequencing

Vector surveillance is vital for public health, yet traditional methods often face limitations. This study assessed the use of metagenomic sequencing for analyzing mosquitoes, cockroaches, flies, and rodents from Zhongshan, China. The results revealed a high concordance between metagenomic and Sanger sequencing for species identification (Kappa = 0.667, P = 0.011). Metagenomic analysis identified vector-specific dominant bacteria, including Wolbachia in mosquitoes, Blattabacterium sp. in cockroaches, Bartonella tribocorum and Leptospira interrogans in rodents, and Vagococcus teuberi in flies. It also detected numerous classical pathogens, such as Enterococcus faecalis, L. interrogans, and Klebsiella pneumoniae. Among the identified fungi and viruses, 19 were human pathogens, while seven posed a threat to agriculture, notably Pepper mild mottle virus and Tomato brown rugose fruit virus. These findings systematically characterize the microbial communities associated with various vectors, highlight the potential of metagenomics for broad pathogen screening, and underscore its utility in large-scale vector surveillance and public health protection. ImportanceVectors are major carriers of numerous diseases and present risks fot cross-border transmission. Existing detection methods for vectors and their pathogens remain inefficient, insensitive, and prone to difficulties in species identification. Despite extensive efforts in vector identification, the potential threats posed by their associated pathogens remain unclear, demonstrating the need for more effective diagnostic strategies. Metagenomic sequencing offers a promising solution to this challenge. In this study, we applied metagenomic sequencing to vectors collected from urban and port areas in Zhongshan, China, to evaluate its efficacy in pathogen detection. Overall, our work contributes to a better understanding of the public health risks posed by vector-borne pathogens.

microbiology↗