Acidic Nanoparticles Restore Lysosomal Acidification and Rescue Metabolic Dysfunction in Pancreatic ß-Cells under Lipotoxic Conditions.

Type 2 diabetes (T2D), a prevalent metabolic disorder lacking effective treatments, is associated with lysosomal acidification dysfunction, as well as autophagic and mitochondrial impairments. Here, we report a series of biodegradable poly(butylene tetrafluorosuccinate-co-succinate) polyesters, comprising a 1,4-butanediol linker and varying ratios of tetrafluorosuccinic acid (TFSA) and succinic acid as components, to engineer lysosome-acidifying nanoparticles (NPs). The synthesized NPs are spherical with diameters of ≈100 nm and have low polydispersity and good stability. Notably, TFSA NPs, which are composed entirely of TFSA, exhibit the strongest degradation capability and superior acidifying properties. We further reveal significant downregulation of lysosomal vacuolar (H+)-ATPase subunits, which are responsible for maintaining lysosomal acidification, in human T2D pancreatic islets, INS-1 ß-cells under chronic lipotoxic conditions, and pancreatic tissues of high-fat-diet (HFD) mice. Treatment with TFSA NPs restores lysosomal acidification, autophagic function, and mitochondrial activity, thereby improving the pancreatic function in INS-1 cells and HFD mice with lipid overload. Importantly, the administration of TFSA NPs to HFD mice reduces insulin resistance and improves glucose clearance. These findings highlight the therapeutic potential of lysosome-acidifying TFSA NPs for T2D.

Data Mining of Microarray Datasets in Translational Neuroscience.

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective.

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.