Biology subjects

You, Y.

Publications and source records attributed to You, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Anxious vision: Trait-like visual cortical hyperactivity in trait anxiety

Sensory processing varies across individuals, often manifesting as distinct traits. Some traits, particularly sensory hypersensitivity, even to basic non-valenced stimuli, are closely linked to emotional traits and implicated in a diversity of psychiatric disorders. Extant theories attribute this sensory-emotion linkage to limbic and prefrontal modulation, but empirical support remains limited. Meanwhile, it is increasingly recognized that sensory cortex supports flexible, value-based processing beyond labeled-line sensory analysis. Assaying early visual cortical activity (using visual evoked potentials/VEPs) across four high-density electroencephalogram (hd-EEG) experiments, involving three independent samples (N = 150) and repeated assessments (up to three waves), we identify a reliable and generalizable pattern of visual cortical hyperactivity related to high trait anxiety. Notably, this effect was specific to the parvocellular (vs. magnocellular) pathway, emerging as early as 46 ms and localized to V1. Underscoring its trait-like stability, the effect was reproducible across different arousal states, neutral and negative stimuli, extended time intervals, and varied experimental paradigms. Moreover, its ecological validity was affirmed by its presence in response to both basic low-level stimuli (Gabor patches) and complex real-world images. Crucially, an excitation-inhibition (E/I) ratio (indexed by the resting-state EEG aperiodic exponent; 1/f slope) predicted parvocellular VEP magnitude in subjects with low but not high trait anxiety, suggesting disrupted E/I modulation in trait anxiety underpins this hyperactivity. We conclude that in trait anxiety, early visual processing deviates from strict "fidelity" to physical-world input or mere "subservience" to top-down modulation, instead adapting to an individuals biological propensity at the earliest processing stage. HighlightsO_LIHyperactivity in primary visual cortex is stable, trait-like C_LIO_LIIt occurs as early as 46 ms post-stimulus, during initial feedforward sweep C_LIO_LIIt covaries with trait anxiety, a key personality factor and risk predisposition for psychiatric disorders C_LIO_LIIt is related to disrupted excitation-inhibition modulation of visual cortex in trait anxiety C_LI

neuroscience↗

bioRxiv2025

Igniting full-length isoform analysis in single-cell and spatial RNA-seq data with FLAMESv2

Long-read single-cell RNA-sequencing enables the profiling of RNA isoform expression and alternative splicing at single cell resolution. However, diverse single-cell technologies and sparse isoform data demand flexible and accurate analysis tools. We introduce FLAMESv2, a highly modular and protocol-agnostic R/Bioconductor package for long-read single-cell RNA-seq data analysis. FLAMESv2 supports a wide range of single-cell and spatial protocols, is highly configurable, scales to allow multi-sample analysis and provides versatile visualisation and analysis outputs. We demonstrate its compatibility with both droplet-based and combinatorial barcoding single-cell methods, as well as spatial transcriptomics workflows. Benchmarking confirms FLAMESv2 achieves field-leading performance across key analysis tasks. Applying FLAMESv2 to in vitro differentiation of stem cells into neurons, we identify cell-types, differentiation trajectories, expression of annotated and novel isoforms and isoform expression diversity and heterogeneity within individual cells. FLAMESv2 provides a comprehensive, flexible approach to analysing long-read single-cell RNA-sequencing, unlocking this powerful methodology for RNA isoform characterisation.

bioinformatics↗

bioRxiv2025

A ratio-based framework using Quartet reference materials for integrating long- and short-read RNA-seq

Long-read RNA sequencing (lrRNA-seq) enables full-length transcript profiling but is confounded by technical batch effects that compromise quantification and prevent data integration across platforms, protocols, and laboratories. The lack of a transcriptome-wide biological ground truth has hindered objective benchmarking. To address these dual challenges, we leveraged certified Quartet reference materials to generate one of the largest multi-center lrRNA-seq resources to date: over one billion long reads from 144 libraries across four PacBio and Nanopore protocols in four independent laboratories. We first establish that ratio-based quantification against built-in reference samples effectively removes technical noise, revealing underlying biological signals. We then constructed the first ratio-based reference datasets for full-length transcripts-- comprising 10,218 isoforms and 6,032 alternative splicing (AS) events--and orthogonally validated them with RT-qPCR. Finally, a comprehensive benchmark using these ground truths reveals that a hybrid strategy integrating long- and short-read data (hybrid-seq) achieves the highest quantification accuracy for both isoforms and AS events. Our work provides a foundational framework and resource for evaluating lrRNA-seq technologies and accelerating the standardization of full-length transcriptomics for research and clinical applications.

genomics↗

bioRxiv2025

Benchmarking long-read RNA-sequencing technologies with LongBench: a cross-platform reference dataset profiling cancer cell lines with bulk and single-cell approaches

Long-read RNA sequencing enables full-length transcript profiling and improved isoform resolution, but variable platforms and evolving chemistries demand careful benchmarking for reliable application. We present LongBench, a matched, multi-platform reference dataset spanning bulk, single-cell, and single-nucleus transcriptomics across eight human lung cancer cell lines with synthetic spike-in controls. LongBench in-corporates three state-of-the-art long-read protocols alongside Illumina short reads: Oxford Nanopore Technologies (ONT) PCR-cDNA, ONT direct RNA, and PacBio Kinnex. We systematically evaluate transcript capture, quantification accuracy, differential expression, isoform usage, variant detection, and allele-specific analyses. Our results show high concordance in gene-level differential analyses across protocols, but reduced consistency for transcript-level and isoform analyses due to length- and platform-dependent biases. Single-cell long-read data are highly concordant with bulk for high-confidence features, though single-nuclei data show reduced feature detection. LongBench provides one of the largest publicly available long-read benchmarking resources, enabling rigorous cross-platform evaluation and guiding technology selection for transcriptomic research.

genomics↗

bioRxiv2025

Macropinocytosis-Mediated Recyclable LYTACs (McR-TACs) for Membrane and Extracellular Protein Degradation

The degradation of cell membrane and extracellular proteins with lysosome-targeting chimeras (LYTACs) is hampered by non-recyclable and lysosome-shuttling receptor-dependent mechanisms, limiting the wide application of this emerging technology. Here, we developed macropinocytosis-mediated recyclable LYTACs (McR-TACs) that could durably degrade cell membrane and extracellular proteins in a receptor-independent manner. This McR-TAC platform demonstrated the feasibility of harnessing endogenous transport pathways for developing next-generation recyclable protein degraders with broad applicability.

biochemistry↗

bioRxiv2025

Comparative Analysis of Single-Nucleus and Single-Cell RNA Sequencing in Human Bone Marrow Mononuclear Cells: Methodological Insights and Trade-offs

Bone marrow mononuclear cells (BMMCs) are a heterogeneous pool of hematopoietic progenitors and mature immune cells that collectively sustain hematopoiesis and coordinate immune responses. The bone marrow serves not only as the primary site for blood cell production but also as a niche for various disorders, including blood cancers. Advances in single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) have significantly enhanced our understanding of the cellular biology and molecular dynamics within this complex microenvironment. The choice between these two approaches, however, is often shaped or constrained by the study design, such as research objectives, sample type, and preservation conditions. Consequently, methodological differences in library preparation and transcript capture efficiency can introduce systematic biases that complicate downstream analyses and interpretation, underscoring the need to identify and account for method-specific features. In this study, we conducted a comparative analysis of matched snRNA-seq and scRNA-seq datasets from 11 pairs of healthy donor bone marrow mononuclear cell samples, generated using the popular 10x Genomics platform. We evaluated method-specific biases using multiple quality metrics and compared cell type proportions and transcriptomic signatures captured by each approach. Integrative analysis of these datasets is feasible but not advisable due to systematic gene length biases that were observed between these approaches. Our results showed that despite inherent differences in library complexity, both protocols reliably captured all major cell types. This comparative analysis highlights intrinsic differences between snRNA-seq and scRNA-seq data, providing valuable insights into their respective advantages, limitations, and trade-offs. These findings can assist researchers in selecting the optimal method tailored to specific biological questions and sample characteristics, and also enable more method-aware data analysis and interpretation.

bioinformatics↗

bioRxiv2025

γδ T cells modulate anti-tumor immunity in small cell lung cancer

Small cell lung cancer (SCLC) is a highly aggressive neoplasm with limited sensitivity to anti-PD-(L)1 blockade, likely due to the epigenetic silencing of MHC-I. Elucidating MHC-I-independent immune recognition mechanisms is therefore crucial for enhancing treatment responses and improving clinical outcomes in a greater number of patients. Leveraging single cell approaches, we discovered {gamma}{delta} T cell infiltration in biospecimens from patients with SCLC. Despite PD-1 expression, {gamma}{delta} T cells maintained a cytotoxic transcriptional profile, suggestive of an anti-tumor role. Indeed, high {gamma}{delta} T cell infiltration predicted improved response to anti-PD-L1 immunotherapy in patients with SCLC. Moreover, using pre-clinical models, we demonstrated that {gamma}{delta} T cells are effective at tarlatamab (DLL3-CD3 BiTE) redirected SCLC killing and that zoledronate, an FDA-approved compound, can sensitize SCLC cells to {gamma}{delta} T cell-mediated killing. Thus, our findings suggest that engaged {gamma}{delta} T cells are potentially valuable targets for SCLC therapy.

cancer biology↗

bioRxiv2025

Sleeping Beauty mutagenesis identifies BACH2 and other regulators of CD8+T cell exhaustion, persistence in vivo, and CAR-T function under tumor-associated chronic antigen stimulation

Genes that enhance T cell function represent promising targets for improving engineered T cell therapies for cancer. While extensive CRISPR knockout screens have identified key genes enhancing T cell persistence, employing Sleeping Beauty (SB) insertional mutagenesis, which induces both gain-(GOF) and loss-of-function (LOF) mutations via the generation of fusion transcripts with endogenous genes, may uncover additional critical factors that previous approaches have overlooked. We developed transgenic mice carrying Doxycycline (Dox)-inducible SB mutagenesis system (DiSBey) in primary T cells. Using DiSBey, we conducted screens for genetic alterations enhancing T cell persistence under chronic antigen exposure. Specifically, CD8 T cells from Dox-fed DiSBey mice were subjected to repeated anti-CD3 stimulation over 18 days to mimic chronic antigenic stimulation. We then identified SB transposon genomic insertion sites and corresponding fusion transcripts from the persistent DiSBey CD8 T cells using enhanced-specificity tagmentation sequencing (esTag-seq) and RNA-seq, respectively. Under chronic stimulation, SB-mutagenized CD8 T cells exhibited improved persistence and reduced terminal exhaustion phenotype. Across six independent screens, we identified 38 genes that were recurrently targeted by the SB transposon T2/Onc2 and differentially expressed under chronic anti-CD3 stimulation stress. Among these, T2/Onc2 insertions into Bach2 and Elmo1 were repeatedly found at the genomic level and were associated with altered nascent transcript expression. Bach2, known as a key regulator of T cell memory formation and resistance to chronic viral infection but less characterized in engineered T cells for cancer therapy, was found to enhance in vivo tumor persistence in the B16-Ova tumor model. We showed that ectopic Bach2 expression levels influence engineered T cell differentiation lineage. A Bach2low signature allowed differentiation into both KLRG1 and CD62L phenotypes, whereas Bach2high restricted differentiation predominantly to the CD62L subset. Finally, in human CART19-28{zeta} cells, BACH2 overexpression enhanced cytotoxicity and improved tumor control following chronic cancer stimulation. Controllable SB mutagenesis using DiSBey mice provides a novel platform for functional screening of genes that improve T cell therapeutic phenotypes. Our findings highlight a dose-dependent role of BACH2 in enhancing the function of engineered T cells under conditions of chronic antigenic stimulation.

immunology↗

bioRxiv2025

Tau-seed interactome analysis reveals distinct functional signatures in Alzheimer's disease across model systems.

Tau aggregates propagate through the brain in a prion-like manner in Alzheimers disease (AD) and other tauopathies, but the molecular identity and functional partners of the seeding-competent Tau species remain poorly defined. Here, we present an unbiased proteomic profiling of a high-molecular-weight (HMW) Tau-seed isolated from AD patient brains. We contrast this interactome with that of a biochemically similar, seeding-incompetent HMW-Tau species from age-matched healthy controls. Despite comprising less than 5% of total Tau in the brain, Tau-seed associates with a distinct set of proteins enriched in synaptic, mitochondrial, and vesicle-trafficking functions. Cross-species functional screening in Drosophila and mouse models identifies interactors that modulate Tau toxicity and seeding. Spatially resolved analysis of postmortem AD brains reveals heterogenous co-deposition of these proteins with Tau aggregates, suggesting functionally distinct Tau-seed complexes. Together, this dataset provides a framework for understanding selective Tau-seed toxicity and identifies candidate regulators of Tau propagation with therapeutic potential.

neuroscience↗

bioRxiv2025

Sex specific correction of maternal inflammation-induced behavioral abnormalities by the inhibition of colony-stimulating factor 1 receptor

We have previously reported the therapeutic effect of depletion and regeneration of endogenous microglia on autism spectrum disorder-like behaviors in offspring from the dams under maternal immune activation (MIA) by dampening their neuritogenic activation. Here we show a long-lasting pathological effect by MIA, leading to abnormal behaviors in offspring mice in a sex-specific manner at 3 months of age. MIA was induced by injecting Polyinosinic:polycytidylic acid [Poly(I:C)] at 10mg/kg at E9.5 with preselected dams based on the immunoreactivity to low-dose Poly(I:C) injection. MIA offspring show impairments in sociability, repetitive behavior, and spatial working and associative memories in both sexes, whereas only male offspring show social novelty deficit. MIA has no effect on anxiety-like behavior nor sensori-motor or locomotor activity. Administering colony stimulating factor receptor (CSF1R) inhibitor in young adult MIA offspring regenerates microglia and ameliorates sociability deficits in males and spatial working and associative memory impairments in both sexes without effect on social novelty impairments. Transcriptomic analysis of prefrontal cortex and hippocampal tissues reveals MIA and sex-specific pathways and highlights potential key genes involved in the corrective effect of microglial regeneration in MIA offspring. Our results underscore the potential of the sex-specific therapeutic applications of CSF1R inhibitor for MIA-related social and cognitive disorders.

animal behavior and cognition↗

bioRxiv2025

stPipe: A flexible and streamlined R/Bioconductor pipeline for preprocessing sequencing-based spatial transcriptomics data

Spatial transcriptomics technology has developed rapidly in recent years, with various sequencing-based platforms such as 10x Visium, Slide-seq and Stereo-seq becoming widely used by researchers. Each platform brings its own set of protocols and customised data analysis pipelines which presents challenges when the goal is to obtain uniformly preprocessed data that is conveniently formatted for downstream analysis. To address the need for simpler, open-source solutions that deal with sequencing-based spatial transcriptomics (sST) data from different platforms, we present stPipe, a comprehensive and modular pipeline for analysing sST data. stPipe handles various analysis steps including (i) data processing from raw paired end FASTQ files to create a spatially resolved gene count matrix; (ii) the collation of relevant quality control metrics during preprocessing to ensure unwanted artefacts can be filtered from further analysis; and (iii) the adoption of standardised data storage containers to allow results to be easily passed on to a wide range of downstream analysis packages tailored to different goals (such as clustering, cell-cell communication analysis and differential expression analysis). stPipe is implemented as an R/Bioconductor package that builds upon functionality in the scPipe software, and offers a flexible preprocessing pipeline that can manage data from all current main-stream sST plaforms. A key use case for stPipe is in methods benchmarking, and we demonstrate how the uniform processing of sST data collected on reference tissue samples from the cadasSTre and SpatialBenchVisium projects is made easier, allowing comparisons between different technology platforms and downstream analysis tools. Our framework thus aims to advance the standardization and optimization of spatial transcriptomics analyses, fostering collaboration and innovation within the research community.

bioinformatics↗

bioRxiv2025

Efficient genetic code expansion tools enable in vivo study of lysine acetylation in non-model bacteria

Recent proteomic advancements have revealed widespread N{varepsilon}-lysine acetylation in pathways governing pathogenicity, metabolism, and antibiotic resistance in bacteria. The spontaneous, non-specific nature of this modification in prokaryotes obscures its biological role, necessitating prokaryotic specific in vivo interrogation systems. Genetic Code Expansion (GCE) offers a powerful method to investigate the roles and regulation dynamics of acetyl-lysine in vivo with the precise incorporation of a suite of non-canonical amino acids, including acetyl-lysine analogs. However, its use has been largely restricted to E. coli strains due to challenges associated with implementation and optimization of the technology in more diverse bacterial strains. Here, we present a bacterial host-agnostic, readily optimizable GCE platform designed to site-specifically incorporate non-canonical amino acids into target proteins within living bacteria. We further demonstrate the versatility of this technology by showcasing, for the first time, the successful incorporation of acetyl-lysine in a non-E. coli bacterium.

synthetic biology↗

bioRxiv2025

Mouse Cortical Cellular Diversification Through Lineage Progression of Radial Glia

Cortical radial glia (RGs) sequentially generate pyramidal neurons (PyNs) and glia. In this study, we investigated the cell intrinsic programs underlying cortical cellular diversification using time-series scRNA-seq and snATAC-seq on purified mouse cortical progenitors across embryonic and postnatal stages. Our data revealed that RGs transition from early to late over time, sequentially producing intermediate neuronal progenitors (INPs) and intermediate glial progenitors (IGPs). While INPs expand exclusively to generate PyNs, IGPs progress from young to old, sequentially producing cortical astrocytes, oligodendrocytes, and olfactory bulb interneurons. We constructed comprehensive molecular maps that reflect cell lineage progression. In particularly, we found that chromatin accessibility drives cortical cellular diversification by restricting broadly expressed transcription factors to specific stages and cell types. Developmental changes in chromatin accessibility confine Lhx2-induced neurogenesis to early-stage RGs, leading to the loss of neurogenic competence and the acquisition of gliogenic competence as corticogenesis progresses.

neuroscience↗

bioRxiv2025

Building Foundation Models to Characterize Cellular Interactions via Geometric Self-Supervised Learning on Spatial Genomics

Cellular interactions form the fundamental/core circuits that drive development, physiology, and disease within tissues. Advances in spatial genomics (SG) and artificial intelligence (AI) offer unprecedented opportunities to computationally analyze and predict the behavior of cell intricate networks, and to identify interactions that drive disease states. However, challenges arise in both methodology and scalability: (i) how to computationally characterize complicated cellular interactions of multi-scale nature where chemical genes/circuits in individual cells process information and drive interactions among large numbers of diverse cell types, and (ii) how to scale up the pipeline to accommodate the increasing volumes of SG data that map transcriptome-scale gene expression and spatial proximity across millions of cells. In this paper, we introduce the Cellular Interaction Foundation Model (CI-FM), an AI foundation model functioning to analyze and simulate cellular interactions within living tissues. In the CI-FM pipeline, we explicitly capture and embed cellular interactions within microenvironments by leveraging the powerful and scalable geometric graph neural network model, and optimize the characterization of cellular interactions with a novel self-supervised learning objective - we train it to infer gene expressions of cells based upon their interacting microenvironment. As a result, we construct CI-FM with 100 million parameters by consuming SG data of 23 million cells. Our benchmarking experiments show CI-FM effectively infers gene expressions conditional on the microenvironmental contexts: we achieve a high correlation and a low mismatch error (MSE of 1.1% relative to the square median expression), with 79.4% of cells on average being annotated as the similar cell type based on their predicted and actual expressions. We demonstrate the downstream utility of CI-FM by: (i) applying CI-FM to embed tumor samples to capture cellular interactions within tumor microenvironments (ROC-AUC score of 0.76 on classifying sample conditions via linear probing on embeddings), and identifying shared signatures across samples; and (ii) using CI-FM to simulate changes in microenvironmental composition in response to T cell infiltration, which highlights how CI-FM can be leveraged to model cellular responses to tissue perturbations - an essential step toward constructing "AI virtual tissues". Our model is open source and publicly accessible at https://huggingface.co/ynyou/CIFM.

bioinformatics↗

bioRxiv2024

A Systematic Comparison of Single-Cell Perturbation Response Prediction Models

Predicting single-cell transcriptional responses to perturbations is central to dissecting gene regulation and accelerating therapeutic design, yet the field lacks a rigorous, task-spanning assessment of model behavior. We present a large-scale benchmark of 13 representative methods and baselines across 25 datasets spanning diverse perturbation modalities and species, including two primary immune-cell drug-response resources. We evaluated three core tasks--generalization to unseen single-gene perturbations, prediction of combinatorial interactions, and transfer across cell types--using 24 metrics covering expression-level accuracy, relative changes, differential expression recovery, and distributional similarity. Across tasks, performance depended strongly on perturbation effect size and evaluation perspective: expression-level agreement was highest for small-effect perturbations resembling controls, whereas delta- and DE-based metrics improved with larger effects, providing clearer signals. Models shared a conservative bias, with fine-tuned foundation models compressing variance and underestimating synergistic effects in combinations. PerturbNet showed superior recovery of DE signatures in Tasks 1 and 2, while no method consistently generalized across cell types in Task 3, where biological consistency dominated outcomes. This benchmark establishes current methodological limits, clarifies that different metrics probe distinct biological signals rather than redundant summaries of the same prediction problem, and provides a foundation for developing virtual-cell models that more faithfully capture heterogeneous perturbation responses. Teaser: A large-scale benchmark reveals strengths and limits of models predicting single-cell perturbation responses.

bioinformatics↗

bioRxiv2024

Multiomic foundation model predicts epigenetic regulation by zero-shot

Deciphering cell type-specific perturbation effects on genes and cellular states demands considerable experimental resources. To overcome this challenge, we propose Perturbation Transformer (PertFormer), a foundation model to uncover functional and regulatory mechanisms through interpretable in silico perturbation process. PertFormer comprises 3 billion parameters pretrained on both bulk and single-cell datasets covering 9 types of multiomics (55 billion bp bulk multiomics and the largest-to-date 1.5 billion paired multiomic samples from 1 million single cells), capturing regulation of 300 kb around every genic region. PertFormer enables zero-shot prediction of functional perturbations and cell fate dynamics, outperforming existing methods by 20.9%-480.2%. PertFormer identifies novel tumor treatment targets, validated experimentally. The generalization capabilities of PertFormer have the potential to accelerate the discovery of biological and clinical targets.

genomics↗

bioRxiv2024

TIRE-seq: an Integrated Sample Extraction and Transcriptomics Workflow for High Throughput Perturbation Studies

RNA sequencing (RNA-seq) is widely used in biomedical research, advancing our understanding of gene expression across biological systems. Traditional methods require upstream RNA extraction from biological inputs, adding time and expense to workflows. We developed TIRE-seq (Turbocapture Integrated RNA Expression Sequencing) to address these challenges. TIRE-seq integrates mRNA purification directly into library preparation, eliminating the need for a separate extraction step. This streamlined approach reduces turnaround time, minimizes sample loss, and improves data quality. A comparative study with the widely used Prime-seq protocol demonstrates TIRE-seqs superior sequencing efficiency with crude cell lysates as inputs. TIRE-seqs utility was demonstrated across three biological applications. It captured transcriptional changes in stimulated human T cells, revealing activation-associated gene expression profiles. It also identified key genes driving murine dendritic cell differentiation, providing insights into lineage commitment. Lastly, TIRE-seq analyzed the dose-response and time-course effects of temozolomide on patient-derived neurospheres, identifying differentially expressed genes and enriched pathways linked to the drugs mechanism of action. With its simplified workflow and high sequencing efficiency, TIRE-seq offers a cost-effective solution for large-scale gene expression studies across diverse biological systems.

genomics↗

bioRxiv2024

Loss of virulence of Botrytis cinerea mutants defective in phytotoxin production is restored by modifying inoculation medium

Botrydial, botcinic acid and their derivatives are major phytotoxic metabolites produced by the necrotrophic fungal pathogen Botrytis cinerea. These phytotoxins are able to induce programmed cell death in the host and thereby promote plant susceptibility to B. cinerea. We observed that a{Delta} bot2{Delta}boa6 double mutant strain, which synthesizes neither botrydial nor botcinic acid, was almost avirulent on tomato leaves when the disease assay was performed using synthetic minimal Gamborg B5 medium. However, virulence of this mutant was restored when the inoculation medium was supplemented with yeast extract. Further virulence assays which compared the double mutant with other multiple mutants using both inoculation media, revealed a prominent contribution of botrydial and botcinic acid to the full virulence of B. cinerea. Therefore, we performed an RNA-sequencing experiment to identify B. cinerea genes that contribute to the phenotypic switch from an "incompatible" to a "compatible" interaction between tomato and the{Delta} bot2{Delta}boa6 double mutant. Four genes encoding cell death-inducing effector proteins were upregulated in B. cinerea by the addition of yeast extract, and their transcript profiles grouped within a co-expression module that was positively correlated with the compatible interaction. Functional analyses of these effector genes were performed by overexpressing them individually in the{Delta} bot2{Delta}boa6 background, followed by disease assays with the Gamborg B5 medium without yeast extract. ImportanceThe grey mould fungus Botrytis cinerea is a model for necrotrophic plant pathogens due to its wide host range, economic impact, well-assembled genome, and versatile mechanisms for inducing host cell death during colonization. Botrydial and botcinic acid have previously been characterized as major phytotoxins produced by B. cinerea. However, studies from different groups reported variable results regarding the contributions of these phytotoxins to fungal virulence. Here we demonstrate that botrydial and botcinic acid make a prominent contribution to the full virulence of B. cinerea, by performing infection assays with mutants that are defective in phytotoxin production and/or multiple cell death-inducing proteins using different inoculation media. This work highlights the pivotal roles of these phytotoxins as compared to other virulence factors, as well as the significant impact of inoculation conditions on compatible and incompatible interactions between the fungus and its hosts.

microbiology↗