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.

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.

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.

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.

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.

Quantifying intracellular trafficking of silica-coated magnetic nanoparticles in live single cells by site-specific direct stochastic optical reconstruction microscopy.

BACKGROUND: Nanoparticles have been used for biomedical applications, including drug delivery, diagnosis, and imaging based on their unique properties derived from small size and large surface-to-volume ratio. However, concerns regarding unexpected toxicity due to the localization of nanoparticles in the cells are growing. Herein, we quantified the number of cell-internalized nanoparticles and monitored their cellular localization, which are critical factors for biomedical applications of nanoparticles. METHODS: This study investigates the intracellular trafficking of silica-coated magnetic nanoparticles containing rhodamine B isothiocyanate dye [MNPs@SiO2(RITC)] in various live single cells, such as HEK293, NIH3T3, and RAW 264.7 cells, using site-specific direct stochastic optical reconstruction microscopy (dSTORM). The time-dependent subdiffraction-limit spatial resolution of the dSTORM method allowed intracellular site-specific quantification and tracking of MNPs@SiO2(RITC). RESULTS: The MNPs@SiO2(RITC) were observed to be highly internalized in RAW 264.7 cells, compared to the HEK293 and NIH3T3 cells undergoing single-particle analysis. In addition, MNPs@SiO2(RITC) were internalized within the nuclei of RAW 264.7 and HEK293 cells but were not detected in the nuclei of NIH3T3 cells. Moreover, because of the treatment of the MNPs@SiO2(RITC), more micronuclei were detected in RAW 264.7 cells than in other cells. CONCLUSION: The sensitive and quantitative evaluations of MNPs@SiO2(RITC) at specific sites in three different cells using a combination of dSTORM, transcriptomics, and molecular biology were performed. These findings highlight the quantitative differences in the uptake efficiency of MNPs@SiO2(RITC) and ultra-sensitivity, varying according to the cell types as ascertained by subdiffraction-limit super-resolution microscopy.

Decrease in membrane fluidity and traction force induced by silica-coated magnetic nanoparticles.

BACKGROUND: Nanoparticles are being increasingly used in biomedical applications owing to their unique physical and chemical properties and small size. However, their biophysical assessment and evaluation of side-effects remain challenging. We addressed this issue by investigating the effects of silica-coated magnetic nanoparticles containing rhodamine B isothiocyanate [MNPs@SiO2(RITC)] on biophysical aspects, such as membrane fluidity and traction force of human embryonic kidney 293 (HEK293) cells. We further extended our understanding on the biophysical effects of nanoparticles on cells using a combination of metabolic profiling and transcriptomic network analysis. RESULTS: Overdose (1.0 µg/µL) treatment with MNPs@SiO2(RITC) induced lipid peroxidation and decreased membrane fluidity in HEK293 cells. In addition, HEK293 cells were morphologically shrunk, and their aspect ratio was significantly decreased. We found that each traction force (measured in micropillar) was increased, thereby increasing the total traction force in MNPs@SiO2(RITC)-treated HEK293 cells. Due to the reduction in membrane fluidity and elevation of traction force, the velocity of cell movement was also significantly decreased. Moreover, intracellular level of adenosine triphosphate (ATP) was also decreased in a dose-dependent manner upon treatment with MNPs@SiO2(RITC). To understand these biophysical changes in cells, we analysed the transcriptome and metabolic profiles and generated a metabotranscriptomics network, which revealed relationships among peroxidation of lipids, focal adhesion, cell movement, and related genes and metabolites. Furthermore, in silico prediction of the network showed increment in the peroxidation of lipids and suppression of focal adhesion and cell movement. CONCLUSION: Taken together, our results demonstrated that overdose of MNPs@SiO2(RITC) impairs cellular movement, followed by changes in the biophysical properties of cells, thus highlighting the need for biophysical assessment of nanoparticle-induced side-effects.

mAHTPred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation.

MOTIVATION: Cardiovascular disease is the primary cause of death globally accounting for approximately 17.7 million deaths per year. One of the stakes linked with cardiovascular diseases and other complications is hypertension. Naturally derived bioactive peptides with antihypertensive activities serve as promising alternatives to pharmaceutical drugs. So far, there is no comprehensive analysis, assessment of diverse features and implementation of various machine-learning (ML) algorithms applied for antihypertensive peptide (AHTP) model construction. RESULTS: In this study, we utilized six different ML algorithms, namely, Adaboost, extremely randomized tree (ERT), gradient boosting (GB), k-nearest neighbor, random forest (RF) and support vector machine (SVM) using 51 feature descriptors derived from eight different feature encodings for the prediction of AHTPs. While ERT-based trained models performed consistently better than other algorithms regardless of various feature descriptors, we treated them as baseline predictors, whose predicted probability of AHTPs was further used as input features separately for four different ML-algorithms (ERT, GB, RF and SVM) and developed their corresponding meta-predictors using a two-step feature selection protocol. Subsequently, the integration of four meta-predictors through an ensemble learning approach improved the balanced prediction performance and model robustness on the independent dataset. Upon comparison with existing methods, mAHTPred showed superior performance with an overall improvement of approximately 6-7% in both benchmarking and independent datasets. AVAILABILITY AND IMPLEMENTATION: The user-friendly online prediction tool, mAHTPred is freely accessible at http://thegleelab.org/mAHTPred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effect of silica-coated magnetic nanoparticles on rigidity sensing of human embryonic kidney cells.

BACKGROUND: Nanoparticles (NPs) can enter cells and cause cellular dysfunction. For example, reactive oxygen species generated by NPs can damage the cytoskeleton and impair cellular adhesion properties. Previously, we reported that cell spreading and protrusion structures such as lamellipodia and filopodia was reduced when cells are treated with silica-coated magnetic nanoparticles incorporating rhodamine B isothiocyanate (MNPs@SiO2(RITC)), even at 0.1 µg/µL. These protruded structures are involved in a cell's rigidity sensing, but how these NPs affect rigidity sensing is unknown. RESULTS: Here, we report that the rigidity sensing of human embryonic kidney (HEK293) cells was impaired even at 0.1 µg/µL of MNPs@SiO2(RITC). At this concentration, cells were unable to discern the stiffness difference between soft (5 kPa) and rigid (2 MPa) flat surfaces. The impairment of rigidity sensing was further supported by observing the disappearance of locally contracted elastomeric submicron pillars (900 nm in diameter, 2 µm in height, 24.21 nN/µm in stiffness k) under MNPs@SiO2(RITC) treated cells. A decrease in the phosphorylation of paxillin, which is involved in focal adhesion dynamics, may cause cells to be insensitive to stiffness differences when they are treated with MNPs@SiO2(RITC). CONCLUSIONS: Our results suggest that NPs may impair the rigidity sensing of cells even at low concentrations, thereby affecting cell adhesion and spreading.

Metabolomic approaches to polyamines including acetylated derivatives in lung tissue of mice with asthma.

INTRODUCTION: Recently, the relationship between polyamine (PA) metabolism and asthma has been studied in severe asthmatic therapy, but systematic PA metabolism including their acetylated derivatives was not fully understood. OBJECTIVES: Profiling analysis of polyamines (PAs) was performed to understand the biochemical events and monitor altered PA metabolism in lung tissue of mice with asthma. METHODS: Polyamine profiling of lung tissue of mice with asthma was performed without derivatization by liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with star pattern recognition analysis. The PA levels between control and asthma groups were evaluated by multivariate analysis. RESULTS: In mouse lung tissue, seven PAs were determined by LC-MS/MS in multiple reaction monitoring (MRM) mode. Their levels were normalized to the corresponding mean levels of the control group for star pattern analysis, which showed distorted heptagonal shapes with characteristic and readily distinguishable patterns for each group. Levels of putrescine (p < 0.0034), N1-acetylputrescine (p < 0.0652), and N8-acetylspermidine (p < 0.0827) were significantly increased in asthmatic lung tissue. The separation of the two groups was evaluated using multivariate analysis. In unsupervised learning, acetylated PAs including N1-acetylspermine were the main metabolites for discrimination. In supervised learning, putrescine and N1-acetylputrescine were evaluated as important metabolites. CONCLUSIONS: The present results provide basic data for understanding polyamine metabolism in asthma and may help to improve the therapy for severe asthma patients.

Silica-coated magnetic nanoparticles induce glucose metabolic dysfunction in vitro via the generation of reactive oxygen species.

Nanoparticles are a useful material in biomedicine given their unique properties and biocompatibility; however, there is increasing concern regarding the potential toxicity of nanoparticles with respect to cell metabolism. Some evidence suggests that nanoparticles can disrupt glucose and energy homeostasis. In this study, we investigated the metabolomic, transcriptomic, and integrated effects of silica-coated magnetic nanoparticles containing rhodamine B isothiocyanate dye [MNPs@SiO2(RITC)] on glucose metabolism in human embryonic kidney 293 (HEK293) cells. Using gas chromatography-tandem mass spectrometry, we analysed the metabolite profiles of 14 organic acids (OAs), 20 amino acids (AAs), and 13 fatty acids (FAs) after treatment with 0.1 or 1.0 µg/µl MNPs@SiO2(RITC) for 12 h. The metabolic changes were highly related to reactive oxygen species (ROS) generation and glucose metabolism. Additionally, effects on the combined metabolome and transcriptome or "metabotranscriptomic network" indicated a relationship between ROS generation and glucose metabolic dysfunction. In the experimental validation, MNPs@SiO2(RITC) treatment significantly decreased the amount of glucose in cells and was associated with a reduction in glucose uptake efficiency. Decreased glucose uptake efficiency was also related to ROS generation and impaired glucose metabolism in the metabotranscriptomic network. Our results suggest that exposure to high concentrations of MNPs@SiO2(RITC) produces maladaptive alterations in glucose metabolism and specifically glucose uptake as well as related metabolomic and transcriptomic disturbances via increased ROS generation. These findings further indicate that an integrated metabotranscriptomics approach provides useful and sensitive toxicological assessment for nanoparticles.

A Molecular Dynamics Approach to Explore the Intramolecular Signal Transduction of PPAR-α.

Dynamics and functions of the peroxisome proliferator-activated receptor (PPAR)-α are modulated by the types of ligands that bind to the orthosteric sites. While several X-ray crystal structures of PPAR-α have been determined in their agonist-bound forms, detailed structural information in their apo and antagonist-bound states are still lacking. To address these limitations, we apply unbiased molecular dynamics simulations to three different PPAR-α systems to determine their modulatory mechanisms. Herein, we performed hydrogen bond and essential dynamics analyses to identify the important residues involved in polar interactions and conformational structural variations, respectively. Furthermore, betweenness centrality network analysis was carried out to identify key residues for intramolecular signaling. The differences observed in the intramolecular signal flow between apo, agonist- and antagonist-bound forms of PPAR-α will be useful for calculating maps of information flow and identifying key residues crucial for signal transductions. The predictions derived from our analysis will be of great help to medicinal chemists in the design of effective PPAR-α modulators and additionally in understanding their regulation and signal transductions.

Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy.

Cell-penetrating peptides (CPPs) can enter cells as a variety of biologically active conjugates and have various biomedical applications. To offset the cost and effort of designing novel CPPs in laboratories, computational methods are necessitated to identify candidate CPPs before in vitro experimental studies. We developed a two-layer prediction framework called machine-learning-based prediction of cell-penetrating peptides (MLCPPs). The first-layer predicts whether a given peptide is a CPP or non-CPP, whereas the second-layer predicts the uptake efficiency of the predicted CPPs. To construct a two-layer prediction framework, we employed four different machine-learning methods and five different compositions including amino acid composition (AAC), dipeptide composition, amino acid index, composition-transition-distribution, and physicochemical properties (PCPs). In the first layer, hybrid features (combination of AAC and PCP) and extremely randomized tree outperformed state-of-the-art predictors in CPP prediction with an accuracy of 0.896 when tested on independent data sets, whereas in the second layer, hybrid features obtained through feature selection protocol and random forest produced an accuracy of 0.725 that is better than state-of-the-art predictors. We anticipate that our method MLCPP will become a valuable tool for predicting CPPs and their uptake efficiency and might facilitate hypothesis-driven experimental design. The MLCPP server interface along with the benchmarking and independent data sets are freely accessible at www.thegleelab.org/MLCPP .

Analysis of the free fatty acid metabolome in the plasma of patients with systemic lupus erythematosus and fever.

INTRODUCTION: Systemic lupus erythematosus (SLE) is a multifactorial autoimmune disease with heterogeneous clinical manifestations mediated by immune dysregulation. OBJECTIVES: We aimed to analyze the metabolomic differences in free fatty acids (FFAs) between patients with SLE and healthy controls (HCs). METHODS: In this study, the levels of 24 FFAs, as their tert-butyldimethylsilyl derivatives, in the plasma of 41 patients with SLE and 41 HCs, were investigated using gas chromatography with mass spectrometry in selected-ion monitoring mode. RESULTS: The results showed that patients with SLE and HCs had significantly different levels of 13 of the 24 FFAs. The levels of myristic, palmitoleic, oleic, and eicosenoic acids were significantly higher, whereas the levels of caproic, caprylic, linoleic, stearic, arachidonic, eicosanoic, behenic, lignoceric, and hexacosanoic acids were significantly lower in patients with SLE, than in the HCs. In the partial-correlation analysis of the FFA profiles and markers of disease activity of SLE, several metabolic markers correlated with SLE disease activity. CONCLUSIONS: Our results provide a comprehensive understanding of the relationship between FFAs and markers of SLE disease activity. Thus, this approach has promising potential for the discovery of metabolic biomarkers of SLE.

Disruption of phosphofructokinase activity and aerobic glycolysis in human bronchial epithelial cells by atmospheric ultrafine particulate matter.

Exposure to ambient ultrafine particulate matter (UPM) causes respiratory disorders; however, the underlying molecular mechanisms remain unclear. In this study, we synthesized simulated UPM (sUPM) with controlled physicochemical properties using the spark-discharge method. Subsequently, we investigated the biological effects of sUPM using BEAS-2B human bronchial epithelial cells (HBECs) and a mouse intratracheal instillation model. High throughput RNA-sequencing and bioinformatics analyses revealed that dysregulation of the glycolytic metabolism is involved in the inhibited proliferation and survival of HBECs by sUPM treatment. Furthermore, signaling pathway and enzymatic analyses showed that the treatment of BEAS-2B cells with sUPM induces the inactivation of extracellular signal-regulated kinase (ERK) and protein kinase B (PKB, also known as AKT), resulting in the downregulation of phosphofructokinase 2 (PFK2) S483 phosphorylation, PFK enzyme activity, and aerobic glycolysis in HBECs in an oxidative stress-independent manner. Additionally, intratracheal instillation of sUPM reduced the phosphorylation of ERK, AKT, and PFK2, decreased proliferation, and increased the apoptosis of bronchial epithelial cells in mice. The findings of this study imply that UPM induces pulmonary toxicity by disrupting aerobic glycolytic metabolism in lung epithelial cells, which can provide novel insights into the toxicity mechanisms of UPM and strategies to prevent their toxic effects.

AMPK-HIF-1α signaling enhances glucose-derived de novo serine biosynthesis to promote glioblastoma growth.

BACKGROUND: Cancer cells undergo cellular adaptation through metabolic reprogramming to sustain survival and rapid growth under various stress conditions. However, how brain tumors modulate their metabolic flexibility in the naturally serine/glycine (S/G)-deficient brain microenvironment remain unknown. METHODS: We used a range of primary/stem-like and established glioblastoma (GBM) cell models in vitro and in vivo. To identify the regulatory mechanisms of S/G deprivation-induced metabolic flexibility, we employed high-throughput RNA-sequencing, transcriptomic analysis, metabolic flux analysis, metabolites analysis, chromatin immunoprecipitation (ChIP), luciferase reporter, nuclear fractionation, cycloheximide-chase, and glucose consumption. The clinical significances were analyzed in the genomic database (GSE4290) and in human GBM specimens. RESULTS: The high-throughput RNA-sequencing and transcriptomic analysis demonstrate that the de novo serine synthesis pathway (SSP) and glycolysis are highly activated in GBM cells under S/G deprivation conditions. Mechanistically, S/G deprivation rapidly induces reactive oxygen species (ROS)-mediated AMP-activated protein kinase (AMPK) activation and AMPK-dependent hypoxia-inducible factor (HIF)-1α stabilization and transactivation. Activated HIF-1α in turn promotes the expression of SSP enzymes phosphoglycerate dehydrogenase (PHGDH), phosphoserine aminotransferase 1 (PSAT1), and phosphoserine phosphatase (PSPH). In addition, the HIF-1α-induced expression of glycolytic genes (GLUT1, GLUT3, HK2, and PFKFB2) promotes glucose uptake, glycolysis, and glycolytic flux to fuel SSP, leading to elevated de novo serine and glycine biosynthesis, NADPH/NADP+ ratio, and the proliferation and survival of GBM cells. Analyses of human GBM specimens reveal that the levels of overexpressed PHGDH, PSAT1, and PSPH are positively correlated with levels of AMPK T172 phosphorylation and HIF-1α expression and the poor prognosis of GBM patients. CONCLUSION: Our findings reveal that metabolic stress-enhanced glucose-derived de novo serine biosynthesis is a critical metabolic feature of GBM cells, and highlight the potential to target SSP for treating human GBM.

Recent Trends on the Development of Machine Learning Approaches for the Prediction of Lysine Acetylation Sites.

Acetylation on lysine residues is considered one of the most potent protein post-translational modifications, owing to its crucial role in cellular metabolism and regulatory processes. Recent advances in experimental techniques have unraveled several lysine acetylation substrates and sites. However, owing to its cost-ineffectiveness, cumbersome process, time-consumption, and labor-intensiveness, several efforts have been geared towards the development of computational tools. In particular, machine learning (ML)-based approaches hold great promise in the rapid discovery of lysine acetylation modification sites, which could be witnessed by the growing number of prediction tools. Recently, several ML methods have been developed for the prediction of lysine acetylation sites, owing to their time- and cost-effectiveness. In this review, we present a complete survey of the state-of-the-art ML predictors for lysine acetylation. We discuss a variety of key aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, validation techniques, and software utility. Initially, we review lysine acetylation site databases, current ML approaches, working principles, and their performances. Lastly, we discuss the shortcomings and future directions of ML approaches in the prediction of lysine acetylation sites. This review may act as a useful guide for the experimentalists in choosing the right ML tool for their research. Moreover, it may help bioinformaticians in the development of more accurate and advanced MLbased predictors in protein research.

The Impact of Fine Particulate Matter 2.5 on the Cardiovascular System: A Review of the Invisible Killer.

Air pollution exerts several deleterious effects on the cardiovascular system, with cardiovascular disease (CVD) accounting for 80% of all premature deaths caused by air pollution. Short-term exposure to particulate matter 2.5 (PM2.5) leads to acute CVD-associated deaths and nonfatal events, whereas long-term exposure increases CVD-associated risk of death and reduces longevity. Here, we summarize published data illustrating how PM2.5 may impact the cardiovascular system to provide information on the mechanisms by which it may contribute to CVDs. We provide an overview of PM2.5, its associated health risks, global statistics, mechanistic underpinnings related to mitochondria, and hazardous biological effects. We elaborate on the association between PM2.5 exposure and CVD development and examine preventive PM2.5 exposure measures and future strategies for combating PM2.5-related adverse health effects. The insights gained can provide critical guidelines for preventing pollution-related CVDs through governmental, societal, and personal measures, thereby benefitting humanity and slowing climate change.

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics.

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the development and identification of ACPs are time-consuming and expensive in traditional wet-lab-based approaches. Thus, the application of artificial intelligence on the approaches can save time and reduce the cost to identify candidate ACPs. Recently, machine learning (ML), deep learning (DL), and hybrid learning (ML combined DL) have emerged into the development of ACPs without experimental analysis, owing to advances in computer power and big data from the power system. Additionally, we suggest that combination therapy with classical approaches and ACPs might be one of the impactful approaches to increase the efficiency of cancer therapy.