Category Ranking

98%

Total Visits

921

Avg Visit Duration

2 minutes

Citations

20

Article Abstract

Neph et al. (2012) (Circuitry and dynamics of human transcription factor regulatory networks. Cell, 150: 1274-1286) reported the transcription factor (TF) regulatory networks of 41 human cell types using the DNaseI footprinting technique. This provides a valuable resource for uncovering regulation principles in different human cells. In this paper, the architectures of the 41 regulatory networks and the distributions of housekeeping and specific regulatory interactions are investigated. The TF regulatory networks of different human cell types demonstrate similar global three-layer (top, core and bottom) hierarchical architectures, which are greatly different from the yeast TF regulatory network. However, they have distinguishable local organizations, as suggested by the fact that wiring patterns of only a few TFs are enough to distinguish cell identities. The TF regulatory network of human embryonic stem cells (hESCs) is dense and enriched with interactions that are unseen in the networks of other cell types. The examination of specific regulatory interactions suggests that specific interactions play important roles in hESCs.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4227771PMC
http://dx.doi.org/10.1093/nar/gku923DOI Listing

Publication Analysis

Top Keywords

regulatory networks
20
cell types
16
transcription factor
12
factor regulatory
12
networks human
12
human cell
12
regulatory
9
networks cell
8
specific regulatory
8
regulatory interactions
8

Similar Publications

Noncoding RNA regulatory networks play crucial roles in human breast cancer. The aim of this study was to establish a network containing multi-type RNAs and RBPs in triple-negative breast cancer (TNBC). Differential expression analyses of lncRNAs, miRNAs, and genes were performed using the GEO2R tool.

View Article and Find Full Text PDF

The E76K mutation in protein tyrosine phosphatase (PTP) SHP2 is a recurrent driver of developmental disorders and cancers, yet the mechanism by which this single-site substitution promotes persistent activation remains elusive. Here, we combine path-based conformational sampling, unbiased molecular dynamics (MD) simulations, Markov state models (MSMs), and neural relational inference (NRI) to elucidate how E76K reshapes the activation landscape and regulatory architecture of SHP2. Using a minimum-action trajectory derived from experimentally determined closed and open structures, we generated representative transition intermediates to guide the unbiased MD simulations.

View Article and Find Full Text PDF

Enoxaparin sodium (ES), a low molecular weight heparin derivative, has recently been recognized for its diverse biological activities. In particular, the ability of heparin to modulate inflammation has been utilized to enhance the biocompatibility of bone implant materials. In this study, we utilized poly (methyl methacrylate) (PMMA), a drug loading bone implant material, as a matrix and combined this with enoxaparin sodium (ES) to create enoxaparin sodium PMMA cement (ES-PMMA) to investigate the regulatory effects of ES on inflammatory responses in bone tissue from an animal model.

View Article and Find Full Text PDF

Background: Heat illness is a dangerous condition marked by a widespread inflammatory response. Although Pogostemon cablin (Blanco) Benth and its derivatives are clinically used, their mechanisms remain unclear.

Methods: 11 heat illness patients and 14 healthy volunteers from Southwest Medical University Affiliated Hospital were enrolled.

View Article and Find Full Text PDF

The barriers for uptake of artificial intelligence in hepatology and how to overcome them.

J Hepatol

July 2025

Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Department of Medicine I, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, 01307 Dresden, Germany; Medical Oncology, National Center for Tumor Disease

Artificial intelligence (AI) methods in hepatology have proliferated since the mid-2010s, with numerous publications and some regulatory approvals. Yet, adoption of AI methods in real-world clinical practice and clinical research remains limited. Despite clear benefits of using AI to analyze complex data types in hepatology, such as histopathology, radiology images, multi-omics and more recently, natural language patient data, there are still substantial barriers and challenges to its integration into routine clinical workflows.

View Article and Find Full Text PDF