Background: Intracellular signaling networks transmit signals from the cell membrane to the nucleus, via biochemical interactions. The goal is to regulate some target molecules, to properly control the cell function. Regulation of the target molecules occurs through the communication of several intermediate molecules that convey specific signals originated from the cell membrane to the specific target outputs.
Results: In this study we propose to model intracellular signaling network as communication channels. We define the fundamental concepts of transmission error and signaling capacity for intracellular signaling networks, and devise proper methods for computing these parameters. The developed systematic methodology quantitatively shows how the signals that ligands provide upon binding can be lost in a pathological signaling network, due to the presence of some dysfunctional molecules. We show the lost signals result in message transmission error, i.e., incorrect regulation of target proteins at the network output. Furthermore, we show how dysfunctional molecules affect the signaling capacity of signaling networks and how the contributions of signaling molecules to the signaling capacity and signaling errors can be computed. The proposed approach can quantify the role of dysfunctional signaling molecules in the development of the pathology. We present experimental data on caspese3 and T cell signaling networks to demonstrate the biological relevance of the developed method and its predictions.
Conclusions: This study demonstrates how signal transmission and distortion in pathological signaling networks can be modeled and studied using the proposed methodology. The new methodology determines how much the functionality of molecules in a network can affect the signal transmission and regulation of the end molecules such as transcription factors. This can lead to the identification of novel critical molecules in signal transduction networks. Dysfunction of these critical molecules is likely to be associated with some complex human disorders. Such critical molecules have the potential to serve as proper targets for drug discovery.
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http://dx.doi.org/10.1186/s12918-014-0089-z | DOI Listing |
J Inflamm Res
January 2025
Department of Pharmacology, School of Pharmaceutical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, People's Republic of China.
Background: Chronic kidney disease (CKD) is a progressive condition that arises from diverse etiological factors, resulting in structural alterations and functional impairment of the kidneys. We aimed to establish the Anoikis-related gene signature in CKD by bioinformatics analysis.
Methods: We retrieved 3 datasets from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs), followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) of them, which were intersected with Anoikis-related genes (ARGs) to derive Anoikis-related differentially expressed genes (ARDEGs).
Drug Des Devel Ther
January 2025
Shanxi Key Laboratory of Innovative Drug for the Treatment of Serious Diseases Basing on the Chronic Inflammation, College of Traditional Chinese Medicine and Food Engineering, Shanxi University of Chinese Medicine, Jinzhong, People's Republic of China.
Background: Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease in which macrophages produce cytokines that enhance inflammation and contribute to the destruction of cartilage and bone. Additive Sishen decoction (ASSD) is a widely used traditional Chinese medicine for the treatment of RA; however, its active ingredients and the mechanism of its therapeutic effects remain unclear.
Methods: To predict the ingredients and key targets of ASSD, we constructed "drug-ingredient-target-disease" and protein-protein interaction networks.
Cytotechnology
April 2025
Department of Critical Care Medicine, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), No. 1017, North Dongmen Road, Luohu District, Shenzhen, 518020 Guangdong China.
This study aimed to investigate the role of circular RNAs (circRNAs) in sepsis-induced acute gastrointestinal injury (AGI), focusing on their potential as biomarkers and their involvement in disease progression. Peripheral blood samples from 14 patients with sepsis-induced AGI and healthy volunteers were collected. RNA sequencing was performed to profile circRNA and miRNA expression.
View Article and Find Full Text PDFRationale: Individuals homozygous for the Alpha-1 Antitrypsin (AAT) Z allele (Pi*ZZ) exhibit heterogeneity in COPD risk. COPD occurrence in non-smokers with AAT deficiency (AATD) suggests inflammatory processes may contribute to COPD risk independently of smoking. We hypothesized that inflammatory protein biomarkers in non-AATD COPD are associated with moderate-to-severe COPD in AATD individuals, after accounting for clinical factors.
View Article and Find Full Text PDFMed Image Comput Comput Assist Interv
October 2024
Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, Chapel Hill, USA.
Delineating the normative developmental profile of functional connectome is important for both standardized assessment of individual growth and early detection of diseases. However, functional connectome has been mostly studied using functional connectivity (FC), where undirected connectivity strengths are estimated from statistical correlation of resting-state functional MRI (rs-fMRI) signals. To address this limitation, we applied regression dynamic causal modeling (rDCM) to delineate the developmental trajectories of effective connectivity (EC), the directed causal influence among neuronal populations, in whole-brain networks from infancy to adolescence (0-22 years old) based on high-quality rs-fMRI data from Baby Connectome Project (BCP) and Human Connectome Project Development (HCP-D).
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