Biology subjects

Cen, X.

Publications and source records attributed to Cen, X..

9 recordsLinked to original sources

bioRxiv2026

FITdb, an Integrated Functional Immunogenomics and Transcriptomics Database

Genetic screens in immune cells enable the systematic interrogation of gene function at scale, uncovering key regulators of cell functions such as tumor cell killing and persistence. However, existing datasets typically focus on specific biological questions, employ targeted gene panels, are generated under diverse experimental conditions, and are not readily accessible, which together limit their integration and future usability. To address this, we developed the Functional Immunogenomics and Transcriptomics Database (FITdb), a freely accessible resource that harmonizes functional genomics datasets for the study of immune cell biology. FITdb currently integrates 43 independent functional genetics screens, including 32 pooled and 11 single-cell screens, spanning 20, 696 mouse genes and 22, 293 human genes across 199 immune cell types and conditions. All datasets are uniformly re-analyzed to enable cross-study comparisons. FITdb provides intuitive, gene-centric visualizations, detailed exploration of individual screens, and access to sgRNA-level data. Additionally, built-in tools such as "Compare MyGeneSet" and "Compare MyScreen" identify statistically significant overlaps between user-defined gene lists and functional gene sets in FITdb, and enable direct comparison of user-generated screening data with existing datasets, respectively. Together, FITdb provides a comprehensive, user-friendly platform for accelerating the discovery of immune regulatory programs. The database is freely available at https://fitdb.lji.org. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/741304v1_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@114d978org.highwire.dtl.DTLVardef@1d15d04org.highwire.dtl.DTLVardef@31c2f4org.highwire.dtl.DTLVardef@f63dfe_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Proteostasis Stress Drives Stem Cell Aging, Clonal Hematopoiesis and Leukemia

Aging is the primary risk factor for clonal hematopoiesis and the development of hematologic malignancies (1-5), yet the selective pressures that shape stem cell behavior and clonal expansion during aging remain poorly defined. Here, we identify proteostasis stress as a central driver of hematopoietic stem cell (HSC) aging and clonal evolution. We show that Heat shock factor 1 (Hsf1) is activated in aging HSCs to preserve proteostasis and sustain self-renewal. However, this physiological, age-associated adaptive mechanism is co-opted by pre-leukemic Dnmt3a-mutant HSCs to resist proteostasis and inflammatory stress required to fuel clonal expansion during aging. In the context of co-occurring Dnmt3a and Nras mutations, which are frequently observed in human acute myeloid leukemia (AML) (6-13), mutant HSCs and progenitors exhibit heightened dependence on Hsf1 for expansion, malignant transformation and disease progression. Loss of Hsf1, or disruption of proteostasis, impairs expansion of mutant progenitors, delays leukemia onset, and prolongs survival. Together, these findings reveal proteostasis as a key constraint in the aging hematopoietic system that imposes a selective bottleneck. Hsf1 activation enables both physiological adaptation in aging stem cells and pathological clonal outgrowth in pre-leukemic and leukemic states, establishing proteostasis control as a pivotal mechanism linking stem cell aging to clonal hematopoiesis and malignancy.

cancer biology↗

bioRxiv2024

Development of the Follow-Up Discourse Observation Protocol (FUDOP) for characterizing instructor active-learning follow-up behaviors

Instructors can provide feedback to their class in multiple ways. One way is through their follow-up behaviors, which are the specific strategies instructors implement after active learning activities. These behaviors could play an important role in student learning as students can receive feedback from the instructor. However, there is little research on the effects of different types of follow-up behaviors. Follow-up after active learning can be seen as a form of discourse between the instructor and the entire class. Previous researchers developed the Classroom Discourse Observation Protocol (CDOP) to analyze discourse between the instructor and individual students or small groups. We used CDOP as a starting point to develop and validate a new protocol, the Follow-Up Discourse Observation Protocol (FUDOP), to characterize instructional follow-up behaviors to the entire class after active-learning activities. We then used FUDOP to characterize follow-up behaviors of multiple instructors in introductory biology courses at three different universities. We measured consistent differences in these behaviors across instructors but not within instructors, demonstrating that instructors may engage in consistent follow-up behaviors. FUDOP could allow instructors and researchers to better measure and analyze follow-up behaviors and their effects, which could in turn provide guidance to instructors and faculty developers.

scientific communication and education↗

bioRxiv2024

An interpretable integration model improving disease-free survival prediction for gastric cancer based on CT images and clinical parameters

Preoperative prediction of disease-free survival of gastric cancer is significantly important in clinical practice. Existing studies showed the potentials of CT images in identifying predicting the disease-free survival of gastric cancer. However, no studies to date have combined deep features with radiomics features and clinical features. In this study, we proposed a model which embedded radiomics features and clinical features into deep learning model for improving the prediction performance. Our models showed a 3%-5% C-index improvement and 10% AUC improvement in predicting DFS and disease event. Interpretation analysis including T-SNE visualization and Grad-CAM visualization revealed that the model extract biologically meaning features, which are potentially useful in predicting disease trajectory and reveal tumor heterogeneity. The embedding of radiomics features and clinical features into deep learning model could guide the deep learning to learn biologically meaningful information and further improve the performance on the DFS prediction of gastric cancer. The proposed model would be extendable to related problems, at least in few-shot medical image learning. Key PointsO_LIAn integration model combining deep features, radiomics features and clinical parameters improved disease-free-survival prediction of gastric cancer by 3%-5% C-index. C_LIO_LIEmbedding radiomics and clinical features into deep learning model through concatenation and loss design improved feature extraction ability of deep network. C_LIO_LIThe model revealed disease progression trajectory and tumor heterogeneity. C_LI

cancer biology↗

bioRxiv2023

HGNNPIP: A Hybrid Graph Neural Network framework for Protein-protein Interaction Prediction

A deep understanding of Protein-protein interactions (PPIs) can provide comprehensive insights into many biological functions, thereby facilitating drug target identification and novel therapeutic design. Recent developments in artificial intelligence (AI)-driven computational methods have enabled the discovery of previously uncharacterized PPIs from large-scale interactome datasets. Almost all existing machine learning methods rely on Subcellular Localization (SL) to construct balanced datasets based on positive interactions to achieve predictions. Despite high fitting accuracy, the generalization ability of these models is questionable. To solve this problem, we analyzed existing methods and found that the high false positives in these methods are due to the bias in data distribution caused by SL. Therefore, we proposed a new strategy for negative instance sampling in PPI prediction and developed a Hybrid Graph Neural Network framework for Protein-protein Interaction Prediction (HGNNPIP). The experimental results showed that HGNNPIP works well on six benchmark datasets. Comparison analysis demonstrated that our model outperformed the other four existing methods. We also used HGNNPIP to explore the molecular contacts involved in the rice-pathogen interaction system. In vivo experiments confirmed multiple regulations related to disease resistance in rice. In summary, this study provides new insights into establishing a computational framework for PPI prediction with high reliability.

bioinformatics↗

bioRxiv2023

Morphine Re-arranges Chromatin Spatial Architecture of Primate Cortical Neurons

The expression of linear DNA sequences is precisely regulated by the three-dimensional (3D) architecture of chromatin. Morphine-induced aberrant gene networks of neurons have been extensively investigated; however, how morphine impacts the 3D genomic architecture of neuorns is still unknown. Here, we applied digestion-ligation-only high-throughput chromosome conformation capture (DLO Hi-C) technology to investigate the affection of morphine on 3D chromatin architecture of primate cortical neurons. After receiving continuous morphine administration for 90 days on rhesus monkeys, we discovered that morphine re-arranged chromosome territories, with a total of 391 segmented compartments being switched. Morphine altered over half of the detected topologically associated domains (TADs), most of which exhibited a variety of shifts, followed by separating and fusing types. Analysis of the looping events at kilobase-scale resolution revealed that morphine increased not only the number but also the length of differential loops. Moreover, all identified differentially expressed genes (DEGs) from the RNA sequencing (RNA-seq) were mapped to the specific TAD boundaries or differential loops, and were further validated to be significantly changed. Collectively, an altered 3D genomic architecture of cortical neurons may regulate the gene networks associated-morphine effects. Our finding provides critical hubs connecting chromosome spatial organization and gene networks associated with the morphine effects in humans.

bioinformatics↗

bioRxiv2022

Effect of time spent on active learning on exam performance: A controlled case study on a course with different instructors but identical teaching materials

Active learning, including student thinking and discussion in class, has been shown to increase student learning gains. However, it is less clear how variations in how instructors implement active learning affect student gains. Our study aims to investigate the extent to which the time spent on individual episodes of active learning activities influences student performance. We hypothesized that instructors who let students spend more time on peer discussion and individual thinking on practice problems associated with particular learning objectives will have better student exam scores on exam questions addressing those objectives. To test this hypothesis, we obtained a large data set of classroom recordings and student exam scores from an introductory biology course at a large four-year university, where three instructors shared identical teaching materials and exams for different course offerings. Contrary to our hypothesis, although the three instructors spent significantly different amounts of time on episodes of thinking and peer discussion, there was no correlation between the total time spent on active learning activities and student performance on exam questions. Linear mixed-effects modeling of the effect of length of episodes of student thinking and discussion on exam score found that the amount of course time spent on active learning activities did not reliably predict student performance on associated exam questions. This result held true even when only considering learning objectives with high variations in performance between offerings, difficult exam questions, exam questions requiring higher-order thinking skills, or within-instructor performance. Although our study was only conducted in one course, our results imply that time spent per individual episode of student thinking or peer discussion may not be the primary factor explaining the positive effects of active learning and that it may be worthwhile to explore other factors.

scientific communication and education↗

bioRxiv2021

The YAP/TAZ antagonist, PTPN14, stabilizes SMAD4 through direct interactions in endothelial cells: Implications for Hereditary Hemorrhagic Telangiectasia

Hereditary Hemorrhagic Telangiectasia (HHT) results from germline loss-of-function mutations of ENG, ACVRL1, or SMAD4, encoding TGF{beta}/BMP signaling components. Telangiectasias occur in most patients, and pulmonary, visceral, or cerebral arteriovenous malformations (AVMs) in 20-50% of these. How HHT mutations cause these clinical manifestations and why some patients suffer more serious sequelae than others is unknown. PTPN14 is a genetic modifier of pulmonary AVM incidence, and here we show by gene expression network analysis of a large panel of genetically diverse mouse lung RNA samples, that Ptpn14 is ontologically associated with markers of angiogenesis, vascular remodeling, and BMP/TGF{beta} and Rho kinase signaling. We demonstrate physical interaction between protein tyrosine phosphatase non-receptor, type 14 (PTPN14) and SMAD4 in nucleus and cytoplasm of primary human endothelial cells. PTPN14 suppresses ubiquitination and turnover of SMAD4 to augment tonic SMAD-mediated transcriptional readouts. This is the first report that PTPN14 binds and stabilizes SMAD4, a key component of the HHT signaling pathway. Through this mechanism, and its inhibition of YAP/TAZ signaling, PTPN14 levels may protect against development of AVMs in HHT. We discuss potential druggable targets for HHT within the ENG-ALK1-SMAD4-PTPN14 network. One Sentence SummaryPTPN14 binds and stabilizes SMAD4 to potentiate BMP9 signaling in endothelial cells and components of the PTPN14 network may be drug targets for HHT.

cell biology↗

bioRxiv2020

Structure-Based Design, Synthesis and Biological Evaluation of Peptidomimetic Aldehydes as a Novel Series of Antiviral Drug Candidates Targeting the SARS-CoV-2 Main Protease

SARS-CoV-2 is the etiological agent responsible for the COVID-19 outbreak in Wuhan. Specific antiviral drug are urgently needed to treat COVID-19 infections. The main protease (Mpro) of SARS-CoV-2 is a key CoV enzyme that plays a pivotal role in mediating viral replication and transcription, which makes it an attractive drug target. In an effort to rapidly discover lead compounds targeting Mpro, two compounds (11a and 11b) were designed and synthesized, both of which exhibited excellent inhibitory activity with an IC50 value of 0.05 M and 0.04 M respectively. Significantly, both compounds exhibited potent anti-SARS-CoV-2 infection activity in a cell-based assay with an EC50 value of 0.42 M and 0.33 M, respectively. The X-ray crystal structures of SARS-CoV-2 Mpro in complex with 11a and 11b were determined at 1.5 [A] resolution, respectively. The crystal structures showed that 11a and 11b are covalent inhibitors, the aldehyde groups of which are bound covalently to Cys145 of Mpro. Both compounds showed good PK properties in vivo, and 11a also exhibited low toxicity which is promising drug leads with clinical potential that merits further studies.

biochemistry↗