Nanopore-based glycan sequencing: state of the art and future prospects.

Sequencing of biomacromolecules is a crucial cornerstone in life sciences. Glycans, one of the fundamental biomolecules, derive their physiological and pathological functions from their structures. Glycan sequencing faces challenges due to its structural complexity and current detection technology limitations. As a highly sensitive sensor, nanopores can directly convert nucleic acid sequence information into electrical signals, spearheading the revolution of third-generation nucleic acid sequencing technologies. However, their potential for deciphering complex glycans remains untapped. Initial attempts demonstrated the significant sensitivity of nanopores in glycan sensing, which provided the theoretical basis and insights for the realization of nanopore-based glycan sequencing. Here, we present three potential technical routes to employ nanopore technology in glycan sequencing for the first time. The three novel technical routes include: strand sequencing, capturing glycan chains as they translocate through nanopores; sequential hydrolysis sequencing, capturing released monosaccharides one by one; splicing sequencing, mapping signals from hydrolyzed glycan fragments to an oligosaccharide database/library. Designing suitable nanopores, enzymes, and motors, and extracting characteristic signals pose major challenges, potentially aided by artificial intelligence. It would be highly desirable to design an all-in-one high-throughput glycan sequencer instrument by integrating a sample processing unit, nanopore array, and signal acquisition system into a microfluidic device. The nanopore sequencer invention calls for intensive multidisciplinary cooperation including electrochemistry, glycochemistry, engineering, materials, enzymology, etc. Advancing glycan sequencing will promote the development of basic research and facilitate the discovery of glycan-based drugs and disease markers, fostering progress in glycoscience and even life sciences.

Direct Identification of Complex Glycans via a Highly Sensitive Engineered Nanopore.

The crucial roles that glycans play in biological systems are determined by their structures. However, the analysis of glycan structures still has numerous bottlenecks due to their inherent complexities. The nanopore technology has emerged as a powerful sensor for DNA sequencing and peptide detection. This has a significant impact on the development of a related research area. Currently, nanopores are beginning to be applied for the detection of simple glycans, but the analysis of complex glycans by this technology is still challenging. Here, we designed an engineered α-hemolysin nanopore M113R/T115A to achieve the sensing of complex glycans at micromolar concentrations and under label-free conditions. By extracting characteristic features to depict a three-dimensional (3D) scatter plot, glycans with different numbers of functional groups, various chain lengths ranging from disaccharide to decasaccharide, and distinct glycosidic linkages could be distinguished. Molecular dynamics (MD) simulations show different behaviors of glycans with ß1,3- or ß1,4-glycosidic bonds in nanopores. More importantly, the designed nanopore system permitted the discrimination of each glycan isomer with different lengths in a mixture with a separation ratio of over 0.9. This work represents a proof-of-concept demonstration that complex glycans can be analyzed using nanopore sequencing technology.

Matching biomedical ontologies with GCN-based feature propagation.

With an increasing number of biomedical ontologies being evolved independently, matching these ontologies to solve the interoperability problem has become a critical issue in biomedical applications. Traditional biomedical ontology matching methods are mostly based on rules or similarities for concepts and properties. These approaches require manually designed rules that not only fail to address the heterogeneity of domain ontology terminology and the ambiguity of multiple meanings of words, but also make it difficult to capture structural information in ontologies that contain a large amount of semantics during matching. Recently, various knowledge graph (KG) embedding techniques utilizing deep learning methods to deal with the heterogeneity in knowledge graphs (KGs), have quickly gained massive attention. However, KG embedding focuses mainly on entity alignment (EA). EA tasks and ontology matching (OM) tasks differ dramatically in terms of matching elements, semantic information and application scenarios, etc., hence these methods cannot be applied directly to biomedical ontologies that contain abstract concepts but almost no entities. To tackle these issues, this paper proposes a novel approach called BioOntGCN that directly learns embeddings of ontology-pairs for biomedical ontology matching. Specifically, we first generate a pair-wise connectivity graph (PCG) of two ontologies, whose nodes are concept-pairs and edges correspond to property-pairs. Subsequently, we learn node embeddings of the PCG to predicate the matching results through following phases: 1) A convolutional neural network (CNN) to extract the similarity feature vectors of nodes; 2) A graph convolutional network (GCN) to propagate the similarity features and obtain the final embeddings of concept-pairs. Consequently, the biomedical ontology matching problem is transformed into a binary classification problem. We conduct systematic experiments on real-world biomedical ontologies in Ontology Alignment Evaluation Initiative (OAEI), and the results show that our approach significantly outperforms other entity alignment methods and achieves state-of-the-art performance. This indicates that BioOntGCN is more applicable to ontology matching than the EA method. At the same time, BioOntGCN substantially achieves superior performance compared with previous ontology matching (OM) systems, which suggests that BioOntGCN based on the representation learning is more effective than the traditional approaches.