Attenuating mitochondrial dysfunction and morphological disruption with PT320 delays dopamine degeneration in MitoPark mice.

BACKGROUND: Mitochondria are essential organelles involved in cellular energy production. Changes in mitochondrial function can lead to dysfunction and cell death in aging and age-related disorders. Recent research suggests that mitochondrial dysfunction is closely linked to neurodegenerative diseases. Glucagon-like peptide-1 receptor (GLP-1R) agonist has gained interest as a potential treatment for Parkinson's disease (PD). However, the exact mechanisms responsible for the therapeutic effects of GLP-1R-related agonists are not yet fully understood. METHODS: In this study, we explores the effects of early treatment with PT320, a sustained release formulation of the GLP-1R agonist Exenatide, on mitochondrial functions and morphology in a progressive PD mouse model, the MitoPark (MP) mouse. RESULTS: Our findings demonstrate that administration of a clinically translatable dose of PT320 ameliorates the reduction in tyrosine hydroxylase expression, lowers reactive oxygen species (ROS) levels, and inhibits mitochondrial cytochrome c release during nigrostriatal dopaminergic denervation in MP mice. PT320 treatment significantly preserved mitochondrial function and morphology but did not influence the reduction in mitochondria numbers during PD progression in MP mice. Genetic analysis indicated that the cytoprotective effect of PT320 is attributed to a reduction in the expression of mitochondrial fission protein 1 (Fis1) and an increase in the expression of optic atrophy type 1 (Opa1), which is known to play a role in maintaining mitochondrial homeostasis and decreasing cytochrome c release through remodeling of the cristae. CONCLUSION: Our findings suggest that the early administration of PT320 shows potential as a neuroprotective treatment for PD, as it can preserve mitochondrial function. Through enhancing mitochondrial health by regulating Opa1 and Fis1, PT320 presents a new neuroprotective therapy in PD.

Proteomic Network of Antibiotic-Induced Outer Membrane Vesicles Released by Extensively Drug-Resistant Elizabethkingia anophelis.

Elizabethkingia anophelis, a nonfermenting Gram-negative bacterium, causes life-threatening health care-associated infections. E. anophelis harbors multidrug resistance (MDR) genes and is intrinsically resistant to various classes of antibiotics. Outer membrane vesicles (OMVs) are secreted by Gram-negative bacteria and contain materials involved in bacterial survival and pathogenesis. OMVs specialize and tailor their functions by carrying different components to challenging environments and allowing communication with other microorganisms or hosts. In this study, we sought to understand the characteristics of E. anophelis OMVs under different antibiotic stress conditions. An extensively drug-resistant clinical isolate, E. anophelis C08, was exposed to multiple antibiotics in vitro, and its OMVs were characterized using nanoparticle tracking analysis, transmission electron microscopy, and proteomic analysis. Protein functionality analysis showed that the OMVs were predominantly involved in metabolism, survival, defense, and antibiotic resistance processes, such as the Rag/Sus family, the chaperonin GroEL, prenyltransferase, and an HmuY family protein. Additionally, a protein-protein interaction network demonstrated that OMVs from imipenem-treated E. anophelis showed significant enrichments in the outer membrane, adenyl nucleotide binding, serine-type peptidase activity, the glycosyl compound metabolic process, and cation binding proteins. Collectively, the OMV proteome expression profile indicates that the role of OMVs is immunologically relevant and related to bacterial survival in antibiotic stress environments rather than representing a resistance point. IMPORTANCE Elizabethkingia anophelis is a bacterium often associated with nosocomial infection. This study demonstrated that imipenem-induced E. anophelis outer membrane vesicles (OMVs) are immunologically relevant and crucial for bacterial survival under antibiotic stress conditions rather than being a source of antibiotic resistance. Furthermore, this is the first study to discuss the protein-protein interaction network of the OMVs released by E. anophelis, especially under antibiotic stress. Our findings provide important insights into clinical antibiotic stewardship.

DeF-GPU: Efficient and effective deletions finding in hepatitis B viral genomic DNA using a GPU architecture.

Hepatitis B viral (HBV) infection is strongly associated with an increased risk of liver diseases like cirrhosis or hepatocellular carcinoma (HCC). Many lines of evidence suggest that deletions occurring in HBV genomic DNA are highly associated with the activity of HBV via the interplay between aberrant viral proteins release and human immune system. Deletions finding on the HBV whole genome sequences is thus a very important issue though there exist underlying the challenges in mining such big and complex biological data. Although some next generation sequencing (NGS) tools are recently designed for identifying structural variations such as insertions or deletions, their validity is generally committed to human sequences study. This design may not be suitable for viruses due to different species. We propose a graphics processing unit (GPU)-based data mining method called DeF-GPU to efficiently and precisely identify HBV deletions from large NGS data, which generally contain millions of reads. To fit the single instruction multiple data instructions, sequencing reads are referred to as multiple data and the deletion finding procedure is referred to as a single instruction. We use Compute Unified Device Architecture (CUDA) to parallelize the procedures, and further validate DeF-GPU on 5 synthetic and 1 real datasets. Our results suggest that DeF-GPU outperforms the existing commonly-used method Pindel and is able to exactly identify the deletions of our ground truth in few seconds. The source code and other related materials are available at https://sourceforge.net/projects/defgpu/.

Aligning to the sample-specific reference sequence to optimize the accuracy of next-generation sequencing analysis for hepatitis B virus.

BACKGROUND: Hepatitis B virus (HBV) quasispecies are crucial in the pathogenesis of chronic liver disease. Next-generation sequencing (NGS) is powerful for identifying viral quasispecies. To improve mapping quality and single nucleotide variant (SNV) calling accuracy in the NGS analysis of HBV, we compared different mapping references, including the sample-specific reference sequence, same genotype sequences and different genotype sequences, according to the sample. METHODS: Real Illumina HBV datasets from 86 patients, and simulated datasets from 158 HBV strains in the GenBank database, were used to assess mapping quality. SNV calling accuracy was evaluated using different mapping references to align Real Illumina datasets from a single HBV clone. RESULTS: Using the sample-specific reference sequence as a mapping reference produced the largest number of mappable reads and coverages. With a different genotype mapping reference, the consensus sequence derived from the Real Illumina datasets of the single HBV clone showed 21 false SNV callings in polymerase and surface genes, the regions most divergent between the mapping reference and this HBV clone. A ~6 % coverage of most of these false SNVs was yielded even with a same genotype mapping reference, but none with the sample-specific reference sequence. CONCLUSIONS: Using sample-specific reference sequences as a mapping reference in NGS analysis optimized mapping quality and the SNV calling accuracy for HBV quasispecies.