Biology subjects

Shen, L.

Publications and source records attributed to Shen, L..

At least 91 records · Page 5Linked to original sources

bioRxiv2024

Missing Wedge Completion via Unsupervised Learning with Coordinate Networks

Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, which limits reconstruction quality due to incomplete data collection angles. Recently, supervised deep learning methods leveraging convolutional neural networks (CNNs) have considerably addressed this issue; however, their pretraining requirements render them susceptible to inaccuracies and artifacts, particularly when representative training data is scarce. To overcome these limitations, we introduce a proof-of-concept unsupervised learning approach using coordinate networks (CNs) that optimizes network weights directly against input projections. This eliminates the need for pretraining, reducing reconstruction runtime by 3 - 20x compared to supervised methods. Our in silico results show improved shape completion and reduction of missing wedge artifacts, assessed through several voxel-based image quality metrics in real space and a novel directional Fourier Shell Correlation (FSC) metric. Our study illuminates benefits and considerations of both supervised and unsupervised approaches, guiding the development of improved reconstruction strategies.

molecular biology↗

bioRxiv2024

FABEL: Forecasting Animal Behavioral Events with Deep Learning-Based Computer Vision

Behavioral neuroscience aims to provide a connection between neural phenomena and emergent organism-level behaviors. This requires perturbing the nervous system and observing behavioral outcomes, and comparing observed post-perturbation behavior with predicted counterfactual behavior and therefore accurate behavioral forecasts. In this study we present FABEL, a deep learning method for forecasting future animal behaviors and locomotion trajectories from historical locomotion alone. We train an offline pose estimation network to predict animal body-part locations in behavioral video; then sequences of pose vectors are input to deep learning time-series forecasting models. Specifically, we train an LSTM network that predicts a future food interaction event in a specified time window, and a Temporal Fusion Transformer that predicts future trajectories of animal body-parts, which are then converted into probabilistic label forecasts. Importantly, accurate prediction of food interaction provides a basis for neurobehavioral intervention in the context of compulsive eating. We show promising results on forecasting tasks between 100 milliseconds and 5 seconds timescales. Because the model takes only behavioral video as input, it can be adapted to any behavioral task and does not require specific physiological readouts. Simultaneously, these deep learning models may serve as extensible modules that can accommodate diverse signals, such as in-vivo fluorescence imaging and electrophysiology, which may improve behavior forecasts and elucidate invervention targets for desired behavioral change.

neuroscience↗

bioRxiv2024

Transcriptional characterization of cocaine withdrawal versus extinction within nucleus accumbens

Substance use disorder is characterized by a maladaptive imbalance wherein drug seeking persists despite negative consequences or drug unavailability. This imbalance correlates with neurobiological alterations some of which are amplified during forced abstinence, thereby compromising the capacity of extinction-based approaches to prevent relapse. Cocaine use disorder (CUD) exemplifies this phenomenon in which neurobiological modifications hijack brain reward regions such as the nucleus accumbens (NAc) to manifest craving and withdrawal-like symptoms. While increasing evidence links transcriptional changes in the NAc to specific phases of addiction, genome-wide changes in gene expression during withdrawal vs. extinction (WD/Ext) have not been examined in a context- and NAc-subregion-specific manner. Here, we used cocaine self-administration (SA) in rats combined with RNA-sequencing (RNA-seq) of NAc subregions (core and shell) to transcriptionally profile the impact of experiencing withdrawal in the home cage or in the previous drug context or experiencing extinction training. As expected, home-cage withdrawal maintained drug seeking in the previous drug context, whereas extinction training reduced it. By contrast, withdrawal involving repetitive exposure to the previous drug context increased drug-seeking behavior. Bioinformatic analyses of RNA-seq data revealed gene expression patterns, networks, motifs, and biological functions specific to these behavioral conditions and NAc subregions. Comparing transcriptomic analysis of the NAc of patients with CUD highlighted conserved gene signatures, especially with rats that were repetitively exposed to the previous drug context. Collectively, these behavioral and transcriptional correlates of several withdrawal-extinction settings reveal fundamental and translational information about potential molecular mechanisms to attenuate drug-associated memories.

neuroscience↗

bioRxiv2024

Differential gene expression and chromatin accessibility in the rat medial prefrontal cortex mediating individual variability in vulnerability to opioid use

We characterized gene transcriptional activity in the medial prefrontal cortex of rats associated with individual differences in vulnerability to three distinct phases of opioid use disorder (OUD). Resilient rats showed many more changes in canonical pathway activity than Vulnerable rats in models of both early and advanced OUD, involving passive opioid exposure and opioid self-administration (SA), respectively. The Resilient/Vulnerable phenotype was also associated across phases with functionally specific gene networks, including those mediating epigenetic, neuroimmune, and neuroplasticity function. In contrast, we identified two phase-specific effects. First, differential activity of a myelination-related gene network was associated with Resilience/Vulnerability measured after passive morphine exposure. Second, expression of the calmodulin-inhibitor Pcp4, a gene recently implicated in a rat opioid SA GWAS analysis, was associated with Resilience/Vulnerability measured after SA but not after passive morphine exposure. Thus, we have identified both general and phase-specific transcriptional signatures involved in OUD vulnerability across its trajectory. TeaserAdaptations in the brain transcriptome are associated with resilience and vulnerability to opioid use disorder.

neuroscience↗

bioRxiv2024

An Engineered IGF2 Mutant for Lysosomal Targeting Chimeras Development and Membrane Proteins Degradation

Lysosome-targeting chimeras (LYTACs) have emerged as a promising strategy for targeted degradation of membrane proteins, offering potential applications in drug development. Currently, two main methods for developing LYTACs exist: chemically modified antibodies [1-2] and wild-type insulin-like growth factor 2 (IGF2) fusion proteins (iLYTACs) [3]. However, the fusion of the IGF2 arm within iLYTACs carries the risk of activating IGF1R tyrosine kinase activity and promoting tumor development. To address this concern, we introduce eiLYTACs, a technology that employs engineered IGF2 fusion antibodies to induce degradation of endogenous membrane proteins. Compared to the wild-type IGF2, the engineered IGF2 mutant exhibited minimal binding affinity for IGF1R but demonstrated a significant 100-fold increase in its binding affinity for IGF2R. In contrast to wild-type IGF2, which promotes tumor growth, the cells incubated with the engineered IGF2 showed no stimulation of tumor growth. The eiLYTACs strategy effectively inhibits tumor cell proliferation by degrading specific targets, resulting in a significant reduction in xenograft tumor size in experimental nude mice. More interestingly, our research revealed that eiLYTACs simultaneously degrade both homo- and heterodimers of disease-relevant proteins,which offer a promising strategy to address the activation of compensatory bypass signaling pathways, drug resistance, and tumor heterogeneity.

biochemistry↗

bioRxiv2024

QOT: Efficient Computation of Sample Level Distance Matrix from Single-Cell Omics Data through Quantized Optimal Transport

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell technologies have emerged as a transformative technology enabling high-dimensional characterization of cell populations at an unprecedented scale. The datas innate complexity and voluminous nature pose significant computational and analytical challenges, especially in comparative studies delineating cellular architectures across various biological conditions (i.e., generation of sample level distance matrices). Optimal Transport (OT) is a mathematical tool that captures the intrinsic structure of data geometrically and has been applied to many bioinformatics tasks. In this paper, we propose QOT (Quantized Optimal Transport), a new method enables efficient computation of sample level distance matrix from large-scale single-cell omics data through a quantization step. We apply our algorithm to real-world single-cell genomics and pathomics datasets, aiming to extrapolate cell-level insights to inform sample level categorizations. Our empirical study shows that QOT outperforms OT-based algorithms in terms of accuracy and robustness when obtaining a distance matrix at the sample level from high throughput single-cell measures. Moreover, the sample level distance matrix could be used in downstream analysis (i.e. uncover the trajectory of disease progression), highlighting its usage in biomedical informatics and data science.

bioinformatics↗

bioRxiv2024

Auditory stimuli extend the temporal window of visual integration by modulating alpha-band oscillations

In multisensory environments, signals from different modalities interact to shape perception, but the underlying temporal dynamics remain unclear. We investigated how a brief auditory stimulus affects the temporal integration of successive visual events using electroencephalography (EEG). Participants viewed two consecutive flashes, with or without an auditory beep accompanying the first flash, and reported whether they perceived one or two flashes. The beep shifted the psychometric fusion threshold to longer interstimulus intervals, indicating a wider temporal integration window. This behavioural shift was accompanied by a transient reduction in poststimulus instantaneous alpha frequency, and individual differences in alpha-cycle lengthening correlated with threshold prolongation. Auditory input also weakened the relationship between prestimulus alpha frequency and perceptual report while strengthening the corresponding relationship with prestimulus alpha phase. In a follow-up transcranial alternating current stimulation (tACS) experiment, the effects of lower- versus upper-alpha stimulation on fusion thresholds differed between the visual-only and audiovisual conditions. A computational model based on phase resetting and perceptual cycles qualitatively reproduced the main findings. Together, the results suggest that a brief sound can reorganize alpha-band timing and thereby widen the temporal window of visual integration.

neuroscience↗

bioRxiv2023

A spectrum of altered non-rapid eye movement sleep in schizophrenia

BackgroundMultiple facets of sleep neurophysiology, including electroencephalography (EEG) metrics such as non-rapid eye movement (NREM) spindles and slow oscillations (SO), are altered in individuals with schizophrenia (SCZ). However, beyond group-level analyses which treat all patients as a unitary set, the extent to which NREM deficits vary among patients is unclear, as are their relationships to other sources of heterogeneity including clinical factors, illness duration and ageing, cognitive profiles and medication regimens. Using newly collected high density sleep EEG data on 103 individuals with SCZ and 68 controls, we first sought to replicate our previously reported (Kozhemiako et. al, 2022) group-level mean differences between patients and controls (original N=130). Then in the combined sample (N=301 including 175 patients), we characterized patient-to-patient variability in NREM neurophysiology. ResultsWe replicated all group-level mean differences and confirmed the high accuracy of our predictive model (Area Under the ROC Curve, AUC = 0.93 for diagnosis). Compared to controls, patients showed significantly increased between-individual variability across many (26%) sleep metrics, with patterns only partially recapitulating those for group-level mean differences. Although multiple clinical and cognitive factors were associated with NREM metrics including spindle density, collectively they did not account for much of the general increase in patient-to-patient variability. Medication regimen was a greater (albeit still partial) contributor to variability, although original group mean differences persisted after controlling for medications. Some sleep metrics including fast spindle density showed exaggerated age-related effects in SCZ, and patients exhibited older predicted biological ages based on an independent model of ageing and the sleep EEG. ConclusionWe demonstrated robust and replicable alterations in sleep neurophysiology in individuals with SCZ and highlighted distinct patterns of effects contrasting between-group means versus within-group variances. We further documented and controlled for a major effect of medication use, and pointed to greater age-related change in NREM sleep in patients. That increased NREM heterogeneity was not explained by standard clinical or cognitive patient assessments suggests the sleep EEG provides novel, nonredundant information to support the goals of personalized medicine. Collectively, our results point to a spectrum of NREM sleep deficits among SCZ patients that can be measured objectively and at scale, and that may offer a unique window on the etiological and genetic diversity that underlies SCZ risk, treatment response and prognosis.

neuroscience↗

bioRxiv2023

Potential clinical value and influence of conductivity in conductive cardiac patches for reducing post-MI arrythmia risks

Conductive cardiac patches can help to restore electric signal conduction of the diseased myocardium after myocardial infarction (MI). However, none of the conductive cardiac patches reported in literature has entered clinical trials. Bench-to-bedside translation of conductive patches has long been hindered by the lack of knowledge of the optimal patch conductivity and deep understanding of the potential clinical benefits and risks in patients. Here, we first prepared conductive cardiac patches with conductivities covering 5 orders of magnitude (10-3-101 S/cm). Disagreeing with the mainstream opinion that patch conductivity close to native myocardium (10-3-10-2 S/cm) is most favorable, our results showed that patches with conductivity two orders of magnitudes higher than native myocardium (10-1-100 S/cm) are most effective in restoring cardiac conduction and lowering inducibility quotient. Conduction velocity (CV) is the essence of the observed results. Rat experiments showed that the low-conductivity patch could increase the CV of infarcted myocardium, but did not fully compensate the mismatch in the CVs of infarct and healthy myocardium. Moderate-conductivity patches could increase myocardial CV to the same level of healthy myocardium, while high-conductivity patches further increased myocardial CV, causing a reversed mismatch. The relationship between patch conductivity and improved CVs in myocardium can be explained by monodomain model theory. Based on the theory, 3D finite element simulation of a MI patient heart predicted that a suitable, patch-improved myocardial CV could reduce the number of reentrants, and stabilize the remaining reentry circuits in the myocardium of the MI patient, which indicated its clinical value.

bioengineering↗

bioRxiv2023

Homological landscape of human brain functional sub-circuits

Human whole-brain functional connectivity networks have been shown to exhibit both local/quasilocal (e.g., set of functional sub-circuits induced by node or edge attributes) and non-local (e.g., higher-order functional coordination patterns) properties. Nonetheless, the non-local properties of topological strata induced by local/quasilocal functional sub-circuits have yet to be addressed. To that end, we proposed a homological formalism that enables the quantification of higher-order characteristics of human brain functional sub-circuits. Our results indicated that each homological order uniquely unravels diverse, complementary properties of human brain functional sub-circuits. Noticeably, the H1 homological distance between rest and motor task were observed at both whole-brain and sub-circuit consolidated level which suggested the self-similarity property of human brain functional connectivity unraveled by homological kernel. Furthermore, at the whole-brain level, the rest-task differentiation was found to be most prominent between rest and different tasks at different homological orders: i) Emotion task (H0), ii) Motor task (H1), and iii) Working memory task (H2). At the functional sub-circuit level, the rest-task functional dichotomy of default mode network is found to be mostly prominent at the first and second homological scaffolds. Also at such scale, we found that the limbic network plays a significant role in homological reconfiguration across both task- and subject-domain which sheds light to subsequent investigations on the complex neuro-physiological role of such network. From a wider perspective, our formalism can be applied, beyond brain connectomics, to study non-localized coordination patterns of localized structures stretching across complex network fibers.

neuroscience↗

bioRxiv2023

YAP1 status defines two intrinsic subtypes of LCNEC with distinct molecular features and therapeutic vulnerabilities

Large cell neuroendocrine carcinoma (LCNEC) is a high-grade neuroendocrine malignancy that, like the more common small cell lung cancer (SCLC), is associated with an absence of druggable oncogenic driver mutations, a clinically aggressive disease course, and dismal prognosis. In contrast to SCLC, however, there is little evidence to guide optimal treatment strategies which are, instead, often adapted from SCLC and non-small cell lung cancer (NSCLC) approaches. While there have been some efforts to describe the molecular landscape of LCNEC, to date there are few links between distinct biologic phenotypes of LCNEC and therapeutic vulnerabilities. Here, we demonstrate that the presence or absence of the transcription factor YAP1 distinguishes two roughly equal subsets of LCNEC. The YAP1-high subset is mesenchymal and inflamed and characterized, alongside TP53 mutations, by co-occurring alterations in CDKN2A/B and SMARCA4. Therapeutically, the YAP1-high subset demonstrates vulnerability to MEK and AXL targeting strategies, including a novel preclinical AXL CAR-T cell, as well as predicted vulnerability to SMARCA2 degraders and CDK4/6 inhibitors. Meanwhile, the YAP1-low subset is epithelial and immune-cold and more commonly features TP53 and RB1 co-mutations, similar to those observed in pure SCLC. Notably, the YAP1-low subset is also characterized by expression of SCLC subtype-defining transcription factors -especially ASCL1 and NEUROD1 - and, as expected given its transcriptional similarities to SCLC, exhibits putative vulnerabilities reminiscent of SCLC, including Delta-like ligand 3 (DLL3) and CD56 targeting, as with novel preclinical DLL3 and CD56 CAR T-cells, and DNA damage repair (DDR) inhibition. These findings highlight the potential for YAP1 to guide the first personalized treatment strategies for LCNEC.

cancer biology↗

bioRxiv2023

ATAT: Automated Tissue Alignment and Traversal in Spatial Transcriptomics with Self-Supervised Learning

Spatial transcriptomics (ST) has enhanced RNA analysis in tissue biopsies, but interpreting these data is challenging without expert input. We present Automated Tissue Alignment and Traversal (ATAT), a novel computational framework designed to enhance ST analysis in the context of multiple and complex tissue architectures and morphologies, such as those found in biopsies of the gastrointestinal tract. ATAT utilizes self-supervised contrastive learning on hematoxylin and eosin (H&E) stained images to automate the alignment and traversal of ST data. This approach addresses a critical gap in current ST analysis methodologies, which rely heavily on manual annotation and pathologist expertise to delineate regions of interest for accurate gene expression modeling. Our framework not only streamlines the alignment of multiple ST samples, but also demonstrates robustness in modeling gene expression transitions across specific regions. Additionally, we highlight the ability of ATAT to traverse complex tissue topologies in real-world cases from various individuals and conditions. Our method successfully elucidates differences in immune infiltration patterns across the intestinal wall, enabling the modeling of transcriptional changes across histological layers. We show that ATAT achieves comparable performance to the state-of-the-art method, while alleviating the burden of manual annotation and enabling alignment of tissue samples with complex morphologies. AvailabilityATAT is available at: https://github.com/StevenSong/tissue-alignment

bioinformatics↗

bioRxiv2023

Integration of ATAC-seq and RNA-seq identifies novel candidate genes associated with drought tolerance in Zoysia japonica Steud.

HighlightIntegration of ATAC-seq and RNA-seq identifies novel candidate genes associated with drought tolerance in Zoysia japonica Steud. The warm-season turfgrass Zoysia japonica is renowned for its drought resistance and serves as an exceptional domestic turfgrass in China. In order to unlock the potential of this native grass, identify drought-resistant genes, enhance the genetic transformation system, and maximize its utilization benefits, we conducted physiological characterization, multi-omics analysis, and RT-qPCR experimental verification in Zoysia japonica. This study suggested that 63 high-confidence genes related to drought stress and 6 motifs regulating drought responses were identified using a combined omics approach and RT-qPCR validation. The study discovered a positive correlation between ATAC-Seq peak intensity and gene expression levels. The expression of high-confidence genes was linked to Zoysia japonica resistance evaluation and phenotypic traits, implying that these genes are involved in responding to external drought stress. This study combined ATAC-seq and RNA-seq technologies for the first time to identify drought-related genes in Zoysia japonica, elucidating the grasss adaptation to environmental stress and the regulatory mechanisms underlying stress responses, and laying the groundwork for Zoysia japonica improvement and breeding.

genomics↗

bioRxiv2023

A Method for Calculating the Least Mutated Sequence in DNA Alignment Based on Point Mutation Sites

It is hard to decide the root of a phylogenetic tree when no suitable outgroup sequence is available, especially in a high mutation rate system. Here we propose a bioinformatics method for calculating the least mutated sequence in DNA alignment based on point mutation sites. By parsimony principle, the least mutated sequence should be the ancestor (phylogenetic root) of the other sequences in an alignment result. The intuition suggests that our method would be sound only if enough sequences were collected. In this study, we used DNA mutation simulation data to test our method. The simulation results show that our method is very reliable and just a small proportion of sequences is required to find the least mutated sequence when the sampling is random. When the ancestral sequence is present in the dataset, the least mutated sequence calculated by our method is the ancestral sequence itself, i.e., the phylogenetic root of the dataset. When the ancestral sequence is not in the dataset, the least mutated sequence calculated by our method is the one closest to the phylogenetic root. Furthermore, the simulation demonstrates that our method is robust against reverse mutation and saturation mutation. The performance of our method improves with the increasing number of sequences. We developed the program based on this method which also calculates the transition/transversion ratio for each sequence in an DNA alignment. It can be used as a rough measure for estimating selection pressure and evolutionary stability for a sequence. In conclusion, our method and the corresponding programs could be a useful auxiliary tool for bioinformaticians and evolutionary biologists.

bioinformatics↗

bioRxiv2023

Serotonin transporter-dependent histone serotonylation in placenta contributes to the neurodevelopmental transcriptome

Brain development requires appropriate regulation of serotonin (5-HT) signaling from distinct tissue sources across embryogenesis. At the maternal-fetal interface, the placenta is thought to be an important contributor of offspring brain 5-HT and is critical to overall fetal health. Yet, how placental 5-HT is acquired, and the mechanisms through which 5-HT influences placental functions, are not well understood. Recently, our group identified a novel epigenetic role for 5-HT, in which 5-HT can be added to histone proteins to regulate transcription, a process called H3 serotonylation. Here, we show that H3 serotonylation undergoes dynamic regulation during placental development, corresponding to gene expression changes that are known to influence key metabolic processes. Using transgenic mice, we demonstrate that placental H3 serotonylation largely depends on 5-HT uptake by the serotonin transporter (SERT/SLC6A4). SERT deletion robustly reduces enrichment of H3 serotonylation across the placental genome, and disrupts neurodevelopmental gene networks in early embryonic brain tissues. Thus, these findings suggest a novel role for H3 serotonylation in coordinating placental transcription at the intersection of maternal physiology and offspring brain development.

molecular biology↗

bioRxiv2023

Aryl hydrocarbon receptor restricts axon regeneration of DRG neurons in response to injury

Neurons must balance stress responses with regenerative demands following injury, but the mechanism is unclear. Here, we identify the aryl hydrocarbon receptor (AhR), a ligand-activated bHLH-PAS transcription factor, as a key regulator of this stress-growth switch. We show that ligand mediated AhR signaling restrains axon growth, whereas neuronal deletion or pharmacological inhibition of AhR promotes axonal regeneration and functional recovery in both peripheral nerve and spinal cord injury models. Mechanistic studies revealed that axotomy-induced AhR activation in dorsal root ganglion (DRG) neurons enforces proteostasis and stress-response programs to preserve tissue integrity. In contrast, AhR ablation redirects the neuronal response toward elevated de novo translation and pro-growth signaling, enabling axon regeneration. This growth-promoting effect required HIF-1, with shared transcriptional targets enriched for metabolic and regenerative pathways. Single-cell and epigenomic analyses further revealed that the AhR regulon engages the integrated stress response (ISR) and DNA hydroxymethylation to rewire neuronal injury programs. Together, our findings establish AhR as a neuronal brake on axon regeneration, integrating environmental sensing, protein homeostasis, and metabolic signaling to control the balance between stress adaptation and axonal repair.

neuroscience↗

bioRxiv2023

Shared and divergent transcriptomic regulation in nucleus accumbens D1 and D2 medium spiny neurons by cocaine and morphine

Substance use disorders (SUDs) induce widespread molecular dysregulation in the nucleus accumbens (NAc), a brain region pivotal for coordinating motivation and reward. These molecular changes are thought to support lasting neural and behavioral disturbances that promote drug-seeking in addiction. However, different drug classes exert unique influences on neural circuits, cell types, physiology, and gene expression despite the overlapping symptomatology of SUDs. To better understand common and divergent molecular mechanisms governing SUD pathology, our goal was to survey cell-type-specific restructuring of the NAc transcriptional landscape in after psychostimulant or opioid exposure. We combined fluorescence-activated nuclei sorting and RNA sequencing to profile NAc D1 and D2 medium spiny neurons (MSNs) across cocaine and morphine exposure paradigms, including initial exposure, prolonged withdrawal after repeated exposure, and re-exposure post-withdrawal. Our analyses reveal that D1 MSNs display many convergent transcriptional responses across drug classes during exposure, whereas D2 MSNs manifest mostly divergent responses between cocaine and morphine, with morphine causing more adaptations in this cell type. Utilizing multiscale embedded gene co-expression network analysis (MEGENA), we discerned transcriptional regulatory networks subserving biological functions shared between cocaine and morphine. We observed largely integrative engagement of overlapping gene networks across drug classes in D1 MSNs, but opposite regulation of key D2 networks, highlighting potential therapeutic gene network targets within MSNs. These studies establish a landmark, cell-type-specific atlas of transcriptional regulation induced by cocaine and by morphine that can serve as a foundation for future studies towards mechanistic understanding of SUDs. Our findings, and future work leveraging this dataset, will pave the way for the development of targeted therapeutic interventions, addressing the urgent need for more effective treatments for cocaine use disorder and enhancing the existing strategies for opioid use disorder.

neuroscience↗

bioRxiv2023

Claudin-2 limits pancreatitis development through regulating tight junction-controlled pancreatic ductal transport

Pancreatitis is an inflammatory disease of the pancreas that can arise due to various factors, including environmental risks such as diet, alcohol, and smoking, as well as genetic predispositions. In some cases, pancreatitis may progress and become chronic, leading to irreversible damage and impaired pancreatic function. Genome-wide association studies (GWAS) have identified polymorphisms at the X-linked CLDN2 locus as risk factors for both sporadic and alcohol-related chronic pancreatitis. CLDN2 encodes claudin-2 (CLDN2), a paracellular cation-selective channel localized at tight junctions and expressed in the pancreas and other secretory organs. However, whether and how CLDN2 may modify pancreatitis susceptibility remains poorly understood. We aimed to clarify the potential role of CLDN2 in the onset and progression of pancreatitis. We employed multiple methodologies to examine the role of CLDN2 in human pancreatic tissue, caerulein-induced experimental pancreatitis mouse model, and pancreatic ductal epithelial organoids. In both human chronic pancreatitis tissues and caerulein-induced experimental pancreatitis, CLDN2 protein was significantly upregulated in pancreatic ductal epithelial cells. Our studies using pancreatic ductal epithelial organoids and mice demonstrated the inflammatory cytokine IFN{gamma} upregulates claudin-2 expression at both RNA and protein levels. Following caerulein treatment, Ifng KO mice had diminished upregulation of CLDN2 relative to WT mice, indicating that caerulein-induced claudin-2 expression is partially driven by IFN{gamma}. Functionally, Cldn2 knockout mice developed more severe caerulein-induced experimental pancreatitis, indicating CLDN2 plays a protective role in pancreatitis development. Pancreatic ductal epithelial organoid-based studies demonstrated that CLDN2 is critical for sodium-dependent water transport and necessary for cAMP-driven, CFTR-dependent fluid secretion. These findings suggest that functional crosstalk between CLDN2 and CFTR is essential for fluid transport in pancreatic ductal epithelium, which may protect against pancreatitis by adjusting pancreatic ductal secretion to prevent worsening autodigestion and inflammation. In conclusion, our studies suggest CLDN2 upregulation during pancreatitis may play a protective role in limiting disease development, and decreased CLDN2 function may increase pancreatitis severity. These results point to the possibility of modulating pancreatic ductal CLDN2 function as an approach for therapeutic intervention of pancreatitis.

pathology↗