A green edge-hosted zinc single-site heterogeneous catalyst for superior Fenton-like activity.

Developing green heterogeneous catalysts with excellent Fenton-like activity is critical for water remediation technologies. However, current catalysts often rely on toxic transitional metals, and their catalytic performance is far from satisfactory as alternatives of homogeneous Fenton-like catalysts. In this study, a green catalyst based on Zn single-atom was prepared in an ammonium atmosphere using ZIF-8 as a precursor. Multiple characterization analyses provided evidence that abundant intrinsic defects due to the edge sites were created, leading to the formation of a thermally stable edge-hosted Zn-N4 single-atom catalyst (ZnN4-Edge). Density functional theory calculations revealed that the edge sites equipped the single-atom Zn with a super catalytic performance, which not only promoted decomposition of peroxide molecule (HSO5-) but also greatly lowered the activation barrier for •OH generation. Consequently, the as-prepared ZnN4-Edge exhibited extremely high Fenton-like performance in oxidation and mineralization of phenol as a representative organic contaminant in a wide range of pH, realizing its quick detoxification. The atom-utilization efficiency of the ZnN4-Edge was ~104 higher than an equivalent amount of the control sample without edge sites (ZnN4), and the turnover frequency was ~103 times of the typical benchmark of homogeneous catalyst (Co2+). This study opens up a revolutionary way to rationally design and optimize heterogeneous catalysts to homogeneous catalytic performance for Fenton-like application.

Integrative Metabolomics and Proteomics Allow the Global Intracellular Characterization of Bacillus subtilis Cells and Spores.

Reliable and comprehensive multi-omics analysis is essential for researchers to understand and explore complex biological systems more completely. Bacillus subtilis (B. subtilis) is a model organism for Gram-positive spore-forming bacteria, and in-depth insight into the physiology and molecular basis of spore formation and germination in this organism requires advanced multilayer molecular data sets generated from the same sample. In this study, we evaluated two monophasic methods for polar and nonpolar compound extraction (acetonitrile/methanol/water; isopropanol/water, and 60% ethanol) and two biphasic methods (chloroform/methanol/water, and methyl tert-butyl ether/methanol/water) on coefficients of variation of analytes, identified metabolite composition, and the quality of proteomics profiles. The 60% EtOH protocol proved to be the easiest in sample processing and was more amenable to automation. Collectively, we annotated 505 and 484 metabolites and identified 1665 and 1562 proteins in B. subtilis vegetative cells and spores, respectively. We also show differences between vegetative cells and spores from a multi-omics perspective and demonstrate that an integrative multi-omics analysis can be implemented from one sample using the 60% EtOH protocol. The results obtained by the 60% EtOH protocol provide comprehensive insight into differences in the metabolic and protein makeup of B. subtilis vegetative cells and spores.

Melatonin suppresses TLR4-mediated RSV infection in the central nervous cells by inhibiting NLRP3 inflammasome formation and autophagy.

Respiratory syncytial virus (RSV) infects neuronal cells in the central nervous system (CNS), resulting in neurological symptoms. In the present study, we intended to explore the mechanism of RSV infection-induced neuroinflammatory injury from the perspective of the immune response and sought to identify effective protective measures against the injury. The findings showed that toll-like receptor 4 (TLR4) was activated after RSV infection in human neuronal SY5Y cells. Furthermore, TLR4 activation induced autophagy and apoptosis in neuronal cells, promoted the formation of the NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome, and increased the secretion of downstream inflammatory cytokines such as interleukin-1ß (IL-1ß), interleukin-18 (IL-18) and tumour necrosis factor-α (TNF-α). Interestingly, blockade of TLR4 or treatment with exogenous melatonin significantly suppressed TLR4 activation as well as TLR4-mediated apoptosis, autophagy and immune responses. Therefore, we infer that melatonin may act on the TLR4 to ameliorate RSV-induced neuronal injury, which provides a new therapeutic target for RSV infection.

Molecular physiological characterization of the dynamics of persister formation in Staphylococcus aureus.

Bacteria possess the ability to enter a growth-arrested state known as persistence in order to survive antibiotic exposure. Clinically, persisters are regarded as the main causative agents for chronic and recurrent infectious diseases. To combat this antibiotic-tolerant population, a better understanding of the molecular physiology of persisters is required. In this study, we collected samples at different stages of the biphasic kill curve to reveal the dynamics of the cellular molecular changes that occur in the process of persister formation. After exposure to antibiotics with different modes of action, namely, vancomycin and enrofloxacin, similar persister levels were obtained. Both shared and distinct stress responses were enriched for the respective persister populations. However, the dynamics of the presence of proteins linked to the persister phenotype throughout the biphasic kill curve and the molecular profiles in a stable persistent population did show large differences, depending on the antibiotic used. This suggests that persisters at the molecular level are highly stress specific, emphasizing the importance of characterizing persisters generated under different stress conditions. Additionally, although generated persisters exhibited cross-tolerance toward tested antibiotics, combined therapies were demonstrated to be a promising approach to reduce persister levels. In conclusion, this investigation sheds light on the stress-specific nature of persisters, highlighting the necessity of tailored treatment approaches and the potential of combined therapy.

MXene-Decorated Nanofibrous Membrane with Programmed Antibacterial and Anti-Inflammatory Effects via Steering NF-κB Pathway for Infectious Cutaneous Regeneration.

Although antibiotic is still the main choice for antibacteria both in hospital and community, phototherapy has become a possibly one of the alternative approaches in the treatment of microbe-associated infections nowadays because of its considerable potential in effective eradication of pathogenic bacteria. However, overwhelming reactive oxygen species (ROS) generated from phototherapy inevitably provoke an inflammatory response, complicating the healing process. To address this outstanding issue, a MXene-decorated nanofibrious is devised that not only yield localized heat but also elevate ROS levels under near-infrared laser exposure ascribed to the synergistic photothermal/photodynamic effect, for potent bacterial inactivation. After being further loaded with aspirin, the nanofibrous membranes exhibit benign cytocompatibility, boosting cell growth and suppressing the (nuclear factor kappa-B ( NF-κB) signaling pathways through RNA sequencing analysis, indicating an excellent anti-inflammatory effect. Interestingly, in vivo investigations also corroborate that the nanofibrous membranes accelerate infectious cutaneous regeneration by efficiently killing pathogenic bacteria, promoting collagen deposition, boosting angiogenesis, and dampening inflammatory reaction via steering NF-κB pathway. As envisaged, this work furnishes a decorated nanofibrous membrane with programmed antibacterial and anti-inflammatory effects for remedy of refractory bacteria-invaded wound regeneration.

SRDFM: Siamese Response Deep Factorization Machine to improve anti-cancer drug recommendation.

Predicting the response of cancer patients to a particular treatment is a major goal of modern oncology and an important step toward personalized treatment. In the practical clinics, the clinicians prefer to obtain the most-suited drugs for a particular patient instead of knowing the exact values of drug sensitivity. Instead of predicting the exact value of drug response, we proposed a deep learning-based method, named Siamese Response Deep Factorization Machines (SRDFM) Network, for personalized anti-cancer drug recommendation, which directly ranks the drugs and provides the most effective drugs. A Siamese network (SN), a type of deep learning network that is composed of identical subnetworks that share the same architecture, parameters and weights, was used to measure the relative position (RP) between drugs for each cell line. Through minimizing the difference between the real RP and the predicted RP, an optimal SN model was established to provide the rank for all the candidate drugs. Specifically, the subnetwork in each side of the SN consists of a feature generation level and a predictor construction level. On the feature generation level, both drug property and gene expression, were adopted to build a concatenated feature vector, which even enables the recommendation for newly designed drugs with only chemical property known. Particularly, we developed a response unit here to generate weighted genetic feature vector to simulate the biological interaction mechanism between a specific drug and the genes. For the predictor construction level, we built this level integrating a factorization machine (FM) component with a deep neural network component. The FM can well handle the discrete chemical information and both low-order and high-order feature interactions could be sufficiently learned. Impressively, the SRDFM works well on both single-drug recommendation and synergic drug combination. Experiment result on both single-drug and synergetic drug data sets have shown the efficiency of the SRDFM. The Python implementation for the proposed SRDFM is available at at https://github.com/RanSuLab/SRDFM Contact: ran.su@tju.edu.cn, gbx@mju.edu.cn and weileyi@sdu.edu.cn.

Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases.

It's challenging work to identify disease-causing genes from the next-generation sequencing (NGS) data of patients with Mendelian disorders. To improve this situation, researchers have developed many phenotype-driven gene prioritization methods using a patient's genotype and phenotype information, or phenotype information only as input to rank the candidate's pathogenic genes. Evaluations of these ranking methods provide practitioners with convenience for choosing an appropriate tool for their workflows, but retrospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate. In this research, the performance of ten recognized causal-gene prioritization methods was benchmarked using 305 cases from the Deciphering Developmental Disorders (DDD) project and 209 in-house cases via a relatively unbiased methodology. The evaluation results show that methods using Human Phenotype Ontology (HPO) terms and Variant Call Format (VCF) files as input achieved better overall performance than those using phenotypic data alone. Besides, LIRICAL and AMELIE, two of the best methods in our benchmark experiments, complement each other in cases with the causal genes ranked highly, suggesting a possible integrative approach to further enhance the diagnostic efficiency. Our benchmarking provides valuable reference information to the computer-assisted rapid diagnosis in Mendelian diseases and sheds some light on the potential direction of future improvement on disease-causing gene prioritization methods.

TLR4 TIR domain and nucleolin GAR domain synergistically mediate RSV infection and induce neuronal inflammatory damage in SH-SY5Y cells.

Previous research results of our group showed that Toll-like receptor 4 (TLR4) and nucleolin synergistically mediate respiratory syncytial virus (RSV) infection in human central neuron cells, but the specific mechanism remains unclear. Here we designed and synthesized lentiviruses with TIR (674-815 aa), TLR4 (del 674-815 aa), GAR (645-707 aa), and NCL (del 645-707 aa) domains, and obtained stable overexpression cell lines by drug screening, and subsequently infected RSV at different time points. Laser confocal microscopy and coimmunoprecipitation were used for the observation of co-localization and interaction of TIR/GAR domains. Western blot analysis was used for the detection of p-NF-κB and LC3 protein expression. Real-time PCR was used for the detection of TLR4/NCL mRNA expression. ELISA assay was used to measure IL-6, IL-1ß, and TNF-α concentrations and flow cytometric analysis was used for the study of apoptosis. Our results suggest that overexpression of TIR and GAR domains can exacerbate apoptosis and autophagy, and that TIR and GAR domains can synergistically mediate RSV infection and activate the NF-κB signaling pathway, which regulates the secretion of downstream inflammatory factors, such as IL-6, IL-1ß, and TNF-α, and ultimately leads to neuronal inflammatory injury.

Enhanced Recovery Protocol Versus Conventional Care in Patients Undergoing Esophagectomy for Cancer: Advantages in Clinical and Patient-Reported Outcomes.

BACKGROUND: This study was designed to compare the clinical and patient-reported outcomes (PROs) between the enhanced recovery after surgery (ERAS) protocol and conventional care in patients undergoing esophagectomy for cancer, which have not previously been compared. METHODS: This single-center retrospective study included prospective PRO data from August 2019 to June 2021. Clinical outcomes included perioperative complications and postoperative length of stay (PLOS). Patient-reported outcomes were assessed by using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core-30 (QLQ-C30) and esophagus-specific module (QLQ-OES18) preoperatively to 6 months postoperatively. Mixed-effects models were used to longitudinally compare quality of life (QOL) scores between the two modes. RESULTS: Patients undergoing conventional care and ERAS were analyzed (n = 348 and 109, respectively). The ERAS group had fewer overall complications, pneumonia, arrhythmia, and a shorter PLOS than the conventional group, and outperformed the conventional group in five functional QLQ-C30 domains and five symptom QLQ-OES18 domains, including less dysphagia (p < 0.0001), trouble talking (p = 0.0006), and better eating (p < 0.0001). These advantages persisted for 3 months postoperatively. For the cervical circular stapled anastomosis, the initial domains and duration of benefit were reduced in the ERAS group. CONCLUSIONS: The ERAS protocol has significant advantages over conventional care in terms of clinical outcomes, lowering postoperative symptom burden, and improving functional QOL in patients who have undergone esophagectomy. Selection of the optimal technique for cervical anastomosis is a key operative component of ERAS that maintains the symptom domains and duration of the advantages of PROs.

Theory of Defect-Induced Crystal Field Perturbations in Rare-Earth Magnets.

We present a theory describing the single-ion anisotropy of rare-earth (RE) magnets in the presence of point defects. Taking the RE-lean 1∶12 magnet class as a prototype, we use first-principles calculations to show how the introduction of Ti substitutions into SmFe_{12} perturbs the crystal field, generating new coefficients due to the lower symmetry of the RE environment. We then demonstrate that these perturbations can be described extremely efficiently using a screened point charge model. We provide analytical expressions for the anisotropy energy that can be straightforwardly implemented in atomistic spin dynamics simulations, meaning that such simulations can be carried out for an arbitrary arrangement of point defects. The significant crystal field perturbations calculated here demonstrate that a sample that is single phase from a structural point of view can nonetheless have a dramatically varying anisotropy profile at the atomistic level if there is compositional disorder, which may influence localized magnetic objects like domain walls or skyrmions.

Identification of Sulfur-Containing Chlorinated Paraffin Structural Analogues in Human Serum: Origination from Biotransformation or Bioaccumulation?

Chlorinated paraffins (CPs) are manufactured and used in high quantities and have diverse structural analogues. It is generally recognized that sulfur-containing structural analogues of CPs are mainly derived from sulfate-conjugated phase II metabolism. In this study, we non-targeted identified three classes of sulfur-containing CP structural analogues (CPs-S) in human serum, including 44 CP sulfates (CPs-SO4H/CPs-SO4H-OH), 14 chlorinated benzene sulfates (CBs-SO4H), and 19 CP sulfite esters (CPs-SO3/CPs-S2O6), which were generated during the production of commercial mixtures of CPs and, thus, bioaccumulated via environmental exposures. We first wrote a program to screen CPs-S, which were baseline-separated from CPs according to their polar functional groups. Then, mass spectral analyses of alkalization-acidification liquid-liquid extracts of serum samples and Orbitrap mass spectrometry analyses in the presence and absence of tetraphenylphosphonium chloride (Ph4PCl), respectively, were performed to determine the ionization forms ([M + Cl]- or [M - H]-) of CPs-S. The presence of fragment ions (SO4H-, SO3-, SO2Cl-, and HSO3-) revealed the structures of CPs-S, which were validated by their detections in commercial mixtures of CPs. The estimated total concentrations of CPs-S in the human serum samples were higher than the concentrations of medium- and long-chain CPs. The profiles of CPs-S in human serum were similar to those detected in CP commercial mixtures and rats exposed to the commercial mixtures, but CPs-S were not detected in human liver S9 fractions or rat tissues after exposure to CP standards. These results, together with the knowledge of the processes used to chemically synthesize CPs, demonstrate that CPs-S in humans originates from environmental bioaccumulation.

Analysis of Risk Factors for Velopharyngeal Insufficiency and Palatal Fistula After Sommerlad-Furlow Palatoplasty.

This study aimed to introduce a surgery technique-Sommerlad-Furlow palatoplasty (SFP) and analyze the risk factors of velopharyngeal insufficiency (VPI) and palatal fistula after SFP. Cases after SFP under the age of 5 between 2011 and 2021 were reviewed, and the cases with complete follow-up information were included. Univariate and multivariate logistic regression were used to evaluate the effects of surgical age, surgery technique, surgeon's experience, and cleft type on velopharyngeal function and the occurrence of palatal fistula. SFP is a safe and effective procedure to increase the palatal length and reconstruct the levator veli palatini sling. The speech outcome after SFP was associated with cleft type and age at operation. Age = 1.285 years is the best cutoff value. The fistula occurrence was associated with cleft type only.

Climate warming interacts with other global change drivers to influence plant phenology: A meta-analysis of experimental studies.

Shifts in plant phenology influence ecosystem structures and functions, yet how multiple global change drivers interact to affect phenology remains elusive. We conducted a meta-analysis of 242 published articles to assess interactions between warming (W) and other global change drivers including nitrogen addition (N), increased precipitation (IP), decreased precipitation (DP) and elevated CO2 (eCO2 ) on multiple phenophases in experimental studies. We show that leaf out and first flowering were most strongly affected by warming, while warming and decreased precipitation were the most pronounced drivers for leaf colouring. Moreover, interactions between warming and other global change drivers were common and both synergistic and antagonistic interactions were observed: interactions W + IP and W + eCO2 were frequently synergistic, whereas interactions W + N and W + DP were mostly antagonistic. These findings demonstrate that global change drivers often affect plant phenology interactively. Incorporating the multitude of interactions into models is crucial for accurately predicting plant responses to global changes.

Engineered Bio-Heterojunction with Infection-Primed H2 S Liberation for Boosted Angiogenesis and Infectious Cutaneous Regeneration.

Photodynamic therapy (PDT) acts as a powerful weapon against infectious diseases for its enormous antimicrobial activity that quickly elicits storms of reactive oxygen species (ROS). Nevertheless, redundant ROS during treatment inevitably bring detriments in revascularization. To address this dilemma, an innovative P-N bio-heterojunction (bio-HJ) material consisting of p-type copper sulfide (p-CuS), n-type bismuth sulfide (n-Bi2 S3 ), and lactate oxidase (LOx) for effective treatment of recalcitrant infectious wounds by promoting angiogenesis is devised. LOx exhausts lactic acid accumulated in infection environment and converts it to hydrogen peroxide (H2 O2 ), which subsequently yields bactericidal hydroxyl radicals (·OH) via Fenton-like reactions. Ultimately, the P-N bio-HJs exert synergistic photothermal, photodynamic, and chemodynamic effects for rapid bacterial annihilation. Moreover, in vitro and RNA-seq analyses reveal that the crafted bio-HJs dramatically expedite the proliferation of L929 cells and promote angiogenesis by up-regulating angiogenic gene expression in hypoxia-inducible factor-1 (HIF-1) signaling pathway, which may ascribe to the evolution of H2 S in response to the infection microenvironment. Critically, results of in vivo experiments have authenticated that the bio-HJs significantly boost healing rates of full-thickness wounds by slaughtering bacteria, elevating angiogenesis, and promoting cytothesis. As envisioned, this work furnishes a novel tactic for the effective treatment of bacteria-invaded wound using H2 S-liberating P-N bio-HJs.

Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data.

Single-cell transcriptomics technologies have vast potential in advancing our understanding of cellular heterogeneity in complex tissues. While methods to interpret single-cell transcriptomics data are developing rapidly, challenges in most analysis pipeline still remain, and the major limitation is a reliance on manual annotations for cell-type identification that is time-consuming, irreproducible, and sometimes lack canonical markers for certain cell types. There is a growing realization of the potential of machine learning models as a supervised classification approach that can significantly aid decision-making processes for cell-type identification. In this work, we performed a comprehensive and impartial evaluation of 10 machine learning models that automatically assign cell phenotypes. The performance of classification methods is estimated by using 20 publicly accessible single-cell RNA sequencing datasets with different sizes, technologies, species and levels of complexity. The performance of each model for within dataset (intra-dataset) and across datasets (inter-dataset) experiments based on the classification accuracy and computation time are both evaluated. Besides, the sensitivity to the number of input features, different annotation levels and dataset complexity was also been estimated. Results showed that most classifiers perform well on a variety of datasets with decreased accuracy for complex datasets, while the Linear Support Vector Machine (linear-SVM) and Logistic Regression classifier models have the best overall performance with remarkably fast computation time. Our work provides a guideline for researchers to select and apply suitable machine learning-based classification models in their analysis workflows and sheds some light on the potential direction of future improvement on automated cell phenotype classification tools based on the single-cell sequencing data.

Mapping of Lymph Node Metastasis and Efficacy Index in Thoracic Esophageal Squamous Cell Carcinoma: A Large-Scale Retrospective Analysis.

BACKGROUND: Esophageal squamous cell carcinoma has a high mortality rate in China. The metastatic pattern in the lymph nodes and the value of their dissection on the overall survival of these patients remain controversial. The primary aim of this study was to provide a basis for accurate staging of esophageal cancer and to identify the relationship between esophageal cancer surgery, lymph node dissection, and overall survival rates. METHODS: We utilized our hospital database to retrospectively review the data of 1727 patients with esophageal cancer who underwent R0 esophagectomy from January 2010 to December 2017. The lymph nodes were defined according to Japanese Classification of Esophageal Cancer, 11th Edition. The Efficacy Index (EI) was calculated by multiplying the frequency (%) of metastases to a zone and the 5-year survival rate (%) of patients with metastases to that zone, and then dividing by 100. RESULTS: The EI was high in the supraclavicular and mediastinal zones in patients with upper esophageal tumors, and the EI of 101R was 17.39, which was the highest among the lymph node stations. In patients with middle esophageal tumors, the EI was highest in the mediastinal zone, followed by the celiac and supraclavicular zones. Furthermore, the EI was highest in the celiac zone, followed by the mediastinal zones in patients with lower esophageal tumors. CONCLUSIONS: The EI of resected lymph nodes was found to vary between stations and was related to the primary location of the tumor.

Quantum Phase Diagram and Spontaneously Emergent Topological Chiral Superconductivity in Doped Triangular-Lattice Mott Insulators.

The topological superconducting state is a highly sought-after quantum state hosting topological order and Majorana excitations. In this Letter, we explore the mechanism to realize the topological superconductivity (TSC) in the doped Mott insulators with time-reversal symmetry (TRS). Through large-scale density matrix renormalization group study of an extended triangular-lattice t-J model on the six- and eight-leg cylinders, we identify a d+id-wave chiral TSC with spontaneous TRS breaking, which is characterized by a Chern number C=2 and quasi-long-range superconducting order. We map out the quantum phase diagram with by tuning the next-nearest-neighbor (NNN) electron hopping and spin interaction. In the weaker NNN-coupling regime, we identify a pseudogaplike phase with a charge stripe order coexisting with fluctuating superconductivity, which can be tuned into d-wave superconductivity by increasing the doping level and system width. The TSC emerges in the intermediate-coupling regime, which has a transition to a d-wave superconducting phase with larger NNN couplings. The emergence of the TSC is driven by geometrical frustrations and hole dynamics which suppress spin correlation and charge order, leading to a topological quantum phase transition.

Effect of fluorine doping on the NO2-sensing properties of MoS2nanoflowers.

The somewhat slow recovery kinetics of NO2sensing at low temperatures are still challenging to overcome. To enhance the gas sensing property, fluorine is doped to MoS2nanoflowers by facile hydrothermal method. Extensive characterization data demonstrate that F was effectively incorporated into the MoS2nanoflowers, and that the microstructure of the MoS2nanoflowers did not change upon F doping. The two MoS2doped with varying concentrations of fluorine were tested for their sensing property to NO2gas. Both of them show good repeatability and stability. A smaller recovery time was seen in the F-MoS2-1 sample with a little amount of F loading, which was three times quicker than that of pure MoS2. The key reason for the quicker recovery time of this material was found to be the fluorine ions that had been adsorbed on the surface of F-MoS2-1 would take up some of the NO2adsorption site. Additionally, the sample F-MoS2-2 with a higher F doping level demonstrated increased sensitivity. The F-MoS2-2 sensor's high sensitivity was mostly due to the lattice fluorine filled to the sulfur vacancy, which generated impurity levels and reduced the energy required for its electronic transition. This study might contribute to the development of new molybdenum sulfide based gas sensor.

Enzyme-Catalyzed Hydrogen-Deuterium Exchange between Environmental Pollutants and Enzyme-Regulated Endogenous Metabolites.

Environmental pollutants can disrupt the homeostasis of endogenous metabolites in organisms, leading to metabolic disorders and syndromes. However, it remains highly challenging to efficiently screen for critical biological molecules affected by environmental pollutants. Herein, we found that enzyme could catalyze hydrogen-deuterium (H-D) exchange between a deuterium-labeled environmental pollutant [D38-bis(2-ethylhexyl) phthalate (D38-DEHP)] and several groups of enzyme-regulated metabolites [cardiolipins (CLs), monolysocardiolipins (MLCLs), phospholipids (PLs), and lysophospholipids (LPLs)]. A high-throughput scanning identified the D-labeled endogenous metabolites in a simple enzyme [phospholipase A2 (PLA2)], enzyme mixtures (liver microsomes), and living organisms (zebrafish embryos) exposed to D38-DEHP. Mass fragmentation and structural analyses showed that similar positions were D-labeled in the CLs, MLCLs, PLs, and LPLs, and this labeling was not attributable to natural metabolic transformations of D38-DEHP or incorporation of its D-labeled side chains. Molecular docking and competitive binding analyses revealed that DEHP competed with D-labeled lipids for binding to the active site of PLA2, and this process mediated H-D exchange. Moreover, competitive binding of DEHP against biotransformation enzymes could interfere with catabolic or anabolic lipid metabolism and thereby affect the concentrations of endogenous metabolites. Our findings provide a tool for discovering more molecular targets that complement the known toxic endpoints of metabolic disruptors.

Spatially Resolved Co-Imaging of Polyhalogenated Xenobiotics and Endogenous Metabolites Reveals Xenobiotic-Induced Metabolic Alterations.

A large group of polyhalogenated compounds has been added to the list of persistent organic pollutants in a global convention endorsed by over 100 nations. Once entering the biotas, these pollutants are transported to focal sites of toxicological action and affected endogenous metabolites, which exhibited distinct tissue or organ distribution patterns. However, no study is available to achieve simultaneous mapping of the spatial distributions of xenobiotics and endogenous metabolites for clarifying the molecular mechanism of toxicities. Herein, we present a sensitive mass spectrometry imaging method─tetraphenyl phosphonium chloride-enhanced ionization coupled with air flow-assisted ionization-Orbitrap mass spectrometry─which simultaneously determined the spatial distributions of polyhalogenated xenobiotics and endogenous metabolites. The spatially resolved toxicokinetics and toxicodynamics of typical polyhalogenated compounds (chlorinated paraffins (CPs) and hexabromocyclododecane (HBCD)) were assessed in zebrafish. Co-imaging of polyhalogenated compounds and metabolites visualized the major accumulation organs and maternal transfer of HBCD and CPs, and it clarified the reproductive toxicity of HBCD. CPs were accumulated in the liver, heart, and brain and decreased the concentrations of polyamine/inosine-related metabolites and lipid molecules in these organs. HBCD accumulated in the ovary and was effectively transferred to eggs, and it also disrupted normal follicular development and impaired the production of mature eggs from the ovary by inhibiting expressions of the luteinizing hormone/choriogonadotropin receptor gene. The toxic effects of metabolic disruptions were validated by organ-specific histopathological examinations. These results highlight the necessity to assess the distributions and bioeffects of pollutants in a spatial perspective.