Moxifloxacin rescues SMA phenotypes in patient-derived cells and animal model

A novel microbe-drug association prediction model based on graph attention networks and bilayer random forest

BACKGROUND

In recent years, the extensive use of drugs and antibiotics has led to increasing microbial resistance. Therefore, it becomes crucial to explore deep connections between drugs and microbes. However, traditional biological experiments are very expensive and time-consuming. Therefore, it is meaningful to develop efficient computational models to forecast potential microbe-drug associations.

RESULTS

In this manuscript, we proposed a novel prediction model called GARFMDA by combining graph attention networks and bilayer random forest to infer probable microbe-drug correlations. In GARFMDA, through integrating different microbe-drug-disease correlation indices, we constructed two different microbe-drug networks first. And then, based on multiple measures of similarity, we constructed a unique feature matrix for drugs and microbes respectively. Next, we fed these newly-obtained microbe-drug networks together with feature matrices into the graph attention network to extract the low-dimensional feature representations for drugs and microbes separately. Thereafter, these low-dimensional feature representations, along with the feature matrices, would be further inputted into the first layer of the Bilayer random forest model to obtain the contribution values of all features. And then, after removing features with low contribution values, these contribution values would be fed into the second layer of the Bilayer random forest to detect potential links between microbes and drugs.

CONCLUSIONS

Experimental results and case studies show that GARFMDA can achieve better prediction performance than state-of-the-art approaches, which means that GARFMDA may be a useful tool in the field of microbe-drug association prediction in the future. Besides, the source code of GARFMDA is available at https://github.com/KuangHaiYue/GARFMDA.git.

Unlocking the Complexity of Neuromuscular Diseases: Insights from Human Pluripotent Stem Cell-Derived Neuromuscular Junctions

Over the past 20 years, the use of pluripotent stem cells to mimic the complexities of the human neuromuscular junction has received much attention. Deciphering the key mechanisms underlying the establishment and maturation of this complex synapse has been driven by the dual goals of addressing developmental questions and gaining insight into neuromuscular disorders. This review aims to summarise the evolution and sophistication of in vitro neuromuscular junction models developed from the first differentiation of human embryonic stem cells into motor neurons to recent neuromuscular organoids. We also discuss the potential offered by these models to decipher different neuromuscular diseases characterised by defects in the presynaptic compartment, the neuromuscular junction, and the postsynaptic compartment. Finally, we discuss the emerging field that considers the use of these techniques in drug screening assay and the challenges they will face in the future.

Elucidation of bioinformatic-guided high-prospect drug repositioning candidates for DMD via Swanson linking of target-focused latent knowledge from text-mined categorical metadata

Duchenne Muscular Dystrophy (DMD)'s complex multi-system pathophysiology, coupled with the cost-prohibitive logistics of multi-year drug screening and follow-up, has hampered the pursuit of new therapeutic approaches. Here we conducted a systematic historical and text mining-based pilot feasibility study to explore the potential of established or previously tested drugs as prospective DMD therapeutic agents. Our approach utilized a Swanson linking-inspired method to uncover meaningful yet largely hidden deep semantic connections between pharmacologically significant DMD targets and drugs developed for unrelated diseases. Specifically, we focused on molecular target-based MeSH terms and categories as high-yield bioinformatic proxies, effectively tagging relevant literature with categorical metadata. To identify promising leads, we comprehensively assembled published reports from 2011 and sampling from subsequent years. We then determined the earliest year when distinct MeSH terms or category labels of the relevant cellular target were referenced in conjunction with the drug, as well as when the pertinent target itself was first conclusively identified as holding therapeutic value for DMD. By comparing the earliest year when the drug was identifiable as a DMD treatment candidate with that of the first actual report confirming this, we computed an Index of Delayed Discovery (IDD), which serves as a metric of Swanson-linked latent knowledge. Using these findings, we identified data from previously unlinked articles subsetted via MeSH-derived Swanson linking or from target classes within the DrugBank repository. This enabled us to identify new but untested high-prospect small-molecule candidates that are of particular interest in repurposing for DMD and warrant further investigations.

Mechanistic and biophysical insight into the inhibitory and disaggregase role of antibiotic moxifloxacin on human lysozyme amyloid formation

Lysozyme amyloidosis is a systemic non-neuropathic disease caused by the accumulation of amyloids of mutant lysozyme. Presently, therapeutic interventions targeting lysozyme amyloidosis, remain elusive with only therapy available for lysozyme amyloidosis being supportive management. In this work, we examined the effects of moxifloxacin, a synthetic fluoroquinolone antibiotic on the amyloid formation of human lysozyme. The ability of moxifloxacin to interfere with lysozyme amyloid aggregation was examined using various biophysical methods like Rayleigh light scattering, Thioflavin T fluorescence assay, transmission electron microscopy and docking method. The reduction in scattering and ThT fluorescence along with extended lag phase in presence of moxifloxacin, suggest that the antibiotic inhibits and impedes the lysozyme fibrillation in concentration dependent manner. From ANS experiment, we deduce that moxifloxacin is able to decrease the hydrophobicity of the protein molecule thereby preventing aggregation. Our CD and DLS results show that moxifloxacin stabilizes the protein in its native monomeric structure, thus also showing retention of lytic activity upto 69% and inhibition of cytotoxicity at highest concentration of moxifloxacin. The molecular docking showed that moxifloxacin forms a stable complex of -7.6 kcal/mol binding energy and binds to the aggregation prone region of lysozyme thereby stabilising it and preventing aggregation. Moxifloxacin also showed disaggregase potential by disrupting fibrils and decreasing the β-sheet content of the fibrils. Our current study, thus highlight the anti-amyloid and disaggregase property of an antibiotic moxifloxacin and hence sheds light on the future of antibiotics against protein aggregation, a hallmark event in many neurodegenerative diseases.