Urine-Derived Stem Cells Reverse Bleomycin­Induced Experimental Pulmonary Fibrosis by Inhibition of the TGF-ß1-Smad2/3 Pathway.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is characterized by progressive lung interstitial lesions with the disease pathophysiology incompletely understood, which is a serious and fatal disorder with limited treatment options. Mesenchymal stem cells (MSCs) have exhibited promising therapeutic capability for IPF. While most types of MSCs are obtained invasively, urine-derived stem cells (USCs) can be gained in a safe, noninvasive, and inexpensive procedure, which are readily available and reported to exhibit no risk of teratoma formation or oncogenic potential in vivo, sounding alternative to other MSCs. This study aims to investigate the therapeutic effect and mechanism of USCs on IPF, using a bleomycin (BLM)-induced IPF model in mice. METHODS: Cell surface marker examination by flow cytometry analysis and cell differentiation culture were used to characterize USCs obtained from healthy individuals. BLM was instilled endotracheally in adult C57BL/6 mice, followed by USCs or human bone marrow-derived mesenchymal stem cells (BMSCs) treatment by tail vein injection on day 14. Mice were euthanized on day 14 before administration or day 21 for the evaluation of pulmonary histopathology and hydroxyproline (HYP) content. Inflammatory factors of the lung, including transforming growth factor (TGF)-ß1, TNF-α, IL-6, MMP2 were analyzed by quantitative real-time PCR (qRT-PCR). Additionally, immunohistochemistry (IHC) and western blotting (WB) were applied to evaluate the expression of α-SMA and activation of TGF-ß1-Smad2/3 in lung. RESULTS: USCs highly expressed CD29 and CD90, showing negative expression of hematopoietic stem cell markers (CD45, CD34) and could differentiate into, at least, bone and fat in vitro. In mice challenged with BLM, septal thickening and prominent fibrosis were observed on day 14, with higher HYP content and mRNA levels of TGF-ß1, TNF-α and IL-6 exhibited, compared to untreated mice. USCs could migrate to lung and accumulate there in mouse model after intravenous injection. Transplantation of USCs into BLM-induced mice improved their pulmonary histopathology, decreasing Ashcroft score, Szapiel score, HYP content and mRNA levels of TGF-ß1 and MMP2 of lung, similar to the effects of BMSCs. IHC and WB further revealed that USCs could inhibit activation of the TGF­ß1-Smad2/3 pathway of lung in vivo. CONCLUSIONS: Transplantation of USCs effectively reverses pulmonary fibrotic phenotype in an experimental IPF model, inhibiting the TGF-ß1-Smad2/3 pathway, a key driver of fibrosis. These results suggest the therapeutic application of USCs for IPF, instead of other types of MSCs obtained invasively.

Electrospun nanofibers electrostatically adsorb heterotrophic nitrifying and aerobic denitrifying bacteria to degrade nitrogen in wastewater.

Nanofibers were prepared by electrospinning a mixture of polycaprolactone and silica, and modified to improve the hydrophilicity and stability of the material and to degrade nitrogenous wastewater by adsorbing heterotrophic nitrifying aerobic denitrifying (Ochrobactrum anthropic). The immobilized bacteria showed highly efficient simultaneous nitrification-denitrification ability, which could convert nearly 90 % of the initial nitrogen into gaseous nitrogen under aerobic conditions, and the average TN removal rate reached 5.59 mg/L/h. The average ammonia oxidation rate of bacteria immobilized by modified nanofibers was 7.36 mg/L/h, compared with 6.3 mg/L/h for free bacteria and only 4.23 mg/L/h for unmodified nanofiber-immobilized bacteria. Kinetic studies showed that modified nanofiber-immobilized bacteria complied with first-order degradation kinetics, and the effects of extreme pH, temperature, and salinity on immobilized bacteria were significantly reduced, while the degradation rate of free bacteria produced larger fluctuations. In addition, the immobilized bacterial nanofibers were reused five times, and the degradation rate remained stable at more than 80 %. At the same time, the degradation rate can still reach 50 % after 6 months of storage at 4 °C. It also demonstrated good nitrogen removal in practical wastewater treatment.

DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy.

Virulence factors (VFs) enable pathogens to infect their hosts. A wealth of individual, disease-focused studies has identified a wide variety of VFs, and the growing mass of bacterial genome sequence data provides an opportunity for computational methods aimed at predicting VFs. Despite their attractive advantages and performance improvements, the existing methods have some limitations and drawbacks. Firstly, as the characteristics and mechanisms of VFs are continually evolving with the emergence of antibiotic resistance, it is more and more difficult to identify novel VFs using existing tools that were previously developed based on the outdated data sets; secondly, few systematic feature engineering efforts have been made to examine the utility of different types of features for model performances, as the majority of tools only focused on extracting very few types of features. By addressing the aforementioned issues, the accuracy of VF predictors can likely be significantly improved. This, in turn, would be particularly useful in the context of genome wide predictions of VFs. In this work, we present a deep learning (DL)-based hybrid framework (termed DeepVF) that is utilizing the stacking strategy to achieve more accurate identification of VFs. Using an enlarged, up-to-date dataset, DeepVF comprehensively explores a wide range of heterogeneous features with popular machine learning algorithms. Specifically, four classical algorithms, including random forest, support vector machines, extreme gradient boosting and multilayer perceptron, and three DL algorithms, including convolutional neural networks, long short-term memory networks and deep neural networks are employed to train 62 baseline models using these features. In order to integrate their individual strengths, DeepVF effectively combines these baseline models to construct the final meta model using the stacking strategy. Extensive benchmarking experiments demonstrate the effectiveness of DeepVF: it achieves a more accurate and stable performance compared with baseline models on the benchmark dataset and clearly outperforms state-of-the-art VF predictors on the independent test. Using the proposed hybrid ensemble model, a user-friendly online predictor of DeepVF (http://deepvf.erc.monash.edu/) is implemented. Furthermore, its utility, from the user's viewpoint, is compared with that of existing toolkits. We believe that DeepVF will be exploited as a useful tool for screening and identifying potential VFs from protein-coding gene sequences in bacterial genomes.

Increased visceral fat area to skeletal muscle mass ratio is positively associated with the risk of cardiometabolic diseases in a Chinese natural population: A cross-sectional study.

AIMS: Visceral adiposity and skeletal muscle loss may be positively correlated with cardiometabolic outcomes. This study aimed to explore the associations between the visceral fat area to skeletal muscle mass ratio (VSR) and the risk of cardiometabolic diseases in a Chinese natural population. MATERIALS AND METHODS: A total of 5158 participants were included in this study. Body composition, anthropometrical, and biochemical measurements were performed. Body composition was assessed via the direct segmental multi-frequency bioelectrical impedance analysis method. The associations between VSR and metabolic associated fatty liver disease (MAFLD), hyperglycemia, hypertension, dyslipidemia, and hyperuricemia were analysed. RESULTS: With the increase of VSR by one quartile, the odds ratio (OR) increased significantly for all five cardiometabolic diseases in both genders (ptrend  < 0.001). With regard to the highest versus the lowest quartile of VSR, the ORs for cardiometabolic diseases were significantly higher in women than in men. Restricted cubic splines showed that there were significant non-linear relationships between VSR and the risk of MAFLD, dyslipidemia, hyperglycemia, and hypertension in both genders (p for non-linearity <0.05). The risk was relatively flat until VSR reached 3.078 cm2 /kg in men and 4.750 cm2 /kg in women and started to increase rapidly afterwards. In men, however, the risk slowed down after the VSR value reached around 4 cm2 /kg. CONCLUSIONS: VSR was positively associated with cardiometabolic diseases regardless of gender. As VSR increased, the risk of cardiometabolic diseases was significantly higher in women than in men. TRIAL REGISTRATION: www.chictr.org.cn (Registration number: ChiCTR2100044305).

AcrHub: an integrative hub for investigating, predicting and mapping anti-CRISPR proteins.

Anti-CRISPR (Acr) proteins naturally inhibit CRISPR-Cas adaptive immune systems across bacterial and archaeal domains of life. This emerging field has caused a paradigm shift in the way we think about the CRISPR-Cas system, and promises a number of useful applications from gene editing to phage therapy. As the number of verified and predicted Acrs rapidly expands, few online resources have been developed to deal with this wealth of information. To overcome this shortcoming, we developed AcrHub, an integrative database to provide an all-in-one solution for investigating, predicting and mapping Acr proteins. AcrHub catalogs 339 non-redundant experimentally validated Acrs and over 70 000 predicted Acrs extracted from genome sequence data from a diverse range of prokaryotic organisms and their viruses. It integrates state-of-the-art predictors to predict potential Acrs, and incorporates three analytical modules: similarity analysis, phylogenetic analysis and homology network analysis, to analyze their relationships with known Acrs. By interconnecting all modules as a platform, AcrHub presents enriched and in-depth analysis of known and potential Acrs and therefore provides new and exciting insights into the future of Acr discovery and validation. AcrHub is freely available at http://pacrispr.erc.monash.edu/AcrHub/.

BastionHub: a universal platform for integrating and analyzing substrates secreted by Gram-negative bacteria.

Gram-negative bacteria utilize secretion systems to export substrates into their surrounding environment or directly into neighboring cells. These substrates are proteins that function to promote bacterial survival: by facilitating nutrient collection, disabling competitor species or, for pathogens, to disable host defenses. Following a rapid development of computational techniques, a growing number of substrates have been discovered and subsequently validated by wet lab experiments. To date, several online databases have been developed to catalogue these substrates but they have limited user options for in-depth analysis, and typically focus on a single type of secreted substrate. We therefore developed a universal platform, BastionHub, that incorporates extensive functional modules to facilitate substrate analysis and integrates the five major Gram-negative secreted substrate types (i.e. from types I-IV and VI secretion systems). To our knowledge, BastionHub is not only the most comprehensive online database available, it is also the first to incorporate substrates secreted by type I or type II secretion systems. By providing the most up-to-date details of secreted substrates and state-of-the-art prediction and visualized relationship analysis tools, BastionHub will be an important platform that can assist biologists in uncovering novel substrates and formulating new hypotheses. BastionHub is freely available at http://bastionhub.erc.monash.edu/.

First Report of Leaf Spot on Soybean Caused by Epicoccum sorghinum in Heilongjiang Province, China.

Soybean (Glycine max (Linn.) Merr.) is one of the important oil crops in China. In September 2022, a new soybean leaf spot disease was found in Zhaoyuan County, Suihua City, Heilongjiang Province, China. Symptoms of the initial formation of irregular brown lesions on the leaves, dark brown inside, the periphery is yellow, vein chlorotic yellow, severe leaf spots connected into pieces, late fall off, not the same as previously reported soybean leaf spot (Fig. 1A). The leaf samples of infected plants were collected, and the leaf tissue (5 × 5 mm) was cut from the edge of the lesion, and then surface sterilized with 3% sodium hypochlorite for 5 min, rinsed with sterile distilled water for 3 times, and inoculated on potato dextrose agar (PDA) at 28°C. Isolates growing around the tissues from samples were subcultured on PDA, and 3 isolates were obtained using the single-spore isolation method. The fungal hyphae were white or grayish white in early stage, and the hyphae with light green concentric ring appeared on the front of the colony after 3 days, appeared orange, pink or white convex, irregular shape, reddish brown on the front of the colony for 10 days and black spherical pycnidium can be produced in the hyphae layer for 15 days (Fig.1D, E). Conidia were oval, hyaline, unicellular, aseptate, and 2.3 to 3.7 × 4.1 to 6.8 µm (n=30, Fig. 1F). Chlamydospores were subglobose, light brown, unicellular or multicellular, and 7.2 to 14.7 × 12.2 to 43.9 µm (n=30, Fig. 1H, I). Pycnidia mostly spheroid, brown, and 47.1 to 114.4 × 72.6 to 167.4 µm (n=30, Fig. 1G). A cetyl trimethyl ammonium bromide method was used to extract DNA from 7-day-old. Internal transcribed spacer (ITS), RNA polymerase II (RPB2) and ß-tubulin (TUB) gene were amplified using ITS1/ITS4 (White et al. 1990), RPB2-5F/RPB2-7cR (Liu et al. 1999) and BT2a/Bt2b (O'Donnell et al. 1997) primers respectively. The sequences obtained by polymerase chain reaction (PCR) were sequenced and the results showed that the DNA sequences of the 3 isolates were identical. Therefore, the sequence of isolate DNES22-01, DNES22-02 and DNES22-03 was submitted to GenBank. According to BLAST search, the ITS (OP884646), RPB2 (OP910000) and TUB (OP909999) sequences showed 99.81% similarity to Epicoccum sorghinum strain LC12103 (MN215621.1), 99.07% to strain P-XW-9A (MW446946.1), and 98.85% with the strain UMS (OM048108.1), respectively. Phylogenetic analysis by maximum likelihood method (MEGA7.0) generated based on the ITS, RPB2 and TUB sequences indicated that the isolates formed a supported clade to the related E. sorghinum type sequences. Isolates was found to be most closely related to E. sorghinum and far from other species. Based on morphological and phylogenetic characteristics, isolates DNES22-01, DNES22-02 and DNES22-03 was identified as E. sorghinum (Bao et al. 2019; Chen et al. 2021; Zhang et al. 2022). At the 4-leaf-stage, 10 soybean plants were inoculated by spraying with a conidial suspension (1 × 106 spores·ml-1). Sterile water served as a control. The test was repeated 3 times. All samples were incubated in a growth chamber at 27°C. Symptoms typical developed on the leaves after 7 days, but control samples remained healthy (Fig.1B, C). The fungus was reisolated from symptomatic tissues and identified as E. sorghinum by morphology characteristics and molecular characterization. To our knowledge, this is the first report of E. sorghinum causing leaf spot on soybean in Heilongjiang, China. The results can provide the basis for future studies on the occurrence, prevention, and management of this disease.

Latent profiles of nurses' moral resilience and compassion fatigue.

BACKGROUND: Compassion fatigue is often associated with moral distress in the nursing practice among registered nurses. Moral resilience is an important ability to maintain, restore, or promote their physical and mental health in response to ethical dilemmas in nursing. Moral resilience can be utilized as a potential solution to aid registered nurses in effectively managing compassion fatigue. AIM: To identify latent profiles of moral resilience among registered nurses and to explore the relationships of these profiles with compassion fatigue. RESEARCH DESIGN: From August 2022 to December 2022, 569 nurses were recruited in two general hospitals, in China. A Rushton Moral Resilience Scale and the Chinese version of Compassion Fatigue-Short Scale were given to the participants. A latent profile analysis was conducted to explore moral resilience latent profiles. Predictors of profiles membership was evaluated using multinomial logistic regression analysis, and the compassion fatigue scores of each latent profile were compared using a one-way analysis of variance. ETHICAL CONSIDERATIONS: We obtained ethical approval from the Institution Review Board of Xiangya School of Nursing, Central South University (IRB No. E202293, approved 15/July/2022). RESULTS: A four-profile moral resilience model best fit the data. Different levels and shapes differentiated the four profiles: high moral resilience (28.7%), moderate moral resilience (52.3%), low responses and high efficacy (16.2%), and low moral resilience (2.8%). Nurses with bachelor's degrees were more likely to belong to the high moral resilience (OR = 0.118, p = .038) and moderate moral resilience (OR = 0.248, p = .045); Nurses who were divorced or separated (OR = 11.746, p = .025) and very dissatisfied with their work (OR = 0.001, p = .049) were more probably belonging to low moral resilience. Nurses who had received ethical training in the hospital were more likely involved in high moral resilience (OR = 5.129, p = .003) and low responses and high efficacy (OR = 5.129, p = .003). In each profile of moral resilience, compassion fatigue was experienced differently by the participants (F = 13.05, p < .001). CONCLUSIONS: Developing and implementing interventions tailored to each nurse's moral resilience profile would maximize interventions' effectiveness and reduce nurses' compassion fatigue.

PaCRISPR: a server for predicting and visualizing anti-CRISPR proteins.

Anti-CRISPRs are widespread amongst bacteriophage and promote bacteriophage infection by inactivating the bacterial host's CRISPR-Cas defence system. Identifying and characterizing anti-CRISPR proteins opens an avenue to explore and control CRISPR-Cas machineries for the development of new CRISPR-Cas based biotechnological and therapeutic tools. Past studies have identified anti-CRISPRs in several model phage genomes, but a challenge exists to comprehensively screen for anti-CRISPRs accurately and efficiently from genome and metagenome sequence data. Here, we have developed an ensemble learning based predictor, PaCRISPR, to accurately identify anti-CRISPRs from protein datasets derived from genome and metagenome sequencing projects. PaCRISPR employs different types of feature recognition united within an ensemble framework. Extensive cross-validation and independent tests show that PaCRISPR achieves a significantly more accurate performance compared with homology-based baseline predictors and an existing toolkit. The performance of PaCRISPR was further validated in discovering anti-CRISPRs that were not part of the training for PaCRISPR, but which were recently demonstrated to function as anti-CRISPRs for phage infections. Data visualization on anti-CRISPR relationships, highlighting sequence similarity and phylogenetic considerations, is part of the output from the PaCRISPR toolkit, which is freely available at http://pacrispr.erc.monash.edu/.

Magnesium sulfate reduces postoperative pain in women with cesarean section: A meta-analysis of randomized controlled trials.

OBJECTIVES: The use of magnesium sulfate (MgSO4 ) as an adjunct in different anesthetic regimens for cesarean section (CS) delivery often reports conflicting results. This study aimed to review the effectiveness of MgSO4 on improving postoperative analgesia after CS systematically. METHODS: PubMed, Embase, and the Cochrane library were searched for randomized controlled trials (RCTs) published from inception to February 2020. RESULTS: A total of 880 women were included (440 in each group). MgSO4 had a statistically significant effect compared to the control group on the highest VAS (weighted mean difference [WMD] = -0.74, 95% confidence interval [CI] = -1.03 to -0.46, p < 0.001, I2  = 91.7%, pheterogeneity  < 0.001) and the last VAS (WMD = -0.47, 95% CI = -0.71 to -0.23, p < 0.001, I2  = 95.0%, pheterogeneity  < 0.001). MgSO4 prolonged the time to the first use of analgesia compared to the control group (standardized mean difference [SMD] = -3.03 min, 95% CI = -4.32 to -1.74, p < 0.001, I2  = 96.3%, pheterogeneity  < 0.001). MgSO4 decreased the consumption of analgesia compared to the control group (SMD = -3.20 mg of IV morphine equivalent, 95% CI: -5.45 to -0.95, p = 0.005, I2  = 97.6%, pheterogeneity  < 0.001). DISCUSSION: MgSO4 decreases the highest VAS in women who underwent general anesthesia, spinal anesthesia, or epidural for CS (all p < 0.05). Additional MgSO4 significantly reduces postoperative pain in women undergoing CS.

[Preparation and characterization of near-infrared responsive sinomenine hydrochloride reservoir microneedles].

The present study designed and prepared near-infrared responsive sinomenine hydrochloride(SIN) reservoir microneedles and evaluated the feasibility of this type of microneedles in increasing the drug loading and transdermal absorption by characterizing their mechanical properties and in vitro release characteristics.SIN was selected as the model drug, and methoxy poly(ethylene glycol) poly(caprolactone)(mPEG-PCL) copolymers and indocyanine green(ICG) were employed as amphiphilic block copolymers and light inductor to prepare near-infrared responsive nanoparticles.Based on the preparation principle of bubble microneedles, near-infrared responsive SIN reservoir microneedles were designed and prepared.The features of the near-infrared responsive SIN reservoir microneedles were characterized by measuring the morphology, length, mechanical properties, and skin penetration of microneedles.Meanwhile, the drug release performance of reservoir microneedles was evaluated by in vitro release assay.The results showed that the prepared SIN microneedles were conical, with an exposed tip height of about 650 µm.Each needle could load about 0.5 mg of drugs per square centi-meter, and this type of microneedle showed good mechanical properties and performance in skin penetration.The results of the in vitro release assay showed that the 24 h cumulative release per unit area and release rate of the microneedle were 825.61 µg·cm~(-2) and 74.3%, respectively, which indicated that its release kinetics was in line with the first-order kinetic model.This study preliminarily proved that the reservoir microneedle could effectively increase the drug loading with good mechanical properties and release perfor-mance.

Computational analysis and prediction of lysine malonylation sites by exploiting informative features in an integrative machine-learning framework.

As a newly discovered post-translational modification (PTM), lysine malonylation (Kmal) regulates a myriad of cellular processes from prokaryotes to eukaryotes and has important implications in human diseases. Despite its functional significance, computational methods to accurately identify malonylation sites are still lacking and urgently needed. In particular, there is currently no comprehensive analysis and assessment of different features and machine learning (ML) methods that are required for constructing the necessary prediction models. Here, we review, analyze and compare 11 different feature encoding methods, with the goal of extracting key patterns and characteristics from residue sequences of Kmal sites. We identify optimized feature sets, with which four commonly used ML methods (random forest, support vector machines, K-nearest neighbor and logistic regression) and one recently proposed [Light Gradient Boosting Machine (LightGBM)] are trained on data from three species, namely, Escherichia coli, Mus musculus and Homo sapiens, and compared using randomized 10-fold cross-validation tests. We show that integration of the single method-based models through ensemble learning further improves the prediction performance and model robustness on the independent test. When compared to the existing state-of-the-art predictor, MaloPred, the optimal ensemble models were more accurate for all three species (AUC: 0.930, 0.923 and 0.944 for E. coli, M. musculus and H. sapiens, respectively). Using the ensemble models, we developed an accessible online predictor, kmal-sp, available at http://kmalsp.erc.monash.edu/. We hope that this comprehensive survey and the proposed strategy for building more accurate models can serve as a useful guide for inspiring future developments of computational methods for PTM site prediction, expedite the discovery of new malonylation and other PTM types and facilitate hypothesis-driven experimental validation of novel malonylated substrates and malonylation sites.

PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins.

MOTIVATION: Gram-positive bacteria have developed secretion systems to transport proteins across their cell wall, a process that plays an important role during host infection. These secretion mechanisms have also been harnessed for therapeutic purposes in many biotechnology applications. Accordingly, the identification of features that select a protein for efficient secretion from these microorganisms has become an important task. Among all the secreted proteins, 'non-classical' secreted proteins are difficult to identify as they lack discernable signal peptide sequences and can make use of diverse secretion pathways. Currently, several computational methods have been developed to facilitate the discovery of such non-classical secreted proteins; however, the existing methods are based on either simulated or limited experimental datasets. In addition, they often employ basic features to train the models in a simple and coarse-grained manner. The availability of more experimentally validated datasets, advanced feature engineering techniques and novel machine learning approaches creates new opportunities for the development of improved predictors of 'non-classical' secreted proteins from sequence data. RESULTS: In this work, we first constructed a high-quality dataset of experimentally verified 'non-classical' secreted proteins, which we then used to create benchmark datasets. Using these benchmark datasets, we comprehensively analyzed a wide range of features and assessed their individual performance. Subsequently, we developed a two-layer Light Gradient Boosting Machine (LightGBM) ensemble model that integrates several single feature-based models into an overall prediction framework. At this stage, LightGBM, a gradient boosting machine, was used as a machine learning approach and the necessary parameter optimization was performed by a particle swarm optimization strategy. All single feature-based LightGBM models were then integrated into a unified ensemble model to further improve the predictive performance. Consequently, the final ensemble model achieved a superior performance with an accuracy of 0.900, an F-value of 0.903, Matthew's correlation coefficient of 0.803 and an area under the curve value of 0.963, and outperforming previous state-of-the-art predictors on the independent test. Based on our proposed optimal ensemble model, we further developed an accessible online predictor, PeNGaRoo, to serve users' demands. We believe this online web server, together with our proposed methodology, will expedite the discovery of non-classically secreted effector proteins in Gram-positive bacteria and further inspire the development of next-generation predictors. AVAILABILITY AND IMPLEMENTATION: http://pengaroo.erc.monash.edu/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparison of the Distinct, Host-Specific Response of Three Solanaceae Hosts Induced by Phytophthora infestans.

Three Solanaceae hosts (TSHs), S. tuberosum, N. benthamiana and S. lycopersicum, represent the three major phylogenetic clades of Solanaceae plants infected by Phytophthora infestans, which causes late blight, one of the most devastating diseases seriously affecting crop production. However, details regarding how different Solanaceae hosts respond to P. infestans are lacking. Here, we conducted RNA-seq to analyze the transcriptomic data from the TSHs at 12 and 24 h post P. infestans inoculation to capture early expression effects. Macroscopic and microscopic observations showed faster infection processes in S. tuberosum than in N. benthamiana and S. lycopersicum under the same conditions. Analysis of the number of genes and their level of expression indicated that distinct response models were adopted by the TSHs in response to P. infestans. The host-specific infection process led to overlapping but distinct in GO terms and KEGG pathways enriched for differentially expressed genes; many were tightly linked to the immune response in the TSHs. S. tuberosum showed the fastest response and strongest accumulation of reactive oxygen species compared with N. benthamiana and S. lycopersicum, which also had similarities and differences in hormone regulation. Collectively, our study provides an important reference for a better understanding of late blight response mechanisms of different Solanaceae host interactions.

Bastion3: a two-layer ensemble predictor of type III secreted effectors.

MOTIVATION: Type III secreted effectors (T3SEs) can be injected into host cell cytoplasm via type III secretion systems (T3SSs) to modulate interactions between Gram-negative bacterial pathogens and their hosts. Due to their relevance in pathogen-host interactions, significant computational efforts have been put toward identification of T3SEs and these in turn have stimulated new T3SE discoveries. However, as T3SEs with new characteristics are discovered, these existing computational tools reveal important limitations: (i) most of the trained machine learning models are based on the N-terminus (or incorporating also the C-terminus) instead of the proteins' complete sequences, and (ii) the underlying models (trained with classic algorithms) employed only few features, most of which were extracted based on sequence-information alone. To achieve better T3SE prediction, we must identify more powerful, informative features and investigate how to effectively integrate these into a comprehensive model. RESULTS: In this work, we present Bastion3, a two-layer ensemble predictor developed to accurately identify type III secreted effectors from protein sequence data. In contrast with existing methods that employ single models with few features, Bastion3 explores a wide range of features, from various types, trains single models based on these features and finally integrates these models through ensemble learning. We trained the models using a new gradient boosting machine, LightGBM and further boosted the models' performances through a novel genetic algorithm (GA) based two-step parameter optimization strategy. Our benchmark test demonstrates that Bastion3 achieves a much better performance compared to commonly used methods, with an ACC value of 0.959, F-value of 0.958, MCC value of 0.917 and AUC value of 0.956, which comprehensively outperformed all other toolkits by more than 5.6% in ACC value, 5.7% in F-value, 12.4% in MCC value and 5.8% in AUC value. Based on our proposed two-layer ensemble model, we further developed a user-friendly online toolkit, maximizing convenience for experimental scientists toward T3SE prediction. With its design to ease future discoveries of novel T3SEs and improved performance, Bastion3 is poised to become a widely used, state-of-the-art toolkit for T3SE prediction. AVAILABILITY AND IMPLEMENTATION: http://bastion3.erc.monash.edu/. CONTACT: selkrig@embl.de or wyztli@163.com or or trevor.lithgow@monash.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors.

Motivation: Many Gram-negative bacteria use type VI secretion systems (T6SS) to export effector proteins into adjacent target cells. These secreted effectors (T6SEs) play vital roles in the competitive survival in bacterial populations, as well as pathogenesis of bacteria. Although various computational analyses have been previously applied to identify effectors secreted by certain bacterial species, there is no universal method available to accurately predict T6SS effector proteins from the growing tide of bacterial genome sequence data. Results: We extracted a wide range of features from T6SE protein sequences and comprehensively analyzed the prediction performance of these features through unsupervised and supervised learning. By integrating these features, we subsequently developed a two-layer SVM-based ensemble model with fine-grain optimized parameters, to identify potential T6SEs. We further validated the predictive model using an independent dataset, which showed that the proposed model achieved an impressive performance in terms of ACC (0.943), F-value (0.946), MCC (0.892) and AUC (0.976). To demonstrate applicability, we employed this method to correctly identify two very recently validated T6SE proteins, which represent challenging prediction targets because they significantly differed from previously known T6SEs in terms of their sequence similarity and cellular function. Furthermore, a genome-wide prediction across 12 bacterial species, involving in total 54 212 protein sequences, was carried out to distinguish 94 putative T6SE candidates. We envisage both this information and our publicly accessible web server will facilitate future discoveries of novel T6SEs. Availability and implementation: http://bastion6.erc.monash.edu/. Supplementary information: Supplementary data are available at Bioinformatics online.

Expression and functional analysis of the transcription factor-encoding Gene CsERF004 in cucumber during Pseudoperonospora cubensis and Corynespora cassiicola infection.

BACKGROUND: Cucumber downy mildew, caused by P. cubensis, is an important leaf disease that can severely affect cucumber production. In recent years, cucumber target spot, caused by C. cassiicola, has been reported in both Asia and Europe and is now considered as a major disease disrupting cucumber production. Single-disease-resistant cucumber varieties have been unable to satisfy production needs. To explore the molecular mechanisms of cucumber resistance to these two diseases, cucumber cultivars D9320 (resistant to downy mildew and target spot) and D0401 (susceptible to downy mildew and target spot) were used as experimental materials in this study. We used transcriptome sequencing technology to identify genes related to disease resistance and verified using transgenic technology. RESULTS: We screened out the cucumber resistance-related gene CsERF004 using transcriptome sequencing technology. Induction by pathogens, salicylic acid (SA), and ethylene (ET) resulted in the up-regulation of CsERF004. Three treatments, namely, inoculation with C. cassiicola alone, inoculation with P. cubensis alone, and simultaneous inoculation with both pathogens, all resulted in the significant and sustained up-regulation of CsERF004 in the resistant cultivar D9320, during the early stage of infection. In the susceptible cultivar D0401, CsERF004 expression was also significantly up-regulated at the later stage of infection but to a lesser extent and for a shorter duration than in the resistant cultivar D9320. The CsERF004 gene encodes a protein localizes to the nucleus. The over-expression of CsERF004 in the susceptible cultivar D0401 resulted in the significant up-regulation of the CsPR1 and CsPR4 genes and increased the levels of SA and ET, which enhanced the resistance of cucumber to downy mildew and target spot. CONCLUSIONS: Analyses of the CsERF004 expression pattern in disease-resistant and susceptible cucumber cultivars and transgenic validation indicate that CsERF004 confers resistance to P. cubensis and C. cassiicola. The findings of this study can help to better understanding of mechanisms of response to pathogens and in establishment the genetic basis for the development of cucumber broad-spectrum resistant cultivars.

[Analgesic effect of buccal acupuncture on acute arthritis in rabbits and underlying mechanisms].

OBJECTIVE: To observe the analgesic effect of acupuncture and to explore its central analgesic mechanism in rheumatoid arthritis rabbits.
 Methods: A total of 60 flap-eared white rabbits were randomly assigned into a normal control group (n=6), a model group (n=6), a body-acupuncture group (n=24), and a buccal acupuncture group (n=24). The later 2 groups were further randomly assigned into 0, 0.5, 1, and 2 h subgroups, with 6 cases in each group. The rheumatoid arthritis model was established by induction of egg-albumin. In the body acupuncture group, bilateral "Xiyan" and "Zusanli" were punctured for 15 s while in the buccal acupuncture group, acupuncture was applied to "Xi" for 15 s, with the needle retaining for 30 min. The pain threshold was detected with PL-200, taking struggle movements of rabbits as a measurement index, response latency from irradiation to struggling movements as the rabbit's pain threshold. The contents of ß-endorplhin (ß-EP) and cholecystokinin-8 (CCK-8) in cerebrospinal fluid were examined by radioimmunoassay.
 Results: Compared with the control group, pain threshold and CCK-8 levels decreased significantly (P<0.01) and the concentration of ß-EP significantly increased (P<0.05) in the model group. The pain threshold in the body-acupuncture group and the buccal acupuncture group at 0 and 1 h (P<0.05 or P<0.01) increased significantly, while the ß-EP and CCK-8 contents in the body-acupuncture group and the buccal acupuncture group were significantly higher than those in the model group (P<0.01 or P<0.05). Both ß-EP and CCK-8 contents in the buccal acupuncture group at 0 h were significantly higher than those in the body-acupuncture group (P<0.05).
 Conclusion: The analgesic effect of buccal acupuncture is superior to that of body-acupuncture. Both buccal acupuncture and body-acupuncture can effectively raise the pain threshold in acute arthritis rabbits, which is closely associated with their effects in the up-regulation of ß-EP and CCK-8 contents in cerebrospinal fluid.

Novel anticancer oridonin derivatives possessing a diazen-1-ium-1,2-diolate nitric oxide donor moiety: Design, synthesis, biological evaluation and nitric oxide release studies.

Oridonin (1) is a complex ent-kaurane diterpenoid with unique antitumor profile, which is isolated from Isodon rubescens. In order to develop novel derivatives of oridonin with improved potency, a series of nitric oxide (NO)-releasing oridonin derivatives were synthesized by coupling diazeniumdiolates with oridonin and its semisynthesized analogues. Their in vitro antiproliferative activity, nitric oxide release ability, and preliminary anticancer mechanism were further evaluated. The results displayed that all the target compounds exhibited potent antiproliferative activities, with IC50 values ranging from 1.84 to 17.01µM. Besides, it was observed that in most cases, the antiproliferative activity correlated well with levels of intracellular NO release. More interestingly, preliminary mechanism studies revealed that the most potent compound 14d induced apoptosis and arrested the cell cycle at the S phase in Bel-7402 cells, which is different from parent compound oridonin. Together, the above promising results warrant the further development of oridonin/NO hybrids as potential antitumor leads.

Accelerating spliced alignment of long RNA sequencing reads using parallel maximal exact match retrieval.

The genomics landscape has undergone a revolutionary transformation with the emergence of third-generation sequencing technologies. Fueled by the exponential surge in sequencing data, there is an urgent demand for accurate and rapid algorithms to effectively handle this burgeoning influx. Under such circumstances, we developed a parallelized, yet accuracy-lossless algorithm for maximal exact match (MEM) retrieval to strategically address the computational bottleneck of uLTRA, a leading spliced alignment algorithm known for its precision in handling long RNA sequencing (RNA-seq) reads. The design of the algorithm incorporates a multi-threaded strategy, enabling the concurrent processing of multiple reads simultaneously. Additionally, we implemented the serialization of index required for MEM retrieval to facilitate its reuse, resulting in accelerated startup for practical tasks. Extensive experiments demonstrate that our parallel algorithm achieves significant improvements in runtime, speedup, throughput, and memory usage. When applied to the largest human dataset, the algorithm achieves an impressive speedup of 10.78 × , significantly improving throughput on a large scale. Moreover, the integration of the parallel MEM retrieval algorithm into the uLTRA pipeline introduces a dual-layered parallel capability, consistently yielding a speedup of 4.99 × compared to the multi-process and single-threaded execution of uLTRA. The thorough analysis of experimental results underscores the adept utilization of parallel processing capabilities and its advantageous performance in handling large datasets. This study provides a showcase of parallelized strategies for MEM retrieval within the context of spliced alignment algorithm, effectively facilitating the process of RNA-seq data analysis. The code is available at https://github.com/RongxingWong/AcceleratingSplicedAlignment.