Biology subjects

You, J.

Publications and source records attributed to You, J..

At least 19 recordsLinked to original sources

bioRxiv2026

An immunocompetent model of MCPyV-driven Merkel cell carcinoma reveals tumor evolution under immune selection

Merkel cell carcinoma (MCC) is a neuroendocrine skin tumor that is frequently driven by integration of Merkel cell polyomavirus (MCPyV). In MCC, the MCPyV genome is truncated, but expression of the viral tumor antigens, truncated large tumor antigen (LTT) and small tumor antigen (sT), is maintained and drives uncontrolled proliferation. We introduced constitutive expression of the MCPyV T antigens (TAs) into primary mouse dermal fibroblasts (MDFs) to determine whether these cells are susceptible to MCPyV-driven transformation. TA expression alone in MDFs induced key MCC markers, cytokeratin-20 (CK20) and Sry-box transcription factor 2 (SOX2), and promoted anchorage-independent growth indicative of cellular transformation. Subcutaneous implantation of TA-transformed fibroblasts produced high-grade MCC-like tumors that grew persistently in immunodeficient NSG mice but not in immunocompetent C57BL/6 mice. Serial in vivo passaging of the tumor cell line enhanced tumor growth, reduced expression of p53-target genes and MHC-1, and was accompanied by a shift in T antigen isoform expression, with decreased LTT and increased sT expression. Our data demonstrate that MCPyV-driven tumors acquire immune-evasive adaptions during tumor progression in vivo and suggest that the anti-tumor immune response exerts selective pressure in MCC that favors expression of sT rather than LTT. The model established in this study provides a unique platform for studying evolution of MCPyV-driven tumors under immune pressure and identifying mechanisms of immune evasion in MCC that could be used to develop new therapeutic strategies. Significance StatementMCPyV tumor antigen expression transforms mouse dermal fibroblasts to generate MCC-like tumors. Serial in vivo passaging reveals tumor evolution under immune pressure, providing a model to study immune evasion mechanisms in MCC.

cancer biology↗

bioRxiv2026

X-Gutor: a high-fidelity in vitro platform for long-term gut microbiome recovery

In vitro gut microbiome-culturing models are essential for studying host-microbe interactions, yet achieving high microbial recovery and long-term stability remains a long-standing challenge. Here we devise X-Gutor, an ecologically designed in vitro platform that recapitulates the monogastric digestive system with precise control of pH, redox potential, and a mucin-based biofilm matrix. Using an integrated assessment framework, microbial source tracking, a curated gut metabolism database (GutDB), and short-chain fatty acid profiling, we benchmarked gut community recovery. Building on this platform, HanGutor and PigGutor were developed to recover 96.27{+/-}4.92% and 90.48{+/-}5.95% of human and swine gut microbiota, respectively. Ecological fine-tuning revealed that redox potential drives the Prevotella-Bacteroides trade-off and that spatial biofilm structure suppresses cheater (Succinivibrio) growth. The X-Gutor provides a scalable, modular platform for gut microbiome engineering, dietary intervention, and microbiota-targeted therapy development.

microbiology↗

bioRxiv2026

Targeting melanosome pH is an effective method for the treatment of oculocutaneous albinism

Oculocutaneous albinism (OCA) is a genetic condition associated with impaired visual acuity and increased skin cancer risk. When OCA is due to defects in melanosome ion transport, abnormally acidic conditions in the melanosome lumen inhibit tyrosinase, the critical pigment synthetic enzyme. Hence, a therapeutic approach that optimizes melanosome pH to increase pigment production presents a potential treatment for OCA and a method for decreasing skin cancer risk. Here, we report that reduction in sAC (ADCY10) activity via naturally occurring human variants in ADCY10 restores OCA pigmentation, and sAC inhibition increases melanin synthesis in both human and mouse OCA models. These findings demonstrate that targeting melanosome pH is an effective, previously untapped therapeutic strategy for OCA and elevated skin cancer risk.

physiology↗

bioRxiv2026

Patient-specific "Physical Twin" artery-on-chip platform reveals complex flow-dependent VWF mechanobiology and guides personalized antithrombotic therapy

Ischemic stroke triggered by carotid atherosclerosis remains unpredictable because the thrombosis embolization is governed by patient-specific vascular geometry and hemodynamics that cannot be recapitulated in conventional models. Here, we engineered "Physical Twin" artery-on-chip platforms that reproduce individualized carotid anatomy with humanized subendothelial matrix composition and arterial endothelial phenotype under physiological flow patterns. We then established thrombotic microenvironments following laser-induced injury. Computational fluid dynamics-guided experiments across distinct patient geometries reveal that local shear dictates a mechanobiological hierarchy: high-shear bifurcations ([~]3,000 s-{superscript 1}) produce von Willebrand factor (VWF) A1-dependent thrombi suppressible by conformationally sensitive inhibitors, whereas low-shear stenoses generate fragile aggregates where VWF-integrin IIb{beta} coupling governs embolization overgrowth. Pulsatile flow suppresses thrombotic growth independent of mean shear. Molecular dynamics simulations reveal why conformationally sensitive nanobodies (caplacizumab) outperform shear-independent aptamers (ARC1172) in high-shear bifurcation flow zones. This Physical Twin platform provides a mechanistic blueprint for geometry-informed, personalized antithrombotic therapy selection.

bioengineering↗

bioRxiv2026

Postweaning social isolation induces gene expression alterations and histone modification dysregulations in nucleus accumbens (NAc) neurons

Lack of social interaction results in various behavioral abnormalities in rodents, including increased anxiety levels, altered sociability, and impaired cognitive ability. Epigenetic factors regulate gene expression, however, how they are involved in juvenile social isolation (jSI)-induced behavioral alterations remains largely unknown. Here, we focused on the nucleus accumbens (NAc), a critical brain region of the reward system that regulates motivation-related behaviors. We first performed RNA-seq on neuronal nuclei and found alterations in genes related to neuronal function, as well as in transcriptional and epigenetic regulation. Protein-protein interaction (PPI) analysis of differentially expressed genes (DEGs) showed that top key nodes among down-regulated genes include membrane receptors (Ntrk2, Grin3a, and Grik1) and an apoptosis regulator (Bcl2). To further investigate the association between jSI-induced gene expression alterations and histone modifications, we next performed CUT&Tag for four histone modifications (H3K4me1, H3K4me3, H3K27ac, and H3K27me3), and the results implied that epigenetic alterations may also play a role in neuronal function as well as transcriptional regulation. Reanalysis of previously published RNA-seq data on the manipulation of histone modification-associated factors (including Kdm6b, Brd4, and Setd1a) suggested that these enzymes are candidate regulators of jSI-induced gene expression alterations. Taken together, our comprehensive analysis characterizes the association between histone modification and jSI-related alterations of gene expression in the NAc of female mice.

neuroscience↗

bioRxiv2026

Programming T cells for Early Cancer Detection with Customized Protease-Activatable Receptors

Early cancer detection has the potential to reduce cancer mortality, yet endogenous tumor-shed biomarkers lack sensitivity for early-stage disease. We report OncoSCOUT, a cancer detection strategy using T cells engineered with protease-activatable receptors (PARs) that conditionally recognize tumor cells and release a synthetic biomarker for detection in urine. These PARs comprise masked synthetic Notch receptors in which antigen binding is blocked by a peptide mimotope tethered via a protease-cleavable linker. We demonstrate that requiring both extracellular protease activity and tumor antigen recognition improves spatial specificity and minimizes off-tumor activation of PAR T cells in vivo. To identify tumor-selective PARs, we adoptively transferred a HER2-targeted PAR library displaying [~]160,000 unique 4-mer amino acid linkers and discovered multiple variants significantly enriched in a HER2-positive cancer xenograft model. Using a single customized PAR, we show that OncoSCOUT can detect total tumor burdens as small as 10-30 mm3 with significantly improved sensitivity than the protein biomarker CA 15-3 or a 20-plex circulating tumor DNA (ctDNA) assay.

bioengineering↗

bioRxiv2026

Predictive coding narrows the gap between convolutional networks and human brain function in misspelled-word reading

Humans can readily recognize words even when they are misspelled, though with slower responses. We investigated whether predictive coding could be a feasible computational mechanism to explain both the robustness and the additional processing time. By incorporating brain-inspired predictive coding dynamics into a convolutional neural network (CNN), we assessed whether the resulting interplay between feed-back predictions and feed-forward errors enhanced the models brain-likeness in misspelled-word reading. The initial CNN was trained to classify images of rendered text from a 1000-word Finnish vocabulary (supervised), and then enhanced with feedback predictive coding connections, which were trained with the learning objective of reconstructing the activity in the previous layer (unsupervised). The model, with and without the predictive coding dynamics enabled, was then evaluated using the same real and misspelled word stimuli that were presented to human participants during a magnetoencephalography (MEG) recording. The predictive coding dynamics improved model performance on misspelled words, particularly reducing the accuracy gap between real and word-like misspelled words, thereby aligning overall performance more closely with human behavioral patterns. Furthermore, representational similarity analysis (RSA) and multivariate regression showed a stronger correspondence between model activations and human MEG responses when predictive coding dynamics were enabled. These findings provide converging evidence for predictive coding dynamics as a biologically plausible computational mechanism for the brains ability to cope with misspelled words. Author summaryWhen we read, we can often understand a word even if it is misspelled, e.g, "lamguage". Hence, computational models of visual word recognition in the brain should also exhibit such flexibility. Convolutional neural networks (CNNs) are gaining popularity as models of visual processing in the brain, including reading, yet they typically fail when faced with misspelled words. In our study, we enhanced a CNN by adding feedback connections that perform predictive coding, i.e. continuously attempt to reconstruct the input from the initial output and adjust the output until it can do so. Through this predictive coding loop, the model continuously refines its internal representations, mimicking how the brain may "clean up" noisy inputs. We found that this predictive coding mechanism not only allowed the model to better identify the closest real word form of misspelled words, but also produced activity patterns the more closely resembling those measured from human brain using magnetoencephalography. These results suggest that predictive coding could be a key computational principle underlying the brains remarkable flexibility in reading and provide insights into how biological mechanisms could inspire brain-like computational models.

neuroscience↗

bioRxiv2025

Discovery and characterization of a lactonase in gut microbiota that initiates the metabolism of ellagic acid

Ellagic acid (EA), a natural plant polyphenol, and its metabolite urolithin (Uro) exhibit significant bioactivity, with Uro being recognized as the active compound responsible for the in vivo effects of EA. Although specific intestinal microorganisms that can metabolize EA have been discovered, the corresponding biochemical routes and genetic mechanisms involved in these processes have yet to be fully elucidated. In this study, we employed bioinformatics, biochemical assays, and genetic analyses to discover and characterize an EA lactonase (EAL) from the intestinal bacterium Gordonibacter urolithinfaciens DSM 27213. EAL specifically catalyzes the cleavage of the ester bond in EA, yielding Uro-M5. Notably, EAL and its extensive homologs form a unique subclass within the lactonase and amidohydrolase families. Structural predictions indicate that the active pocket of EAL comprises a propeller-like structure formed by seven {beta}-strands. Additionally, molecular modeling analyses further revealed that EA establishes specific interactions with key catalytic residues within the enzymes binding site. These insights enhance our understanding of the role of intestinal microbial communities in metabolizing plant-derived phenolic compounds, potentially shaping health outcomes and disease management through dietary interventions. Significance StatementEllagic acid (EA), which is abundant in the human gut microbiome, can be metabolized by gut microbes into urolithin A (Uro-A), however, the biochemical mechanisms underlying these transformations remain incompletely understood. In this study, we identified and characterized an EA lactonase (EAL) from the gut bacterium Gordonibacter urolithinfaciens DSM 27213, which serves as a key enzyme in the initial metabolic breakdown of EA. We elucidate the function, distribution, and structure of the EAL. This discovery advances our understanding of Uro-A production within the human body and may provide a foundation for further investigations into the relationship between Uros and human health.

biochemistry↗

bioRxiv2025

Prediction of zoonotic virus-host transmissibility using comparative airway organoids

AbstractThe emergence of new pandemic zoonotic diseases is accelerating. However, current approaches to control after outbreaks often allow the epidemic or pandemic spread of new zoonotic diseases, exemplified by the case of COVID-19. Prediction of zoonotic transmissibility will help minimize the outbreaks and socioeconomic damage. Here, we established highly efficient comparative airway organoids with minimal components from a panel of mammalian species for long-term culture and passaging and validated their utility to predict the transmissibility of zoonotic pathogens. Airway organoids from each species recapitulated the airway histology and contained the major cell types in the airway. They recapitulated the known susceptibility of their host species and tissues to diverse viruses. Transcriptomic analyses revealed that organoids from susceptible species induced genes of anti-viral immune responses only in common upon exposure to the same virus, although each species organoids displayed differential gene expression profiles. Also, organoids of the same species displayed differential transcriptomic responses against different viruses but induced anti-viral immune genes only in common. Our work provides a roadmap for comprehensive species- and tissue-level organoid panels for quick and reproducible uncharacterized pathogen surveillance to predict and prevent the next pandemic.

developmental biology↗

bioRxiv2025

The two-component nuclease-active KELShedu system confers broad antiphage activity via abortive infection

Bacteriophages and bacteria engage in a continuous evolutionary arms race, driving the development of intricate bacterial defense systems such as CRISPR-Cas, BREX, Gabija, and Shedu. Here, we characterize a two-component KELShedu system in Escherichia coli that confers resistance to phages via abortive infection. The KELShedu system comprises KELA, a dsDNA-binding protein, and KELB, a metal ion-dependent nuclease harboring the DUF4263 domain. In addition, we find that physiological levels of NTP inhibit the DNA cleavage activity of the KELShedu system, suggesting that KELShedus activation depends on reduced intracellular NTP levels during phage invasion. Our research demonstrates that the KELShedu system responds to nucleotide depletion triggered by phage replication, leading to non-specific degradation of cellular DNA and ultimately inducing abortive infection. These insights into the KELShedu system expand the repertoire of bacterial anti-phage mechanisms and lay the groundwork for novel applications in microbial engineering and therapeutic development. TeaserThe KELShedu system exerts anti-phage function by aborting infection in response to physiological changes following phage infection

microbiology↗

bioRxiv2025

Merits and challenges of plasma proteomics on association replicability

Developments in proteomic platforms have enabled the generation of large-scale high-throughput plasma proteomics data [1-3]. With recent breakthroughs in AI modelling, these data have significantly enhanced our understanding of molecular mechanisms underlying human behaviors and diseases [4-6]. However, the replicability of associations between plasma proteomics and phenotypes remains underexplored. Here, we systematically assessed the replicability of associations with recent plasma proteomics data in the UK biobank. Over 75% of cognitive function and mental health traits demonstrated high overall (proteomics-wide) replicability when brain-related traits were considered as phenotypes. Although mean cortical thickness (CT) as phenotype exhibited clearly reduced replicability, total cortical surface area (CSA) and cortical volume (CV) showed high overall replicability across hemispheres and over twenty brain regions. In comparative multi-omics analyses based on the same cohort of participants, proteomics outperformed genomics across all brain-related traits, and exceeded metabolomics for over half of traits where metabolomics also exhibited high overall replicability. Furthermore, we developed a predictive framework to estimate the replicability for potential future proteomics panels based on the crucial influential factors including dilution level, proportion of samples below the limit of detection (LOD), and sample size. Moreover, we constructed an individual replicability index for proteins and identified eleven proteins with highly replicable associations across cognitive function and mental health traits, which was in line with the recent identifications of pleiotropic proteins in large-scale population studies. Collectively, our results revealed the challenges in the association replicability of plasma proteomics under reduced data quality (from "Explore" to "Expansion" assay panels), and we further explored how to sustain high replicability in potential future panels. Fundamentally, our findings affirm the merits of plasma proteomics: this molecular omics platform enables highly replicable associations for mapping biomedical signatures.

bioinformatics↗

bioRxiv2025

A short intrinsically disordered domain of MCPyV ALTO regulates host TBK1 signaling and MCPyV latency

Merkel Cell Polyomavirus (MCPyV) is an oncogenic human polyomavirus that latently infects most adults. Although the causative link between MCPyV and Merkel Cell Carcinoma (MCC) is well established, the molecular mechanisms that govern viral latency and prevent oncogenic progression remain poorly understood. We previously reported that the MCPyV early region protein ALTO is a key modulator of the STING-TBK1 signaling axis, enabling the virus to co-opt host innate immune pathways to suppress excessive viral replication and promote latency over transformation. In this study, we expand on this model by identifying a short, essential domain within ALTO that is required for TBK1 activation. This domain, which we term LIT (Lost in Tau), is necessary for ALTO-TBK1 interaction but dispensable for ALTO trafficking and its interactions with STING or Src. When expressed alone, the LIT domain functions as a dominant negative inhibitor of wild-type ALTO, competitively blocking TBK1 activation through a novel TBK1 interaction domain. Deletion of the LIT domain from ALTO not only abolishes TBK1 interaction and downstream phosphorylation but also eliminates TBK1-mediated suppression of MCPyV replication during early infection of human dermal fibroblasts (HDFs). These findings provide mechanistic insight into how ALTO promotes viral persistence and immune evasion. More broadly, they highlight the functional importance of intrinsically disordered regions in modulating host-virus interactions and suggest that MCPyV latency is actively maintained through a finely tuned balance of pro- and anti-viral signaling. Targeting domains such as LIT may offer new strategies for regulating TBK1 activity or disrupting viral persistence. ImportanceMerkel cell polyomavirus causes lifelong, latent infections in the skin of most people. When this latency is perturbed, the virus can give rise to Merkel Cell Carcinoma, an aggressive and difficult-to-treat skin cancer. Efforts to prevent this cancer depend on understanding what controls viral latency and persistence. We previously reported that MCPyV stimulates the hosts STING-TBK1 signaling axis to limit its own replication. In this work, we identify and characterize a short amino acid motif within the ALTO proteins intrinsically disordered region that is required for this immune-stimulating activity. This region appears to be critical for helping the virus maintain latency by fine-tuning the hosts response. Our findings provide new insight into MCPyVs latency mechanism and may help guide future approaches to prevent or treat Merkel cell carcinoma by targeting viral or cellular factors involved in long-term infection.

microbiology↗

bioRxiv2025

Dynamic feedforward and feedback influences on left ventral occipitotemporal cortex: evidence from word and pseudoword reading

Left ventral occipitotemporal cortex (vOT) is crucial in reading, yet its functional role is viewed either as a prelexical feedforward hub or a bidirectional interface between sensory and higher-order linguistic systems. The two competing views have usually been considered mutually exclusive. To comprehensively explain the functional complexity of left vOT, we investigated the temporal and spectral dynamics of information flows involving left vOT during visual word and pseudoword reading using magnetoencephalography (MEG) and two directed connectivity metrics, i.e., phase slope index (PSI) and Granger causality (GC). Specifically, feedforward connectivity from low-level visual areas to vOT was observed for all conditions, with the strength of orthographic information flow to left superior temporal cortex (ST) modulated by stimulus word-likeness. Conversely, feedback flow from left ST to vOT appeared for pseudowords that allow top-down linguistic constraints to facilitate reading, and occurred later for word-like than complete pseudowords. Our findings suggest that left vOT during word reading functions in a hybrid manner: operating in efficient feedforward mode for familiar word recognition while flexibly recruiting bidirectional processing for unfamiliar pseudowords. By disentangling feedforward and feedback dynamics with high temporal and spectral resolution, we empirically reconcile competing theories of vOT function.

neuroscience↗

bioRxiv2025

Interventionally targeting somatic CAG expansions can be a rapid disease-modifying therapeutic avenue: Preclinical evidence.

Huntington disease (HD) is caused by inherited CAG expansions, which continue expanding somatically in affected brain regions to hasten disease onset and progression. Therapeutically diminishing somatic expansions is expected to be clinically beneficial. However, it is not known if interventionally modifying somatic CAG expansions will actually modify in vivo clinically-relevant phenotypes, what the therapeutic window is, or which phenotypes will be altered. Here we show that acute (6-week) delivery of the contraction-inducing slipped-CAG DNA ligand naphthyridine-azaquinolone to young (4-week-old) (CAG)120 HD mice, induces contractions throughout brain regions, improves motor function (locomotion, balance, coordination, muscle strength), molecular disease landmarks (mHTT aggregates, nuclear envelope morphology, nucleocytoplasmic mRNA transport, transcriptomic dysregulation, neuroinflammation), and neurodegeneration. Beneficial effects of modifying somatic expansions were also evident in muscle and blood, where blood CAG instability correlated with brain instability and blood serum had diminished levels of neurofilament light (a biomarker for neurodegeneration) -- offering blood as having elements of target engagement and efficacy. These data support that targeting somatic repeat expansions can be a rapid disease-modifying therapeutic avenue for HD and possibly other repeat expansion diseases. Our findings support an etiologic pathway interconnected to somatic CAG expansions that will inform the design of clinical trials expecting clinical benefit by modulating somatic expansions.

genetics↗

bioRxiv2025

Learning and predicting fishing activities from AIS data

Marine life significantly impacts our planet by providing essential resources such as food, oxygen, and biodiversity. The surveillance of illegal fishing activities is critical for the sustainable management of marine resources. In this study, we investigate the use of two publicly available data sources to automatically detect fishing activities. One dataset is the Automatic Identification System (AIS) data, the other is the Catch Reports from the Norwegian fishing authorities. The data from fisheries along the Norwegian coast, specifically from vessels with a length of 15 meters or more, covers a period of 1 year and 11 months (January 2022 to November 2023). The AIS data was cleaned by removing duplicates and outliers, then interpolated using piecewise linear interpolation and resampling at a five-minute rate to turn the AIS data into time series of one-hour length. The time series were then tagged using the Catch Report data. The dataset is severely unbalanced with fishing and non-fishing tags. For better learning, the total data was resampled to create 100 balanced bootstrap sets, ensuring equal representation of fishing and non-fishing activities. This process resulted in a benchmark dataset containing about 30 million data points in about 2.5 million time series. We applied a range of classification methods, based on random forests and deep convolution networks. Three types of features were used, related to secant speed, distance to shore, and curvature. We achieved an accuracy ranging from about 92% to 93.7% depending on the features and classification methods used. The predictive capabilities of the classifiers are investigated and significance of the features are studied. The uncertainty of the classification was assessed using the bootstrap sets, providing robust evaluation metrics. Overall, this study demonstrates the effectiveness of leveraging AIS and Catch Report data and advanced data processing techniques for automatic and accurate monitoring of marine fishing activities.

biophysics↗

bioRxiv2025

The Use of DeepQSAR Models for The Discovery of Peptides with Enhanced Antimicrobial and Antibiofilm Potential.

Withdrawal StatementThe authors have withdrawn this manuscript because issues discovered in the dataset required removal of certain external peptides and a recalculation of performance metrics. Revised analyses will be presented separately. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

bioinformatics↗

bioRxiv2025

Deep Modeling of Gain of Function Mutations on Androgen Receptor

The efficiency of Androgen Receptor (AR) pathway inhibitors for prostate cancer (PCa) is on decline due to resistance mechanisms including the occurrence of gain-of-function mutations on human androgen receptor (AR). Hence, understanding and predicting such mutations is crucial for developing effective PCa treatment strategies. Leveraging accumulated data on clinically relevant AR mutants with recent advances in deep modeling techniques, this study aims to unveil and quantify critical AR mutation-drug relationships. By incorporating molecular descriptors for drugs and mutated genes sequences, this work represented these features as single vectors and demonstrates their effectiveness in modeling AR mutant responses to conventional antiandrogens. The developed approach achieves up to 80% accuracy in predicting the gain-of-function behavior of AR mutants and therefore can potentially uncover unknown agonist/antagonist relationships among mutant-drug pairs.

bioinformatics↗

bioRxiv2025

Misspelled-word reading modulates late cortical dynamics

Literate humans can effortlessly interpret tens of thousands of words, even when the words are sometimes written incorrectly. This phenomenon suggests a flexible nature of reading that can endure a certain amount of noise. In this study, we investigated where and when brain responses diverged for conditions where misspelled words were resolved as real words or not. We used magnetoencephalography (MEG) to track the cortical activity as the participants read words with different degrees of misspelling that were perceived to range from real words to complete pseudowords, as confirmed by their behavioral responses. In particular, we were interested in how lexical information survives (or not) along the uncertainty spectrum, and how the corresponding brain activation patterns evolve spatiotemporally. We identified three brain regions that were notably modulated by misspellings: left ventral occipitotemporal cortex (vOT), superior temporal cortex (ST), and precentral cortex (pC). This suggests that resolving misspelled words into stored concepts involves an interplay between orthographic, semantic, and phonological processing. Temporally, these regions showed fairly late and sustained responses selectively to misspelled words. Specifically, an increasing level of misspelling increased the response in ST from 300 ms after stimulus onset; a functionally fairly similar but weaker effect was observed in pC. In vOT, misspelled words were sharply distinguished from real words notably later, after 700 ms. A linear mixed effects (LME) analysis further showed that pronounced and long-lasting misspelling effects appeared first in ST and then in pC, with shorter-lasting activation also observed in vOT. We conclude that reading misspelled words engages brain areas typically associated with language processing, but in a manner that cannot be interpreted merely as a rapid feedforward mechanism. Instead, feedback interactions likely contribute to the late effects observed during misspelled-word reading.

neuroscience↗