Biology subjects

Sim, D.

Publications and source records attributed to Sim, D..

7 recordsLinked to original sources

bioRxiv2025

Synergetic hallmark knockouts immortalize bovine muscle stem cells for cellular agriculture

Achieving scalable and sustainable production of cultivated meat hinges on developing robust livestock muscle and fat cell lines that can proliferate and differentiate effectively, while meeting regulatory standards and consumer expectations. In this study, bovine satellite cells (BSCs) were immortalized using CRISPR/Cas9 to knockdown PTEN (phosphatase and tensin homolog), TP53 (cellular tumor antigen) and SMAD4 (SMAD [Sma and Mad proteins] family member 4). The resulting cell line, named "CriBSC2," exhibited consistent growth, maintained muscle cell characteristics, and successfully differentiated into multinucleated myotubes after more than 150 cell divisions. In contrast, another cell line, "CriBSC1," achieved immortalization with TP53 knockout alone but lacked differentiation capacity. CriBSC2 were further cultured on gelatin scaffolds to evaluate their anchorage-dependent responses, laying the groundwork for their potential application in tissue engineering for cellular agriculture. Ultimately, CriBSC2 cells demonstrate suitable proliferation and differentiation capabilities crucial for advancing cellular agriculture and future food technologies.

cell biology↗

bioRxiv2023

Targeting NGF but not VEGF or BDNF signaling reduces endometriosis-associated pain in mice

IntroductionEndometriosis is a chronic inflammatory disease that affects [~]10% of women. A significant fraction of patients experience limited or no efficacy with current therapies. Tissue adjacent to endometriosis lesions often exhibits increased neurite and vascular density, suggesting that disease pathology involves neurotrophic activity and angiogenesis. ObjectivesWe aim to evaluate the potential for key tyrosine-kinase-receptor-coupled neurotrophic molecules to contribute to endometriosis-associated pain in mice. MethodsThe levels of VEGFR1 regulators (VEGFA, VEGFB, PLGF, and sVEGFR1) were quantified by ELISA in peritoneal fluid from endometriosis patients undergoing surgery and used to calculate VEGFR1 occupancy. We used genetic depletion, neutralizing antibody, and pharmacological approaches to specifically block ligand (NGF or BDNF) or neurotrophic receptor (VEGFR1, TRKs) in a murine model of endometriosis-associated pain. Endometriosis-associated pain was determined using the von Frey filaments method, quantification of spontaneous abdominal pain-related behavior, and thermal discomfort. Diseases parameters were evaluated by lesion size and prevalence. ResultsWe found that entrectinib (pan-Trk inhibitor) or anti-NGF treatments reduced evoked pain, spontaneous pain, and thermal discomfort. In contrast, even though receptor occupancy revealing that VEGFR1 agonist levels are sufficient to support pain, blocking VEGFR1 signaling via antibody or tamoxifen-induced knockout did not reduce pain or lesion size in mice. Targeting BDNF-TrkB with an anti-BDNF antibody also proved ineffective. ConclusionsThis suggests NGF-TrkA signaling, but not BDNF-TrkB or VEGF-VEGFR1, mediates endometriosis-associated pain. Moreover, entrectinib blocks endometriosis-associated pain and reduces lesion sizes. Our results also indicated that entrectinib-like molecules are promising candidates for endometriosis treatment. Credit author statementConceptualization: T.H. Zaninelli, V. Fattori, and M.S. Rogers; investigation and data curation: T.H. Zaninelli, V. Fattori, O.K. Heintz, K.R. Wright; A.C. Andrello, W.A. Verri Jr, M.S. Rogers; funding acquisition: M.S. Rogers,, S.A. Missmer, R.M. Anchan; methodology: T.H. Zaninelli, V. Fattori, and M.S. Rogers; human sample collection: S.A. Missmer, A.F. Vitonis, K.L. Terry, R.M. Anchan; animal breading and VEGFR1 ablation: D. Sim and H. Bukhari; resources: A.C. Andrello; D. Bree, T. Zheng, J. Wagner, W.A. Verri Jr, and M.S. Rogers; project administration: T.H. Zaninelli; supervision: V. Fattori, W.A. Verri Jr, and M.S. Rogers; visualization: T.H. Zaninelli, V. Fattori, W.A. Verri Jr, and M.S. Rogers; writing-original draft: T.H. Zaninelli; writing - editing and reviewing: all authors. All authors have read and approved the final version of the manuscript.

pharmacology and toxicology↗

bioRxiv2021

PRSS2 promotes tumor growth and progression by repressing Tsp-1 in the tumor microenvironment via binding to LRP1

In the earliest stages of tumor development, epithelial tumors (carcinomas) are physically confined to the area of the tissue in which they form. These nascent lesions (carcinomas in situ) are sequestered from the tissue parenchyma by the basement membrane. Within the tissue parenchyma lie a myriad of cell types comprised of fibroblasts, immune and inflammatory cells and endothelial cells. Upon invasion across the basement membrane and into the tissue parenchyma, tumors must manipulate the expression of pro- and anti-tumorigenic proteins such that pro-tumorigenic factors are produced in excess to anti-tumorigenic proteins. One such anti-tumorigenic protein is Thrombospondin-1 (Tsp-1). We have previously demonstrated that stimulation of Tsp-1 in the tumor microenvironment (TME) potently inhibits tumor growth and progression and in some cases induces tumor regression. Here, we identify a novel tumor-mediated mechanism to repress the expression of Tsp-1 in the TME via secretion of the serine protease PRSS2. We demonstrate that PRSS2 represses Tsp-1, not via its enzymatic activity, but by binding to low-density lipoprotein receptor-related protein 1 (LRP1). These findings describe a novel activity for PRSS2 through binding to LRP1 and represent a potential therapeutic strategy to treat cancer by blocking the PRSS2-mediated repression of Tsp-1.

cancer biology↗

bioRxiv2020

Reverse Pneumatic Artificial Muscles for Application in Low-Cost Artificial Respirators

One of the main challenges associated with mechanical ventilators is their limited availability in pandemics and other emergencies. Therefore, there is a great demand for mechanical ventilators to address this issue. In this work, we propose a low-cost, portable, yet high-performance design for a volume-controlled mechanical ventilator. We are employing pneumatic artificial muscles, such as air cylinders, in the reverse mode of operation to achieve mechanical ventilation. The current design of the device can operate in two modes: controlled mode and assisted mode. Unlike most ICU ventilators, our device does not need a high-pressure air pipeline to operate. With the current design, mechanical ventilation for respiration rate ranging from 10 b/min to 30 b/min with a tidal volume range of 150 mL to 1000 mL and I:E ratio of 1:1 to 1:5 can be performed. We achieved a total cost of less $400 USD to make one device. We estimate the device to cost less than $250 USD when produced in larger volumes.

bioengineering↗

bioRxiv2020

Notch1 regulates breast cancer stem cell function via a non-canonical cleavage-independent pathway

Current treatment of triple negative breast cancer patients is hindered by a high incidence of chemoresistance (30-50%). The prevailing theory is that resistance and subsequent recurrence is driven by cancer stem cells. Unfortunately, the functional characterization of cancer stem cells at the molecular level is still incomplete. We show here, that within the canonical breast cancer stem cell population, a subset of cells characterized by high Notch1 expression possesses the tumor-initiating property associated with cancer stem cells. Moreover, the tumor initiating property of these high Notch1-expressing breast cancer stem cells is mediated by a cleavage independent Notch signaling pathway culminating in the repression of SIRT1. Of note, the Notch1-mediated repression of SIRT1 is required not only for tumor initiation, but also for chemoresistance in breast cancer stem cells. Strikingly, inhibition of SIRT1 obviates the requirement for Notch1, marking the first example of conferring cancer stem cell function by inhibiting the activity of a single protein. We also demonstrate that progenitor-like mammary epithelial cells, which possess both luminal and basal properties, are also characterized by high Notch1 expression and repression of SIRT1 via the non-canonical pathway. These findings provide the first functional mechanistic requirements for tumor initiation by breast cancer stem cells and suggest that activation of the non-canonical Notch1 pathway is hardwired into tumor-initiating progenitor cells and thus a prerequisite for tumor initiation. Statement of SignificanceWe demonstrate that chemoresistant and tumor-initiating properties of breast cancer stem cells are driven by repression of SIRT1 via non-canonical Notch signaling, suggesting a novel therapeutic strategy for triple negative breast cancer.

cancer biology↗

bioRxiv2020

The effect of drug dose and timing of treatment on the emergence of drug resistance in vivo in a malaria model

Background and objectivesThere is significant interest in identifying clinically effective drug treatment regimens that minimize the de novo evolution of antimicrobial resistance in pathogen populations. However, in vivo studies that vary treatment regimens and directly measure drug resistance evolution are rare. Here, we experimentally investigate the role of drug dose and treatment timing on resistance evolution in an animal model. MethodologyIn a series of experiments, we measured the emergence of atovaquone-resistant mutants of Plasmodium chabaudi in laboratory mice, as a function of dose and timing of treatment with the antimalarial drug atovaquone. ResultsIncreasing the concentration of atovaquone increased the likelihood of high-level resistance emergence. Treating very early or late in infection reduced the risk of resistance, likely as a result of population size at time of treatment, but we were not able to exclude influence of the immune response in the latter. When we varied starting inoculum, resistance was more likely at intermediate inoculum sizes, but this did not correlate directly with population sizes at time of treatment. Conclusions and implications(i) Higher doses do not always minimize resistance emergence and can result in competitive release of parasites with high-level resistance. (ii) Altering treatment timing affects the risk of resistance emergence, but not as a simple function of population size at the time of treatment. (iii) Finding the right dose and right time to maximize clinical gains and limit resistance emergence can vary depending on biological context and was non-trivial even in our simplified experiments.

evolutionary biology↗

bioRxiv2019

Feasibility of Automated Deep Learning Design for Medical Image Classification by Healthcare Professionals with Limited Coding Experience

Deep learning has huge potential to transform healthcare. However, significant expertise is required to train such models and this is a significant blocker for their translation into clinical practice. In this study, we therefore sought to evaluate the use of automated deep learning software to develop medical image diagnostic classifiers by healthcare professionals with limited coding - and no deep learning - expertise.\n\nWe used five publicly available open-source datasets: (i) retinal fundus images (MESSIDOR); (ii) optical coherence tomography (OCT) images (Guangzhou Medical University/Shiley Eye Institute, Version 3); (iii) images of skin lesions (Human against Machine (HAM)10000) and (iv) both paediatric and adult chest X-ray (CXR) images (Guangzhou Medical University/Shiley Eye Institute, Version 3 and the National Institute of Health (NIH)14 dataset respectively) to separately feed into a neural architecture search framework that automatically developed a deep learning architecture to classify common diseases. Sensitivity (recall), specificity and positive predictive value (precision) were used to evaluate the diagnostic properties of the models. The discriminative performance was assessed using the area under the precision recall curve (AUPRC). In the case of the deep learning model developed on a subset of the HAM10000 dataset, we performed external validation using the Edinburgh Dermofit Library dataset.\n\nDiagnostic properties and discriminative performance from internal validations were high in the binary classification tasks (range: sensitivity of 73.3-97.0%, specificity of 67-100% and AUPRC of 0.87-1). In the multiple classification tasks, the diagnostic properties ranged from 38-100% for sensitivity and 67-100% for specificity. The discriminative performance in terms of AUPRC ranged from 0.57 to 1 in the five automated deep learning models. In an external validation using the Edinburgh Dermofit Library dataset, the automated deep learning model showed an AUPRC of 0.47, with a sensitivity of 49% and a positive predictive value of 52%. The quality of the open-access datasets used in this study (including the lack of information about patient flow and demographics) and the absence of measurement for precision, such as confidence intervals, constituted the major limitation of this study.\n\nAll models, except for the automated deep learning model trained on the multi-label classification task of the NIH CXR14 dataset, showed comparable discriminative performance and diagnostic properties to state-of-the-art performing deep learning algorithms. The performance in the external validation study was low. The availability of automated deep learning may become a cornerstone for the democratization of sophisticated algorithmic modelling in healthcare as it allows the derivation of classification models without requiring a deep understanding of the mathematical, statistical and programming principles. Future studies should compare several application programming interfaces on thoroughly curated datasets.

bioinformatics↗