Biology subjects

Li, J.-J.

Publications and source records attributed to Li, J.-J..

9 recordsLinked to original sources

bioRxiv2026

dbGIST: An LLM-Assisted Multi-Omics Resource for Target Exploration and Cross-Dataset Validation in Gastrointestinal Stromal Tumors

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal neoplasms of the gastrointestinal tract, yet GIST-specific omics evidence remains scattered across small cohorts and is not represented as a dedicated disease project in major cancer genomics resources, limiting reproducible target exploration. Here, we present dbGIST (https://www.dbgist.com), a dedicated GIST-focused multi-omics resource built to make dispersed GIST evidence searchable, analyzable, and reusable. dbGIST harmonizes data from 37 centers and 1,991 samples, including pathologically verified in-house cohorts, across genomics, bulk transcriptomics, proteomics, phosphoproteomics, and single-cell transcriptomics, and couples these data with curated clinical annotations covering survival, mutation status, risk stratification, metastasis or recurrence, mitotic index, tumor site and size, and imatinib response. The platform supports cohort-level molecular-clinical association, survival, enrichment, immune-infiltration, drug-sensitivity, and single-cell analyses through interactive visualizations, downloadable source data, and public APIs for programmatic access to reusable analysis outputs and visualization-ready data. An optional LLM-assisted interface helps users navigate analyses and interpret outputs. Using MCM7 as a case study, dbGIST linked a resource-derived candidate to survival, risk features, metastatic or recurrent disease, imatinib-response phenotypes, proliferative cell states, and in vitro GIST-cell behavior. dbGIST therefore provides a traceable and interoperable resource for target exploration and precision oncology research in GIST.

cancer biology↗

bioRxiv2025

Genetic changes linked to two different syndromic forms of autism enhance reinforcement learning in adolescent male but not female mice

Autism Spectrum Disorder (ASD) is characterized by restricted and repetitive behaviors and social differences, both of which may manifest, in part, from underlying differences in corticostriatal circuits and reinforcement learning. Here, we investigated reinforcement learning in developing mice with mutations in either Tsc2 or Shank3, both high-confidence ASD risk genes associated with major syndromic forms of ASD. Using an odor-based two-alternative forced choice (2AFC) task, we tested early adolescent mice of both sexes and found male Tsc2 and Shank3B heterozygote (Het) mice showed enhanced learning performance compared to their wild type (WT) siblings. No gain of function was observed in females. Using a novel reinforcement learning (RL) based computational model to infer learning rate as well as policy-level task engagement and disengagement, we found that the gain of function in males was driven by an enhanced positive learning rate in both Tsc2 and Shank3B Het mice. The gain of function in Het males was absent when mice were trained with a probabilistic reward schedule. These findings in two ASD mouse models reveal a convergent learning phenotype that shows similar sensitivity to sex and environmental uncertainty. These data can inform our understanding of both strengths and challenges associated with autism, while providing further evidence that sex and experience of uncertainty modulate autism-related phenotypes. Significance StatementReinforcement learning is a foundational form of learning that is widely used in behavioral interventions for autism. Here, we measured reinforcement learning in developing mice carrying genetic mutations linked to two different syndromic forms of autism. We found that males showed strengths in reinforcement learning compared to their wild type siblings, while females showed no differences. This gain of function in males was no longer observed when uncertainty was introduced into the reward schedule for correct choices. These findings support a model in which diverse genetic changes interact with sex to generate common phenotypes underlying autism. Our data further support the idea that autism risk genes may produce strengths as well as challenges in behavioral function.

neuroscience↗

bioRxiv2024

Neural mechanisms of awareness of action

The origins of awareness of action (AoA), the ability to report an action just performed, remain elusive. Differing theories ascribe AoA to pre-action, efferent motor/volitional mechanisms versus post-action, afferent sensory/perceptual neural mechanisms. To study these two types of mechanisms and others, we developed a paradigm where very similar aware and unaware actions occur repeatedly. Aware actions demonstrated larger neurophysiological signals both preceding and following movement. The differences included well-known volitional and perceptual event related potentials (PMP, N140, P300), as well as frontal midline theta, event-related alpha/beta desynchronization, and post-move blink rates. On longer time scales, we identified a novel event related potential preceding unaware moves, and found behavioral and pupillometric evidence for decreased attention and arousal over minutes concurrent with AoA loss. Our findings suggest that both dynamic, individual action-associated volitional and perceptual neural activity, as well as long-term attention and arousal states play a role in maintaining AoA.

neuroscience↗

bioRxiv2024

Super homotypic targeting by exosome surface engineering

Homotypic targeting is the inherent ability of cells for preferential interaction with cells of similar or identical types, a phenomenon commonly seen in cell adhesion, tissue formation, and immune responses. Unfortunately, its full potential remains largely untapped. Here we introduce an approach to drastically boost the homotypic targeting capabilities of cells via exosomes (nanoscale extracellular vesicles secreted by cells). By engineering exosome surfaces with lanthanides, we amplify specific cell-exosome interactions by more than 25-fold, significantly accelerating the selective capture of exosomes by cells of the same lineage. This substantial enhancement in cellular homophilicity opens up an entirely new class of applications, two of which we showcase here with unprecedented performance: using cells to detect specific exosomes and using exosomes to detect specific cells. The concept of "super homotypic targeting" offers enormous potential to transform cancer diagnostics, immunotherapy, targeted drug delivery, tissue engineering, and vaccine development.

cell biology↗

bioRxiv2023

Dynamic noise estimation: A generalized method for modeling noise in sequential decision-making behavior

Computational cognitive modeling is an important tool for understanding the processes supporting human and animal decision-making. Choice data in decision-making tasks are inherently noisy, and separating noise from signal can improve the quality of computational modeling. Common approaches to model decision noise often assume constant levels of noise or exploration throughout learning (e.g., the{epsilon} -softmax policy). However, this assumption is not guaranteed to hold - for example, a subject might disengage and lapse into an inattentive phase for a series of trials in the middle of otherwise low-noise performance. Here, we introduce a new, computationally inexpensive method to dynamically infer the levels of noise in choice behavior, under a model assumption that agents can transition between two discrete latent states (e.g., fully engaged and random). Using simulations, we show that modeling noise levels dynamically instead of statically can substantially improve model fit and parameter estimation, especially in the presence of long periods of noisy behavior, such as prolonged attentional lapses. We further demonstrate the empirical benefits of dynamic noise estimation at the individual and group levels by validating it on four published datasets featuring diverse populations, tasks, and models. Based on the theoretical and empirical evaluation of the method reported in the current work, we expect that dynamic noise estimation will improve modeling in many decision-making paradigms over the static noise estimation method currently used in the modeling literature, while keeping additional model complexity and assumptions minimal.

neuroscience↗

bioRxiv2022

Differential genetic variation underlying Ammonium and Nitrate responses in Arabidopsis thaliana

Nitrogen is an essential element required for plant growth and productivity. Understanding the mechanisms and natural genetic variation underlying nitrogen use in plants will facilitate engineering plant nitrogen use to maximize crop productivity while minimizing environmental costs. To understand the scope of natural variation that may influence nitrogen use, we grew 1135 Arabidopsis thaliana natural genotypes on two nitrogen sources, nitrate and ammonium, and measured both developmental and defense metabolite traits. By using different environments and focused on multiple traits, we identified a wide array of different nitrogen responses. These responses are associated with a large number of genes, most of them not previously associated with nitrogen responses. Only a small portion of these genes appear to be shared between environments or traits while most of the detected genes are predominantly specific to a developmental or defense trait under a specific nitrogen source. Finally, by using a large population we were able to identify unique nitrogen responses, like preferring ammonium or nitrate, that appear to be generated by combinations of loci rather than a few large effect loci. This suggests that it may be possible to obtain novel phenotypes in complex nitrogen responses by manipulating sets of genes with small effects rather than solely focusing on large effect single gene manipulations. One Sentence SummaryUsing a large collection of natural genotypes, and studying both developmental and metabolic responses, we found a large number of genes that are involved in the plants nitrogen response.

plant biology↗

bioRxiv2022

Optical control of CRISPR-Cas editing with cyclically caged guide RNAs

The CRISPR/Cas system has been proved as one of the most powerful tools for precise gene editing. However, the approaches for precise control over the genome editing and regulatory events are still desirable. Here, we reported a spatiotemporal and efficient CRISPR/Cas9 and Cpf1-mediated editing with photo-sensitive circular gRNAs. This approach relies on only two or three pre-installed photolabile substituents followed by a simple covalent cyclization, which provides a robust synthesize approach in comparison to heavily modified gRNAs. In established cells stably expressing Cas9, the circular gRNA in coordination with light irradiation could direct a precise cleavage of GFP and VEGFA within a pre-defined cutting region. We have also achieved light-mediated MSTN gene editing in embryos, whereas a new bow-knot-type gRNA showed no background editing in the absence of light irradiation. Together, our work provides a significantly improved method to precisely manipulate where and when genes are edited.

biochemistry↗

bioRxiv2021

Decreased overall neuronal activity in a rodent model of impaired consciousness during absence seizures

Absence seizures are characterized by a brief behavioural impairment including apparent loss of consciousness. Neuronal mechanisms determining the behavioural impairment of absence seizures remain unknown, and their elucidation might highlight therapeutic options for reducing seizure severity. However, recent studies have questioned the similarity of animal spike-wave-discharges (SWD) to human absence seizures both behaviourally and neuronally. Here, we report that Genetic Absence Epilepsy Rats from Strasbourg recapitulate the decreased neuroimaging signals and loss of consciousness characteristic of human absence seizures. Overall neuronal firing is decreased but rhythmic in the somatosensory cortex and thalamus during these seizures. Interestingly, individual neurons in both regions tend to consistently express one of four distinct patterns of seizure-associated activity. These patterns differ in firing rate dynamics and in rhythmicity during seizure. One group of neurons showed a transient initial peak in firing at SWD onset, accounting for the brief initial increase in overall neuronal firing seen in cortex and thalamus. The largest group of neurons in both cortex and thalamus showed sustained decreases in firing during SWD. Other neurons showed either sustained increases or no change in firing. These findings suggest that certain classes of cortical and thalamic neurons may be particularly responsible for the paroxysmal oscillations and consequent loss of consciousness in absence epilepsy.

neuroscience↗

bioRxiv2018

Targeting BMI1 and MCL1 for Lung Adenocarcinoma Treatment

Lung cancer is the leading cause of cancer-associated death worldwide. Early metastasis and the recurrence remain major challenges for lung cancer treatment in clinic. Targeting the cancer stemness could be a potential strategy to restrain tumor progression. In the current study, we found that in lung adenocarcinoma (LAC), BMI1 and MCL1 play crucial roles in invasion, chemo-resistance, and tumor initiation. JNK signaling is a link between oncogenic pathway or environment stress to cancer stemness. The activation of JNK, either by EGFR or chemotherapy agent, stabilized BMI1 and MCL1 proteins through suppressing the expression of E3-ubiquitin ligase HUWE1. In lung cancer patient samples, high level of BMI1 is correlated with poor survival, and the expression of BMI1 is positively correlated with MCL1. A novel small-molecule BI-44 was synthesized, which effectively suppressed BMI1/MCL1 expression and inhibited tumor formation and progression in preclinical models. Targeting BMI1/MCL1 provides the basis for a new therapeutic approach in the treatment of LAC.

cancer biology↗