Biology subjects

Ke, J.

Publications and source records attributed to Ke, J..

At least 19 recordsLinked to original sources

bioRxiv2026

A direct RNA sequencing workflow for simultaneous profiling of tRNA abundance and modifications in mouse liver

Transfer RNA (tRNA) is highly modified and difficult to profile using reverse-transcription-based sequencing. Here, we established a direct RNA sequencing (tRNA-DRS) workflow to simultaneously characterize tRNA abundance and modification signals in mouse liver. The workflow included small-RNA enrichment, deacylation, splint-adapter ligation, barcoding, reverse transcription, and nanopore sequencing on PromethION with modification-aware basecalling. From 1,678,666 raw reads, 1,421,279 (84.67%) reads were retained after 20-200 nt length filtering, and 17,094 uniquely mapped reads (MAPQ [≥] 10) were obtained. These reads covered 244 tRNA genes and represented all 23 tRNA isotypes. tRNA abundance was strongly skewed, with Asp, Gly, Ser, and Lys accounting for 56.6% of uniquely mapped reads. Read-body coverage indicated that most uniquely mapped reads spanned substantial portions of tRNA molecules (mean 71.1%, median 75.9%; 83.3% reads covering [≥]50% of the reference). Modification analysis identified 41,013 candidate sites, of which 711 passed stringent filtering (modification fraction [≥]50%, coverage [≥]5), spanning eight modification types and dominated by pseudouridine and m5C. Filtered sites showed position-specific clustering and isotype-dependent patterns. Together, these results support tRNA-DRS as a practical single-assay framework for simultaneous tRNA expression and epitranscriptomic profiling in mammalian tissue.

molecular biology↗

bioRxiv2026

A common connectome-based neural reference space across affective, autonomic, and wakefulness arousal

Arousal is often invoked to explain state-dependent variability in attention, emotion, memory, and decision-making. However, the term is used inconsistently, referring variously to affective experience, autonomic activation, and states of wakefulness. The extent to which different operationalizations of arousal reflect a shared neurobiological basis remains unclear, limiting efforts to unify findings across studies. We applied dynamic connectome predictive models to five fMRI datasets spanning naturalistic movie watching, story listening, wakeful rest, and sleep. We derived measures of affective, autonomic, and wakefulness arousal from subjective ratings, pupil dilation, and EEG, respectively. Models trained to predict arousal in one dataset generalized across datasets, indicating that dynamic connectivity captures shared arousal-related dynamics across measures and task contexts. The models also predicted manually scored sleep stages, suggesting that they capture arousal dynamics that extend to the graded fluctuations in arousal during sleep. Decoded arousal dynamics during movie-viewing predicted how well participants later recalled movie events, reproducing classic arousal-dependent memory enhancement effects. Model predictions were supported by overlapping functional connections, including a subset shared across all models. The largest proportion of shared connections was between the salience and somatomotor networks, suggesting that increased coordination between salience detection and action readiness may be a common feature across multiple operationalizations of arousal. Together, these results are consistent with a connectome-based neural reference space for arousal, in which different varieties share a core set of predictive connections. Our findings offer a quantitative framework for integrating arousal-related findings across tasks and modalities.

neuroscience↗

bioRxiv2026

Neural dynamics of updating social impressions during movie watching

Updating impressions of others is essential to navigating social life. As we get to know an individual, we update our impressions of them in accordance with new information. How the brain dynamically revises impressions in real time under naturalistic conditions remains unclear. Here, we address this question using functional magnetic resonance imaging (fMRI) and natural language analysis in a naturalistic social cognition paradigm. Across 10 runs, participants viewed a character-driven TV episode, reported moments of insight, and described their impressions of the characters. Results reveal that impressions progressively evolved over time. Individuals with more similar existing impressions exhibit greater neural synchrony during movie-watching, which predicts convergence in post-movie impressions. Neural synchrony in the right superior temporal sulcus (STS) mediates the influence of initial similarity on later alignment. Insight moments accompany neural pattern shifts widespread across cortical regions, including the temporoparietal junction (TPJ), dorsomedial prefrontal cortex (dmPFC) and STS, and the magnitude of the shifts tracks the degree of impression updating. Specifically, distinct forms of insight selectively update complementary components of impressions: character insight updates person-centered representations, whereas non-character insight shapes social-event structure. Together, these findings show that people update their impressions of others through dynamic shifts in distributed brain activity patterns at moments of insight, providing a novel and ecologically grounded neural account of social cognition. Significance statementPeople do not simply form first impressions and keep them. As we learn new things about others, our judgments change--sometimes gradually, sometimes in a flash of insight. By tracking brain activity while people watched a TV show and described how their impressions of the characters evolved, we found that people who saw a character in more similar ways also show more similar brain responses when watching them onscreen, which in turn leads to convergence in subsequent impressions. Such updating is closely tied to "aha" moments, when a person is suddenly understood in a new way. These moments were marked by rapid shifts in the brain representational patterns, and larger shifts predicted greater changes in impressions. These findings offer a more naturalistic account of how the brain revises our understanding of other people in everyday life.

neuroscience↗

bioRxiv2026

ReMind: A Retrospective Self-Report Paradigm for Studying Mind-Wandering Onset During Reading

Mind-wandering (MW) is a frequent and pervasive phenomenon, yet it is commonly assessed using self-reports or probe-based methods that offer limited temporal precision regarding its onset. In this study, we introduce a novel paradigm, ReMind, that estimates the onset and duration of MW episodes during natural reading by combining retrospective self-reports with eye-tracking. Participants indicated the words where they believed their mind started and stopped wandering, and these reports were aligned with gaze timestamps to estimate MW onset. Using data from 44 participants, we examined whether knowledge of MW onset improves the detection of MW from eye-tracking signals. To evaluate relevance for both self-report and thought-probe paradigms, we additionally simulated thought probes by randomly sampling time points during reading. Logistic regression classifiers trained on eye-tracking features extracted from time windows anchored to MW onset achieved AUROC scores of 0.659 and 0.621 under the self-report and simulated thought-probe paradigms, respectively, using leave-one-subject-out cross-validation. In both cases, onset-aligned windows outperformed classifiers trained using arbitrary MW windows. Sliding-window analyses further revealed systematic temporal changes around MW onset, with classification performance peaking at approximately 3 seconds after onset. Feature-level analyses showed reduced fixation rate and fixation dispersion, along with increased pupil size following MW onset. Together, these findings characterize the temporal progression from on-task reading to MW. Overall, ReMind provides a useful framework for studying the temporal dynamics of MW during naturalistic reading.

bioengineering↗

bioRxiv2025

TaoChongBao: A Large-Scale Caenorhabditis elegans Mutagenesis and Missense Variant Database Bridging Worm and Human Genomes

We generated and sequenced 12,069 viable Caenorhabditis elegans strains produced by ethyl methanesulfonate mutagenesis, identifying 20,315,536 variants, including 541,102 unique missense mutations across 20,914 genes. To organize and visualize this resource, we developed TaoChongBao, an open-access database and strain repository that integrates C. elegans mutation data with AlphaMissense-predicted pathogenicity scores and ClinVar clinical annotations. TaoChongBao enables users to explore worm missense variants, identify conserved residues corresponding to human pathogenic sites, and access viable strains for experimental validation. Compared with the previous Million Mutation Project in C. elegans, TaoChongBao expands mutation coverage over twenty-fold and emphasizes amino acid-altering variants. This resource provides a scalable platform for functional residuomics, variant interpretation, and comparative analyses between C. elegans and human genomes.

bioinformatics↗

bioRxiv2025

Conserved Gαq-PLCβ-PKC signaling mediates trametinib resistance in BRAFV600E melanoma

Oncogenic BRAFV600E mutations drive constitutive MAPK signaling across various cancers; however, resistance to MEK inhibitors such as trametinib undermines clinical benefits, and the mechanisms sustaining MAPK activity under treatment remain unclear. We developed a lin-45(V627E) Caenorhabditis elegans model to mimic oncogenic BRAF signaling and found that these animals tolerate ultra-high doses of trametinib. This survival is driven by an adaptive resistance bypass, mediated by Gq-PLC{beta}-PKC signaling, which sustains MAPK activity despite MEK inhibition. Transcriptomic analyses of patient tumors and resistant melanoma cells reveal upregulation of the Gq subunits and GPCR PAR2 upon acquisition of resistance. Functional inhibition of PAR2 or downstream Gq signaling suppresses ERK reactivation, selectively impairs the viability of resistant cells. Furthermore, combined inhibition of MEK and Gq-PLC{beta}-PKC signaling synergistically suppresses tumor growth and restores trametinib sensitivity in xenograft models. These findings uncover adaptive GPCR-Gq-driven signaling rewiring as an evolutionarily conserved and therapeutically actionable vulnerability in MAPK-driven cancers.

cancer biology↗

bioRxiv2025

Common brain network dynamics capture attention fluctuations in tasks and movies

Attentional states are highly dynamic and variable, fluctuating from moment to moment and showing stark differences across contexts. To what extent does functional brain reorganization capture variability in attentional states? In the present study we utilize a time-resolved measure of functional MRI connectivity to examine and compare the extent to which univariate activity and functional networks reflect second-to-second sustained attentional fluctuations. Sustained attention was measured objectively, using auditory and visual tasks, and subjectively while participants watched and listened to narratives. Results revealed that objective measures of sustained attention to images and sounds involved common patterns of neural activity and functional interactions. Additionally, networks related to sustained attentional performance during controlled tasks also predicted fluctuations in subjective attentional engagement while participants watched movies and listened to a podcast. Generalization between experimental and everyday task contexts highlights the robustness of time-resolved functional networks for capturing dynamic fluctuations in sustained attentional states.

neuroscience↗

bioRxiv2025

MAESTRO uncovers tandem paralog dynamics as a core driver of fungal stress adaptation

Understanding how fungi adapt to diverse stresses is critical for mitigating emerging drug resistance and harnessing their robustness for biotechnology. Pichia kudriavzevii is a stress-tolerant and intrinsically drug-resistant yeast, with dual industrial and clinical importance. We analyzed 170 strains using MAESTRO, a machine-learning-assisted GWAS pipeline optimized for small cohorts. MAESTRO identified biologically meaningful features and revealed that copy number variation (CNV) of tandem paralogs (TPs) is a core mechanism of multi-stress adaptation. TPs were recurrently linked to tolerance of industrial inhibitors (HMF, phenolics, heat) and antifungal drugs (fluconazole, azoles), and deletion of the TP pair gene4260/gene4261 confirmed pleiotropic effects across stresses. These findings support a TP CNV model where recombination-driven TP CNVs and gene fusions enable rapid stress adaptation. Importantly, our results suggest that antifungal resistance can arise through co-option of mechanisms originally evolved for environmental stressors, raising a One Health concern about the environmental origins of drug-resistant pathogens.

genomics↗

bioRxiv2025

Ongoing thoughts at rest reflect functional brain organization and behavior

Resting-state functional connectivity (rsFC)--brain connectivity observed when people rest with no external tasks--predicts individual differences in behavior. Yet, rest is not idle; it involves streams of thoughts. Are these ongoing thoughts reflected in FC and do they contribute to the relationship between rsFC and behavior? To test this question, we developed an annotated rest paradigm where participants rated and verbally described their thoughts after each rest period during functional MRI (N = 60). Our findings revealed rich and idiosyncratic thoughts across individuals. Similarity in thoughts was associated with more similar FC patterns within and across individuals. In addition, both thought ratings and topics could be decoded from FC. Furthermore, neuromarkers of these thoughts generalized to unseen individuals in the Human Connectome Project dataset (N = 908), where decoded thought patterns during rest predicted positive vs. negative trait-level individual differences. Together, our findings reveal that ongoing thoughts at rest are reflected in brain dynamics and these network patterns predict everyday cognition and experiences. Understanding subjective in-scanner experiences is thus crucial in characterizing the relationship between individual differences in functional brain organization and behavior.

neuroscience↗

bioRxiv2025

Discovering disease genetic variation impacting gene expression in 103 brain tissues with the Brain Ontology Expression (BRONTE) graph neural network model

Uncovering genes dysregulated in neuropsychiatric diseases promises to put forward therapeutic targets. By linking disease risk variants to tissue-specific gene regulation, the Genotype-Tissue Expression (GTEx) project has enabled discovery of disease genes and the brain tissues where these pathogenic effects occur. But, GTEx requires huge investment in obtaining human brain tissues, limiting the sample size and tissue diversity available. Crucially, this resource does not contain all key tissues where brain diseases are known to originate. Here, we propose a novel graph convolutional model that integrates the GTEx brain expression data with the Allen Brain Atlas, a resource spanning over 100 fine grained brain tissues that are annotated with an ontology of tissue organization. Our model, Brain Ontology Expression (BRONTE), allows completion of the GTEx data for 221 individuals to impute expanded expression in 103 uncollected detailed brain tissues. We show that expression data imputed by BRONTE recovers measured variation in expression across subjects, genes, and brain tissues. Most importantly, our method both recapitulates known relationships between genetic variations and gene expression, and simultaneously discovers both new genes impacted by genetic variants, and the tissues impacted. This allows us to put forward novel mechanisms of effect for genetic variation associated with 30 brain-related traits, supplying therapeutic targets in overlooked tissues of the brain.

bioinformatics↗

bioRxiv2025

Cortical reinstatement of causally related events sparks narrative insights by updating neural representation patterns

We make sense of everyday events by reasoning about their underlying causes. When we connect causal links between events separated in time, we often experience a sudden feeling of "aha!", or a moment of insight. What cognitive and neural processes underlie these moments of understanding? We hypothesized that narrative insight accompanies retrieving causally related past events in memory and updating the current event representation. To test this, we designed an fMRI study in which participants watched a TV episode that was cut into multiple events and presented in temporally scrambled orders. Participants pressed an "aha" button whenever they understood something new and verbally explained why they pressed in those moments at the end of each run. Supporting our prediction, more than 40% of insights included the retrieval of past events that were causally related to the current event. Neural patterns representing causally related past events were reinstated in cortical areas. This neural reinstatement drove sudden shifts in cortical representation patterns [~]2 s prior to aha button presses, reflecting an update in situational representation at moments of insight. Moreover, distributed areas in the brain represented causally related events with similar neural patterns, beyond their shared semantic or perceptual features. Together, the study suggests that we comprehend events by reinstating causally related past events via shared neural patterns, followed by updating neural patterns at moments of insight.

neuroscience↗

bioRxiv2025

Emotional arousal enhances narrative memories through functional integration of large-scale brain networks

Emotional events tend to be vividly remembered. While growing evidence suggests that emotions have their basis in brain-wide network interactions, it is unclear if and how these whole-brain dynamics contribute to memory encoding. We combined fMRI, graph theory, text analyses, and pupillometry in a naturalistic context where participants recalled complex narratives in their own words. Across three independent datasets, emotionally arousing moments during narrative perception were associated with an integrated brain state characterized by increased cohesion across functional modules, which in turn predicted the fidelity of subsequent recall. Network integration mediated the influence of emotional arousal on recall fidelity, with consistent within- and between-network interactions supporting the mediation across datasets. Together, these results suggest that emotional arousal enhances memory encoding via strengthening functional integration across brain networks. Our findings advance a cross-level understanding of emotional memories that bridges large-scale brain network dynamics, affective states and ongoing cognition.

neuroscience↗

bioRxiv2025

Sustained attention is more closely related to long-term memory than to attentional control

Individuals differ in their ability to sustain attention. However, whether differences in sustained attention reflect differences in processes related to attentional control and working memory or long-term memory (LTM) remains underexplored. In Experiment 1, we conducted an online study (n = 136) measuring participants sustained attention, attention control and working memory, and LTM. We measured sustained attention with an audio-visual continuous performance task (avCPT) in which participants responded to images while inhibiting responses to infrequent targets; attention control and working memory with Flanker, change localization, and Simon tasks; and LTM with recognition and source memory tests. Factor analyses revealed that sustained attention formed a distinct factor from attention control and working memory and LTM. Individual differences in the sustained attention factor robustly predicted individual differences in LTM and, to a lesser extent, attention control and working memory. In Experiment 2, to test how neural signatures of sustained attention related to attention control and working memory and LTM, we analyzed fMRI functional connectivity patterns collected as 20 participants performed the avCPT. A pre-trained connectome-based model of sustained attention predicted participants performance on out-of-scanner LTM, but not attention control and working memory, tasks. Together these results suggest that individual differences in sustained attention, although correlated with attention control and working memory, are more closely related to LTM.

neuroscience↗

bioRxiv2025

SynSeg: Generating Synthetic Datasets for Accurate Subcellular Segmentation with U-net

Accurate segmentation of subcellular components is crucial for understanding cellular processes, but traditional methods struggle with noise and complex structures. Convolutional neural networks improve accuracy but require large, time-consuming, and biased manually annotated datasets. Here, we developed SynSeg, a pipeline that generates synthetic training data to train a U-net model for subcellular structure segmentation, eliminating the need for manual annotation. SynSeg leverages synthetic datasets with variations in intensity, morphology, and signal distribution to deliver context-aware segmentations, even in challenging imaging conditions. We demonstrate SynSegs superior performance in segmenting vesicles and cytoskeletal filaments from culture cells and live C. elegans, outperforming traditional methods such as Otsus thresholding, ILEE, and FilamentSensor 2.0. Additionally, SynSeg effectively quantified disease-associated microtubule morphology in live cells, uncovering structural defects caused by mutant Tau proteins linked to neurodegenerative diseases. These results highlight the potential of synthetic data-driven approaches to advance biological segmentation and enhance microscopy techniques. Significance StatementThis study introduces a novel approach for accurately segmenting cellular structures, such as microtubules and vesicles, using synthetic datasets and advanced deep learning techniques. By leveraging a U-Net model trained on thousands of artificially generated images, our method eliminates the need for labor-intensive experimental data and simplifies the data creation process. Importantly, it incorporates noise and variability into the training datasets to make the model more robust and biologically relevant. Our findings demonstrate that the model can successfully identify cellular components, paving the way for its application in real-world microscopy images. This innovation has the potential to accelerate discoveries in cell biology by providing an efficient, scalable tool for analyzing complex cellular structures, even in challenging imaging conditions.

bioinformatics↗

bioRxiv2024

Functional brain networks predicting sustained attention are not specific to perceptual modality

Sustained attention is essential for daily life and can be directed to information from different perceptual modalities including audition and vision. Recently, cognitive neuroscience has aimed to identify neural predictors of behavior that generalize across datasets. Prior work has shown strong generalization of models trained to predict individual differences in sustained attention performance from patterns of fMRI functional connectivity. However, it is an open question whether predictions of sustained attention are specific to the perceptual modality in which they are trained. In the current study we test whether connectome-based models predict performance on attention tasks performed in different modalities. We show first that a predefined network trained to predict adults visual sustained attention performance generalizes to predict auditory sustained attention performance in three independent datasets (N1=29, N2=60, N3=17). Next, we train new network models to predict performance on visual and auditory attention tasks separately. We find that functional networks are largely modality-general, with both model-unique and shared model features predicting sustained attention performance in independent datasets regardless of task modality. Results support the supposition that visual and auditory sustained attention rely on shared neural mechanisms and demonstrate robust generalizability of whole-brain functional network models of sustained attention.

neuroscience↗

bioRxiv2024

Gut Microbiome-Driven Metabolites Influence Skin Pigmentation in TYRP1 Mutant Oujiang Color Common Carp

The gut microbiome is one of the major regulators of the gut-skin axis and is partly regulated by host genetics. In the present study, using comparative high-throughput omics data on CRISPR/Cas9-mediated TYRP1 mutant and wild-color common carp populations, we quantified the proportion of inter-individual variation in the skin transcriptome and blood metabolome by genetic architecture and gut microbiomes. We found 525 differential metabolites (DMs) and 45 differential gut microbial genera in TYRP1 mutant fishes relative to the wild type. Through interaction analysis and causal mediation analyses, we revealed that the TYRP1-mutant derived genetic background may exert an inflammatory Acinetobacter - Leukotrience-C4 and - Spermine metabolic pathway under the regulation of an anti-inflammatory cardio-vascular genetic network underlying the upregulating expression of COMT, PLG, C2, C3, F10, TDO2, MHC1, and SERPINF2 gene for evolving unusual coffee-like color phenotype. This unique network appears to underlie the "coffee-like" color phenotype. We propose that the COMT-mediated causal effect of the unusual gut microbiome on the atypical skin gene expression patterns through the gut-skin metabolic pathway. Article SummaryR.N. Mandal et al. report on the causal effect of gut microbiome-driven metabolites on the expression pattern of regulatory genes underlying an unusual color phenotype. It suggests that TYRP1 Mutation may rise an unusual inflammatory gut microbiome-skin metabolic pathway that may be balanced by an anti-inflammatory cardio-vascular genetic network leading to unique coloration in Oujiang Color Common Carp.

genetics↗

bioRxiv2023

Transposase-Assisted Tagmentation: An Economical and Scalable Strategy for Single-Worm Whole-Genome Sequencing

AlphaMissense identifies 23 million human missense variants as likely pathogenic, but only 0.1% have been clinically classified. To experimentally validate these predictions, chemical mutagenesis presents a rapid, cost-effective method to produce billions of mutations in model organisms. However, the prohibitive costs and limitations in the throughput of whole-genome sequencing (WGS) technologies, crucial for variant identification, constrain its widespread application. Here, we introduce a Tn5 transposase-assisted tagmentation technique for conducting WGS in C. elegans, E. coli, S. cervisiae, and C. reinhardtii. This method, demands merely 20 minutes of hands-on time for a single worm or single-cell clones and incurs a cost below 10 US dollars. It effectively pinpoints causal mutations in mutants defective in cilia or neurotransmitter secretion and in mutants synthetically sterile with a variant analogous to the oncogenic BRAF(V600E) mutation. Integrated with chemical mutagenesis, our approach can generate and identify missense variants economically and efficiently, facilitating experimental investigations of missense variants in diverse species.

genomics↗

bioRxiv2023

Dynamic Functional Connectivity Encodes Generalizable Representations of Emotional Arousal But Not Valence

Human affective experience varies along the dimensions of valence (positivity or negativity) and arousal (high or low activation). It remains unclear how these dimensions are encoded in the brain and if the representations are shared across different individuals and diverse situational contexts. Here we utilized two publicly available functional MRI datasets of participants watching movies to build predictive models of moment-to-moment valence and arousal from dynamic functional brain connectivity. We tested the models both within and across datasets and identified a generalizable arousal representation characterized by the interaction between multiple large-scale functional networks. The arousal representation generalized to two additional movie-watching datasets. Predictions based on multivariate patterns of activation underperformed connectome-based predictions and did not generalize. In contrast, we found no evidence of a generalizable valence representation. Taken together, our findings reveal a generalizable representation of arousal encoded in patterns of dynamic functional connectivity, revealing an underlying similarity in how arousal is encoded across individuals and situational contexts.

neuroscience↗