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Biology subjects

Ding, E.

Publications and source records attributed to Ding, E..

11 recordsLinked to original sources

Asynchronous structural rearrangements govern constriction of the human nuclear pore complex

The nuclear pore complex (NPC) is responsible for transport between the cytoplasm and nucleus. CryoET experiments have resolved multiple conformations of the pore that are dilated or constricted. Since the dilation state will impact transport properties, there is much interest in understanding the energetic factors that regulate dilation/constriction. To this end, we applied a structure-based (G[o]-like) model to study the mechanistic properties of large-scale constriction ( [~] 100[A]) in the human NPC. Even though the structures are symmetric, the simulations implicate asynchronous rear-rangements during constriction. By considering variants of the model, we further characterize the relative contributions of individual proteins to this collective process. Together, this provides an energetic/structural foundation that can guide development of precise physical approaches for studying this elusive motion.

biophysics↗

Frequency-dependent cellular microrheology with pyramidal atomic force microscopy probes

Atomic force microscopy (AFM) is widely used to measure the elastic properties of living cells, typically by extracting the Youngs (elastic) modulus from force-indentation curves acquired at a fixed loading rate. However, it is increasingly clear that cells relax applied stresses over time and exhibit significant frequency-dependent loss properties, a phenomenon which figures centrally in many biological problems. AFM has been employed to capture these dynamic mechanical properties via microscale frequency sweeps, where the AFM probe indents the sample in an oscillatory fashion over a range of frequencies and the oscillatory sample response is recorded. The relationship between dynamic stress and strain is then used to extract frequency-dependent microrheological properties. Despite the power of AFM oscillatory microrheology, the method remains surprisingly underutilized. This may be due to the traditional call for sphere-tipped AFM probes, which produce comparatively low indentation pressures yet are often expensive to purchase and difficult to fabricate. There remains a need for a framework in which standard blunt pyramidal AFM probes can be used "off the shelf" for oscillatory microrheology. In this study, we present such a framing. We derive expressions to extract rheological moduli from the data and explore practical experimental issues such as calibration and parameter optimization, validating the method using agarose hydrogel standards. Finally, we perform mechanical measurements on cultured cells treated with cytoskeletal inhibitors nocodazole and cytochalasin D, which yield rheological changes consistent with expected contributions of the corresponding cytoskeletal networks. Statement of SignificanceAtomic force microscopy (AFM) is often used in a force spectroscopy mode to measure an apparent elastic modulus at a single indentation rate, but in reality the mechanical behavior of cells is strongly rate-dependent, reflecting cellular viscoelasticity. While cellular viscoelastic properties can be measured by AFM oscillatory microrheology, the method traditionally calls for sphere-tipped probes, which are expensive to purchase and difficult to reproducibly fabricate. Fewer studies have used pyramidal AFM probes. Here, we examine this method in detail. We walk through data analysis expressions, compare force spectroscopy and frequency sweep measurements of a soft hydrogel, and discuss experimental setup. We then demonstrate measurement and interpretation of viscoelastic parameters in a model cell line treated with cytoskeletal inhibitors.

biophysics↗

Alcohol Use Disorder Associated Gene FNDC4 Alters Glutamatergic and GABAergic Neurogenesis

Large-cohort genome-wide association studies (GWAS) for alcohol use disorder (AUD) and AUD-related phenotypes have identified more than one hundred genetic loci. Functional study of those GWAS-identified loci might represent an important step toward understanding AUD pathophysiology. We found that genetic loci which are splicing quantitative trait loci (sQTLs) for the fibronectin III domain containing 4 (FNDC4) gene in the brain were identified by GWAS for both AUD drug treatment outcomes and AUD risk. However, FNDC4 function in the brain and how it might contribute to AUD pathophysiology remain unknown. In the present study, we characterized GWAS locus-associated FNDC4 splice isoforms, studies which suggested that FNDC4 alternative splicing results in loss-of-function for FNDC4. We also investigated FNDC4 function using CRISPR/cas9 gene editing, and the creation of human induced pluripotent stem cell (iPSC)-derived neural organoids joined with single-nucleus RNA sequencing. We observed that knock-out (KO) of FNDC4 resulted in a striking shift in the relative proportions of glutamatergic and GABAergic neurons in iPSC-derived neural organoids, suggesting a possible important role for FNDC4 in neurogenesis. We also explored potential mechanism(s) of FNDC4-dependent neurogenesis with results that suggested a role for FNDC4 in mediating neural cell-cell interaction. In summary, this series of experiments indicates that FNDC4 plays a role in regulating cerebral cortical neurogenesis in the brain. This regulation may contribute to the response to AUD pharmacotherapy as well as the effects of alcohol on the brain.

genomics↗

TARRAGON: Therapeutic Target Applicability Ranking and Retrieval-Augmented Generation Over Networks

The identification of therapeutic protein targets is fundamental to the success of drug development and repurposing. Traditional approaches for target selection require extensive preclinical evaluation for toxicity and efficacy, making the process time-intensive and resource-heavy. Computational tools that efficiently prioritize and validate novel targets are needed to streamline drug discovery workflows. To address this gap, we developed TARRAGON: Therapeutic Target Applicability Ranking and Retrieval-Augmented Generation Over Networks, a computational framework that integrates data mining and machine learning to identify, rank, and assess target-disease relationships to nominate new therapeutic targets. TARRAGON mines knowledge graphs to uncover meta-paths, or rules of graph traversal, linking potential therapeutic targets to diseases. It employs a classification model to rank target-disease hypotheses based on evidence patterns and utilizes a retrieval-augmented generation workflow to prompt a large language model for generating feasibility reports on prioritized targets. Using TARRAGON, we prioritized potential drug targets for non-muscle invasive urinary bladder cancer. Top-ranked candidates were validated using CRISPR gene effect and expression data from the Broad Institute DepMap portal. We further proposed chemical modulators for these targets to inform combination drug screening alongside approved bladder cancer therapeutics. TARRAGON introduces a novel, interpretable computational pipeline for therapeutic target discovery and pharmaceutical candidate nomination, offering the potential to accelerate drug development across diverse disease areas.

bioinformatics↗

Targeting de novo lipogenesis improves gemcitabine efficacy in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive disease with few treatment options and poor survivability. In this work we sought to characterise metabolic adaptations to gemcitabine (GEMC)-based chemotherapy exposure to discover new therapeutic targets for improving treatment efficacy. We show that GEMC resistance (GEMR) upregulates de novo lipogenesis in Panc1 and MiaPaCa2 cells through increased activity and expression of acetyl-CoA carboxylase (ACC), fatty acid synthase (FAS) and stearoyl-CoA desaturase 1 (SCD1). We also discovered alternate fatty acid desaturase 2 (FADS2) activity in Panc1 cells, which led to the production of sapienic acid (FA 16:1n-10, cis) from palmitic acid (FA 16:0). Knockdown of key lipid synthesis enzymes sensitised cells to GEMC treatment, with FAS (both cell lines), SCD1 (MiaPaCa2 only) and SCD1+FADS2 (Panc1) knockdown showing the greatest reduction in cell growth when combined with GEMC treatment. In Panc1 cells, both desaturases upregulated their activity when the alternate was knocked down, necessitating the need for dual desaturase knockdown in this cell line. PDAC cells attenuated to grow in combination GEMC/paclitaxel (CombAT) also displayed enhanced de novo lipogenesis; however, combination chemotherapy significantly downregulated FADS2 expression and activity in Panc1 CombAT cells rendering them more sensitive to SCD1 knockdown. We conclude that co-targeting lipid synthesis in PDAC could be a viable strategy for improving the efficacy of both GEMC monotherapy and combination GEMC/PTX therapy.

cancer biology↗

Neuroendocrine differentiation (ND) in sensitivity of neuroendocrine tumor (NET) cells to ONC201/TIC10 cancer therapeutic

Prostate cancer (PCa) neuroendocrine tumor (NET)-like cells with low or absent androgen receptor (AR) signaling cause hormone therapy resistance and poor prognosis. Small cell lung carcinoma (SCLC), a high-grade NET, presents with metastasis early and has poor survival. ONC201/TIC10 is a first-in-class cancer therapeutic with clinical activity in diffuse gliomas and neuroendocrine tumors. We hypothesized that markers of neuroendocrine differentiation, activation of the integrated stress response (ISR) and the TRAIL pathway, as well as the expression of ClpP, contribute to neuroendocrine tumor cell death and sensitivity to ONC201. We show that PCa and SCLC cell lines (N=6) are sensitive to ONC201, regardless of the extent of neuroendocrine differentiation. Endogenous levels of some NET markers (CgA, FoxO1, ENO2, PGP9.5, SOX2) are present in a spectrum in PCa and SCLC cell lines. Overexpression of neural transcription factor BRN2 in DU145 PCa cells does not increase expression of NET differentiation markers FoxO1, ENO2, PGP9.5, and CgA at 48 hours. However, the transient BRN2 overexpression showed slight decreases in some NET markers on the spectrum while maintaining sensitivity of PCa cells to ONC201 before any phenotypic change related to NET differentiation. Our results show that ONC201 has preclinical activity against PCa including those without NET markers or in PCa cells with transient overexpression of neural transcription factor BRN2. Our results have relevance to activity of ONC201 in PCa where most castrate-resistant androgen-independent cancers are not therapy resistant due to NET differentiation. Importantly, NET differentiation does not promote resistance to ONC201 supporting further clinical investigations across the spectrum of PCa.

cancer biology↗

Neuroendocrine Prostate Cancer Drivers SOX2 and BRN2 Confer Differential Responses to Imipridones ONC201, ONC206, and ONC212 in Prostate Cancer Cell Lines

Prostate cancer (PCa) is the leading cause death from cancer in men worldwide. Approximately 30% of castrate-resistant PCas become refractory to therapy due to neuroendocrine differentiation (NED) that is present in <1% of androgen-sensitive tumors. First-in-class imipridone ONC201/TIC10 has shown clinical activity against midline gliomas, neuroendocrine tumors and PCa. We explored the question of whether NED promotes sensitivity to imipridones ONC201 and ONC206 by inducible overexpression of SOX2 and BRN2, well-known neuroendocrine drivers, in human PCa cell lines DU145 or LNCaP. Slight protection from ONC201 or ONC206 with SOX2 and BRN2 overexpression was observed in the inducible LNCaP cells but not in the DU145 cells. At 2 months, there was an apparent increase in CLpP expression in LNCaP SOX2-overexpressing cells but this did not confer enhanced sensitivity to ONC201. DU145 SOX2-overexpressing cells had a significantly reduced ONC201 sensitivity than DU145 control cells. The results support the idea that treatment of castrate-resistant prostate cancer by imipridones may not be significantly impacted by neuroendocrine differentiation as a therapy-resistance mechanism. The results support further testing of imipridones across subtypes of androgen-sensitive and castrate-resistant prostate cancer.

cancer biology↗

Critical analysis of polycyclic tetramate macrolactam biosynthetic cluster phylogeny and functional diversity

Polycyclic tetramate macrolactams (PTMs) are bioactive natural products commonly associated with certain actinobacterial and proteobacterial lineages. These molecules have been the subject of numerous structure-activity investigations since the 1970s. New members continue to be pursued in wild and engineered bacterial strains, and advances in PTM biosynthesis suggests their outwardly simplistic biosynthetic gene clusters (BGCs) belie unexpected product complexity. Towards addressing the origins of this complexity and understanding its influence on PTM discovery, we engaged in a combination of bioinformatics to systematically classify PTM BGCs, and PTM-targeted metabolomics to compare the products of select BGC types. By comparing groups of producers and BGC mutants, we exposed knowledge gaps that complicate bioinformatics-driven product predictions. In sum, we provide new insights into the evolution of PTM BGCs while systematically accounting for the PTMs discovered thus far. The combined computational and metabologenomic findings presented here should prove useful for guiding future discovery. IMPORTANCEPolycyclic tetramate macrolactam (PTM) pathways are frequently found within the genomes of biotechnologically-important bacteria, including Streptomyces and Lysobacter spp. Their molecular products are typically bioactive, having substantial agricultural and therapeutic interest. Leveraging bacterial genomics for the discovery of new related molecules is thus desirable, but drawing accurate structural predictions from bioinformatics alone remains challenging. This difficulty stems from a combination of previously underappreciated biosynthetic complexity and remaining knowledge gaps, compounded by a stream of yet-uncharacterized PTM biosynthetic loci gleaned from recently sequenced bacterial genomes. We engaged in the following study to create a useful framework for cataloging historic PTM clusters, identifying new cluster variations, and tracing evolutionary paths for these molecules. Our data suggests new PTM chemistry remains discoverable in nature. However, our metabolomic and mutational analyses emphasize practical limitations to genomics-based discovery by exposing hidden complexity.

microbiology↗

High-throughput detection and quantification of phosphatidylcholines and sphingomyelins from single cells by chip-based nanoelectrospray ionisation

Recent advances in single-cell genomics and transcriptomics technologies have transformed our understanding of cellular heterogeneity in growth, development, ageing and disease; however, methods for single-cell lipidomics have comparatively lagged behind in development. We have developed a high-throughput method for the detection and quantification of a wide range of phosphatidylcholine (PC) and sphingomyelin (SM) species from single cells that combines fluorescence-assisted cell sorting (FACS) with automated chip-based nanoelectrospray ionization (nanoESI) and shotgun lipidomics. We show herein that our method is capable of quantifying more than 50 different PC and SM species from single cells and can easily distinguish between cells of different lineages or cells treated with exogenous fatty acids. Moreover, our method can detect more subtle differences in the lipidome between cell lines of the same cancer type. Our approach can be run in parallel with other single-cell technologies to deliver near-complete multi-omics data on cells with a similar phenotype and has the capacity to significantly advance our current knowledge on cellular heterogeneity.

biochemistry↗

Modeling Zika Virus Spread in Colombia Using Google Search Queries and Logistic Power Models

Public health agencies generally have a small window to respond to burgeoning disease outbreaks in order to mitigate the potential impact. There has been significant interest in developing forecasting models that can predict how and where a disease will spread. However, since clinical surveillance systems typically publish data with a lag of two or more weeks, there is a need for complimentary data streams that can close this gap. We examined the usefulness of Google Trends search data for analyzing the 2016 Zika epidemic in Colombia and evaluating their ability to predict its spread. We calculated the correlation and the time delay between the reported case data and the Google Trends data using variations of the logistic growth model, and showed that the data sets were systematically offset from each other, implying a lead time in the Google Trends data. Our study showed how Internet data can potentially complement clinical surveillance data and may be used as an effective early detection tool for disease outbreaks.

epidemiology↗

Native and engineered clifednamide biosynthesis in multiple Streptomyces spp.

Polycyclic tetramate macrolactam (PTM) natural products are produced by actinomycetes and other bacteria. PTMs are often bioactive, and the simplicity of their biosynthetic clusters make them attractive for bioengineering. Clifednamide-type PTMs from Streptomyces sp. JV178 contain a distinctive ketone group, suggesting the existence of a novel PTM oxidizing enzyme. Here, we report the new cytochrome P450 enzyme (CftA) is required for clifednamide production. Genome mining was used to identify several new clifednamide producers, some having improved clifednamide yields. Using a parallel synthetic biology approach, CftA isozymes were used to engineer the ikarugamycin pathway of Streptomyces sp. NRRL F-2890 to yield clifednamides. Further, we observed that strong CftA expression leads to the production of a new PTM, clifednamide C. We demonstrate the utility of both genome mining and synthetic biology to rapidly increase clifednamide production and identify a PTM tailoring enzyme for rational molecule design.

synthetic biology↗