Biology subjects

Yong, J.

Publications and source records attributed to Yong, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Functional T cells trapped behind a stromal wall: a Brake-with-Wall phenotype redefines pancreatic adenocarcinoma immunotherapy resistance

BackgroundPancreatic ductal adenocarcinoma (PDAC) is the paradigmatic immunotherapy-refractory cancer, with a 5-year survival of approximately 12% and minimal benefit from immune checkpoint blockade (ICB). The dominant mechanistic explanation classifies PDAC as a T cell-excluded "cold" tumor, implying that no functional anti-tumor T cells are available for checkpoint release. Whether this Block-strategy view is correct has not been re-examined under integrated evasion-framework analysis. MethodsWe applied a previously developed 16-module immune evasion framework to TCGA-PAAD (n=183), integrated with hub-cytokine analysis (IL-10/TGF-{beta}), Kv1.3-immune channelome data, and clinical trial mapping (12,007 trials). Single-cell validation used two independent PDAC cohorts retrieved through TISCH2: PAAD_CRA001160 (Peng 2019, 35 samples [24 PDAC + 11 adjacent normal], 57,443 cells) and PAAD_GSE154778 (Lin 2020, 16 samples, 14,953 cells), examined for CD8A, TOX, PRF1, KCNA3, and FAP expression by cell type. ResultsPDAC scored highest in CAF Wall (z=0.768) and Platelet Cloak (z=0.663) modules; strategy classification yielded Brake -- not Block -- driven by a positive KCNA3-survival relationship (HR=0.649, 95% CI 0.43-0.97, p=0.037). Single-cell qualitative analysis of TISCH2 violin plots showed that CD8 exhausted T cells (CD8Tex) carried (i) high CD8A, (ii) the highest TOX expression among annotated cell types, (iii) preserved PRF1, and (iv) high KCNA3 expression. FAP was strongly localized to fibroblasts (peak [~]3.0 vs. <0.5 elsewhere). The pattern was reproduced in the second cohort. The optimal three-module attack (MHC restoration + CAF disruption + VEGF blockade) suppressed 10 of 16 evasion modules in silico (62.5%); zero of 370 PDAC immunotherapy trials test this combination. ConclusionsPDAC may not be T cell-cold but T cell-trapped: CD8 T cells with intact Kv1.3 channels appear immobilized behind a FAP-positive cancer-associated fibroblast wall. ICB monotherapy is mechanistically insufficient because the brake is engaged on T cells that cannot reach the tumor. The framework predicts that triple-targeted intervention -- checkpoint release + CAF wall disruption + vascular normalization -- is the minimum effective strategy. This is a hypothesis-generating computational analysis; prospective experimental and clinical validation are required.

cancer biology↗

bioRxiv2025

A First-in-Class Therapy Targeting Transcription-Replication Conflicts Drives Synthetic Lethality in Homologous Recombination Deficient Cancers

Hidden beneath the layer of tumor malignancy lies unchecked proliferation driven by hyperactive transcription, which fosters the formation of RNA:DNA hybrids. These structural intermediates precipitate collisions between the transcription and replication machineries. Central to mitigating this genomic threat is interleukin enhancer-binding factor 2 (ILF2), which orchestrates the recruitment of RNA:DNA helicases to resolve R-loops and, in doing so, reveals cancer cells dependency on stress-mitigating mechanisms to avert genome catastrophe. Our discovery of Molephantin and its lead derivative, NYH0002 is the outcome of delineating ILF2s function. NYH002 exerts direct binding to the ILF2 complex and disrupts RNA:DNA helicase activity to elicit genome-wide DNA breakages. Tumors with elevated cyclin E and E2F1 expression are sensitive to NYH002, while those deficient in homologous recombination repair exhibit lethality. Collectively, these findings position ILF2 as a key regulator of R-loop homeostasis and reveal a therapeutic vulnerability that NYH002 exploits to suppress tumor malignancy.

cancer biology↗

bioRxiv2025

Alternatively-Spliced CAMKK2 isoforms drive differential metabolic stress response and the regulation of ferroptosis

Calcium/Calmodulin-dependent protein kinase kinase 2 (CAMKK2) is a multifunctional kinase that regulates metabolic processes by phosphorylating downstream targets. CAMKK2 is expressed as several highly similar protein isoforms, although the specific functions of these individual isoforms remain largely unexplored. These isoforms have been shown to display tissue-specific expression patterns, suggesting unique roles in distinct cellular contexts. In this study, we investigated the biochemical and functional relevance of CAMKK2 isoforms, LF and SF, in the context of glucose metabolism. Co-immunoprecipitation experiments revealed that the LF isoform preferentially binds the adaptor protein 14-3-3, while the SF isoform interacts with Calmodulin. Furthermore, the interaction between SF and Calmodulin was enhanced upon glucose starvation, whereas this interaction was not observed for LF. To assess their functional significance, we generated doxycycline-inducible, isoform-specific HeLa cell lines. Under low glucose conditions, cells expressing the LF isoform failed to activate AMPK, while cells expressing the SF isoform exhibited robust increase in AMPK phosphorylation. Moreover, LF-expressing cells accumulated higher levels of reactive oxygen species, upregulated ferroptosis-related genes via BACH1/NRF2, and displayed increased cell death compared to SF-expressing cells. Collectively, these findings demonstrate that CAMKK2 isoforms exhibit differential selectivity for protein partners and mediate distinct downstream signaling pathways, impacting metabolism in cancer.

biochemistry↗

bioRxiv2025

STIM1 and Endoplasmic Reticulum-Plasma Membrane Contact Sites Oscillate Independently of Calcium-Induced Calcium Release

Calcium (Ca{superscript 2}) release from intracellular stores, Ca{superscript 2} entry across the plasma membrane, and their coordination via store-operated Ca{superscript 2} entry (SOCE) are critical for receptor-activated Ca{superscript 2} oscillations. However, the precise mechanism of Ca{superscript 2} oscillations and whether their control loop resides at the plasma membrane or intracellularly remain unresolved. By examining the dynamics of stromal interaction molecule 1 (STIM1)--an endoplasmic reticulum (ER)-localized Ca{superscript 2} sensor that activates the Orai1 channel on the plasma membrane for SOCE--and in mast cells, we found that a significant proportion of cells exhibited STIM1 oscillations with the same periodicity as Ca{superscript 2} oscillations. These cortical oscillations, occurring in the cells cortical region and shared with ER-plasma membrane (ER-PM) contact sites proteins, were only detectable using total internal reflection fluorescence microscopy (TIRFM). Notably, STIM1 oscillations could occur independently of Ca{superscript 2} oscillations. Simultaneous imaging of cytoplasmic Ca{superscript 2} and ER Ca{superscript 2} with SEPIA-ER revealed that receptor activation does not deplete ER Ca{superscript 2}, whereas receptor activation without extracellular Ca{superscript 2} influx induces cyclic ER Ca{superscript 2} depletion. However, under such nonphysiological conditions, cyclic ER Ca{superscript 2} oscillations lead to sustained STIM1 recruitment, indicating that oscillatory Ca{superscript 2} release is neither necessary nor sufficient for STIM1 oscillations. Using optogenetic tools to manipulate ER-PM contact site dynamics, we found that persistent ER-PM contact sites reduced the amplitude of Ca{superscript 2} oscillations without alteration of oscillation frequency. Together, these findings suggest an active cortical mechanism governs the rapid dissociation of ER-PM contact sites, thereby control amplitude of oscillatory Ca{superscript 2} dynamics during receptor-induced Ca{superscript 2} oscillations.

cell biology↗

bioRxiv2025

National Scale Genomic Engine for Precision Medicine: Singapore PRECISE-SG100K Experience

The whole-of-nation National Precision Medicine (NPM) programme in Singapore aims to integrate a genomics backbone into standard healthcare. Phase II recently completed the sequencing of 102,202 genomes from the diverse Asian population of Singapore, generating the PRECISE-SG100K dataset. The scaling from the pilot SG10K_Health phase to national production scale demanded a central shift: from earlier generation workflows to an integrated, industrial grade "Genomic Engine". The initiative was co-developed under a public-private partnership model. Our Genomic Engine was designed with an automation-first approach, as a modular platform linking a purpose-built, centralized biospecimen processing and DNA extraction facility (EXTRACT Lab), a high-throughput sequencing operation (Illumina Data Generation Infrastructure, IDGI), with the cloud-native analytics (GRIDS) running on a fully integrated PRECISE Sample Tracking System (STS). Here we describe the key design features of our Genomic Engine. Highlighting the Stress Test phase where the automated DNA extraction yields failed to meet expectations. We share the operational adjustments which were implemented to stabilize production and resulted in a three-fold reduction in sample triage due to improved DNA recovery. Without establishing the operational guardrails during the Stress Test, the Engine would not have reached a steady-state production evidenced by >3,000 genomes per month, median turnaround time 11 days, 99.6% operational success, within in six months. This framework offers a practical blueprint for other population-scale initiatives seeking to bridge the gap between scientific piloting and industrial delivery.

genomics↗

bioRxiv2024

U1 snRNP-Specific U1C Acts as the Gatekeeper of the Survival of Motor Neurons (SMN) Complex in snRNP Biogenesis

The stability and abundance of spliceosomal snRNPs are determined by the assembly of an Sm protein ring (Sm core) on each snRNA, a process facilitated by the survival of motor neuron (SMN) complex. Whereas the SMN complexs role as a chaperone is well-established, the mechanisms that regulate its activity remain unclear. Our study identifies U1C, a U1 snRNP-specific protein, as a key regulator of the SMN complex. U1C together with U1-70K facilitates U1 snRNP formation, rendering the SMN complex accessible for other snRNAs to assemble Sm cores. Remarkably, a prevalent cancer-associated mutation in U1 snRNA near the U1C binding site not only disrupts Sm core assembly but also sequesters the SMN complex, inhibiting canonical snRNP formation. Our findings provide mechanistic insights into how snRNP-specific proteins regulate the SMN complex and suggest that U1 snRNA mutations in numerous cancers disrupt the SMN complex, leading to perturbation in various RNA metabolism pathways. HighlightsO_LIU1C facilitates the formation of Sm core not only on U1 snRNA but also on other snRNAs. C_LIO_LIU1C together with U1-70K promotes U1 snRNP formation, enabling the SMN complex to recruit Gemin5/other snRNAs complex. C_LIO_LIThe divalent binding of U1C to the SMN complex is mediated by U1Cs N-terminus helix, interacting with U1-70K, and its arginine-methylated C-terminus domain, binding to the SMN protein. C_LIO_LIMultiple U1 snRNA mutations in cancer patients defect in Sm core assembly and potentially sequester the SMN complex, thereby inhibiting canonical snRNP formation. C_LI

biochemistry↗

bioRxiv2024

Dissecting human monoclonal antibody responses from mRNA- and protein-based XBB.1.5 COVID-19 monovalent vaccines

The emergence of highly contagious and immune-evasive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants has required reformulation of coronavirus disease 2019 (COVID-19) vaccines to target those new variants specifically. While previous infections and booster vaccinations can enhance variant neutralization, it is unclear whether the monovalent version, administered using either mRNA or protein-based vaccine platforms, can elicit de novo B-cell responses specific for Omicron XBB.1.5 variants. Here, we dissected the genetic antibody repertoire of 603 individual plasmablasts derived from five individuals who received a monovalent XBB.1.5 vaccination either with mRNA (Moderna or Pfizer/BioNtech) or adjuvanted protein (Novavax). From these sequences, we expressed 100 human monoclonal antibodies and determined binding, affinity and protective potential against several SARS-CoV-2 variants, including JN.1. We then select two vaccine-induced XBB.1.5 mAbs, M2 and M39. M2 mAb was a de novo, antibody, i.e., specific for XBB.1.5 but not ancestral SARS-CoV-2. M39 bound and neutralized both XBB.1.5 and JN.1 strains. Our high-resolution cryo-electron microscopy (EM) structures of M2 and M39 in complex with the XBB.1.5 spike glycoprotein defined the epitopes engaged and revealed the molecular determinants for the mAbs specificity. These data show, at the molecular level, that monovalent, variant-specific vaccines can elicit functional antibodies, and shed light on potential functional and genetic differences of mAbs induced by vaccinations with different vaccine platforms. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=43 SRC="FIGDIR/small/602781v1_ufig1.gif" ALT="Figure 1000"> View larger version (16K): org.highwire.dtl.DTLVardef@7c4708org.highwire.dtl.DTLVardef@11b66acorg.highwire.dtl.DTLVardef@1f1cec7org.highwire.dtl.DTLVardef@3e72fe_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

Protective effect and molecular mechanisms of human non-neutralizing cross-reactive spike antibodies elicited by SARS-CoV-2 mRNA vaccination

Neutralizing antibodies correlate with protection against SARS-CoV-2. Recent studies, however, show that binding antibody titers, in the absence of robust neutralizing activity, also correlate with protection from disease progression. Non-neutralizing antibodies cannot directly protect from infection but may recruit effector cells thus contribute to the clearance of infected cells. Also, they often bind conserved epitopes across multiple variants. We characterized 42 human mAbs from COVID-19 vaccinated individuals. Most of these antibodies exhibited no neutralizing activity in vitro but several non-neutralizing antibodies protected against lethal challenge with SARS-CoV-2 in different animal models. A subset of those mAbs showed a clear dependence on Fc-mediated effector functions. We determined the structures of three non-neutralizing antibodies with two targeting the RBD, and one that targeting the SD1 region. Our data confirms the real-world observation in humans that non-neutralizing antibodies to SARS-CoV-2 can be protective.

immunology↗

bioRxiv2024

Enzyme Bioink for the 3D Printing of Biocatalytic Materials

The field of 3D biofabrication faces major challenges on the road to printing fully functional tissues and organs. One of them is adding functionality to the newly formed tissue for replicating an active biochemical environment. Native extracellular matrices sequester numerous bioactive species, making the microenvironment biochemically active. On the other hand, most 3D-printed constructs have limited activity, serving merely as mechanical scaffolding. Here we demonstrate active scaffolding through the integration of biocatalytic enzymes within the bioink. Enzymes are an attractive class of biocompatible and substrate-specific bioactive agents that can improve tissue regeneration outcomes. However, the difficulty in the application remains in providing enzymes at the targeted site in adequate amounts over an extended time. In this work, a durable biocatalytic active enzyme bioink for 3D extrusion-based bioprinting is developed by covalently attaching the globular enzyme horseradish peroxidase (HRP) to a gelatin methacrylate (Gel-MA) biopolymer scaffold. Upon introducing methacrylate groups on the surface of the enzyme, it undergoes photo-crosslinking in a post-printing step with the methacrylate groups of Gel-MA without compromising its activity. As a result, HRP becomes a fixed part of the hydrogel network and achieves higher stability inside the gel which results in a higher concentration and catalytic activity for a longer time than solely entrapping the protein inside the hydrogel. We also demonstrate the cytocompatibility of this enzyme bioink and show its printing capabilities for precise applications in the field of tissue engineering. Our approach offers a promising solution to enhance the bioactive properties of 3D-printed constructs, representing a critical step towards achieving functional biofabricated tissues.

bioengineering↗

bioRxiv2023

A genome-wide atlas of human cell morphology

A key challenge of the modern genomics era is developing data-driven representations of gene function. Here, we present the first unbiased morphology-based genome-wide perturbation atlas in human cells, containing three genome-scale genotype-phenotype maps comprising >20,000 single-gene CRISPR-Cas9-based knockout experiments in >30 million cells. Our optical pooled cell profiling approach (PERISCOPE) combines a de-stainable high-dimensional phenotyping panel (based on Cell Painting1,2) with optical sequencing of molecular barcodes and a scalable open-source analysis pipeline to facilitate massively parallel screening of pooled perturbation libraries. This approach provides high-dimensional phenotypic profiles of individual cells, while simultaneously enabling interrogation of subcellular processes. Our atlas reconstructs known pathways and protein-protein interaction networks, identifies culture media-specific responses to gene knockout, and clusters thousands of human genes by phenotypic similarity. Using this atlas, we identify the poorly-characterized disease-associated transmembrane protein TMEM251/LYSET as a Golgi-resident protein essential for mannose-6-phosphate-dependent trafficking of lysosomal enzymes, showing the power of these representations. In sum, our atlas and screening technology represent a rich and accessible resource for connecting genes to cellular functions at scale.

genomics↗

bioRxiv2022

Impairment of lipid homoeostasis causes accumulation of protein aggregates in the lysosome

Protein aggregation increases during aging and is a pathological hallmark of many age-related diseases. Protein homeostasis (proteostasis) depends on a core network of factors directly influencing protein production, folding, trafficking, and degradation. Cellular proteostasis also depends on the overall composition of the proteome and numerous environmental variables. Modulating this cellular proteostasis state can influence the stability of multiple endogenous proteins, yet the factors contributing to this state remain incompletely characterized. Here, we performed genome-wide CRISPRi screens to elucidate the modulators of proteostasis state in mammalian cells, using a fluorescent dye to monitor endogenous protein aggregation. These screens identified known components of the proteostasis network and uncovered a novel link between protein and lipid homeostasis. Increasing lipid uptake and/or disrupting lipid metabolism promotes the accumulation of sphingomyelins and cholesterol esters and drives the formation of detergent-insoluble protein aggregates at the lysosome. Proteome profiling of lysosomes revealed ESCRT accumulation, suggesting disruption of ESCRT disassembly, lysosomal membrane repair, and microautophagy. Lipid dysregulation leads to lysosomal membrane permeabilization but does not otherwise impact fundamental aspects of lysosomal and proteasomal functions. Together, these results demonstrate that lipid dysregulation disrupts ESCRT function and impairs proteostasis.

cell biology↗

bioRxiv2022

Constitutive, calcium-independent endoplasmic reticulum-plasma membrane contact site oscillations and its implications in store-operated calcium entry

The endoplasmic reticulum and plasma membrane (ER-PM) contact site dynamics plays a central role for store-operated calcium entry (SOCE). ER localized calcium sensor STIM1 translocates to the contact sites, interacts with Orai and mediates calcium influx from the extracellular environment. Different species of phosphoinositides (PIPs) have been reported to be involved in contact site dynamics as well as STIM1 translocation. However, most of the studies were based on loss-of-function experiments or conditions that generate massive calcium store depletion. The kinetics of ER-PM contact site dynamics during physiological stimuli -induced calcium oscillations are not well understood. Using total internal reflection fluorescence microscopy (TIRFM), we investigated the relationship between dynamics of STIM1 as well as cortical ER (cER) proteins and calcium oscillations in rat basophilic leukemia (RBL) mast cells. Surprisingly, a significant percentage of cells displayed cyclic STIM1 and cER dynamics that were calcium-independent. Using specific lipid sensors, we showed that cyclic ER-PM contact site assembly was in phase with PI(4)P oscillation, but preceded phases of PI(4,5)P2 or PI(3,4,5)P3 oscillation. Optogenetic recruitment of the phosphoinositide 5-phosphatase from INPP5E, which decreased PI(4,5)P2 and increased PI(4)P levels on the plasma membrane, stimulated the translocation of STIM1 and inhibited calcium oscillations. Interestingly, prolonged stable translocation of STIM1 to the plasma membrane had an inhibitory effect on calcium oscillations. Collectively, our findings suggest that ER-PM contact sites formation is PI(4)P-dependent. In addition, reversibility of ER-PM contact sites dynamics and intermediate strength of ER-PM contact are needed for calcium oscillation.

cell biology↗

bioRxiv2021

Multi-omics Data Integration by Generative Adversarial Network

Accurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is now available that can effectively link the genotype to phenotype. However, the interactive relation of multi-omics datasets makes it particularly challenging to incorporate different biological layers to discover the coherent biological signatures and predict phenotypic outcomes. In this study, we introduce omicsGAN, a generative adversarial network (GAN) model to integrate two omics data and their interaction network. The model captures information from the interaction network as well as the two omics datasets and fuse them to generate synthetic data with better predictive signals. Large-scale experiments on The Cancer Genome Atlas (TCGA) breast cancer, lung cancer, and ovarian cancer datasets validate that (1) the model can effectively integrate two omics data (e.g., mRNA and microRNA expression data) and their interaction network (e.g., microRNA-mRNA interaction network). The synthetic omics data generated by the proposed model has a better performance on cancer outcome classification and patients survival prediction compared to original omics datasets. (2) The integrity of the interaction network plays a vital role in the generation of synthetic data with higher predictive quality. Using a random interaction network does not allow the framework to learn meaningful information from the omics datasets; therefore, results in synthetic data with weaker predictive signals.

bioinformatics↗

bioRxiv2021

Ectopic FVIII expression and misfolding in hepatocytes as a potential cause of human hepatocellular carcinoma

Hemophilia A gene therapy targets hepatocytes to express B domain deleted-(BDD) clotting factor VIII (FVIII) to permit viral encapsidation. Since BDD is prone to misfolding in the endoplasmic reticulum (ER) and ER protein misfolding in hepatocytes followed by high fat diet (HFD) can cause hepatocellular carcinoma (HCC), we studied how FVIII misfolding impacts HCC development using hepatocyte DNA delivery to express three proteins from the same parental vector: 1) well-folded cytosolic dihydrofolate reductase (DHFR); 2) BDD-FVIII, which is prone to misfolding in the ER; and 3) N6-FVIII which folds more efficiently than BDD-FVIII. One week after DNA delivery, when FVIII expression was undetectable, mice were fed HFD for 65 weeks. Remarkably, all mice that received BDD-FVIII vector developed liver tumors, whereas only 58% of mice that received N6 and no mice that received DHFR vector developed liver tumors, suggesting that the degree of protein misfolding in the ER increases predisposition to HCC in the context of a HFD and in the absence of viral transduction. Our findings raise concerns of ectopic BDD-FVIII expression in hepatocytes in the clinic, which poses risks independent of viral vector integration. Limited expression per hepatocyte and/or use of proteins that avoid misfolding may enhance safety. Graphical abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

cancer biology↗

bioRxiv2020

Imputation of Spatially-resolved Transcriptomes by Graph-regularized Tensor Completion

High-throughput spatial-transcriptomics RNA sequencing (sptRNA-seq) based on in-situ capturing technologies has recently been developed to spatially resolve transcriptome-wide mRNA expressions mapped to the captured locations in a tissue sample. One major limitation of in-situ capturing is the high dropout rate of mRNAs that fail the capture or the amplification, which leads to incomplete profiling of the gene expressions. In this paper, we introduce a graph-regularized tensor completion model for imputing the missing mRNA expressions in sptRNA-seq data, namely FIST, Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion. We first model sptRNA-seq data as a 3-way sparse tensor in genes (p-mode) and the (x, y) spatial coordinates (x-mode and y-mode) of the observed gene expressions, and then consider the imputation of the unobserved entries as a tensor completion problem in Canonical Polyadic Decomposition (CPD) form. To improve the imputation of highly sparse sptRNA-seq data, we also introduce a protein-protein interaction network to add prior knowledge of gene functions, and a spatial graph to capture the the spatial relations among the capture spots. The tensor completion model is then regularized by a Cartesian product graph of protein-protein interaction network and the spatial graph to capture the high-order relations in the tensor. In the experiments, FIST was tested on ten 10x Genomics Visium spatial transcriptomic datasets of different tissue sections with cross-validation among the known entries in the imputation. FIST significantly outperformed several best performing single-cell RNAseq data imputation methods. We also demonstrate that both the spatial graph and PPI network play an important role in improving the imputation. In a case study, we further analyzed the gene clusters obtained from the imputed gene expressions to show that the imputations by FIST indeed capture the spatial characteristics in the gene expressions and reveal functions that are highly relevant to three different kinds of tissues in mouse kidney. The source code and data are available at https://github.com/kuanglab/FIST. Author summaryBiological tissues are composed of different types of structurally organized cell units playing distinct functional roles. The exciting new spatial gene expression profiling methods have enabled the analysis of spatially resolved transcriptomes to understand the spatial and functional characteristics of these cells in the context of eco-environment of tissue. Similar to single-cell RNA sequencing data, spatial transcriptomics data also suffers from a high dropout rate of mRNAs in in-situ capture. Our method, FIST (Fast Imputation of Spatially-resolved transcriptomes by graph-regularized Tensor completion), focuses on the spatial and high-sparsity nature of spatial transcriptomics data by modeling the data as a 3-way gene-by-(x, y)-location tensor and a product graph of a spatial graph and a protein-protein interaction network. Our comprehensive evaluation of FIST on ten 10x Genomics Visium spatial genomics datasets and comparison with the methods for single-cell RNA sequencing data imputation demonstrate that FIST is a better method more suitable for spatial gene expression imputation. Overall, we found FIST a useful new method for analyzing spatially resolved gene expressions based on novel modeling of spatial and functional information.

bioinformatics↗

bioRxiv2020

Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

By circumventing cellular heterogeneity, single cell omics have now been widely utilized for cell typing in human tissues, culminating with the undertaking of human cell atlas aimed at characterizing all human cell types. However, more important are the probing of gene regulatory networks, underlying chromatin architecture and critical transcription factors for each cell type. Here we report the Genomic Architecture of Cells in Tissues (GeACT), a comprehensive genomic data base that collectively address the above needs with the goal of understanding the functional genome in action. GeACT was made possible by our novel single-cell RNA-seq (MALBAC-DT) and ATAC-seq (METATAC) methods of high detectability and precision. We exemplified GeACT by first studying representative organs in human mid-gestation fetus. In particular, correlated gene modules (CGMs) are observed and found to be cell-type-dependent. We linked gene expression profiles to the underlying chromatin states, and found the key transcription factors for representative CGMs. HighlightsO_LIGenomic Architecture of Cells in Tissues (GeACT) data for human mid-gestation fetus C_LIO_LIDetermining correlated gene modules (CGMs) in different cell types by MALBAC-DT C_LIO_LIMeasuring chromatin open regions in single cells with high detectability by METATAC C_LIO_LIIntegrating transcriptomics and chromatin accessibility to reveal key TFs for a CGM C_LI

genomics↗

bioRxiv2020

mTOR-driven widespread exon skipping renders multifaceted gene regulation and proteome complexity

The mammalian target of rapamycin (mTOR) pathway is crucial in cell proliferation. Previously, we reported transcriptome-wide 3’-untranslated region (UTR) shortening by alternative polyadenylation upon mTOR activation and its impact on the proteome. Here, we further interrogated the mTOR-activated transcriptome and found that hyperactivation of mTOR promotes transcriptome-wide exon-skipping/exclusion, producing short isoform transcripts from genes. This widespread exon skipping confers multifarious regulations in the mTOR-controlled functional proteomics: alternative splicing (AS) in the 5’-UTR controls translation efficiency while AS in coding regions widely affects the protein length and functional domains. They also alter the half-life of proteins and affect the regulatory post-translational modifications. Among the RNA processing factors differentially regulated by mTOR signaling, we found that SRSF3 mechanistically facilitates exon skipping in the mTOR-activated transcriptome. This study reveals a role of mTOR in AS regulation and demonstrates that widespread AS is a multifaceted modulator of the mTOR-regulated functional proteome.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biochemistry↗

bioRxiv2020

APA-Scan: Detection and Visualization of 3'-UTR APA with RNA-seq and 3'-end-seq Data

BackgroundThe eukaryotic genome is capable of producing multiple isoforms from a gene by alternative polyadenylation (APA) during pre-mRNA processing. APA in the 3-untranslated region (3-UTR) of mRNA produces transcripts with shorter or longer 3-UTR. Often, 3-UTR serves as a binding platform for microRNAs and RNA-binding proteins, which affect the fate of the mRNA transcript. Thus, 3-UTR APA is known to modulate translation and provides a mean to regulate gene expression at the post-transcriptional level. Current bioinformatics pipelines have limited capability in profiling 3-UTR APA events due to incomplete annotations and a low-resolution analyzing power: widely available bioinformatics pipelines do not reference actionable polyadenylation (cleavage) sites but simulate 3-UTR APA only using RNA-seq read coverage, causing false positive identifications. To overcome these limitations, we developed APA-Scan, a robust program that identifies 3-UTR APA events and visualizes the RNA-seq short-read coverage with gene annotations. MethodsAPA-Scan utilizes either predicted or experimentally validated actionable polyadenylation signals as a reference for polyadenylation sites and calculates the quantity of long and short 3-UTR transcripts in the RNA-seq data. APA-Scan works in three major steps: (i) calculate the read coverage of the 3-UTR regions of genes; (ii) identify the potential APA sites and evaluate the significance of the events among two biological conditions; (iii) graphical representation of user specific event with 3-UTR annotation and read coverage on the 3-UTR regions. APA-Scan is implemented in Python3. Source code and a comprehensive users manual are freely available at https://github.com/compbiolabucf/APA-Scan. ResultAPA-Scan was applied to both simulated and real RNA-seq datasets and compared with two widely used baselines DaPars and APAtrap. In simulation APA-Scan significantly improved the accuracy of 3-UTR APA identification compared to the other baselines. The performance of APA-Scan was also validated by 3-end-seq data and qPCR on mouse embryonic fibroblast cells. The experiments confirm that APA-Scan can detect unannotated 3 -UTR APA events and improve genome annotation. ConclusionAPA-Scan is a comprehensive computational pipeline to detect transcriptome-wide 3-UTR APA events. The pipeline integrates both RNA-seq and 3-end-seq data information and can efficiently identify the significant events with a high-resolution short reads coverage plots.

bioinformatics↗