Sanghuangporus vaninii extract ameliorates hyperlipidemia in rats by mechanisms identified with transcriptome analysis.

The increasing incidence of hyperlipidemia is a serious threat to public health. The development of effective and safe lipid-lowering drugs with few side effects is necessary. The purpose of this study was to assess the lipid-lowering activity of Sanghuangporus vaninii extract (SVE) in rat experiments and reveal the molecular mechanism by transcriptome analysis. Hyperlipidemia was induced in the animals using a high-fat diet for 4 weeks. At the end of the 4th week, hyperlipidemic rats were assigned into two control groups (model and positive simvastatin control) and three treatment groups that received SVE at 200, 400, or 800 mg kg-1 day-1 for another 4 weeks. A last control group comprised normal chow-fed rats. At the end of the 8th week, rats were sacrificed and lipid serum levels, histopathology, and liver transcriptome profiles were determined. SVE was demonstrated to relieve the lipid disorder and improve histopathological liver changes in a dose-dependent manner. The transcriptomic analysis identified changes in hepatocyte gene activity for major pathways including steroid biosynthesis, bile secretion, cholesterol metabolism, AMPK signaling, thyroid hormone signaling, and glucagon signaling. The changed expression of crucial genes in the different groups was confirmed by qPCR. Collectively, this study revealed that SVE could relieve hyperlipidemia in rats, the molecular mechanism might be to promote the metabolism of lipids and the excretion of cholesterol, inhibit the biosynthesis of cholesterol by activating the AMPK signaling pathway, the thyroid hormone signaling pathway, and the glucagon signaling pathway.

Pd@Pt Core-Shell Nanocrystal-Decorated ZnO Nanosheets for ppt-Level NO2 Detection.

Bimetallic nanocrystals (NCs) have obtained significant attention due to their unique advantages of the intrinsic properties of individual metals and synergistic enhancements resulting from the electronic coupling between two constituent metals. In this work, Pd@Pt core-shell NCs were prepared through a facile one-pot solution-phase method, which had excellent dispersion and uniform size. Concurrently, ZnO nanosheets were prepared via a hydrothermal method. To explore their potential in nitrogen dioxide (NO2) gas sensing applications, sensitive materials based on ZnO nanosheets with varying mass percentages of Pd@Pt NCs were generated through an impregnation process. The sensor based on 0.3 wt % Pd@Pt-ZnO exhibited remarkable performance, demonstrating a substantial response (Rg/Ra = 60.3) to 50 ppb of NO2 at a low operating temperature of 80 °C. Notably, this sensor reached an outstanding low detection limit of 300 ppt. The enhancement in gas sensing capabilities can be attributed to the sensitization and synergistic effects imparted by the exceptional catalytic activity of Pd@Pt NCs, which significantly promoted the reaction. This research introduces a novel approach for the utilization of core-shell structured bimetallic nanocrystals as modifiers in metal-oxide-semiconductor (MOS) materials for NO2 detection.

Deep learning to assist composition classification and thyroid solid nodule diagnosis: a multicenter diagnostic study.

OBJECTIVES: This study aimed to propose a deep learning (DL)-based framework for identifying the composition of thyroid nodules and assessing their malignancy risk. METHODS: We conducted a retrospective multicenter study using ultrasound images from four hospitals. Convolutional neural network (CNN) models were constructed to classify ultrasound images of thyroid nodules into solid and non-solid, as well as benign and malignant. A total of 11,201 images of 6784 nodules were used for training, validation, and testing. The area under the receiver-operating characteristic curve (AUC) was employed as the primary evaluation index. RESULTS: The models had AUCs higher than 0.91 in the benign and malignant grading of solid thyroid nodules, with the Inception-ResNet AUC being the highest at 0.94. In the test set, the best algorithm for identifying benign and malignant thyroid nodules had a sensitivity of 0.88, and a specificity of 0.86. In the human vs. DL test set, the best algorithm had a sensitivity of 0.93, and a specificity of 0.86. The Inception-ResNet model performed better than the senior physicians (p < 0.001). The sensitivity and specificity of the optimal model based on the external test set were 0.90 and 0.75, respectively. CONCLUSIONS: This research demonstrates that CNNs can assist thyroid nodule diagnosis and reduce the rate of unnecessary fine-needle aspiration (FNA). CLINICAL RELEVANCE STATEMENT: High-resolution ultrasound has led to increased detection of thyroid nodules. This results in unnecessary fine-needle aspiration and anxiety for patients whose nodules are benign. Deep learning can solve these problems to some extent. KEY POINTS: • Thyroid solid nodules have a high probability of malignancy. • Our models can improve the differentiation between benign and malignant solid thyroid nodules. • The differential performance of one model was superior to that of senior radiologists. Applying this could reduce the rate of unnecessary fine-needle aspiration of solid thyroid nodules.

Genomic variation in montane bumblebees in Scandinavia: High levels of intraspecific diversity despite population vulnerability.

Populations of many bumblebee species are declining, with distributions shifting northwards to track suitable climates. Climate change is considered a major contributing factor. Arctic species are particularly vulnerable as they cannot shift further north, making assessment of their population viability important. Analysis of levels of whole-genome variation is a powerful way to analyse population declines and fragmentation. Here, we use genome sequencing to analyse genetic variation in seven species of bumblebee from the Scandinavian mountains, including two classified as vulnerable. We sequenced 333 samples from across the ranges of these species in Sweden. Estimates of effective population size (NE ) vary from ~55,000 for species with restricted high alpine distributions to 220,000 for more widespread species. Population fragmentation is generally very low or undetectable over large distances in the mountains, suggesting an absence of barriers to gene flow. The relatively high NE and low population structure indicate that none of the species are at immediate risk of negative genetic effects caused by high levels of genetic drift. However, reconstruction of historical fluctuations in NE indicates that the arctic specialist species Bombus hyperboreus has experienced population declines since the last ice age and we detected one highly inbred diploid male of this species close to the southern limit of its range, potentially indicating elevated genetic load. Although the levels of genetic variation in montane bumblebee populations are currently relatively high, their ranges are predicted to shrink drastically due to the effects of climate change and monitoring is essential to detect future population declines.

Transcriptome analysis of the mechanism of endophytic fungus CHS3 promoting saikosaponin d synthesis in Bupleurum scorzonerifolium Willd. suspension cells.

Saikosaponin d (SSd) is the main component of Bupleuri Radix, a famous traditional Chinese herbal medicine, with high medicinal value. An endophytic fungus (CHS3) was isolated from Bupleurum scorzonerifolium Willd. in the early stage of our research, and we found that CHS3 could promote the accumulation of SSd in Bupleurum scorzonerifolium Willd. suspension cells (BSS cells). It is of practical significance to identify the mechanism that CHS3 promoted the accumulation of SSd and increased the production of SSd in suspension cells. To search the influence of CHS3 on SSd synthesis in the BSS cells, we co-cultured CHS3 with the BSS cells and compared the SSd content in BSS cells before and after co-culture using high-performance liquid chromatography (HPLC). Then the Illumina HiSeq 2500 was performed to detect the transcriptome of the BSS cells before and after co-culture and analyzed for the KEGG enrichment. The expression of genes involved in SSd synthesis was finally corroborated by qPCR analysis. Among which 11 key genes in connection with SSd synthesis were increased in BSS cells of co-cultured group compared with the BSS cells of the control group. In conclusion, CHS3 could promote the accumulation of SSd in BSS cells, and the molecular mechanism was related to its ability to regulate the MVA pathway, the calcium signaling pathway, and the AMPK signaling pathway by upregulating the expressions of ANT, CypD, CaM, AMPK, AATC, HMGS, HMGR, MVK, MVD, SS, and SE.

Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study.

BACKGROUND: Calcification is a common phenomenon in both benign and malignant thyroid nodules. However, the clinical significance of calcification remains unclear. Therefore, we explored a more objective method for distinguishing between benign and malignant thyroid calcified nodules. METHODS: This retrospective study, conducted at two centers, involved a total of 631 thyroid nodules, all of which were pathologically confirmed. Ultrasound image sets were employed for analysis. The primary evaluation index was the area under the receiver-operator characteristic curve (AUROC). We compared the diagnostic performance of deep learning (DL) methods with that of radiologists and determined whether DL could enhance the diagnostic capabilities of radiologists. RESULTS: The Xception classification model exhibited the highest performance, achieving an AUROC of up to 0.970, followed by the DenseNet169 model, which attained an AUROC of up to 0.959. Notably, both DL models outperformed radiologists (P < 0.05). The success of the Xception model can be attributed to its incorporation of deep separable convolution, which effectively reduces the model's parameter count. This feature enables the model to capture features more effectively during the feature extraction process, resulting in superior performance, particularly when dealing with limited data. CONCLUSIONS: This study conclusively demonstrated that DL outperformed radiologists in differentiating between benign and malignant calcified thyroid nodules. Additionally, the diagnostic capabilities of radiologists could be enhanced with the aid of DL.

Preparation of High-Ductility Cement-Calcined Coal-Gangue-Powder-Composite-Based Rapid Repair Material.

Coal gangue is a kind of solid waste. A high-ductility cement-calcined coal-gangue-powder-composite-based rapid repair material (HD-RRM) was prepared by partially replacing cement with calcined coal gangue powder (CCGP) for achieving high ductility and rapid hardening and conforming to the strength requirements of pavement layers. First, the physical and chemical properties and the reactivity of the CCGP were investigated. Second, HD-RRM material was prepared, and its tensile performance characteristic parameters were investigated. Lastly, the hydration products and microstructure of HD-RRM were characterized through tests (e.g., non-evaporable water content, scanning electron microscopy (SEM), X-ray diffraction (XRD), and comprehensive thermogravimetric analysis and differential scanning calorimetry (TG-DSC)). As indicated by the experimental results, the CCGP with a particle size of 1250 mesh exhibited the maximum potential reactivity. The optimal mixing ratio for HD-RRM in the experiments comprised a water-cement ratio of 0.27, a sand-cement ratio of 0.3, a fiber volume fraction of 2%, a cement content of 70%, a CCGP content of 20%, a fly ash (FA) content of 10%, and a superplasticizer content of 0.1%. Using the abovementioned mix design, the prepared HD-RRM was endowed with a 6 h ultimate elongation of 2.75%, an ultimate tensile strength of 7.58 MPa, a compressive strength of 45.4 MPa, and an average crack width of 125.53 µm, which meets the requirements of repair materials and provides a design method for CCGP resource utilization and asphalt concrete road and bridge deck repair.

Combined conventional ultrasonography with clinicopathological features to predict axillary status after neoadjuvant therapy for breast cancer: A case-control study.

OBJECTIVES: This study aimed to evaluate the value of a model combining conventional ultrasonography and clinicopathologic features for predicting axillary status after neoadjuvant therapy in breast cancer. METHODS: This retrospective study included 329 patients with lymph node-positive who underwent neoadjuvant systemic treatment (NST) from June 2019 to March 2022. Ultrasound and clinicopathological characteristics of breast lesions and axillary lymph nodes were analyzed before and after NST. The diagnostic efficacy of ultrasound, clinicopathological characteristics, and combined model were evaluated using multivariate logistic regression and receiver operator characteristic curve (ROC) analyses. RESULTS: The area under ROC (AUC) for the ability of the combined model to predict the axillary pathological complete response (pCR) after NST was 0.882, that diagnostic effectiveness was significantly better than that of the clinicopathological model (AUC of 0.807) and the ultrasound feature model (AUC of 0.795). In addition, eight features were screened as independent predictors of axillary pCR, including clinical N stage, ERBB2 status, Ki-67, and after NST the maximum diameter reduction rate and margins of breast lesions, the short diameter, cortical thickness, and fatty hilum of lymph nodes. CONCLUSIONS: The combined model constructed from ultrasound and clinicopathological features for predicting axillary pCR has favorable diagnostic results, which allowed more accurate identification of BC patients who had received axillary pCR after NST. ADVANCES IN KNOWLEDGE: A combined model incorporated ultrasound and clinicopathological characteristics of breast lesions and axillary lymph nodes demonstrated favorable performance in evaluating axillary pCR preoperatively and non-invasively.

Leveraging deep learning to identify calcification and colloid in thyroid nodules.

Background: Both calcification and colloid in thyroid nodules are reflected as echogenic foci in ultrasound images. However, calcification and colloid have significantly different probabilities of malignancy. We explored the performance of a deep learning (DL) model in distinguishing the echogenic foci of thyroid nodules as calcification or colloid. Methods: We conducted a retrospective study using ultrasound image sets. The DL model was trained and tested on 30,388 images of 1127 nodules. All nodules were pathologically confirmed. The area under the receiver-operator characteristic curve (AUC) was employed as the primary evaluation index. Results: The YoloV5 (You Only Look Once Version 5) transfer learning model for thyroid nodules based on DL detection showed that the average sensitivity, specificity, and accuracy of distinguishing echogenic foci in the test 1 group (n = 192) was 78.41%, 91.36%, and 77.81%, respectively. The average sensitivity, specificity, and accuracy of the three radiologists were 51.14%, 82.58%, and 61.29%, respectively. The average sensitivity, specificity, and accuracy of distinguishing small echogenic foci in the test 2 group (n = 58) was 70.17%, 77.14%, and 73.33%, respectively. Correspondingly, the average sensitivity, specificity, and accuracy of the radiologists were 57.69%, 63.29%, and 59.38%. Conclusions: The study demonstrated that DL performed far better than radiologists in distinguishing echogenic foci of thyroid nodules as calcifications or colloid.

The auxiliary diagnosis of thyroid echogenic foci based on a deep learning segmentation model: A two-center study.

OBJECTIVE: The aim of this study is to develop AI-assisted software incorporating a deep learning (DL) model based on static ultrasound images. The software aims to aid physicians in distinguishing between malignant and benign thyroid nodules with echogenic foci and to investigate how the AI-assisted DL model can enhance radiologists' diagnostic performance. METHODS: For this retrospective study, a total of 2724 ultrasound (US) scans were collected from two independent institutions, encompassing 1038 echogenic foci nodules. All echogenic foci were confirmed by pathology. Three DL segmentation models (DeepLabV3+, U-Net, and PSPNet) were developed, with each model using two different backbones to extract features from the nodular regions with echogenic foci. Evaluation indexes such as Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA), and Dice coefficients were employed to assess the performance of the segmentation model. The model demonstrating the best performance was selected to develop the AI-assisted diagnostic software, enabling radiologists to benefit from AI-assisted diagnosis. The diagnostic performance of radiologists with varying levels of seniority and beginner radiologists in assessing high-echo nodules was then compared, both with and without the use of auxiliary strategies. The area under the receiver operating characteristic curve (AUROC) was used as the primary evaluation index, both with and without the use of auxiliary strategies. RESULTS: In the analysis of Institution 2, the DeepLabV3+ (backbone is MobileNetV2 exhibited optimal segmentation performance, with MIoU = 0.891, MPA = 0.945, and Dice = 0.919. The combined AUROC (0.693 [95% CI 0.595-0.791]) of radiology beginners using AI-assisted strategies was significantly higher than those without such strategies (0.551 [0.445-0.657]). Additionally, the combined AUROC of junior physicians employing adjuvant strategies improved from 0.674 [0.574-0.774] to 0.757 [0.666-0.848]. Similarly, the combined AUROC of senior physicians increased slightly, rising from 0.745 [0.652-0.838] to 0.813 [0.730-0.896]. With the implementation of AI-assisted strategies, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of both senior physicians and beginners in the radiology department underwent varying degrees of improvement. CONCLUSIONS: This study demonstrates that the DL-based auxiliary diagnosis model using US static images can improve the performance of radiologists and radiology students in identifying thyroid echogenic foci.

Value of machine learning-based transrectal multimodal ultrasound combined with PSA-related indicators in the diagnosis of clinically significant prostate cancer.

Objective: To investigate the effect of transrectal multimodal ultrasound combined with serum prostate-specific antigen (PSA)-related indicators and machine learning for the diagnosis of clinically significant prostate cancer. Methods: Based on Gleason score of postoperative pathological results, the subjects were divided into clinically significant prostate cancer groups(GS>6)and non-clinically significant prostate cancer groups(GS ≤ 6). The independent risk factors were obtained by univariate logistic analysis. Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) machine learning models were combined with clinically significant prostate cancer risk factors to establish the machine learning model, calculate the model evaluation indicators, construct the receiver operating characteristic curve (ROC), and calculate the area under the curve (AUC). Results: Independent risk factor items (P< 0.05) were entered into the machine learning model. A comparison of the evaluation indicators of the model and the area under the ROC curve showed the ANN model to be best at predicting clinically significant prostate cancer, with a sensitivity of 80%, specificity of 88.6%, F1 score of 0.897, and the AUC was 0.855. Conclusion: Establishing a machine learning model by rectal multimodal ultrasound and combining it with PSA-related indicators has definite application value in predicting clinically significant prostate cancer.

Effect of Basalt Fiber on Uniaxial Compression-Related Constitutive Relation and Compressive Toughness of Recycled Aggregate Concrete.

The deformation performance of recycled aggregate concrete can be effectively improved when basalt fiber is reasonably added. In this paper, the effects of the basalt fiber volume fraction and the length-diameter ratio on the uniaxial compression-related failure characteristics, feature points of the complete stress-strain curve and the compressive toughness of recycled concrete under different replacement rates of recycled coarse aggregate were studied. The results showed that with the increase in the fiber volume fraction, the peak stress and peak strain of basalt fiber-reinforced recycled aggregate concrete first increased and then decreased. With the increase in the fiber length-diameter ratio, the peak stress and strain of the basalt fiber-reinforced recycled aggregate concrete first increased and then decreased, whereas the effect of the length-diameter ratio on peak stress and strain of the basalt fiber-reinforced recycled aggregate concrete was clearly smaller than that of the fiber volume fraction. Based on the test results, an optimized stress-strain curve model of concrete under uniaxial compression was proposed for the basalt fiber-reinforced recycled aggregate concrete. Furthermore, it was found that the fracture energy is more suitable for evaluating the compressive toughness of the basalt fiber-reinforced recycled aggregate concrete than the tensile-compression ratio.

Preparation of BiOI-Functionalized ZnO Nanorods for Ppb-Level NO2 Detection at Room Temperature.

Light activation is an effective method to improve sensor performance at room temperature (RT). This work realized the effective detection of trace-level NO2 at RT under visible light by combining ZnO with the excellent photocatalyst BiOI. A 1.5 atom % BiOI-ZnO-based sensor under 520 nm light exhibited optimal sensing properties with the maximum responses (13.9 to 1 ppm NO2), fast response/recovery time (66 s/47 s to 1 ppm), and a low detection limit of 25 ppb (theoretically 0.34 ppb). In the meantime, the sensor also possessed excellent selectivity, repeatability, and stability. The excellent properties were attributed to the high concentration of oxygen vacancies and the prolonged lifetime of photogenerated carriers. In addition, the observed photovoltaic effect of the sensor at RT indicated that the sensor held application prospects in the photovoltaic self-power field.

Phylogenetic Analysis of Small Hive Beetles From Native to Introduced Populations.

The small hive beetle (SHB), a social parasite of beehives, is native to sub-Saharan Africa and has spread to America, Europe, and Australia. Recently, these beetles invaded China, causing widespread colony collapses in the honeybee, Apis cerana. In this study, single nucleotide polymorphisms (SNPs) were identified in the beetle genome from its native range (Africa), a region that was invaded by SHBs nearly 30 years ago (America), and more recent invasions (Asia). The beetles in the United States formed the earliest branch and show signs of two decades of gene flow and local adaptation to differentiate this population from the native ones. The beetles in China were deep branched and showed the highest fixation index when compared to the US populations. The number of SNPs in overexpressed genes was significantly higher than the transcriptome. Gene-expression profiles presented here distinguish the characters between adult and larvae SHBs.

Comparative genomics suggests local adaptations in the invasive small hive beetle.

Invasive species are a major driver of ecological and environmental changes that affect human health, food security, and natural biodiversity. The success and impact of biological invasions depend on adaptations to novel abiotic and biotic selective pressures. However, the molecular mechanisms underlying adaptations in invasive parasitic species are inadequately understood. Small hive beetles, Aethina tumida, are parasites of bee nests. Originally endemic to sub-Saharan Africa, they are now found nearly globally. Here, we investigated the molecular bases of the adaptations to novel environments underlying their invasion routes. Genomes of historic and recent adults A. tumida from both the endemic and introduced ranges were compared. Analysis of gene-environment association identified 3049 candidate loci located in 874 genes. Functional annotation showed a significant bias toward genes linked to growth and reproduction. One of the genes from the apoptosis pathway encodes an "ecdysone-related protein," which is a crucial regulator in controlling body size in response to environmental cues for holometabolous insects during cell death and renewal. Genes whose proteins regulate organ size, ovary activation, and oviposition were also detected. Functions of these enriched pathways parallel behavioral differences between introduced and native A. tumida populations, which may reflect patterns of local adaptation. The results considerably improve our understanding of the underlying mechanisms and ecological factors driving adaptations of invasive species. Deep functional investigation of these identified loci will help clarify the mechanisms of local adaptation in A. tumida.

Design and Preliminary Results of Sensing and Detection for an Extravascular Implantable Cardioverter-Defibrillator.

OBJECTIVES: This study reports the sensing and arrhythmia detection performance of a novel extravascular (EV) implantable cardioverter-defibrillator (ICD) in a first-in-human pilot study. BACKGROUND: The EV ICD lead is implanted in the substernal space, resulting in novel sensing and detection challenges. It uses a programmable sensing profile with new or modified discrimination of oversensing and of ventricular tachycardia (VT) from supraventricular tachycardia (SVT). METHODS: Electrograms were post-processed from induced ventricular fibrillation (VF) at implant to determine virtual detection times for each programmable sensitivity and the least-sensitive safe sensitivity setting. In ambulatory patients, programmed sensitivity provided at least a twofold safety margin for detecting induced VF. Noise discrimination was stress tested, and the effects of source, posture, and lead maturation were determined on electrogram amplitude. Telemetry Holter monitors were used to quantify undersensing and oversensing. RESULTS: In 20 patients at implant, the least-sensitive safe sensitivity for VF detection ranged from 0.1 to 0.6 mV. Seventeen patients were followed up for a total of 16.6 patient-years. Electrogram amplitudes were stable over time, but there were significant differences among postures and sensing vectors. For the primary sensing vector, the weighted oversensing and undersensing rates were 1.03% and 0.40% respectively, on a beat-to-beat basis. Oversensing did not cause inappropriate therapy in patients with in situ leads. Oversensing discriminators withheld VF detection in 4 of 5 spontaneous, sustained oversensed episodes. SVT-VT discriminators correctly classified 93% of 128 sustained SVTs in monitor zones. CONCLUSIONS: In the EV ICD pilot study, oversensing did not cause inappropriate therapy during ambulatory follow-up of stable leads.

Two New Nonenolides from Diaporthe sp. SXZ-19, an Endophytic Fungus of Camptotheca Acuminata.

Two new nonenolides named diaportheolides A (1) and B (2) were isolated from the endophytic fungus Diaporthe sp. SXZ-19 of Camptotheca acuminata. The chemical structures of 1 and 2 were elucidated by spectroscopic analyses, including 1D- and 2D-NMR experiments and HR-ESI-MS data analysis. Their in vitro antibacterial activities are established to be insignificant.

Removal of gaseous pollutants by using 3DOM-based catalysts: A review.

Catalytic oxidation is a promising technique to control the emission of gaseous pollutants. Three-dimensionally ordered macroporous (3DOM)-based catalysts have aroused widespread attention because of their high porosity, large surface area and pore volume, superb ability of mass transfer. Therefore, they have been widely used in gaseous pollutants control field, such as soot and methane catalytic combustion, VOCs catalytic oxidation, photocatalytic CO2 reduction and so on. In this review, the recent studies about the preparation and applications of 3DOM catalysts are summarized. At the same time, the advantages and mechanism of the 3DOM catalysts used in gaseous pollutants control are introduced in depth. Finally, the perspective and future direction of 3DOM-based catalysts for gaseous pollutants control are proposed.

Production of 3-desmethyl protostreptovaricin I from the genetically engineered Streptomyces spectabilis CCTCC M2017417.

A new streptovaricin analogue, namely 3-desmethyl protostreptovaricin I (1), was isolated from the culture of the genetically engineered strain ΔstvM2 derived from Streptomyces spectabilis CCTCC M2017417. Its structure was elucidated on the basis of extensive spectroscopic analyses, including 1D and 2D NMR tests, and high resolution mass spectrometry analysis. Compound 1 is the first example of 3-desmethyl streptovaricin analogues reported so far, however, it showed no antibacterial activities against Staphylococcus aureus ATCC 29213.

Uncovering the cytochrome P450-catalyzed methylenedioxy bridge formation in streptovaricins biosynthesis.

Streptovaricin C is a naphthalenic ansamycin antibiotic structurally similar to rifamycins with potential anti-MRSA bioactivities. However, the formation mechanism of the most fascinating and bioactivity-related methylenedioxy bridge (MDB) moiety in streptovaricins is unclear. Based on genetic and biochemical evidences, we herein clarify that the P450 enzyme StvP2 catalyzes the MDB formation in streptovaricins, with an atypical substrate inhibition kinetics. Furthermore, X-ray crystal structures in complex with substrate and structure-based mutagenesis reveal the intrinsic details of the enzymatic reaction. The mechanism of MDB formation is proposed to be an intramolecular nucleophilic substitution resulting from the hydroxylation by the heme core and the keto-enol tautomerization via a crucial catalytic triad (Asp89-His92-Arg72) in StvP2. In addition, in vitro reconstitution uncovers that C6-O-methylation and C4-O-acetylation of streptovaricins are necessary prerequisites for the MDB formation. This work provides insight for the MDB formation and adds evidence in support of the functional versatility of P450 enzymes.