Iron-laden macrophage-mediated paracrine pro-fibrotic signaling induces lung fibroblast activation.

Idiopathic pulmonary fibrosis (IPF) is a devastating condition characterized by progressive lung scarring and uncontrolled fibroblast proliferation, inevitably leading to organ dysfunction and mortality. While elevated iron levels have been observed in patients and animal models of lung fibrosis, the mechanisms linking iron dysregulation to lung fibrosis pathogenesis, particularly the role of macrophages in orchestrating this process, remain poorly elucidated. Here we evaluate iron metabolism in macrophages during pulmonary fibrosis using both in vivo and in vitro approaches. In murine bleomycin- and amiodarone-induced pulmonary fibrosis models, we observed significant iron deposition and lipid peroxidation in pulmonary macrophages. Intriguingly, the ferroptosis regulator glutathione peroxidase 4 (GPX4) was upregulated in pulmonary macrophages following bleomycin instillation, a finding corroborated by single-cell RNA sequencing analysis. Moreover, macrophages isolated from fibrotic mouse lungs exhibited increased transforming growth factor (TGF)-ß1 expression that correlated with lipid peroxidation. In vitro, iron overload in bone marrow-derived macrophages triggered lipid peroxidation and TGF-ß1 upregulation, which was effectively suppressed by ferroptosis inhibitors. When co-cultured with iron-overloaded macrophages, lung fibroblasts exhibited heightened activation, evidenced by increased α-smooth muscle actin and fibronectin expression. Importantly, this pro-fibrotic effect was attenuated by treating macrophages with a ferroptosis inhibitor or blocking TGF-ß receptor signaling in fibroblasts. Collectively, our study elucidates a novel mechanistic paradigm in which the accumulation of iron within macrophages initiates lipid peroxidation, thereby amplifying TGF-ß1 production, subsequently instigating fibroblast activation through paracrine signaling. Thus, inhibiting iron overload and lipid peroxidation warrants further exploration as a strategy to suppress fibrotic stimulation by disease-associated macrophages.

Isolation and identification of bitter peptides during sequential hydrolysis of wheat gluten by enzyme preparations with endo-and exo-activities.

The effect of the released amount and bitterness threshold of bitter peptides on the sensory properties of different wheat gluten hydrolysates (WGHs) after hydrolysis was investigated. The results showed that the endo-activity of the enzyme promoted the release of bitter peptides, leading to enhanced bitterness intensity in WGHs. With the increase in degree of hydrolysis (DH), the bitter threshold of bitter peptides became the main reason affecting bitterness of the WGHs. Proteax exerted the strong exo-activity at the DH of 20%, which could reduce bitterness of Pro-16 hydrolysates. The reason for debittering was the reduction in the content with molecular weights (MWs) of 500-1000 Da and the decrease of surface hydrophobicity (SH) in the Pro-20 M hydrolysates, which led to the increase of the bitterness threshold of bitter peptide. Meanwhile, HPLC-MS/MS analysis demonstrated the reduced proportion of C-terminal hydrophobic amino acids (HAAs) in Pro-20 M extracts verifying the cause of debittering.

Gastrointestinal bleeding risk factors in type A aortic dissection post-aortic arch replacement.

Background: Gastrointestinal bleeding (GIB) is a notable complication in patients diagnosed with aortic dissection (AD). We evaluated the outcomes and identified the risk factors associated with GIB in patients with AD. Methods: A retrospective case-control study was conducted on patients diagnosed with type A aortic dissection (TAAD) who underwent total aortic arch replacement (TAAR) at our institution from July 2021 to July 2023. Comprehensive clinical data, laboratory findings, and imaging results were meticulously gathered and analyzed to identify potential risk factors linked to GIB in this patient cohort. Results: Of the 198 AD patients who underwent TAAR, 38 (19.2%) developed postoperative GIB (GIB group), with a median interval of 7 days between surgery and bleeding onset. The GIB group exhibited significantly higher mortality (26.3% vs. 3.1%, P<0.001), prolonged intensive care unit (ICU) stay {15 [interquartile range (IQR), 8-25] vs. 7 (IQR, 5-12) days, P<0.001}, and extended duration of ventilation [168 (IQR, 120-372) vs. 71 (IQR, 34-148) hours, P<0.001] compared to the control group (n=160, 80.8%). Logistic regression analysis identified age >54 years [odds ratio (OR): 3.529], intraoperative red blood cell (RBC) transfusion >600 mL (OR: 3.865), and concomitant celiac trunk and superior mesenteric artery (SMA) hypoperfusion (OR: 15.974) as independent risk factors for GIB in AD patients. Conclusions: GIB subsequent to TAAR in AD patients is linked to adverse prognosis. Factors such as advanced age, extensive intraoperative transfusion, and gastrointestinal (GI) perfusion abnormalities may heighten the risk of GIB in this patient population.

Platycodin D facilitates antiviral immunity through inhibiting cytokine storm via targeting K63-linked TRAF6 ubiquitination.

Influenza virus infection is a worldwide challenge that causes heavy burdens on public health. The mortality rate of severe influenza patients is often associated with hyperactive immunological abnormalities characterized by hypercytokinemia. Due to the continuous mutations and the occurrence of drug-resistant influenza virus strains, the development of host-directed immunoregulatory drugs is urgently required. Platycodon grandiflorum is among the top 10 herbs of traditional Chinese medicine used to treat pulmonary diseases. As one of the major terpenoid saponins extracted from Platycodon grandiflorum, Platycodin D (PD) has been reported to play several roles, including anti-inflammation, analgesia, anti-cancer, hepatoprotection, and immunoregulation. However, the therapeutic roles of PD to treat influenza virus infection remains unknown. Here, we show that PD can protect the body weight loss in severely infected influenza mice, alleviate lung damage, and thus improve the survival rate. More specifically, PD protects flu mice via decreasing the immune cell infiltration into lungs and downregulating the overactivated inflammatory response. Western blot and immunofluorescence assays exhibited that PD could inhibit the activation of TAK1/IKK/NF-κB and MAPK pathways. Besides that, CETSA, SPR and immunoprecipitation assays indicated that PD binds with TRAF6 to decrease its K63 ubiquitination after R837 stimulation. Additionally, siRNA interference experiments exhibited that PD could inhibit the secretion of IL-1ß and TNF-α in TRAF6-dependent manner. Altogether, our results suggested that PD is a promising drug candidate for treating influenza. Our study also offered a scientific explanation for the commonly used Platycodon grandiflorum in many anti-epidemic classic formulas. Due to its host-directed regulatory role, PD may serve as an adjuvant therapeutic drug in conjunction with other antiviral drugs to treat the flu.

CMTM6 promotes hepatocellular carcinoma progression through stabilizing ß-catenin.

CMTM6, a regulator of PD-L1 stability, has been implicated in the development of various cancers. However, the expression and role of CMTM6 in hepatocellular carcinoma (HCC) remains controversial. Our study revealed a negative correlation between CMTM6 expression and HCC prognosis through bioinformatics analysis and immunofluorescence staining. CMTM6 expression was also positively associated with alpha-fetoprotein (AFP) levels, supporting its potential as a prognostic marker for HCC. Using Cmtm6 knockout mice, we found that Cmtm6 deficiency inhibited HCC formation and cell proliferation in primary liver cancer models induced by DEN and DEN/CCl4. In HCC cell lines, CMTM6 promoted cell proliferation and interacted with ß-catenin, stabilizing it by preventing ubiquitination. In conclusion, our study suggested that CMTM6 upregulation promotes HCC cell proliferation through the ß-catenin pathway, making it a potential therapeutic target for HCC treatment.

Arsenic Removal via the Biomineralization of Iron-Oxidizing Bacteria Pseudarthrobacter sp. Fe7.

Arsenic (As) is a highly toxic metalloid, and its widespread contamination of water is a serious threat to human health. This study explored As removal using Fe(II)-oxidizing bacteria. The strain Fe7 isolated from iron mine soil was classified as the genus Pseudarthrobacter based on 16S rRNA gene sequence similarities and phylogenetic analyses. The strain Fe7 was identified as a strain of Gram-positive, rod-shaped, aerobic bacteria that can oxidize Fe(II) and produce iron mineral precipitates. X-ray diffraction, X-ray photoelectron spectroscopy, and energy-dispersive X-ray spectroscopy patterns showed that the iron mineral precipitates with poor crystallinity consisted of Fe(III) and numerous biological impurities. In the co-cultivation of the strain Fe7 with arsenite (As(III)), 100% of the total Fe and 99.9% of the total As were removed after 72 h. During the co-cultivation of the strain Fe7 with arsenate (As(V)), 98.4% of the total Fe and 96.9% of the total As were removed after 72 h. Additionally, the iron precipitates produced by the strain Fe7 removed 100% of the total As after 3 h in both the As(III) and As(V) pollution systems. Furthermore, enzyme activity experiments revealed that the strain Fe7 oxidized Fe(II) by producing extracellular enzymes. When 2% (v/v) extracellular enzyme liquid of the strain Fe7 was added to the As(III) or As(V) pollution system, the total As removal rates were 98.6% and 99.4%, respectively, after 2 h, which increased to 100% when 5% (v/v) and 10% (v/v) extracellular enzyme liquid of the strain Fe7 were, respectively, added to the As(III) and As(V) pollution systems. Therefore, iron biomineralized using a co-culture of the strain Fe7 and As, iron precipitates produced by the strain Fe7, and the extracellular enzymes of the strain Fe7 could remove As(III) and As(V) efficiently. This study provides new insights and strategies for the efficient remediation of arsenic pollution in aquatic environments.

A Semisynthetic Bioluminescence Sensor for Ratiometric Imaging of Metal Ions In Vivo Using DNAzymes Conjugated to An Engineered Nano-Luciferase.

DNA-based probes have gained significant attention as versatile tools for biochemical analysis, benefiting from their programmability and biocompatibility. However, most existing DNA-based probes rely on fluorescence as the signal output, which can be problematic due to issues like autofluorescence and scattering when applied in complex biological materials such as living cells or tissues. Herein, we report the development of bioluminescent nucleic acid (bioLUNA) sensors that offer laser excitation-independent and ratiometric imaging of the target in vivo. The system is based on computational modelling and mutagenesis investigations of a genetic fusion between circular permutated Nano-luciferase (NLuc) and HaloTag, enabling the conjugation of the protein with a DNAzyme. In the presence of Zn2+ , the DNAzyme sensor releases the fluorophore-labelled strand, leading to a reduction in bioluminescent resonance energy transfer (BRET) between the luciferase and fluorophore. Consequently, this process induces ratiometric changes in the bioluminescent signal. We demonstrated that this bioLUNA sensor enabled imaging of both exogenous Zn2+ in vivo and endogenous Zn2+ efflux in normal epithelial prostate and prostate tumors. This work expands the DNAzyme sensors to using bioluminescence and thus has enriched the toolbox of nucleic acid sensors for a broad range of biomedical applications.

Ultra-low-dose spectral-detector computed tomography for the accurate quantification of pulmonary nodules: an anthropomorphic chest phantom study.

PURPOSE: To assess the quantification accuracy of pulmonary nodules using virtual monoenergetic images (VMIs) derived from spectral-detector computed tomography (CT) under an ultra-low-dose scan protocol. METHODS: A chest phantom consisting of 12 pulmonary nodules was scanned using spectral-detector CT at 100 kVp/10 mAs, 100 kVp/20 mAs, 120 kVp/10 mAs, and 120 kVp/30 mAs. Each scanning protocol was repeated three times. Each CT scan was reconstructed utilizing filtered back projection, hybrid iterative reconstruction, iterative model reconstruction (IMR), and VMIs of 40-100 keV. The signal-to-noise ratio and air noise of images, absolute differences, and absolute percentage measurement errors (APEs) of the diameter, density, and volume of the four scan protocols and ten reconstruction images were compared. RESULTS: With each fixed reconstruction image, the four scanning protocols exhibited no significant differences in APEs for diameter and density (all P > 0.05). Of the four scan protocols and ten reconstruction images, APEs for nodule volume had no significant differences (all P > 0.05). At 100 kVp/10 mAs, APEs for density using IMR were the lowest (APE-mean: 6.69), but no significant difference was detected between VMIs at 50 keV (APE-mean: 11.69) and IMR (P = 0.666). In the subgroup analysis, at 100 kVp/10 mAs, there were no significant differences between VMIs at 50 keV and IMR in diameter and density (all P > 0.05). The radiation dose at 100 kVp/10 mAs was reduced by 77.8% compared with that at 120 kVp/30 mAs. CONCLUSION: Compared with IMR, reconstruction at 100 kVp/10 mAs and 50 keV provides a more accurate quantification of pulmonary nodules, and the radiation dose is reduced by 77.8% compared with that at 120 kVp/30 mAs, demonstrating great potential for ultra-low-dose spectral-detector CT.

The Role of mTORC1 Pathway and Autophagy in Resistance to Platinum-Based Chemotherapeutics.

Cisplatin (cis-diamminedichloroplatinum I) is a platinum-based drug, the mainstay of anticancer treatment for numerous solid tumors. Since its approval by the FDA in 1978, the drug has continued to be used for the treatment of half of epithelial cancers. However, resistance to cisplatin represents a major obstacle during anticancer therapy. Here, we review recent findings on how the mTORC1 pathway and autophagy can influence cisplatin sensitivity and resistance and how these data can be applicable for the development of new therapeutic strategies.

Radiomics-based Machine Learning Methods for Volume Doubling Time Prediction of Pulmonary Ground-glass Nodules With Baseline Chest Computed Tomography.

PURPOSE: Reliable prediction of volume doubling time (VDT) is essential for the personalized management of pulmonary ground-glass nodules (GGNs). We aimed to determine the optimal VDT prediction method by comparing different machine learning methods only based on the baseline chest computed tomography (CT) images. MATERIALS AND METHODS: Seven classical machine learning methods were evaluated in terms of their stability and performance for VDT prediction. The VDT, calculated by the preoperative and baseline CT, was divided into 2 groups with a cutoff value of 400 days. A total of 90 GGNs from 3 hospitals constituted the training set, and 86 GGNs from the fourth hospital served as the external validation set. The training set was used for feature selection and model training, and the validation set was used to evaluate the predictive performance of the model independently. RESULTS: The eXtreme Gradient Boosting showed the highest predictive performance (accuracy: 0.890±0.128 and area under the ROC curve (AUC): 0.896±0.134), followed by the neural network (NNet) (accuracy: 0.865±0.103 and AUC: 0.886±0.097). While regarding stability, the NNet showed the highest robustness against data perturbation (relative SDs [%] of mean AUC: 10.9%). Therefore, the NNet was chosen as the final model, achieving high accuracy of 0.756 in the external validation set. CONCLUSION: The NNet is a promising machine learning method to predict the VDT of GGNs, which would assist in the personalized follow-up and treatment strategies for GGNs reducing unnecessary follow-up and radiation dose.

Nomograms Using CT Morphological Features and Clinical Characteristics to Identify COPD in Patients with Lung Cancer: A Multicenter Study.

Purpose: This study aimed to screen out computed tomography (CT) morphological features and clinical characteristics of patients with lung cancer to identify chronic obstructive pulmonary disease (COPD). Further, we aimed to develop and validate different diagnostic nomograms for predicting whether lung cancer is comorbid with COPD. Patients and Methods: This retrospective study examined data from 498 patients with lung cancer (280 with COPD, 218 without COPD; 349 in training cohort, 149 in validation cohort) from two centers. Five clinical characteristics and 20 CT morphological features were evaluated. Differences in all variables were assessed between COPD and non-COPD groups. Models were developed using multivariable logistic regression to identify COPD, including clinical, imaging, and combined nomograms. Receiver operating characteristic curves were used to evaluate and compare the performance of nomograms. Results: Age, sex, interface, bronchus cutoff sign, spine-like process, and spiculation sign were independent predictors of COPD in patients with lung cancer. In the training and validation cohorts, the clinical nomogram showed good performance to predict COPD in lung cancer patients (areas under the curves [AUCs] of 0.807 [95% CI, 0.761-0.854] and 0.753 [95% CI, 0.674-0.832]); while the imaging nomogram showed slightly better performance (AUCs of 0.814 [95% CI, 0.770-0.858] and 0.780 [95% CI, 0.705-0.856]). For the combined nomogram generated with clinical and imaging features, the performance was further improved (AUC=0.863 [95% CI, 0.824-0.903], 0.811 [95% CI, 0.742-0.880] in the training and validation cohort). At 60% risk threshold, there were more true negative predictions (48 vs 44) and higher accuracy (73.15% vs 71.14%) for the combined nomogram compared with the clinical nomogram in the validation cohort. Conclusion: The combined nomogram developed with clinical and imaging features outperformed clinical and imaging nomograms; this provides a convenient method to detect COPD in patients with lung cancer using one-stop CT scanning.

Surfactant-Assisted Label-Free Fluorescent Aptamer Biosensors and Binding Assays.

Using DNA staining dyes such as SYBR Green I (SGI) and thioflavin T (ThT) to perform label-free detection of aptamer binding has been performed for a long time for both binding assays and biosensor development. Since these dyes are cationic, they can also adsorb to the wall of reaction vessels leading to unstable signals and even false interpretations of the results. In this work, the stability of the signal was first evaluated using ThT and the classic adenosine aptamer. In a polystyrene microplate, a drop in fluorescence was observed even when non-binding targets or water were added, whereas a more stable signal was achieved in a quartz cuvette. Equilibrating the system can also improve signal stability. In addition, a few polymers and surfactants were also screened, and 0.01% Triton X-100 was found to have the best protection effect against fluorescence signal decrease due to dye adsorption. Three aptamers for Hg2+, adenosine, and cortisol were tested for their sensitivity and signal stability in the absence and presence of Triton X-100. In each case, the sensitivity was similar, whereas the signal stability was better for the surfactant. This study indicates that careful control experiments need to be designed to ensure reliable results and that the reliability can be improved by using Triton X-100 and a long equilibration time.

Deep anatomy learning for lung airway and artery-vein modeling with contrast-enhanced CT synthesis.

PURPOSE: Endobronchial intervention requires detailed modeling of pulmonary anatomical substructure, such as lung airway and artery-vein maps, which are commonly extracted from non-contrast computed tomography (NCCT) independently using automatic segmentation approaches. We aim to make the first attempt to jointly train a CNN-based model for airway and artery-vein segmentation along with synthetic contrast-enhanced CT (CECT) generation. METHODS: A multi-task framework is proposed to simultaneously generate three segmentation maps and synthesize CECTs. We first design a collaborative learning model with tissue knowledge interaction for lung airway and artery-vein segmentation. Meanwhile, a conditional adversarial training strategy is applied to generate CECTs from NCCTs guided by artery maps. Additionally, CECT identity and reconstruction help to regularize the model for plausible NCCT to CECT translation. RESULTS: Extensive experiments were conducted to evaluate the performance of the proposed framework based on three datasets (90 NCCTs for the airway task, 55 NCCTs for the artery-vein task and 100 CECTs for the artery task). The results demonstrate the effective improvement of our proposed method compared to other methods and configurations that can achieve more accurate segmentation maps (Dice score coefficients for these three tasks are: 93.6%, 80.7% and 82.4%, respectively) and realistic CECTs simultaneously. The ablation study further verifies the effectiveness of the components of the designed model. CONCLUSION: This study demonstrates the model potential in multi-task learning that integrates anatomically relevant segmentation and performs NCCT to CECT translation. Such an interaction approach promotes mutually for both promising segmentation results and plausible synthesis.

CT-Based Radiomic Nomogram for the Prediction of Chronic Obstructive Pulmonary Disease in Patients with Lung cancer.

RATIONALE AND OBJECTIVES: To develop and validate a model for predicting chronic obstructive pulmonary disease (COPD) in patients with lung cancer based on computed tomography (CT) radiomic signatures and clinical and imaging features. MATERIALS AND METHODS: We retrospectively enrolled 443 patients with lung cancer who underwent pulmonary function test as the primary cohort. They were randomly assigned to the training (n = 311) or validation (n = 132) set in a 7:3 ratio. Additionally, an independent external cohort of 54 patients was evaluated. The radiomic lung nodule signature was constructed using the least absolute shrinkage and selection operator algorithm, while key variables were selected using logistic regression to develop the clinical and combined models presented as a nomogram. RESULTS: COPD was significantly related to the radiomics signature in both cohorts. Moreover, the signature served as an independent predictor of COPD in the multivariate regression analysis. For the training, internal, and external cohorts, the area under the receiver operating characteristic curve (ROC, AUC) values of our radiomics signature for COPD prediction were 0.85, 0.85, and 0.76, respectively. Additionally, the AUC values of the radiomic nomogram for COPD prediction were 0.927, 0.879, and 0.762 for the three cohorts, respectively, which outperformed the other two models. CONCLUSION: The present study presents a nomogram that incorporates radiomics signatures and clinical and radiological features, which could be used to predict the risk of COPD in patients with lung cancer with one-stop chest CT scanning.

Preparation and application of cinnamon-Litsea cubeba compound essential oil microcapsules for peanut kernel postharvest storage.

This research developed a novel, efficient and safe antimildew for peanut kernel postharvest storage. The antimildew, cinnamon-Litsea cubeba compound essential oil (CLCEO) microcapsule (CLCEOM), was synthesized with CLCEO as core materials and ß-cyclodextrin as wall materials. Fourier transform infrared spectroscopy and gas chromatography-mass spectrometry analyses indicated that major antifungal compounds of CLCEO were encapsulated in the cavity of ß-cyclodextrin. The inhibition zone experiment showed that CLCEOM retained antifungal effect on Aspergillus spp. strains even after storage for 2 months at 4 â„ƒ. Besides, CLCEOM reduced total number of fungal colonies, relative abundance of Aspergillus spp., and aflatoxin B1 content of peanut kernels, and had positive effect on slowing down the increase in acid value of peanut oil without causing any adverse effect on the viability and sensory properties during storage process. Overall, CLCEOM presented good preservative effects on peanut kernels, providing evidence for its potential use as antimildew for peanut storage.

Trustworthy learning with (un)sure annotation for lung nodule diagnosis with CT.

Recent evolution in deep learning has proven its value for CT-based lung nodule classification. Most current techniques are intrinsically black-box systems, suffering from two generalizability issues in clinical practice. First, benign-malignant discrimination is often assessed by human observers without pathologic diagnoses at the nodule level. We termed these data as "unsure-annotation data". Second, a classifier does not necessarily acquire reliable nodule features for stable learning and robust prediction with patch-level labels during learning. In this study, we construct a sure-annotation dataset with pathologically-confirmed labels and propose a collaborative learning framework to facilitate sure nodule classification by integrating unsure-annotation data knowledge through nodule segmentation and malignancy score regression. A loss function is designed to learn reliable features by introducing interpretability constraints regulated with nodule segmentation maps. Furthermore, based on model inference results that reflect the understanding from both machine and experts, we explore a new nodule analysis method for similar historical nodule retrieval and interpretable diagnosis. Detailed experimental results demonstrate that our approach is beneficial for achieving improved performance coupled with trustworthy model reasoning for lung cancer prediction with limited data. Extensive cross-evaluation results further illustrate the effect of unsure-annotation data for deep-learning based methods in lung nodule classification.

SESLA suppresses the activation of macrophages and dendritic cells after Gram-positive bacterial challenge.

BACKGROUND: Secoeudesma sesquiterpenes lactone A (SESLA) is a sesquiterpene derived from Inula japonica Thunb. and is known to possess many pharmacological properties, e.g. anti-tumor and anti-inflammatory activities. However, the immunomodulatory role of SESLA in gram-positive (G+) bacterial infection is not clear. MATERIALS AND METHODS: To set up a G+ bacterial infection model in vitro, we carried out a bacterial mimic (PGN or Pam3CSK4) or Methicillin-resistant Staphylococcus aureus (MRSA) stimulated experiment using macrophages or dendritic cells (DCs). ELISA and qPCR were performed to measure the expression of inflammatory cytokines. Flow cytometry was used to detect the expression of MHC II and co-stimulatory molecules on the surface of DCs. The network pharmacology was used to identify the molecular mechanism and potential targets of SESLA that are predicted to be involved in the MRSA-elicited inflammation. Western blot and dual luciferase reporter assay were adopted to certify possible molecular mechanism of SESLA. RESULTS: This study demonstrated that SESLA treatment significantly reduced the levels of inflammatory cytokines stimulated by PGN, Pam3CSK4 or even MRSA in vitro, and it also reduced PGN-induced expression of MHC II and co-stimulatory molecules on the surface of DCs. Mechanistically, the inhibition of IκBα phosphorylation and the suppression of T cells activation could account for its anti-inflammatory activity. CONCLUSION: The present study validated the notable anti-inflammatory activity of SESLA and discovered its previously uncharacterized immunoregulatory role and the underlying mechanism in G+ bacterial infections. Overall, SESLA has a potential to be an antibiotic adjuvant for the treatment of G+ bacterial infections.

Biodegradation of highly crystallized poly(ethylene terephthalate) through cell surface codisplay of bacterial PETase and hydrophobin.

The process of recycling poly(ethylene terephthalate) (PET) remains a major challenge due to the enzymatic degradation of high-crystallinity PET (hcPET). Recently, a bacterial PET-degrading enzyme, PETase, was found to have the ability to degrade the hcPET, but with low enzymatic activity. Here we present an engineered whole-cell biocatalyst to simulate both the adsorption and degradation steps in the enzymatic degradation process of PETase to achieve the efficient degradation of hcPET. Our data shows that the adhesive unit hydrophobin and degradation unit PETase are functionally displayed on the surface of yeast cells. The turnover rate of the whole-cell biocatalyst toward hcPET (crystallinity of 45%) dramatically increases approximately 328.8-fold compared with that of purified PETase at 30 °C. In addition, molecular dynamics simulations explain how the enhanced adhesion can promote the enzymatic degradation of PET. This study demonstrates engineering the whole-cell catalyst is an efficient strategy for biodegradation of PET.

Structural insights into the human PA28-20S proteasome enabled by efficient tagging and purification of endogenous proteins.

The ability to produce folded and functional proteins is a necessity for structural biology and many other biological sciences. This task is particularly challenging for numerous biomedically important targets in human cells, including membrane proteins and large macromolecular assemblies, hampering mechanistic studies and drug development efforts. Here we describe a method combining CRISPR-Cas gene editing and fluorescence-activated cell sorting to rapidly tag and purify endogenous proteins in HEK cells for structural characterization. We applied this approach to study the human proteasome from HEK cells and rapidly determined cryogenic electron microscopy structures of major proteasomal complexes, including a high-resolution structure of intact human PA28αß-20S. Our structures reveal that PA28 with a subunit stoichiometry of 3α/4ß engages tightly with the 20S proteasome. Addition of a hydrophilic peptide shows that polypeptides entering through PA28 are held in the antechamber of 20S prior to degradation in the proteolytic chamber. This study provides critical insights into an important proteasome complex and demonstrates key methodologies for the tagging of proteins from endogenous sources.

The Antifungal Activity of Cinnamon-Litsea Combined Essential Oil against Dominant Fungal Strains of Moldy Peanut Kernels.

The antifungal activity of cinnamon (Cinnamomum cassia Presl), litsea [Litsea cubeba (Lour.) Pers.], clove (Syzygium aromaticum L.), thyme (Thymus mongolicus Ronn.) and citronella (Cymbopogon winterianus Jowitt) essential oils (EOs) against the dominant fungi isolated from moldy peanuts was investigated in this research. Firstly, strain YQM was isolated and identified by morphological characterization and 18S rRNA gene sequence analysis to be Aspergillus flavus (A. flavus). Next, antifungal effects of single or mixed EOs on strain YQM were evaluated by the inhibition zone test. The cinnamon-litsea combined essential oil (CLCEO, Vcinnamon oil:Vlitsea oil = 3:5) displayed the best antifungal effect on strain YQM. The chemical composition of CLCEO was identified and quantified by gas chromatograph-mass spectrometry (GC-MS), and results revealed that the major components of CLCEO were cinnamaldehyde and citral. Finally, the effect of EOs on the microstructure of strain YQM mycelia was observed under scanning electron microscope (SEM). The mycelia exposed to cinnamon essential oil (CEO) and litsea essential oil (LEO) were partly deformed and collapsed, while the mycelia treated with CLCEO were seriously damaged and the deformation phenomena such as shrinking, shriveling and sinking occurred. Therefore, CLCEO has great potential for using as anti-mildew agents during peanut storage.