Biology subjects

Nguyen, N. D.

Publications and source records attributed to Nguyen, N. D..

13 recordsLinked to original sources

bioRxiv2026

Acquired resistance to the PRMT5 inhibitor confers collateral sensitivity to MEK inhibition in MTAP-null non-small cell lung cancer

Protein arginine methyltransferase 5 (PRMT5) is a synthetic lethal target in methylthioadenosine phosphorylase-deleted (MTAP-null) cancers. Second-generation MTA-cooperative PRMT5 inhibitors preferentially target MTAP-null cells while largely sparing MTAP-wildtype (MTAP-WT) cells, thereby improving tumor selectivity over first-generation PRMT5 inhibitors. Despite encouraging efficacy and safety signals in early clinical studies, the modest objective response rates (ORRs) observed with these inhibitors suggest that intrinsic or acquired resistance may limit their clinical benefit. Here, we investigated mechanisms of acquired resistance to the MTA-cooperative PRMT5 inhibitor BMS-986504/MRTX1719 in MTAP-null non-small cell lung cancer (NSCLC) cells and sought to identify therapeutic vulnerabilities that emerge upon resistance. Using multiple in vitro-derived resistant models, we found that acquired resistance was not fully explained by alterations in PRMT5 activity or reduced MTA levels. Instead, resistance was associated with collateral sensitivity to MEK inhibition and enrichment of MAPK-related transcriptional programs. Together, these findings identify MEK inhibition as an actionable collateral vulnerability in MTAP-null NSCLC cells that acquire resistance to PRMT5 inhibition.

cancer biology↗

bioRxiv2025

Discovery of Small Molecules and a Druggable Groove That Regulate DNA Binding and Release of the AP1 Transcription Factor DeltaFOSB

{Delta}FOSB, a member of the AP1 family of transcription factors, mediates long-term neuroadaptations underlying drug addiction, seizure-related cognitive decline, dyskinesias, and several other chronic conditions. AP1 transcription factors are notoriously difficult to modulate pharmacologically due to the absence of well-defined binding pockets. Here, we identify a novel site on {Delta}FOSB, located outside the DNA-binding cleft, that accommodates small molecules. We show that sulfonic acid-containing compounds bind to this site via an induced-fit mechanism, reorienting side chains critical for DNA binding, and that they may hinder the {Delta}FOSB bZIP -helix from binding to the major groove of DNA. In vivo, direct administration of one such compound, JPC0661, into the brain reduces {Delta}FOSB occupancy at genomic AP1 consensus sites by approximately 60% as determined by CUT&RUN-sequencing. These findings suggest that DNA binding and release by AP1 transcription factors can be controlled via small molecules that dock into a novel site that falls outside of the DNA-binding cleft. Minimal sequence conservation across 29 bZIP domain-containing transcription factors in this druggable groove suggests that it can be exploited to develop AP1-subunit-selective compounds. Our studies thus reveal a novel strategy to design small-molecule inhibitors of {Delta}FOSB and other members of the bZIP transcription factor family.

neuroscience↗

bioRxiv2024

Engineering the cyanobacterial ATP-driven BCT1 bicarbonate transporter for functional targeting to C3 plant chloroplasts

The ATP-driven bicarbonate transporter 1 (BCT1), a four-component complex in the cyanobacterial CO2-concentrating mechanism, could enhance photosynthetic CO2 assimilation in plant chloroplasts. However, directing its subunits (CmpA, CmpB, CmpC and CmpD) to three chloroplast sub-compartments is highly complex. Investigating BCT1 integration into Nicotiana benthamiana chloroplasts revealed promising targeting strategies using transit peptides from the intermembrane space protein Tic22 for correct CmpA targeting, while the transit peptide of the chloroplastic ABCD2 transporter effectively targeted CmpB to the inner envelope membrane. CmpC and CmpD were targeted to the stroma by RecA and recruited to the inner envelope membrane by CmpB. Despite successful targeting, expression of this complex in CO2-dependent Escherichia coli failed to demonstrate bicarbonate uptake. We then used rational design and directed evolution to generate new BCT1 forms that were constitutively active. Several mutants were recovered, including a CmpCD fusion. Selected mutants were further characterized and stably expressed in Arabidopsis thaliana, but the transformed plants did not have higher carbon assimilation rates or decreased CO2 compensation points in mature leaves. While further analysis is required, this directed evolution and heterologous testing approach presents potential for iterative modification and assessment of CO2-concentrating mechanism components to improve plant photosynthesis. HighlightWe describe the directed evolution and rational design of a cyanobacterial four-component bicarbonate transporter and the localization of its subunits to various chloroplast sub-compartments for improving C3 plant photosynthesis.

plant biology↗

bioRxiv2023

Optimal transport for mapping senescent cells in spatial transcriptomics

Spatial transcriptomics (ST) provides a unique opportunity to study cellular organization and cell-cell interactions at the molecular level. However, due to the low resolution of the sequencing data additional information is required to utilize this technology, especially for cases where only a few cells are present for important cell types. To enable the use of ST to study senescence we developed scDOT, which combines ST and single cell RNA-Sequencing (scRNA-Seq) to improve the ability to reconstruct single cell resolved spatial maps. scDOT integrates optimal transport and expression deconvolution to learn non-linear couplings between cells and spots and to infer cell placements. Application of scDOT to existing and new lung ST data improves on prior methods and allows the identification of the spatial organization of senescent cells, the identification of their neighboring cells and the identification of novel genes involved in cell-cell interactions that may be driving senescence.

cell biology↗

bioRxiv2022

Cortical reactivations predict future sensory responses

Prevailing theories of offline memory consolidation posit that the pattern of neurons activated during a salient sensory experience will be faithfully reactivated, thereby stabilizing the entire pattern1-3. However, sensory-evoked patterns are not stable, but instead drift across repeated experiences4-7. To investigate potential roles of reactivations in the stabilization and/or drift of sensory representations, we imaged calcium activity of thousands of excitatory neurons in mouse lateral visual cortex. Presentation of a stimulus resulted in transient, stimulus-specific reactivations during the following minute. These reactivations depended on local circuit activity, as they were abolished by local silencing during the preceding stimulus. Contrary to prevailing theories, reactivations systemically differed from previous patterns evoked by the stimulus. Instead, they were more similar to future patterns evoked by the stimulus, thereby predicting representational drift. In particular, neurons that participated more or less in early reactivations than in stimulus response patterns subsequently increased or decreased their future stimulus responses, respectively. The rate and content of these reactivations was sufficient to accurately predict future changes in stimulus responses and, surprisingly, the decreasing similarity of responses to distinct stimuli. Thus, activity patterns during sensory cortical reactivations may guide the drift in sensory responses to improve sensory discrimination8.

neuroscience↗

bioRxiv2022

A unified analysis of atlas single cell data

Recent efforts to generate atlas-scale single cell data provide opportunities for joint analysis across tissues and across modalities. Most of the existing methods for single cell atlas analysis use cells as the reference unit to combine datasets. However, such methods suffer from the inability to effectively integrate cross-modality data, hindering downstream gene-based analysis, and loss of genuine biological variations. Here we present a new data integration method, GIANT, which is for the first time designed for the atlas-scale analysis from the gene perspective. GIANT first converts datasets from different modalities into gene graphs, and then recursively embeds genes in the graphs into a latent space without additional alignment. Applying GIANT to the HuBMAP datasets creates a unified gene embedding space across multiple human tissues and data modalities, where gene representations reflect the functions of genes in their cells. Further evaluations demonstrate the usefulness of GIANT in discovering diverse gene functions, and underlying gene regulations in cells of different tissues.

bioinformatics↗

bioRxiv2022

MYXOBACTERIA FROM VIETNAM: ISOLATION, PHYLOGENETIC ANALYSIS AND BIOPROSPECTION

Forty-three myxobacterial strains were isolated and identified from 20 soil samples collected in Vietnam. The information on morphological characteristics and 16S ribosomal gene sequencing showed that these strains were designated to seven genera belonging to Angiococcus, Archangium, Chondromyces, Corallococcus, Cystobacter, Melittangium, and Myxococcus, in suborder Cystobacterineae and Sorangiineae. The phylogenetic tree was constructed to clarify the genetic relationship between myxobacterial isolates. Myxobacteria were cultured, and crude extracts were obtained after 10-day fermentation in P-medium in the presence of the Amberlite XAD 16N adsorbent resin. Elution was carried out with acetone and methanol to obtain the crude extracts. Evaluation of antioxidant activity used the DPPH and ABTS assay, the minimum inhibitory concentration values were determined by the microdilution method. The total extract from CT21 had the highest total antioxidant activity (IC50 = 52.34 {+/-} 1.47 {micro}g/mL, 30.28 {+/-} 0.74 {micro}g/mL for DPPH and ABTS assays, respectively). The other potential strain was TG131 and GL41 that IC50 values were 40.28 {+/-} 1.13 and 57.24 {+/-} 1.52 {micro}g/mL, respectively (by the DPPH method), and 48.35 {+/-} 0.58 and 42.76 {+/-} 0.50 {micro}g/mL, respectively (by the ABTS method). Interestingly, 100% isolated myxobacterial strains show inhibitory activity against at least one of the tested microorganisms. The potential antimicrobial strain was GL41, which inhibited all tested microorganisms, and the MIC values were 1 {micro}g/mL against MRSA, MSSA, S. faecalis, C. albicans, and A. niger. The highest active strains were members of Myxococcus sp. genus.

microbiology↗

bioRxiv2021

Comparative untargeted metabolomic profiling of induced mitochondrial fusion in pancreatic cancer

Mitochondria are dynamic organelles that constantly alter their shape through the recruitment of specialized proteins, like mitofusin-2 (Mfn2) and dynamin-related protein 1 (Drp1). Mfn2 induces the fusion of nearby mitochondria, while Drp1 mediates mitochondrial fission. We previously found that the genetic or pharmacological activation of mitochondrial fusion was tumor suppressive against pancreatic ductal adenocarcinoma (PDAC) in several model systems [1]. The mechanisms of how these different inducers of mitochondrial fusion reduce pancreatic cancer growth are still unknown. Here, we characterized and compared the metabolic reprogramming of these three independent methods of inducing mitochondrial fusion in KPC cells: overexpression of Mfn2, genetic editing of Drp1, or treatment with leflunomide. We identified significantly altered metabolites via robust, orthogonal statistical analyses and found that mitochondrial fusion consistently produces alterations in the metabolism of amino acids. Our unbiased methodology revealed that metabolic perturbations were similar across all these methods of inducing mitochondrial fusion, proposing a common pathway for metabolic targeting with other drugs.

cancer biology↗

bioRxiv2021

Stromal HIF2 Regulates Immune Suppression in the Pancreatic Cancer Microenvironment

Background & AimsPancreatic ductal adenocarcinoma (PDAC) has a hypoxic, immunosuppressive stroma, which contributes to its resistance to immune checkpoint blockade therapies. The hypoxia-inducible factors (HIFs) mediate the cellular response to hypoxia, but their role within the PDAC tumor microenvironment remains unknown. MethodsWe used a dual recombinase mouse model to delete Hif1 or Hif2 in -smooth muscle actin (SMA)-expressing cancer-associated fibroblasts (CAFs) arising within spontaneous pancreatic tumors. The effects of CAF-Hif2 expression on tumor progression and composition of the tumor microenvironment were evaluated by Kaplan-Meier analysis, quantitative real-time polymerase chain reaction, histology, immunostaining, and by both bulk and single-cell RNA sequencing. CAF-macrophage crosstalk was modeled ex vivo using conditioned media from CAFs after treatment with hypoxia and PT2399, a HIF2 inhibitor currently in clinical trials. Syngeneic flank and orthotopic PDAC models were used to assess whether HIF2 inhibition improves response to immune checkpoint blockade. ResultsCAF-specific deletion of HIF2, but not HIF1, suppressed PDAC tumor progression and growth, and improved survival of mice by 50% (n = 21-23 mice/group, Log-rank P = 0.0009). Deletion of CAF-HIF2 modestly reduced tumor fibrosis and significantly decreased the intratumoral recruitment of immunosuppressive M2 macrophages and regulatory T cells. Treatment with the clinical HIF2 inhibitor PT2399 significantly reduced in vitro macrophage chemotaxis and M2 polarization, and improved tumor responses to immunotherapy in both syngeneic PDAC mouse models. ConclusionsTogether, these data suggest that stromal HIF2 is an essential component of PDAC pathobiology and is a druggable therapeutic target that could relieve tumor microenvironment immunosuppression and enhance immune responses in this disease.

cancer biology↗

bioRxiv2021

deepManReg: a deep manifold-regularized learning model for improving phenotype prediction from multi-modal data

The biological processes from genotype to phenotype are complex involving multi-scale mechanisms. Increasing multi-modal data enables deeper understanding of underlying complex mechanisms in various phenotypes. However, integrating and interpreting such large-scale multi-modal data remains challenging, especially given highly heterogeneous, nonlinear relationships across modalities. To address this, we developed an interpretable regularized learning model, deepManReg to predict phenotypes from multi-modal data. First, deepManReg employs deep neural networks to learn cross-modal manifolds and then align multi-modal features onto a common latent space. This space aims to preserve both global consistency and local smoothness across modalities and reveal higher-order nonlinear cross-modal relationships. Second, deepManReg uses cross-modal manifolds as a feature graph to regularize the classifiers for improving phenotype predictions and also prioritizing the multi-modal features and cross-modal interactions for the phenotypes. We applied deepManReg to recent single cell multi-modal data such as Patch-seq data including transcriptomics and electrophysiology for neuronal cells in the mouse brain. We show that deepManReg significantly improves predicting cellular phenotypes and also prioritizing genes and electrophysiological features for the phenotypes. Finally, deepManReg is open-source and general for phenotype prediction from multi-modal data. deepManReg is open-source available at https://github.com/daifengwanglab/deepManReg.

bioinformatics↗

bioRxiv2020

Differential encoding in prefrontal cortex projection neuron classes across cognitive tasks

Single-cell transcriptomics has been widely applied to classify neurons in the mammalian brain, while systems neuroscience has historically analyzed the encoding properties of cortical neurons without considering cell types. Here we examine how specific transcriptomic types of mouse prefrontal cortex (PFC) projection neurons relate to axonal projections and encoding properties across multiple cognitive tasks. We found that most types projected to multiple targets, and most targets received projections from multiple types, except PFC[->]PAG (periaqueductal gray). By comparing Ca2+-activity of the molecularly homogeneous PFC[->]PAG type against two heterogeneous classes in several two-alternative choice tasks in freely-moving mice, we found that all task-related signals assayed were qualitatively present in all examined classes. However, PAG-projecting neurons most potently encoded choice in cued tasks, whereas contralateral PFC-projecting neurons most potently encoded reward context in an uncued task. Thus, task signals are organized redundantly, but with clear quantitative biases across cells of specific molecular-anatomical characteristics.

neuroscience↗

bioRxiv2020

Varmole: A biologically drop-connect deep neural network model for prioritizing disease risk variants and genes

Population studies such as GWAS have identified a variety of genomic variants associated with human diseases. To further understand potential mechanisms of disease variants, recent statistical methods associate functional omic data (e.g., gene expression) with genotype and phenotype and link variants to individual genes. However, how to interpret molecular mechanisms from such associations, especially across omics is still challenging. To address this, we develop an interpretable deep learning method, Varmole to simultaneously reveal genomic functions and mechanisms while predicting phenotype from genotype. In particular, Varmole embeds multi-omic networks into a deep neural network architecture and prioritizes variants, genes and regulatory linkages via drop-connect without needing prior feature selections. Varmole is available as a Python package on GitHub at https://github.com/daifengwanglab/Varmole.

bioinformatics↗

bioRxiv2018

ManiNetCluster: A Manifold Learning Approach to Reveal the Functional Linkages Across Multiple Gene Networks

The coordination of genome encoded function is a critical and complex process in biological systems, especially across phenotypes or states (e.g., time, disease, organism). Understanding how the complexity of genome-encoded function relates to these states remains a challenge. To address this, we have developed a novel computational method based on manifold learning and comparative analysis, ManiNetCluster, which simultaneously aligns and clusters multiple molecular networks to systematically reveal function links across multiple datasets. Specifically, ManiNetCluster employs manifold learning to match local and non-linear structures among the networks of different states, to identify cross-network linkages. By applying ManiNetCluster to the developmental gene expression datasets across model organisms (e.g., worm, fruit fly), we found that our tool significantly better aligns the orthologous genes than existing state-of-the-art methods, indicating the non-linear interactions between evolutionary functions in development. Moreover, we applied ManiNetCluster to a series of transcriptomes measured in the green alga Chlamy-domonas reinhardtii, to determine the function links between various metabolic processes between the light and dark periods of a diurnally cycling culture. For example, we identify a number of genes putatively regulating processes across each lighting regime, and how comparative analyses between ManiNetCluster and other clustering tools can provide additional insights. ManiNetCluster is available as an R package together with a tutorial at https://github.com/namtk/ManiNetCluster.

bioinformatics↗