4.7 Article

The spatial binding model of the pioneer factor Oct4 with its target genes during cell reprogramming

Journal

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
Volume 17, Issue -, Pages 1226-1233

Publisher

ELSEVIER
DOI: 10.1016/j.csbj.2019.09.002

Keywords

Spatial binding pattern; Cell reprogramming; Pioneer factor; Multivariate linear regression

Funding

  1. National Nature Scientific Foundation of China [61561036, 61702290]
  2. Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region [NJYT-18-B01]
  3. Fund for Excellent Young Scholars of Inner Mongolia [2017JQ04]
  4. Student's Platform for Innovation and Entrepreneurship Training Program of Inner Mongolia University [201714298]

Ask authors/readers for more resources

Understanding the target regulation between pioneer factor and its binding genes is crucial for improving the efficiency of TF-mediated reprogramming. Oct4 as the only one factor that cannot be substituted by other POU members, it is urgent need to develop a quantitative model for describing the spatial binding pattern with its target genes. The dynamic profiles of pioneer factor Oct4-binding showed that the major wave occurs at the intermediate stage of cell reprogramming (from day 7 to day 15), and the promoter is the preferred targeting regions. The Oct4-binding distributions perform significant chromosome bias. The overall enrichment on chromosome 1-11 is higher than that on the others. The dramatic event of TF-mediated reprogramming is mainly concentrated on autosomes. We also found that the spatial binding ability of Oct4 binding can be represented quantitatively by using three parameters of peaks (height, width and distance). The dynamic changes of Oct4-binding demonstrated that the width play more important roles in regulating expression of target genes. At last, a multivariate linear regression was introduced to establish the spatial binding model of the Oct4-binding. The evaluation results confirmed that the height and width is positively correlated with the gene expression. And the additive interaction terms of height and width can better optimize the model performance than the multiplicative terms. The best average coefficients of determination of improved model achieved to 81.38%. Our study will provide new insights into the cooperative regulation of spatial binding pattern of pioneer factors in cell reprogramming. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Computer Science, Information Systems

Human Inertial Thinking Strategy: A Novel Fuzzy Reasoning Mechanism for IoT-Assisted Visual Monitoring

Shuai Liu, Shuai Wang, Xinyu Liu, Jianhua Dai, Khan Muhammad, Amir H. H. Gandomi, Weiping Ding, Mohammad Hijji, Victor Hugo C. de Albuquerque

Summary: Computer vision, particularly visual monitoring technology, has shown great potential in the complex monitoring environment. This article proposes a fuzzy inference-based monitoring method that utilizes human inertial thinking characteristics to infer the target's location and applies an alternative selection strategy based on thinking set. Experimental results on multiple datasets demonstrate the effectiveness and robustness of the proposed method in IoT-assisted monitoring.

IEEE INTERNET OF THINGS JOURNAL (2023)

Article Biochemistry & Molecular Biology

EmAtlas: a comprehensive atlas for exploring spatiotemporal activation in mammalian embryogenesis

Lei Zheng, Pengfei Liang, Chunshen Long, Haicheng Li, Hanshuang Li, Yuchao Liang, Xiang He, Qilemuge Xi, Yongqiang Xing, Yongchun Zuo

Summary: The lack of global embryogenesis repository and systematic analysis tools hinders the progress of research in stem cells, human congenital diseases, and assisted reproduction. EmAtlas provides a comprehensive collection of multi-omics data and multi-scale tools to explore the spatiotemporal activation during mammalian embryogenesis.

NUCLEIC ACIDS RESEARCH (2023)

Article Biology

ACSN: Attention capsule sampling network for diagnosing COVID-19 based on chest CT scans

Cuihong Wen, Shaowu Liu, Shuai Liu, Ali Asghar Heidari, Mohammad Hijji, Carmen Zarco, Khan Muhammad

Summary: Automated diagnostic techniques based on CT scans of the chest have been proposed to detect COVID-19 cases quickly and accurately. Existing capsule networks face challenges in extracting key slices and fusing features from multiple regions. In this study, an attention capsule sampling network (ACSN) is proposed, which achieves high performance with 96.3% accuracy, 98.8% sensitivity, 93.8% specificity, and 98.3% area under the ROC curve on a dataset of 35,000 slices.

COMPUTERS IN BIOLOGY AND MEDICINE (2023)

Article Computer Science, Artificial Intelligence

Visual tracking in complex scenes: A location fusion mechanism based on the combination of multiple visual cognition flows

Shuai Liu, Shichen Huang, Shuai Wang, Khan Muhammad, Paolo Bellavista, Javier Del Ser

Summary: Deep learning has transformed computer vision and is widely used for monitoring in various visual scenes. However, traditional machine-learning methods still have certain advantages in terms of complexity and explainability. Traditional visual tracking approaches, particularly those using correlation filtering, have become popular for understanding complex visual scenes, but they may not fully capture the changing target appearances in dynamic visual scenes, leading to inaccurate target locations.

INFORMATION FUSION (2023)

Article Computer Science, Information Systems

Multi-modal fusion network with complementarity and importance for emotion recognition

Shuai Liu, Peng Gao, Yating Li, Weina Fu, Weiping Ding

Summary: Multimodal emotion recognition, using machine learning to generate multi-modal features from videos, is a research hotspot in the field of artificial intelligence. This paper improves the discrimination between modalities through effective weighting and constructs an attention network to capture the complementary relationship between modalities, resulting in a multi-modal feature with good interaction.

INFORMATION SCIENCES (2023)

Article Biochemical Research Methods

A computational framework of routine test data for the cost-effective chronic disease prediction

Mingzhu Liu, Jian Zhou, Qilemuge Xi, Yuchao Liang, Haicheng Li, Pengfei Liang, Yuting Guo, Ming Liu, Temuqile Temuqile, Lei Yang, Yongchun Zuo

Summary: This study developed a cost-effective framework for predicting chronic diseases using routine blood and biochemical test data. The framework achieved high accuracy and identified important markers for different types of chronic diseases.

BRIEFINGS IN BIOINFORMATICS (2023)

Article Biotechnology & Applied Microbiology

Stratification of ovarian cancer patients from the prospect of drug target-related transcription factor protein activity: the prognostic and genomic landscape analyses

Dongqing Su, Haoxin Zhang, Yuqiang Xiong, Haodong Wei, Yao Yu, Honghao Li, Tao Wang, Yongchun Zuo, Lei Yang

Summary: The expression and activity of transcription factors play crucial roles in controlling normal cellular processes, while dysregulated transcription factor activity in cancer leads to abnormal gene expression related to tumorigenesis and development. Targeted therapy can reduce the carcinogenicity of transcription factors. However, most studies on ovarian cancer have focused on individual transcription factors, disregarding the need to evaluate multiple transcription factors simultaneously to assess their effects on drug therapies.

BRIEFINGS IN FUNCTIONAL GENOMICS (2023)

Article Computer Science, Artificial Intelligence

SCTV-UNet: a COVID-19 CT segmentation network based on attention mechanism

Xiangbin Liu, Ying Liu, Weina Fu, Shuai Liu

Summary: The global outbreak of COVID-19 has become an important healthcare research topic since 2019. RT-PCR is the main method for detecting COVID-19, but the long detection time is an issue. Therefore, the use of CT imaging for pathological studies of COVID-19 is crucial. The proposed SCTV-UNet addresses the problems of misidentifying normal pixels and blurry boundaries in CT image segmentation, and shows improved results compared to state-of-the-art networks.

SOFT COMPUTING (2023)

Article Biochemistry & Molecular Biology

Integrating reduced amino acid composition into PSSM for improving copper ion-binding protein prediction

Shanghua Liu, Yuchao Liang, Jinzhao Li, Siqi Yang, Ming Liu, Chengfang Liu, Dezhi Yang, Yongchun Zuo

Summary: A copper ion-bound protein classifier, RPCIBP, was developed in this study by integrating reduced amino acid composition into position-specific scoring matrix. The classifier accurately predicts copper ion-binding proteins, facilitating further structural and functional studies, and aiding mechanism exploration and target drug development.

INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES (2023)

Review Computer Science, Hardware & Architecture

Empirical Research of Classroom Behavior Based on Online Education: A Systematic Review

Yishu Huang, Changling Peng, Shuai Liu

Summary: The research found that the number of empirical studies on classroom behavior is increasing rapidly, with topics mainly focusing on the influencing factors and characteristics of classroom behavior. The studies mostly used manual data collection and were predominantly quantitative, with a focus on primary and secondary school subjects and incomplete overviews of school divisions and disciplines.

MOBILE NETWORKS & APPLICATIONS (2023)

Proceedings Paper Computer Science, Software Engineering

An Empirical Study of Smart Contract Decompilers

Xia Liu, Baojian Hua, Yang Wang, Zhizhong Pan

Summary: This paper presents a large-scale empirical study of smart contract decompilers, aiming to understand the reliability, limitations, and research challenges of state-of-the-art decompilation tools. The study identifies root causes of decompiler failures, performance issues, and factors affecting decompilation effectiveness. It also proposes a completeness metric and investigates the resilience of decompilers against program transformations. Suggestions are given for decompiler builders and security researchers to improve decompilation tools and make appropriate selections. The findings and suggestions provided in this study can benefit decompiler builders, contract developers, and security researchers.

2023 IEEE INTERNATIONAL CONFERENCE ON SOFTWARE ANALYSIS, EVOLUTION AND REENGINEERING, SANER (2023)

Article Multidisciplinary Sciences

Characterizing Cellular Differentiation Potency and Waddington Landscape via Energy Indicator

Hanshuang Li, Chunshen Long, Yan Hong, Liaofu Luo, Yongchun Zuo

Summary: This study quantitatively evaluated the differentiation potency of stem cells using the Hopfield neural network and found that it can be approximated by Hopfield energy values. The analysis of the energy landscape in embryogenesis and cell reprogramming processes revealed that cell fate decision is a continuous process. The study also deciphered the dynamics of the gene regulatory network involved in driving cell fate transition. These findings propose a new energy indicator for characterizing cellular differentiation potency and provide insights into the potential mechanism of cellular plasticity.

RESEARCH (2023)

Article Computer Science, Artificial Intelligence

Global-local fusion based on adversarial sample generation for image-text matching

Shichen Huang, Weina Fu, Zhaoyue Zhang, Shuai Liu

Summary: In the era of adversarial machine learning (AML), developing robust and generalized algorithms has become a key research topic. This study proposes a global similarity matching module and a global-local cognition fusion training mechanism based on relationship adversarial sample generation to improve image-text matching algorithm. Experimental results show significant improvements in accuracy and robustness, performing well in facing security challenges and promoting the fusion of visual and linguistic modalities.

INFORMATION FUSION (2024)

Article Engineering, Electrical & Electronic

A Multiscale Feature Pyramid SAR Ship Detection Network With Robust Background Interference

Shuai Liu, Pengfei Chen, Yudong Zhang

Summary: Synthetic aperture radar (SAR) ship detection is widely used in various applications, but existing lightweight algorithms have problems such as misjudgment of targets mixed with the background and poor detection performance for targets with few samples. To address these issues, this paper proposes a detection network based on a multiscale feature pyramid network (FPN) that enhances target features, suppresses interference from the background, and reduces misjudgment. Experimental results show that the proposed network outperforms existing algorithms on public datasets.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2023)

Article Medicine, Research & Experimental

Deciphering the decisive factors driving fate bifurcations in somatic cell reprogramming

Chunshen Long, Hanshuang Li, Pengfei Liang, Lemuge Chao, Yan Hong, Junping Zhang, Qilemuge Xi, Yongchun Zuo

Summary: Single-cell studies have revealed that somatic cell reprogramming is a continuous process of cell fate transition. The activation of pluripotency networks and the mesenchymal-epithelial transition are crucial for successful reprogramming. By reconstructing cellular trajectory, we identified key factors and genes driving cell fate bifurcations, and found several drivers for successful reprogramming and alternative fates.

MOLECULAR THERAPY-NUCLEIC ACIDS (2023)

No Data Available