Biology subjects

Duan, K.

Publications and source records attributed to Duan, K..

24 records · Page 2Linked to original sources

bioRxiv2021

Integrin β3 Targeting Biomaterial Preferentially Promotes Secretion of bFGF and Proliferation of iPSC-Derived Vascular Smooth Muscle Cells

Human-induced pluripotent stem cell-derived-vascular smooth muscle cells (hiPSC-VSMC) have been shown to promote angiogenesis and wound healing. However, there is a paucity of research on how the extracellular matrix (ECM) microenvironment may impact the hiPSC-VSMCs function. In this study, our objective was to understand the effect of specific ECM ligand-integrin interaction on hiPSC- VSMCs paracrine secretion, cell proliferation, and morphology. We here showed a precise modulation of hiPSC-VSMC in a fibronectin functionalized fibrillar collagen scaffold by targeting their integrin {beta}3. The secretion of proangiogenic growth factor, basic fibroblast growth factor (bFGF) was found to be fibronectin dependent via v{beta}3 integrin interactions. Also, our data indicate the possible role of a positive feedback loop between integrin {beta}3, bFGF, and matrix metalloproteinase-2 in regulating hiPSC- VSMCs morphology and cell proliferation. Finally, the secretome with improved proangiogenic activity shows potential for future regenerative applications.

bioengineering↗

bioRxiv2020

CDK5 Inhibitor Seliciclib Promotes Osteoblastic Differentiation of MSCs and Suppresses the Migration of MG-63 Osteosarcoma Cells

CDK5 belongs to the cycling dependent kinase family, which is multifunctional and plays an important role in neural differentiation. However, the role of CDK5 in osteoblastic differentiation remains unclear. The present study investigated functions and molecular mechanism of CDK5 in osteoblastic differentiation. It was found that, the addition of CDK5 inhibitor Seliciclib promoted the expression of Runx2, ALP, OCN and OPN of MSCs and the mineralization of MC-3T3E1 cells. Seliciclib enhanced the development of F-actin, nuclear localization of {beta}-catenin and YAP, as well as the expression of RMRP RNA. When F-actin was suppressed by Blebbistatin, the nuclear localization of YAP and {beta}-catenin, and expression of RMRP RNA as well as Runx2 and ALP were decreased. These indicate Seliciclib promotes osteoblastic differentiation mainly by F-actin. Moreover, Seliciclib also suppressed the migration of MG-63, suggesting a potential application for Seliciclib in bone defect repair and inhibition of the migratiion of osteosarcoma cells.

developmental biology↗

bioRxiv2020

An In situ Collagen-HA Hydrogel System Promotes Survival and Preserves the Proangiogenic Secretion of hiPSC-derived Vascular Smooth Muscle Cells

Human induced pluripotent stem cell-derived vascular smooth muscle cells (hiPSC-VSMCs) with proangiogenic properties have huge therapeutic potential. While hiPSC-VSMCs have already been utilized for wound healing using a biomimetic collagen scaffold, an in situ forming hydrogel mimicking the native environment of skin offers the promise of hiPSC-VSMC mediated repair and regeneration. Herein, the impact of a collagen type-I-hyaluronic acid (HA) in situ hydrogel cross-linked using a PEG-based cross-linker on hiPSC-VSMCs viability and proangiogenic paracrine secretion was investigated. Our study demonstrated increases in cell viability, maintenance of phenotype and proangiogenic growth factor secretion, and proangiogenic activity in response to the conditioned medium. The optimally cross-linked and functionalized collagen type-I/HA hydrogel system developed in this study shows promise as an in situ hiPSC-VSMC carrier system for wound regeneration.

bioengineering↗

bioRxiv2020

aNy-way Independent Component Analysis

Multimodal data fusion is a topic of great interest. Several fusion methods have been proposed to investigate coherent patterns and corresponding linkages across modalities, such as joint independent component analysis (jICA), multiset canonical correlation analysis (mCCA), mCCA+jICA, disjoint subspace using ICA (DS-ICA) and parallel ICA. JICA exploits source independence but assumes shared loading parameters. MCCA maximizes correlation linkage across modalities directly but is limited to orthogonal features. While there is no theoretical limit to the number of modalities analyzed together by jICA, mCCA, or the two-step approach mCCA+jICA, these approaches can only extract common features and require the same number of sources/components for all modalities. On the other hand, DS-ICA and parallel ICA can identify both common and distinct features but are limited to two modalities. DS-ICA assumes shared loading parameters among common features, which works well when links are strong. Parallel ICA simultaneously maximizes correlation between modalities and independence of sources, while allowing different number of sources for each modality. However, only a very limited number of modalities and linkage pairs can be optimized. To overcome these limitations, we propose aNy-way ICA, a new model to simultaneously maximize the independence of sources and correlations across modalities. aNy-way ICA combines infomax ICA and Gaussian independent vector analysis (IVA-G) via a shared weight matrix model without orthogonality constraints. Simulation results demonstrate that aNy-way ICA not only accurately recovers sources and loadings, but also the true covariance/linkage patterns, whether different modalities have the same or different number of sources. Moreover, aNy-way ICA outperforms mCCA and mCCA+jICA in terms of source and loading recovery accuracy, especially under noisy conditions. Clinical RelevanceThis establishes a model for N-way data fusion of any number of modalities and linkage pairs, allowing different number of non-orthogonal sources for different modalities.

bioengineering↗

bioRxiv2019

Structural Brain Alterations and Their Association with Cognitive Function and Symptoms in Attention-Deficit/Hyperactivity Disorder Families

Gray matter disruptions have been found consistently in Attention-deficit/Hyperactivity Disorder (ADHD). The organization of these alterations into brain structural networks remains largely unexplored. We investigated 508 participants (281 males) with ADHD (N = 210), their unaffected siblings (N = 108), individuals with subthreshold ADHD (N = 49), and unrelated healthy controls (N = 141) with an age range from 7 - 18 years old from 336 families in the Dutch NeuroIMAGE project. Source based morphometry was used to examine structural brain network alterations and their association with symptoms and cognitive performance. Two networks showed significant reductions in individuals with ADHD compared to unrelated healthy controls after False Discovery Rate correction. Component A, mainly located in bilateral Crus I, showed a case/control difference with sub-clinical cases being intermediate between cases and controls. The unaffected siblings were similar to controls. After correcting for IQ and medication status, component A showed a negative correlation with inattention symptoms across the entire sample. Component B included a maximum cluster in the bilateral insula, where unaffected siblings, similar to cases, showed significantly reduced loadings compared to controls; but no relationship with individual symptoms or cognitive measures was found for component B. This multivariate approach suggests that areas reflecting genetic liability within ADHD are partly separate from those areas modulating symptom severity.

neuroscience↗

bioRxiv2019

SPARSE INFOMAX BASED ON HOYER PROJECTION AND ITS APPLICATION TO SIMULATED STRUCTURAL MRI AND SNP DATA

Independent component analysis has been widely applied to brain imaging and genetic data analyses for its ability to identify interpretable latent sources. Nevertheless, leveraging source sparsity in a more granular way may further improve its ability to optimize the solution for certain data types. For this purpose, we propose a sparse infomax algorithm based on nonlinear Hoyer projection, leveraging both sparsity and statistical independence of latent sources. The proposed algorithm iteratively updates the unmixing matrix by infomax (for independence) and the sources by Hoyer projection (for sparsity), feeding the sparse sources back as input data for the next iteration. Consequently, sparseness propagates effectively through infomax iterations, producing sources with more desirable properties. Simulation results on both brain imaging and genetic data demonstrate that the proposed algorithm yields improved pattern recovery, particularly under low signal-to-noise ratio conditions, as well as improved sparseness compared to traditional infomax.

bioengineering↗