98%
921
2 minutes
20
Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3996537 | PMC |
http://dx.doi.org/10.1038/ncomms4650 | DOI Listing |
J Org Chem
September 2025
State Key Laboratory of Fine Chemicals, School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin 124221, P. R. China.
The Buchwald-Hartwig (B-H) reaction graph, a novel graph for deep learning models, is designed to simulate the interactions among multiple chemical components in the B-H reaction by representing each reactant as an individual node within a custom-designed reaction graph, thereby capturing both single-molecule and intermolecular relationship features. Trained on a high-throughput B-H reaction data set, B-H Reaction Graph Neural Network (BH-RGNN) achieves near-state-of-the-art performance with an score of 0.971 while maintaining low computational costs.
View Article and Find Full Text PDFPhys Rev Lett
August 2025
Gran Sasso Science Institute, The University of Edinburgh, School of Mathematics, Edinburgh EH93FD, United Kingdom and School of Mathematics, 67100 L'Aquila, Italy.
Multilayer networks provide a powerful framework for modeling complex systems that capture different types of interactions between the same set of entities across multiple layers. Core-periphery detection involves partitioning the nodes of a network into core nodes, which are highly connected across the network, and peripheral nodes, which are densely connected to the core but sparsely connected among themselves. In this paper, we propose a new model of core-periphery structure in multilayer networks and a nonlinear spectral method that simultaneously detects the corresponding core and periphery structures of both nodes and layers in weighted and directed multilayer networks.
View Article and Find Full Text PDFACS Nano
September 2025
School of Physics and Key Lab of Quantum Materials and Devices of the Ministry of Education, Southeast University, Nanjing 211189, P. R. China.
While hexagonal boron nitride (hBN) hosts promising room-temperature quantum emitters for hybrid quantum photonic circuits, scalable deterministic integration and insufficient brightness alongside low photon collection and coupling efficiencies remain unresolved challenges. We present a femtosecond laser nanoengineering platform that enables the site-specific generation of hBN single-photon source (SPS) arrays. First-principles density functional theory (DFT) calculations and polarization-resolved spectroscopy confirm the atomic origin of emission as interfacial defects at hBN/SiO heterojunctions.
View Article and Find Full Text PDFPLoS Comput Biol
September 2025
Faculty of Science, Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands.
Predictive coding (PC) proposes that our brains work as an inference machine, generating an internal model of the world and minimizing predictions errors (i.e., differences between external sensory evidence and internal prediction signals).
View Article and Find Full Text PDFHealth Expect
October 2025
Primary Care Research Centre, School of Primary Care, Population Sciences and Medical Education, University of Southampton, Southampton, Hampshire, UK.
Background: Social relationships are important for self-management and outcomes of multiple long-term conditions (MLTC). Previous research indicates MLTC negatively impacts social relationships and people living with MLTC do not feel adequately supported to manage their health. However, there is limited understanding of the processes and contextual factors that influence social relationships in the context of MLTC.
View Article and Find Full Text PDF