Bioreaction-Compatible Bivariate Lanthanide MOF Sensor Enables Stimulus-Multiresponsive Platform for ctDNA On-Site Detection.

Detection of circulating tumor DNA (ctDNA) in liquid biopsy is of great importance for tumor diagnosis but difficult due to its low amount in bodily fluids. Herein, a novel ctDNA detection platform is established by quantifying DNA amplification by-product pyrophosphate (PPi) using a newly designed bivariable lanthanide metal-organic framework (Ln-MOF), namely, Ce/Eu-DPA MOF (CE-24, DPA = pyridine-2,6-dicarboxylic acid). CE-24 MOF exhibits ultrafast dual-response (fluorescence enhancement and enzyme-activity inhibition) to PPi stimuli by virtue of host-guest interaction. The platform is applied to detecting colon carcinoma-related ctDNA (KARS G12D mutation) combined with the isothermal nucleic acid exponential amplification reaction (EXPAR). ctDNA triggers the generation of a large amount of PPi, and the ctDNA quantification is achieved through the ratio fluorescence/colorimetric dual-mode assay of PPi. The combination of the EXPAR and the dual-mode PPi sensing allows the ctDNA assay method to be low-cost, convenient, bioreaction-compatible (freedom from the interference of bioreaction systems), sensitive (limit of detection down to 101 fM), and suitable for on-site detection. To the best of our knowledge, this work is the first application of Ln-MOF for ctDNA detection, and it provides a novel universal strategy for the rapid detection of nucleic acid biomarkers in point-of-care scenarios.

Bioinspired Heterocoordination in Adaptable Cobalt Metal-Organic Framework for DNA Epigenetic Modification Detection.

Metal-organic frameworks (MOFs) show unique advantages in simulating the dynamics and fidelity of natural coordination. Inspired by zinc finger protein, a second linker was introduced to affect the homogeneous MOF system and thus facilitate the emergence of diverse functionalities. Under the systematic identification of 12 MOF species (i.e., metal ions, linkers) and 6 second linkers (trigger), a dissipative system consisting of Co-BDC-NO2 and o-phenylenediamine (oPD) was screened out, which can rapidly and in situ generate a high photothermal complex (η = 36.9%). Meanwhile, both the carboxylation of epigenetic modifications and metal ion (Fe3+, Ni2+, Cu2+, Zn2+, Co2+ and Mn2+) screening were utilized to improve the local coordination environment so that the adaptable Co-MOF growth on the DNA strand was realized. Thus, epigenetic modification information on DNA was converted to an amplified metal ion signal, and then oPD was further introduced to generate bimodal dissipative signals by which a simple, high-sensitivity detection strategy of 5-hydroxymethylcytosine (LOD = 0.02%) and 5-formylcytosine (LOD = 0.025‰) was developed. The strategy provides one low-cost method (< 0.01 $/sample) for quantifying global epigenetic modifications, which greatly promotes epigenetic modification-based early disease diagnosis. This work also proposes a general heterocoordination design concept for molecular recognition and signal transduction, opening a new MOF-based sensing paradigm.

PT-KGNN: A framework for pre-training biomedical knowledge graphs with graph neural networks.

Biomedical knowledge graphs (KGs) serve as comprehensive data repositories that contain rich information about nodes and edges, providing modeling capabilities for complex relationships among biological entities. Many approaches either learn node features through traditional machine learning methods, or leverage graph neural networks (GNNs) to directly learn features of target nodes in the biomedical KGs and utilize them for downstream tasks. Motivated by the pre-training technique in natural language processing (NLP), we propose a framework named PT-KGNN (Pre-Training the biomedical KG with GNNs) to learn embeddings of nodes in a broader context by applying GNNs on the biomedical KG. We design several experiments to evaluate the effectivity of our proposed framework and the impact of the scale of KGs. The results of tasks consistently improve as the scale of the biomedical KG used for pre-training increases. Pre-training on large-scale biomedical KGs significantly enhances the drug-drug interaction (DDI) and drug-disease association (DDA) prediction performance on the independent dataset. The embeddings derived from a larger biomedical KG have demonstrated superior performance compared to those obtained from a smaller KG. By applying pre-training techniques on biomedical KGs, rich semantic and structural information can be learned, leading to enhanced performance on downstream tasks. it is evident that pre-training techniques hold tremendous potential and wide-ranging applications in bioinformatics.

Solvent-tuned perovskite heterostructures enable visual linoleic acid assay and edible oil species discrimination via wavelength shift.

Linoleic acid (LA) detection and edible oils discrimination are essential for food safety. Recently, CsPbBr3@SiO2 heterostructures have been widely applied in edible oil assays, while deep insights into solvent effects on their structure and performance are often overlooked. Based on the suitable polarity and viscosity of cyclohexane, we prepared CsPbBr3@SiO2 Janus nanoparticles (JNPs) with high stability in edible oil and fast halogen-exchange (FHE) efficiency with oleylammonium iodide (OLAI). LA is selectively oxidized by lipoxidase to yield hydroxylated derivative (oxLA) capable of reacting with OLAI, thereby bridging LA content to naked-eye fluorescence color changes through the anti-FHE reaction. The established method for LA in edible oils exhibited consistent results with GC-MS analysis (p > 0.05). Since the LA content difference between edible oils, we further utilized chemometrics to accurately distinguish (100%) the species of edible oils. Overall, such elaborated CsPbBr3@SiO2 JNPs enable a refreshing strategy for edible oil discrimination.

Trinity Strategy: Enabling Perovskite as Hydrophilic and Efficient Fluorescent Nanozyme for Constructing Biomarker Reporting Platform.

Water instability and sensing homogeneity are the Achilles' heel of CsPbX3 NPs in biological fluids application. This work reports the preparation of Mn2+:CsPbCl3@SiO2 yolk-shell nanoparticles (YSNPs) in aqueous solutions created through the integration of ligand, surface, and crystal engineering strategies. The SN2 reaction between 4-chlorobutyric acid (CBA) and oleylamine (OAm) yields a zwitterionic ligand that facilitates the dispersion of YSNPs in water, while the robust SiO2 shell enhances their overall stability. Besides, Mn2+ doping in YSNPs not only introduces a second emission center but also enables potential postsynthetic designability, leading to the switching from YSNPs to MnO2@YSNPs with excellent oxidase (OXD)-like activity. Theoretical calculations reveal that electron transfer from CsPbCl3 to in situ MnO2 and the adsorption-desorption process of 3,3',5,5'-tetramethylbenzidine (TMB) synergistically amplify the OXD-like activity. In the presence of ascorbic acid (AA), Mn4+ in MnO2@YSNPs (fluorescent nanozyme) is reduced to Mn2+ and dissociated, thereby inhibiting the OXD-like activity and triggering fluorescence "turn-on/off", i.e., dual-mode recognition. Finally, a biomarker reporting platform based on MnO2@YSNPs fluorescent nanozyme is constructed with AA as the reporter molecule, and the accurate detection of human serum alkaline phosphatase (ALP) is realized, demonstrating the vast potential of perovskites in biosensing.

Hierarchical Self-Assembly Molecular Building Blocks as Intelligent Nanoplatforms for Ovarian Cancer Theranostics.

Hierarchical self-assembly from simple building blocks to complex polymers is a feasible approach to constructing multi-functional smart materials. However, the polymerization process of polymers often involves challenges such as the design of building blocks and the drive of external energy. Here, a hierarchical self-assembly with self-driven and energy conversion capabilities based on p-aminophenol and diethylenetriamine building blocks is reported. Through ß-galactosidase (ß-Gal) specific activation to the self-assembly, the intelligent assemblies (oligomer and superpolymer) with excellent photothermal and fluorescent properties are dynamically formed in situ, and thus the sensitive multi-mode detection of ß-Gal activity is realized. Based on the overexpression of ß-Gal in ovarian cancer cells, the self-assembly superpolymer is specifically generated in SKOV-3 cells to achieve fluorescence imaging. The photothermal therapeutic ability of the self-assembly oligomer (synthesized in vitro) is evaluated by a subcutaneous ovarian cancer model, showing satisfactory anti-tumor effects. This work expands the construction of intelligent assemblies through the self-driven cascade assembly of small molecules and provides new methods for the diagnosis and treatment of ovarian cancer.

DeepRisk: A deep learning approach for genome-wide assessment of common disease risk.

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant interest. Although widely applied, traditional polygenic risk scoring methods fall short, as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms (SNPs). This presents a limitation, as genetic diseases often arise from complex interactions between multiple SNPs. To address this challenge, we developed DeepRisk, a biological knowledge-driven deep learning method for modeling these complex, nonlinear associations among SNPs, to provide a more effective method for scoring the risk of common diseases with genome-wide genotype data. Evaluations demonstrated that DeepRisk outperforms existing PRS-based methods in identifying individuals at high risk for four common diseases: Alzheimer's disease, inflammatory bowel disease, type 2 diabetes, and breast cancer.