G-quadruplexes formed by Varicella-Zoster virus reiteration sequences suppress expression of glycoprotein C and regulate viral cell-to-cell spread.

G-quadruplex (G4) formed by repetitive guanosine-rich sequences plays important roles in diverse cellular processes; however, its roles in viral infection are not fully understood. In this study, we investigated the genome-wide distribution of G4-forming sequences (G4 motifs) in Varicella-Zoster virus (VZV) and found that G4 motifs are enriched in the internal repeat short and the terminal repeat short regions flanking the unique short region and also in some reiteration (R) sequence regions. A high density of G4 motifs in the R2 region was found on the template strand of ORF14, which encodes glycoprotein C (gC), a virulent factor for viral growth in skin. Analyses such as circular dichroism spectroscopy, thermal difference spectra, and native polyacrylamide gel electrophoresis with oligodeoxynucleotides demonstrated that several G4 motifs in ORF14 form stable G4 structures. In transfection assays, gC expression from the G4-disrupted ORF14 gene was increased at the transcriptional level and became more resistant to suppression by G4-ligand treatment. The recombinant virus containing the G4-disrupted ORF14 gene expressed a higher level of gC mRNA, while it showed a slightly reduced growth. This G4-disrupted ORF14 virus produced smaller plaques than the wild-type virus. Our results demonstrate that G4 formation via reiteration sequences suppresses gC expression during VZV infection and regulates viral cell-to-cell spread.

Multiomics Evaluation of Human iPSCs and iPSC-Derived Neurons.

Human induced pluripotent stem cells (iPSCs) can be differentiated into neurons, providing living human neurons to model brain diseases. However, it is unclear how different types of molecules work together to regulate stem cell and neuron biology in healthy and disease states. In this study, we conducted integrated proteomics, lipidomics, and metabolomics analyses with confident identification, accurate quantification, and reproducible measurements to compare the molecular profiles of human iPSCs and iPSC-derived neurons. Proteins, lipids, and metabolites related to mitosis, DNA replication, pluripotency, glycosphingolipids, and energy metabolism were highly enriched in iPSCs, whereas synaptic proteins, neurotransmitters, polyunsaturated fatty acids, cardiolipins, and axon guidance pathways were highly enriched in neurons. Mutations in the GRN gene lead to the deficiency of the progranulin (PGRN) protein, which has been associated with various neurodegenerative diseases. Using this multiomics platform, we evaluated the impact of PGRN deficiency on iPSCs and neurons at the whole-cell level. Proteomics, lipidomics, and metabolomics analyses implicated PGRN's roles in neuroinflammation, purine metabolism, and neurite outgrowth, revealing commonly altered pathways related to neuron projection, synaptic dysfunction, and brain metabolism. Multiomics data sets also pointed toward the same hypothesis that neurons seem to be more susceptible to PGRN loss compared to iPSCs, consistent with the neurological symptoms and cognitive impairment from patients carrying inherited GRN mutations.

Comparative analysis of machine learning-based approaches for identifying therapeutic peptides targeting SARS-CoV-2.

Coronavirus disease 2019 (COVID-19) has impacted public health as well as societal and economic well-being. In the last two decades, various prediction algorithms and tools have been developed for predicting antiviral peptides (AVPs). The current COVID-19 pandemic has underscored the need to develop more efficient and accurate machine learning (ML)-based prediction algorithms for the rapid identification of therapeutic peptides against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Several peptide-based ML approaches, including anti-coronavirus peptides (ACVPs), IL-6 inducing epitopes and other epitopes targeting SARS-CoV-2, have been implemented in COVID-19 therapeutics. Owing to the growing interest in the COVID-19 field, it is crucial to systematically compare the existing ML algorithms based on their performances. Accordingly, we comprehensively evaluated the state-of-the-art IL-6 and AVP predictors against coronaviruses in terms of core algorithms, feature encoding schemes, performance evaluation metrics and software usability. A comprehensive performance assessment was then conducted to evaluate the robustness and scalability of the existing predictors using well-constructed independent validation datasets. Additionally, we discussed the advantages and disadvantages of the existing methods, providing useful insights into the development of novel computational tools for characterizing and identifying epitopes or ACVPs. The insights gained from this review are anticipated to provide critical guidance to the scientific community in the rapid design and development of accurate and efficient next-generation in silico tools against SARS-CoV-2.

STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction.

Protein post-translational modification (PTM) is an important regulatory mechanism that plays a key role in both normal and disease states. Acetylation on lysine residues is one of the most potent PTMs owing to its critical role in cellular metabolism and regulatory processes. Identifying protein lysine acetylation (Kace) sites is a challenging task in bioinformatics. To date, several machine learning-based methods for the in silico identification of Kace sites have been developed. Of those, a few are prokaryotic species-specific. Despite their attractive advantages and performances, these methods have certain limitations. Therefore, this study proposes a novel predictor STALLION (STacking-based Predictor for ProkAryotic Lysine AcetyLatION), containing six prokaryotic species-specific models to identify Kace sites accurately. To extract crucial patterns around Kace sites, we employed 11 different encodings representing three different characteristics. Subsequently, a systematic and rigorous feature selection approach was employed to identify the optimal feature set independently for five tree-based ensemble algorithms and built their respective baseline model for each species. Finally, the predicted values from baseline models were utilized and trained with an appropriate classifier using the stacking strategy to develop STALLION. Comparative benchmarking experiments showed that STALLION significantly outperformed existing predictor on independent tests. To expedite direct accessibility to the STALLION models, a user-friendly online predictor was implemented, which is available at: http://thegleelab.org/STALLION.

Preliminary investigation into long-term stress by isolated captivity-related changes of reproduction hormones in Cynomolgus monkey.

BACKGROUND: Stress profoundly affects physical and emotional well-being, extending its physiological influence to the female menstrual cycle, impeding the hypothalamus-pituitary-gonadal (HPG) axis, and affecting fertility by suppressing sex-stimulating hormones. METHODS: In this study, we meticulously analyzed menstrual cycles and corresponding hormonal fluctuations in three female Cynomolgus monkeys. RESULTS: The preliminary findings indicated lower-than-normal levels of cortisol, follicle-stimulating hormone (FSH), and estradiol. Anovulatory bleeding occurred in one monkey, which could be linked to stress. In contrast to cortisol, alkaline phosphatase (ALP), which is correlated to cortisol levels, was consistently elevated in menstruating monkeys, suggesting its potential as a stress indicator. The non-menstruating group exhibited stress-related weight loss, emphasizing the observed ALP trends. CONCLUSIONS: Non-menstruating monkeys may experience more stress than menstruating monkeys. The implications of this study extend beyond the confines of primate studies and offer a valuable method for enhancing the welfare of female Cynomolgus monkeys.

Reduced lysosomal activity and increased amyloid beta accumulation in silica-coated magnetic nanoparticles-treated microglia.

Nanoparticles have been used in neurological research in recent years because of their blood-brain barrier penetration activity. However, their potential neuronanotoxicity remains a concern. In particular, microglia, which are resident phagocytic cells, are mainly exposed to nanoparticles in the brain. We investigated the changes in lysosomal function in silica-coated magnetic nanoparticles containing rhodamine B isothiocyanate dye [MNPs@SiO2(RITC)]-treated BV2 murine microglial cells. In addition, we analyzed amyloid beta (Aß) accumulation and molecular changes through the integration of transcriptomics, proteomics, and metabolomics (triple-omics) analyses. Aß accumulation significantly increased in the 0.1 µg/µl MNPs@SiO2(RITC)-treated BV2 cells compared to the untreated control and 0.01 µg/µl MNPs@SiO2(RITC)-treated BV2 cells. Moreover, the MNPs@SiO2(RITC)-treated BV2 cells showed lysosomal swelling, a dose-dependent reduction in proteolytic activity, and an increase in lysosomal swelling- and autophagy-related protein levels. Moreover, proteasome activity decreased in the MNPs@SiO2(RITC)-treated BV2 cells, followed by a concomitant reduction in intracellular adenosine triphosphate (ATP). By employing triple-omics and a machine learning algorithm, we generated an integrated single molecular network including reactive oxygen species (ROS), autophagy, lysosomal storage disease, and amyloidosis. In silico analysis of the single triple omics network predicted an increase in ROS, suppression of autophagy, and aggravation of lysosomal storage disease and amyloidosis in the MNPs@SiO2(RITC)-treated BV2 cells. Aß accumulation and lysosomal swelling in the cells were alleviated by co-treatment with glutathione (GSH) and citrate. These findings suggest that MNPs@SiO2(RITC)-induced reduction in lysosomal activity and proteasomes can be recovered by GSH and citrate treatment. These results also highlight the relationship between nanotoxicity and Aß accumulation.

Far cortical locking constructs for fixation of distal femur fractures in an Asian population: A prospective observational study.

BACKGROUND: The stiffness of locked plates suppresses healing process, prompting the introduction of far cortical locking to address this issue. This study aimed to demonstrate the clinical efficacy of far cortical locking constructs in treating distal femoral fractures in an Asian population. METHODS: This multicenter prospective observational study was conducted at four university hospitals between February 2018 and February 2021. Demographic data, the presence of metaphyseal comminution, and surgical fixation details were recorded. Clinical outcomes, including single-leg standing, EQ-5D, and EQ-VAS scores, and radiologic outcomes, including the RUST score of each cortex, were evaluated and compared according to the presence of metaphyseal comminution. RESULTS: There were 37 patients (14 men and 23 women) with a mean age of 67.3 ± 11.8 years. Twenty-two patients had metaphyseal comminution (59%), and 15 presented simple fractures in metaphyseal areas. Four patients (13%) could stand on one leg >10s at 6 weeks, and 24 patients (92%) at 1 year. EQ-5D increased from 0.022 ± 0.388 to 0.692 ± 0.347, and the mean EQ-VAS 51.1 ± 13.1 to 74.1 ± 24.1 between discharge (n = 37) and post-operative 1 year (n = 33), respectively. RUST score presented increment for time, from 6.2 ± 1.8 at 6 week to 11.6 ± 1.1 at 1 year. Radiological healing demonstrated rapid increase from week 6 (16/28, 43%) to month 3 (27/31, 87%), with no obvious increase was observed in 6 months (23/26, 89%) or 12 months (25/28, 89%). Simple metaphyseal fractures presented significantly higher RUST scores at 6 weeks and 3 months, but there was no difference in RUST scores at 6 months or 1 year according to metaphyseal comminution. CONCLUSIONS: Plate constructs with far cortical locking screws provided safe and effective fixation for distal femoral fractures, with consistent radiological and clinical results, regardless of metaphyseal comminution.

Pellino-1 promotes intrinsic activation of skin-resident IL-17A-producing T cells in psoriasis.

BACKGROUND: Psoriasis is a chronically relapsing inflammatory skin disease primarily perpetuated by skin-resident IL-17-producing T (T17) cells. Pellino-1 (Peli1) belongs to a member of E3 ubiquitin ligase mediating immune receptor signaling cascades, including nuclear factor kappa-light-chain enhancer of activated B cells (NF-κB) pathway. OBJECTIVE: We explored the potential role of Peli1 in psoriatic inflammation in the context of skin-resident T17 cells. METHODS: We performed single-cell RNA sequencing of relapsing and resolved psoriatic lesions with analysis for validation data set of psoriasis. Mice with systemic and conditional depletion of Peli1 were generated to evaluate the role of Peli1 in imiquimod-induced psoriasiform dermatitis. Pharmacologic inhibition of Peli1 in human CD4+ T cells and ex vivo human skin cultures was also examined to evaluate its potential therapeutic implications. RESULTS: Single-cell RNA sequencing analysis revealed distinct T-cell subsets in relapsing psoriasis exhibiting highly enriched gene signatures for (1) tissue-resident T cells, (2) T17 cells, and (3) NF-κB signaling pathway including PELI1. Peli1-deficient mice were profoundly protected from psoriasiform dermatitis, with reduced IL-17A production and NF-κB activation in γδ T17 cells. Mice with conditional depletion of Peli1 treated with FTY720 revealed that Peli1 was intrinsically required for the skin-resident T17 cell immune responses. Notably, pharmacologic inhibition of Peli1 significantly ameliorated murine psoriasiform dermatitis and IL-17A production from the stimulated human CD4+ T cells and ex vivo skin explants modeling psoriasis. CONCLUSION: Targeting Peli1 would be a promising therapeutic strategy for psoriasis by limiting skin-resident T17 cell immune responses.

Integrative machine learning framework for the identification of cell-specific enhancers from the human genome.

Enhancers are deoxyribonucleic acid (DNA) fragments which when bound by transcription factors enhance the transcription of related genes. Due to its sporadic distribution and similar fractions, identification of enhancers from the human genome seems a daunting task. Compared to the traditional experimental approaches, computational methods with easy-to-use platforms could be efficiently applied to annotate enhancers' functions and physiological roles. In this aspect, several bioinformatics tools have been developed to identify enhancers. Despite their spectacular performances, existing methods have certain drawbacks and limitations, including fixed length of sequences being utilized for model development and cell-specificity negligence. A novel predictor would be beneficial in the context of genome-wide enhancer prediction by addressing the above-mentioned issues. In this study, we constructed new datasets for eight different cell types. Utilizing these data, we proposed an integrative machine learning (ML)-based framework called Enhancer-IF for identifying cell-specific enhancers. Enhancer-IF comprehensively explores a wide range of heterogeneous features with five commonly used ML methods (random forest, extremely randomized tree, multilayer perceptron, support vector machine and extreme gradient boosting). Specifically, these five classifiers were trained with seven encodings and obtained 35 baseline models. The output of these baseline models was integrated and again inputted to five classifiers for the construction of five meta-models. Finally, the integration of five meta-models through ensemble learning improved the model robustness. Our proposed approach showed an excellent prediction performance compared to the baseline models on both training and independent datasets in different cell types, thus highlighting the superiority of our approach in the identification of the enhancers. We assume that Enhancer-IF will be a valuable tool for screening and identifying potential enhancers from the human DNA sequences.

Computational prediction of species-specific yeast DNA replication origin via iterative feature representation.

Deoxyribonucleic acid replication is one of the most crucial tasks taking place in the cell, and it has to be precisely regulated. This process is initiated in the replication origins (ORIs), and thus it is essential to identify such sites for a deeper understanding of the cellular processes and functions related to the regulation of gene expression. Considering the important tasks performed by ORIs, several experimental and computational approaches have been developed in the prediction of such sites. However, existing computational predictors for ORIs have certain curbs, such as building only single-feature encoding models, limited systematic feature engineering efforts and failure to validate model robustness. Hence, we developed a novel species-specific yeast predictor called yORIpred that accurately identify ORIs in the yeast genomes. To develop yORIpred, we first constructed optimal 40 baseline models by exploring eight different sequence-based encodings and five different machine learning classifiers. Subsequently, the predicted probability of 40 models was considered as the novel feature vector and carried out iterative feature learning approach independently using five different classifiers. Our systematic analysis revealed that the feature representation learned by the support vector machine algorithm (yORIpred) could well discriminate the distribution characteristics between ORIs and non-ORIs when compared with the other four algorithms. Comprehensive benchmarking experiments showed that yORIpred achieved superior and stable performance when compared with the existing predictors on the same training datasets. Furthermore, independent evaluation showcased the best and accurate performance of yORIpred thus underscoring the significance of iterative feature representation. To facilitate the users in obtaining their desired results without undergoing any mathematical, statistical or computational hassles, we developed a web server for the yORIpred predictor, which is available at: http://thegleelab.org/yORIpred.

Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework.

DNA N6-methyladenine (6mA) represents important epigenetic modifications, which are responsible for various cellular processes. The accurate identification of 6mA sites is one of the challenging tasks in genome analysis, which leads to an understanding of their biological functions. To date, several species-specific machine learning (ML)-based models have been proposed, but majority of them did not test their model to other species. Hence, their practical application to other plant species is quite limited. In this study, we explored 10 different feature encoding schemes, with the goal of capturing key characteristics around 6mA sites. We selected five feature encoding schemes based on physicochemical and position-specific information that possesses high discriminative capability. The resultant feature sets were inputted to six commonly used ML methods (random forest, support vector machine, extremely randomized tree, logistic regression, naïve Bayes and AdaBoost). The Rosaceae genome was employed to train the above classifiers, which generated 30 baseline models. To integrate their individual strength, Meta-i6mA was proposed that combined the baseline models using the meta-predictor approach. In extensive independent test, Meta-i6mA showed high Matthews correlation coefficient values of 0.918, 0.827 and 0.635 on Rosaceae, rice and Arabidopsis thaliana, respectively and outperformed the existing predictors. We anticipate that the Meta-i6mA can be applied across different plant species. Furthermore, we developed an online user-friendly web server, which is available at http://kurata14.bio.kyutech.ac.jp/Meta-i6mA/.

Evaluation of Lentilactobacillus parafarraginis A6-2 strain for aluminum removal and anti-inflammatory effects: implications for alleviating Al toxicity.

AIM: To assess the effectiveness of Lentilactobacillus parafarraginis A6-2 cell lysate for the removal of aluminum (Al), which induces neurotoxicity, and its protective effect at cellular level. METHODS AND RESULTS: The cell lysate of the selected L. parafarraginis A6-2 strain demonstrated superior Al removal compared to live or dead cells. The Al removal efficiency of L. parafarraginis A6-2 cell lysate increased with decreasing pH and increasing temperature, primarily through adsorption onto peptidoglycan. Neurotoxicity mitigation potential of L. parafarraginis A6-2 was evaluated using C6 glioma cells. C6 cells exposed with increasing concentration of Al led to elevated toxicity and inflammation, which were gradually alleviated upon treatment with L. parafarraginis A6-2. Moreover, Al-induced oxidative stress in C6 cells showed a concentration-dependent reduction upon treatment with L. parafarraginis A6-2. CONCLUSIONS: This study demonstrated that L. parafarraginis A6-2 strain, particularly in its lysate form, exhibited enhanced capability for Al removal. Furthermore, it effectively mitigated Al-induced toxicity, inflammation, and oxidative stress.

Chiral LC-MS/MS method for the discrimination of triiodothyronine enantiomers on a crown ether-based chiral stationary phase.

Considering the greater pharmaceutical and clinical interest of triiodothyronine (T3 ) thyroid hormone, an effective D/L-T3 enantiomer separation was performed on a crown ether-based chiral stationary phase by LC-MS/MS. In optimal analytical condition and selected reaction monitoring mode, the two enantiomers of T3 were baseline separated within 10 min. The limit of detection and limit of quantitation were found to be 0.05 and 0.10 ng/µl; 0.20 and 0.50 ng/µl for D- and L-T3 , respectively. During validation, this method proved to be feasible, accurate as well as enantioselective and sensitive for the resolution of T3 enantiomers. For commercial D- and L-T3 chemicals, the enantiomeric impurities as the other enantiomer were 0.11% and 4.61%. On the other hand, the impurity as D-T3 for commercial pharmaceutical products (liothyronine sodium tablets, two suppliers) was 0.68% and 6.57%.

Mitochondrial protease ClpP supplementation ameliorates diet-induced NASH in mice.

BACKGROUND & AIMS: Mitochondrial dysfunction is considered a pathogenic linker in the development of non-alcoholic steatohepatitis (NASH). Inappropriate mitochondrial protein-quality control, possibly induced by insufficiency of the mitochondrial matrix caseinolytic protease P (ClpP), can potentially cause mitochondrial dysfunction. Herein, we aimed to investigate hepatic ClpP levels in a diet-induced model of NASH and determine whether supplementation of ClpP can ameliorate diet-induced NASH. METHODS: NASH was induced by a high-fat/high-fructose (HF/HFr) diet in C57BL/6J mice. Stress/inflammatory signals were induced in mouse primary hepatocytes (MPHs) by treatment with palmitate/oleate (PA/OA). ClpP levels in hepatocytes were reduced using the RNAi-mediated gene knockdown technique but increased through the viral transduction of ClpP. ClpP activation was induced by administering a chemical activator of ClpP. RESULTS: Hepatic ClpP protein levels in C57BL/6J mice fed a HF/HFr diet were lower than the levels in those fed a normal chow diet. PA/OA treatment also decreased the ClpP protein levels in MPHs. Overexpression or activation of ClpP reversed PA/OA-induced mitochondrial dysfunction and stress/inflammatory signal activation in MPHs, whereas ClpP knockdown induced mitochondrial dysfunction and stress/inflammatory signals in these cells. On the other hand, ClpP overexpression or activation improved HF/HFr-induced NASH characteristics such as hepatic steatosis, inflammation, fibrosis, and injury in the C57BL/6J mice, whereas ClpP knockdown further augmented steatohepatitis in mice fed a HF/HFr diet. CONCLUSIONS: Reduced ClpP expression and subsequent mitochondrial dysfunction are key to the development of diet-induced NASH. ClpP supplementation through viral transduction or chemical activation represents a potential therapeutic strategy to prevent diet-induced NASH. LAY SUMMARY: Western diets, containing high fat and high fructose, often induce non-alcoholic steatohepatitis (NASH). Mitochondrial dysfunction is considered pathogenically linked to diet-induced NASH. We observed that the mitochondrial protease ClpP decreased in the livers of mice fed a western diet and supplementation of ClpP ameliorated western diet-induced NASH.

Size Separation of Exosomes and Microvesicles Using Flow Field-Flow Fractionation/Multiangle Light Scattering and Lipidomic Comparison.

Extracellular vesicles (EVs) are cell-derived membrane-bound particles, including exosomes and microvesicles that differ in cellular origin, content, and lipid composition. This study reports that exosomes and microvesicles can be simultaneously separated by size using flow field-flow fractionation (FlFFF) employed with field programming and that the detection of low-concentration EV species can be significantly improved using multiangle light scattering (MALS). The efficiency of ultracentrifugation (UC) and ultrafiltration (UF) in isolating EVs from the culture media of DU145 cells was compared, and the results showed that UF retrieves more EVs than UC. Two size fractions (small and large) of both exosomes and microvesicles were collected during the FlFFF runs and examined using Western blotting to confirm each EV, and transmission electron microscopy was performed for size analysis. Sizes were compared using the root-mean-square radius obtained from the MALS calculation. The collected fractions were further examined using nanoflow ultrahigh-performance liquid chromatography-electrospray ionization-tandem mass spectrometry for the size-dependent lipidomic profiles of exosomes and microvesicles, showing that lipids were more enriched in the fraction containing large exosomes than in that containing small exosomes; however, an opposite trend was observed with microvesicles. The present study demonstrated that UF followed by FlFFF-MALS can be utilized for the size separation of exosomes and microvesicles without sequential centrifugation, which is useful for monitoring the changes in the size distribution of EVs depending on the biological status along with generating size-dependent lipidomic profiles.

Cerebrospinal Fluid Metabolome in Parkinson's Disease and Multiple System Atrophy.

Parkinson's disease (PD) and multiple system atrophy (MSA) belong to the neurodegenerative group of synucleinopathies; differential diagnosis between PD and MSA is difficult, especially at early stages, owing to their clinical and biological similarities. Thus, there is a pressing need to identify metabolic biomarkers for these diseases. The metabolic profile of the cerebrospinal fluid (CSF) is reported to be altered in PD and MSA; however, the altered metabolites remain unclear. We created a single network with altered metabolites in PD and MSA based on the literature and assessed biological functions, including metabolic disorders of the nervous system, inflammation, concentration of ATP, and neurological disorder, through bioinformatics methods. Our in-silico prediction-based metabolic networks are consistent with Parkinsonism events. Although metabolomics approaches provide a more quantitative understanding of biochemical events underlying the symptoms of PD and MSA, limitations persist in covering molecules related to neurodegenerative disease pathways. Thus, omics data, such as proteomics and microRNA, help understand the altered metabolomes mechanism. In particular, integrated omics and machine learning approaches will be helpful to elucidate the pathological mechanisms of PD and MSA. This review discusses the altered metabolites between PD and MSA in the CSF and omics approaches to discover diagnostic biomarkers.

High-Speed Screening of Lipoprotein Components Using Online Miniaturized Asymmetrical Flow Field-Flow Fractionation and Electrospray Ionization Tandem Mass Spectrometry: Application to Hepatocellular Carcinoma Plasma Samples.

This study introduces a high-speed screening method for the quantitative analysis of lipoprotein components in human plasma samples using online miniaturized asymmetrical flow field-flow fractionation and electrospray ionization-tandem mass spectrometry (mAF4-ESI-MS/MS). Using an mAF4 channel, high-density lipoproteins and low-density lipoproteins can be fractionated by size at a high speed (<10 min) and directly fed to ESI-MS/MS for the simultaneous screening of targeted lipid species and apolipoprotein A1 (ApoA1). By employing the heated electrospray ionization probe as an ionization source, an mAF4 effluent flow rate of up to a few tens of microliters per minute can be used, which is adequate for direct feeding to MS without splitting the outflow, resulting in a consistent feed rate to MS for stable MS detection. mAF4-ESI-MS/MS was applied to hepatocellular carcinoma (HCC) plasma samples for targeted quantification of 25 lipid biomarker candidates and ApoA1 compared with healthy controls, the results of which were in statistical agreement with the quantified results obtained by nanoflow ultrahigh performance liquid chromatography-tandem mass spectrometry. Moreover, the present method provided the simultaneous detection of changes in lipoprotein size and the relative amount. This study demonstrated the potential of mAF4-ESI-MS/MS as an alternative high-speed screening platform for the top-down analysis of targeted lipoprotein components in patients with HCC, which is applicable to other diseases that involve the perturbation of lipoproteins.

HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation.

MOTIVATION: Therapeutic peptides failing at clinical trials could be attributed to their toxicity profiles like hemolytic activity, which hamper further progress of peptides as drug candidates. The accurate prediction of hemolytic peptides (HLPs) and its activity from the given peptides is one of the challenging tasks in immunoinformatics, which is essential for drug development and basic research. Although there are a few computational methods that have been proposed for this aspect, none of them are able to identify HLPs and their activities simultaneously. RESULTS: In this study, we proposed a two-layer prediction framework, called HLPpred-Fuse, that can accurately and automatically predict both hemolytic peptides (HLPs or non-HLPs) as well as HLPs activity (high and low). More specifically, feature representation learning scheme was utilized to generate 54 probabilistic features by integrating six different machine learning classifiers and nine different sequence-based encodings. Consequently, the 54 probabilistic features were fused to provide sufficiently converged sequence information which was used as an input to extremely randomized tree for the development of two final prediction models which independently identify HLP and its activity. Performance comparisons over empirical cross-validation analysis, independent test and case study against state-of-the-art methods demonstrate that HLPpred-Fuse consistently outperformed these methods in the identification of hemolytic activity. AVAILABILITY AND IMPLEMENTATION: For the convenience of experimental scientists, a web-based tool has been established at http://thegleelab.org/HLPpred-Fuse. CONTACT: glee@ajou.ac.kr or watshara.sho@mahidol.ac.th or bala@ajou.ac.kr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Silica-coated magnetic nanoparticles activate microglia and induce neurotoxic D-serine secretion.

BACKGROUND: Nanoparticles have been studied for brain imaging, diagnosis, and drug delivery owing to their versatile properties due to their small sizes. However, there are growing concerns that nanoparticles may exert toxic effects in the brain. In this study, we assessed direct nanotoxicity on microglia, the resident macrophages of the central nervous system, and indirect toxicity on neuronal cells exerted by silica-coated magnetic nanoparticles containing rhodamine B isothiocyanate dye [MNPs@SiO2(RITC)]. METHODS: We investigated MNPs@SiO2(RITC)-induced biological changes in BV2 murine microglial cells via RNA-sequencing-based transcriptome analysis and gas chromatography-mass spectrometry-based intracellular and extracellular amino acid profiling. Morphological changes were analyzed by transmission electron microscopy. Indirect effects of MNPs@SiO2(RITC) on neuronal cells were assessed by Transwell-based coculture with MNPs@SiO2(RITC)-treated microglia. MNPs@SiO2(RITC)-induced biological changes in the mouse brain in vivo were examined by immunohistochemical analysis. RESULTS: BV2 murine microglial cells were morphologically activated and the expression of Iba1, an activation marker protein, was increased after MNPs@SiO2(RITC) treatment. Transmission electron microscopy analysis revealed lysosomal accumulation of MNPs@SiO2(RITC) and the formation of vesicle-like structures in MNPs@SiO2(RITC)-treated BV2 cells. The expression of several genes related to metabolism and inflammation were altered in 100 µg/ml MNPs@SiO2(RITC)-treated microglia when compared with that in non-treated (control) and 10 µg/ml MNPs@SiO2(RITC)-treated microglia. Combined transcriptome and amino acid profiling analyses revealed that the transport of serine family amino acids, including glycine, cysteine, and serine, was enhanced. However, only serine was increased in the growth medium of activated microglia; especially, excitotoxic D-serine secretion from primary rat microglia was the most strongly enhanced. Activated primary microglia reduced intracellular ATP levels and proteasome activity in cocultured neuronal cells, especially in primary cortical neurons, via D-serine secretion. Moreover, ubiquitinated proteins accumulated and inclusion bodies were increased in primary dopaminergic and cortical neurons cocultured with activated primary microglia. In vivo, MNPs@SiO2(RITC), D-serine, and ubiquitin aggresomes were distributed in the MNPs@SiO2(RITC)-treated mouse brain. CONCLUSIONS: MNPs@SiO2(RITC)-induced activation of microglia triggers excitotoxicity in neurons via D-serine secretion, highlighting the importance of neurotoxicity mechanisms incurred by nanoparticle-induced microglial activation.

Silica-coated magnetic-nanoparticle-induced cytotoxicity is reduced in microglia by glutathione and citrate identified using integrated omics.

BACKGROUND: Nanoparticles have been utilized in brain research and therapeutics, including imaging, diagnosis, and drug delivery, owing to their versatile properties compared to bulk materials. However, exposure to nanoparticles leads to their accumulation in the brain, but drug development to counteract this nanotoxicity remains challenging. To date, concerns have risen about the potential toxicity to the brain associated with nanoparticles exposure via penetration of the brain blood barrier to address this issue. METHODS: Here the effect of silica-coated-magnetic nanoparticles containing the rhodamine B isothiocyanate dye [MNPs@SiO2(RITC)] were assessed on microglia through toxicological investigation, including biological analysis and integration of transcriptomics, proteomics, and metabolomics. MNPs@SiO2(RITC)-induced biological changes, such as morphology, generation of reactive oxygen species, intracellular accumulation of MNPs@SiO2(RITC) using transmission electron microscopy, and glucose uptake efficiency, were analyzed in BV2 murine microglial cells. Each omics data was collected via RNA-sequencing-based transcriptome analysis, liquid chromatography-tandem mass spectrometry-based proteome analysis, and gas chromatography- tandem mass spectrometry-based metabolome analysis. The three omics datasets were integrated and generated as a single network using a machine learning algorithm. Nineteen compounds were screened and predicted their effects on nanotoxicity within the triple-omics network. RESULTS: Intracellular reactive oxygen species production, an inflammatory response, and morphological activation of cells were greater, but glucose uptake was lower in MNPs@SiO2(RITC)-treated BV2 microglia and primary rat microglia in a dose-dependent manner. Expression of 121 genes (from 41,214 identified genes), and levels of 45 proteins (from 5918 identified proteins) and 17 metabolites (from 47 identified metabolites) related to the above phenomena changed in MNPs@SiO2(RITC)-treated microglia. A combination of glutathione and citrate attenuated nanotoxicity induced by MNPs@SiO2(RITC) and ten other nanoparticles in vitro and in the murine brain, protecting mostly the hippocampus and thalamus. CONCLUSIONS: Combination of glutathione and citrate can be one of the candidates for nanotoxicity alleviating drug against MNPs@SiO2(RITC) induced detrimental effect, including elevation of intracellular reactive oxygen species level, activation of microglia, and reduction in glucose uptake efficiency. In addition, our findings indicate that an integrated triple omics approach provides useful and sensitive toxicological assessment for nanoparticles and screening of drug for nanotoxicity.