Biology subjects

Kang, S.

Publications and source records attributed to Kang, S..

115 records · Page 7Linked to original sources

bioRxiv2019

An Integrated Multiscale, Multicellular Skin Model

Skin is our primary barrier to the outside world, protecting us from physical, biological and chemical threats. Developing innovative products that preserve and improve skin barrier function requires a thorough understanding of the mechanisms underlying barrier response to topical applications. In many fields, computer simulations already facilitate understanding, thus accelerating innovation. Simulations of software models allow scientists to test hypothesized mechanisms by comparing predicted results to physical observations. They also enable virtual product optimization, without physical experiments, once mechanisms have been validated. The physical accessibility and abundant knowledge of skin structure makes it a prime candidate for computational modeling. In this article, we describe a computational multiscale multicellular skin model used to simulate growth and response of the epidermal barrier. The model integrates several modeling styles and mathematical frameworks including ordinary differential equations, partial differential equations, discrete agent-based modeling and discrete element methods. Specifically, to capture cell biology and physical transport, we combined four distinct sub-models from existing literature. We also implemented methods for elastic biomechanics. Our software implementation of the model is compatible with the high-performance computing simulation platform Biocellion. The integrated model recapitulates barrier formation, homeostasis and response to environmental, chemical and mechanical perturbation. This work exemplifies methodology for integrating models of vastly different styles. The methodology enables us to effectively build on existing knowledge and produce "whole-system" tissue models capable of displaying emergent properties. It also illustrates the inherent technical difficulties associated with the mounting complexity of describing biological systems at high fidelity. Among the challenges are validation of the science, the mathematical representations approximating the science and the software implementing these representations. Responsibility for a discrepancy observed between in silico and in vitro results may as easily lie at one of these three levels as at another, demanding that any sustainable modeling endeavor engage expertise from biology, mathematics and computing.

systems biology↗

bioRxiv2019

Inosine is an alternative carbon supply that supports effector T cell proliferation and anti-tumor function under glucose restriction

T cells undergo a characteristic metabolic rewiring that fulfills the dramatically increased bioenergetic, biosynthetic, and redox demands following antigen stimulation. A robust adaptive immune system requires effector T cells to respond and adapt to fluctuations in environmental nutrient levels imposed by infectious and inflammatory sites in different tissues. Inevitably, such responsiveness and adaptation reflect metabolic plasticity, allowing T cells to elicit immune functions by using a wide range of nutrient substrates. Here, we show that effector T cells utilize inosine, as an alternative substrate, to support cell growth and function in the absence of glucose. T cells metabolize inosine into hypoxanthine and phosphorylated ribose by purine nucleoside phosphorylase (PNP). Using Stable Isotope-Resolved Metabolomics (SIRM), we demonstrated that ribose moiety of inosine can enter into central metabolic pathways to provide ATP and biosynthetic precursors. Accordingly, the dependence of T cells on extracellular glucose for growth and effector functions can be relieved by inosine. On the other hand, cancer cells display diverse capacity to utilize inosine as a carbon resource. Moreover, the supplement of inosine enhances the anti-tumor efficacy of immune-checkpoint blockade or adoptive T cell transfer, reflecting the capability of inosine in relieving tumor-imposed metabolic restrictions on T cells in vivo.

immunology↗

bioRxiv2019

Multi-immersion open-top light-sheet microscope for high-throughput imaging of cleared tissues

Recent advances in optical clearing and light-sheet microscopy have provided unprecedented access to structural and molecular information from intact tissues. However, current light-sheet microscopes have imposed constraints on the size, shape, number of specimens, and compatibility with various clearing protocols. Here we present a multi-immersion open-top light-sheet microscope that enables simple mounting of multiple specimens processed with a variety of protocols, which will facilitate wider adoption by preclinical researchers and clinical laboratories. One Sentence SummaryGlaser et al. describe a multi-immersion open-top light-sheet microscope that enables simple and high-throughput imaging of large numbers of preclinical and clinical specimens prepared with a variety of clearing protocols.

bioengineering↗

bioRxiv2018

In silico Drug Repositioning of bortezomib to reverse metastatic effect of GALNT14 in lung cancer

Although many molecular targets for cancer therapy have been discovered, they often show poor druggability, which is a major obstacle to develop targeted drugs. As an alternative route to drug discovery, we adopted an in silico drug repositioning (in silico DR) approach based on large-scale gene expression signatures, with the goal of identifying inhibitors of lung cancer metastasis. Our analysis of clinicogenomic data identified GALNT14, an enzyme involved in O-linked N-acetyl galactosamine glycosylation, as a putative driver of lung cancer metastasis leading to poor survival. To overcome the poor druggability of GALNT14, we leveraged Connectivity Map approach, an in silico screening for drugs that are likely to revert the metastatic expression patterns. It leads to identification of bortezomib (BTZ) as a potent metastatic inhibitor, bypassing direct inhibition of poorly druggable target, GALNT14. The anti-metastatic effect of BTZ was verified in vitro and in vivo. Notably, both BTZ treatment and GALNT14 knockdown attenuated TGF{beta}-mediated gene expression and suppressed TGF{beta}-dependent metastatic genes, suggesting that BTZ acts by modulating TGF{beta} signalingTaken together, these results demonstrate that our in silico DR approach is a viable strategy to identify a candidate drug for undruggable targets, and to uncover its underlying mechanisms.

cancer biology↗

bioRxiv2018

Novel role of Lin28 signaling in regulation of mammalian PNS and CNS axon regeneration

Several signaling molecules involved in cellular reprogramming have been shown to regulate mammalian axon regeneration. We hypothesized that reprogramming factors are key regulators of axon regeneration. Here we investigated the role of Lin28, an important reprogramming factor, in the regulation of axon regeneration. We found that Lin28a and Lin28b and their regulatory partners, let-7 microRNAs (miRNAs), were both necessary and sufficient in regulating mature sensory axon regeneration in vivo. More importantly, overexpression of either Lin28a or Lin28b in mature retinal ganglion cells (RGCs) promoted robust and sustained optic nerve regeneration. Additionally, combined overexpression of Lin28a and downregulation of PTEN in RGCs acted additively to promote optic nerve regeneration by reducing the backward turning of regenerating RGC axons. Our findings not only identified a novel molecule promoting optic nerve regeneration but also suggested that reprogramming factors may play vital roles in regulating axon regeneration in mammals.

neuroscience↗

bioRxiv2017

Restoration longevity among geriatric and adult special needs patients

This study aimed to describe the survival trajectory of dental restorations placed in an outpatient population of geriatric and adult special needs patients over a 15-year span, with particular interest in longevity of subsequent restorations in teeth that received multiple restorations over time. Dental restorations of different types and sizes in patients age [≥]65 years treated between 2000-14 at the University of Iowa, College of Dentistry were followed until they incurred an event (i.e., restoration replacement, extraction of the tooth, or endodontic treatment of the tooth). Survival analysis and extended Cox regression models were used to generate hazards ratios for selected predictor variables. A total of 9184 restorations were followed in 1551 unique patients. During the follow-up period, 28.7% of these restorations incurred an event; and overall the restorations had a median lifespan of 6.25 years. In multivariable regression models, after controlling for gender and age, composite restorations and greater number of restoration surfaces were associated with higher risks of failure; and the initial restoration recorded in the database for each subject tended to have lower risk of failure than restorations placed later that included any of those same surfaces. This information potentially could be helpful to elderly patients considering various restorative treatment options during the dental treatment planning and informed consent process.

bioengineering↗

bioRxiv2017

Edge Detection of Cryptic Lamellipodia Assisted by Deep Learning

Cell protrusion plays important roles in cell migration by pushing plasma membrane forward. Cryptic lamellipodia induce the protrusion of submarginal cells in collective cell migration where cells are attached and move together. Although computational image analysis of cell protrusion has been done extensively, the study on protrusion activities of cryptic lamellipodia is limited due to difficulties in image segmentation. This study seeks to aid in the computational analysis of submarginal cell protrusion in collective cell migration by using deep learning to detect the cryptic lamellipodial edges from fluorescence time-lapse movies. Due to the noisy features within overlapping cells, the conventional image analysis algorithms such as Canny edge detector and intensity thresholding are limited. By combining Canny edge detector, Convolutional Neural Networks (CNNs), and local intensity thresholding, we were able to detect cryptic lamellipodial edges of submarginal cells with high accuracy from the fluorescence time-lapse movies of PtK1 cells stained with a plasma membrane marker. We used relatively small effort to prepare the training set to train the CNN to detect the cryptical lamellipodial edges in fluorescence time-lapse movies. This work demonstrates that deep learning can be combined with the conventional image analysis algorithms to facilitate the computational analysis of highly complex time-lapse movies of collective cell migration.

bioinformatics↗