Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies.

Phages can specifically recognize and kill bacteria, which lead to important application value of bacteriophage in bacterial identification and typing, livestock aquaculture and treatment of human bacterial infection. Considering the variety of human-infected bacteria and the continuous discovery of numerous pathogenic bacteria, screening suitable therapeutic phages that are capable of infecting pathogens from massive phage databases has been a principal step in phage therapy design. Experimental methods to identify phage-host interaction (PHI) are time-consuming and expensive; high-throughput computational method to predict PHI is therefore a potential substitute. Here, we systemically review bioinformatic methods for predicting PHI, introduce reference databases and in silico models applied in these methods and highlight the strengths and challenges of current tools. Finally, we discuss the application scope and future research direction of computational prediction methods, which contribute to the performance improvement of prediction models and the development of personalized phage therapy.

PB-LKS: a python package for predicting phage-bacteria interaction through local K-mer strategy.

Bacteriophages can help the treatment of bacterial infections yet require in-silico models to deal with the great genetic diversity between phages and bacteria. Despite the tolerable prediction performance, the application scope of current approaches is limited to the prediction at the species level, which cannot accurately predict the relationship of phages across strain mutants. This has hindered the development of phage therapeutics based on the prediction of phage-bacteria relationships. In this paper, we present, PB-LKS, to predict the phage-bacteria interaction based on local K-mer strategy with higher performance and wider applicability. The utility of PB-LKS is rigorously validated through (i) large-scale historical screening, (ii) case study at the class level and (iii) in vitro simulation of bacterial antiphage resistance at the strain mutant level. The PB-LKS approach could outperform the current state-of-the-art methods and illustrate potential clinical utility in pre-optimized phage therapy design.

Loss of sodium leak channel (NALCN) in the ventral dentate gyrus impairs neuronal activity of the glutamatergic neurons for inflammation-induced depression in male mice.

BACKGROUND: The dentate gyrus (DG) has been implicated in the pathophysiology of depression. Many studies have revealed the cellular types, neural circuits, and morphological changes of the DG involved in the development of depression. However, the molecular regulating its intrinsic activity in depression is unknown. METHODS: Utilizing the mode of depression induced by lipopolysaccharide (LPS), we investigate the involvement of the sodium leak channel (NALCN) in inflammation-induced depressive-like behaviors of male mice. The expression of NALCN was detected by immunohistochemistry and real-time polymerase chain reaction. DG microinjection of the adeno-associated virus or lentivirus was carried out using a stereotaxic instrument and followed by behavioral tests. Neuronal excitability and NALCN conductance were recorded by whole-cell patch-clamp techniques. RESULTS: The expression and function of NALCN were reduced in both the dorsal and ventral DG in LPS-treated mice; whereas, only knocking down NALCN in the ventral pole produced depressive-like behaviors and this effect of NALCN was specific to ventral glutamatergic neurons. The excitability of ventral glutamatergic neurons was impaired by both the knockdown of NALCN and/or the treatment of LPS. Then, the overexpression of NALCN in the ventral glutamatergic neurons decreased the susceptibility of mice to inflammation-induced depression, and the intracranial injection of substance P (non-selective NALCN activator) into the ventral DG rapidly ameliorated inflammation-induced depression-like behaviors in an NALCN-dependent manner. CONCLUSIONS: NALCN, which drives the neuronal activity of the ventral DG glutamatergic neurons, uniquely regulates depressive-like behaviors and susceptibility to depression. Therefore, the NALCN of glutamatergic neurons in the ventral DG may present a molecular target for rapid antidepressant drugs.

Mesoporous NiO@ZnO nanofiber membranes via single-nozzle electrospinning for urine metabolism analysis of smokers.

An effective matrix is very important for impact laser desorption/ionization mass spectrometry (LDI-MS), and the physicochemical properties of the matrix nanostructures can impact the LDI-MS performance. In this study, a simple and efficient single-nozzle electrospinning strategy using polystyrene (PS) spheres and polyvinyl pyrrolidone (PVP) to construct a mesoporous NiO@ZnO nanofiber membrane was developed. Compared with the NiO and ZnO nanomaterials alone, the obtained NiO@ZnO nanofiber membrane was proven to be an efficient material as the matrix to increase the intensity of the mass spectrum speaks of small molecules. The NiO@ZnO nanofiber membrane was used as the matrix for the LDI-MS method for the urine metabolism analysis of smokers, which revealed differences in the metabolic and the possible metabolic markers of smokers through the statistical analysis of the urine samples of 27 smokers and 11 nonsmoker controls.

Mesoporous ZnO nanosheet as gas sensor for sensitive triethylamine detection.

In this paper, mesoporous ZnO nanosheets were synthesized by the hydrothermal method using polystyrene-polyacrylic acid (PS-PAA) as the template. The morphology, structure, and composition of the samples were characterized by SEM, TEM, XRD, and XPS, and the physical properties of the samples were tested by N2 adsorption-desorption curve. The results showed that the mesoporous ZnO nanosheets presented a flower-like appearance. Each flower is composed of flake petals which consist of nanoparticles of different sizes, with a large specific surface area. Gas sensitivity test results show that the ZnO gas sensor has good triethylamine (TEA) sensing performance. Its response to 50 ppm TEA can reach 43.771, and the detection limit is as low as 1 ppm, showing the characteristics of rapid response/recovery.

High-fidelity standard model reconstruction and verification of an airliner based on point clouds.

Computational fluid dynamics (CFD) numerical simulation as the primary research tool is particularly essential for its credibility during the aerodynamic design of aircraft. To further promote CFD verification and validation on the airliner, a high-fidelity model reconstruction of the airliner is fundamental. Based on this, we put forward a novel framework, to our best knowledge, to reconstruct a high-fidelity standard model for an airliner efficiently, and the feasibility and accuracy of these reconstructed models are accessed by the CFD simulation-based validation method. First and foremost, a laser scanner was placed at each station around the airliner to scan and acquire multiview point clouds. Afterwards, the truncated least-squares-based algorithm was adopted to register these point clouds entirely. Additionally, we fitted the nonuniform rational basis spline surface based on the least-squares progressive and iterative approximation algorithm. Finally, CFD simulation results were compared and analyzed with the aerodynamic data obtained by the aircraft manufacturer under the same Mach number of the uniform model. It turns out that the coincidence between them is high, and the changing trend is basically consistent. Hence, this method is highly feasible and can establish a high-fidelity standard model of an airliner with unified high and low speeds so that its appearance, test data, and research results can be adopted as the standard data for CFD verification and validation.

Reverse vaccinology approach for the identifications of potential vaccine candidates against Salmonella.

Salmonella is a leading cause of foodborne pathogen which causes intestinal and systemic diseases across the world. Vaccination is the most effective protection against Salmonella, but the identification and design of an effective broad-spectrum vaccine is still a great challenge, because of the multi-serotypes of Salmonella. Reverse vaccinology is a new tool to discovery and design vaccine antigens combining human immunology, structural biology and computational biology with microbial genomics. In this study, reverse vaccinology, an in-silico approach was established to screen appropriate immunogen targets by calculating the immunogenicity score of 583 non-redundant outer membrane and secreted proteins of Salmonella. Herein among 100 proteins identified with top-ranked scores, 15 representative antigens were selected randomly. Applying the sequence conservation test, four proteins (FliK, BcsZ, FhuA and FepA) remained as potential vaccine candidates for in vivo evaluation of immunogenicity and immunoprotection. All four candidates were capable to trigger the immune response and stimulate the production of antiserum in mice. Furthermore, top-ranked proteins including FliK and BcsZ provided wide antigenic coverage among the multi-serotype of Salmonella. The S. Typhimurium LT2 challenge model used in mice immunized with FliK and BcsZ showed a high relative percentage survival (RPS) of 52.74 % and 64.71 % respectively. In conclusion, this study constructed an in-silico pipeline able to successfully pre-screen the vaccine targets characterized by high immunogenicity and protective immunity. We show that reverse vaccinology allowed screening of appropriate broad-spectrum vaccines for Salmonella.

Double-layer capsule of mesoporous ZnO@SnO2 for sensitive detection of triethylamine.

To overcome obstacles such as low response and poor selectivity of pure ZnO and SnO2 gas sensors, the ZnO@SnO2 sensor was synthesized by hydrothermal synthesis. The samples were characterized by XRD, XPS, SEM, HRTEM, N2 adsorption-desorption and other techniques. The results show that ZnO@SnO2 forms an n-n-type heterostructure and presents a double-layer capsule with a size of 0.5-4 µm. The results show that compared with pure ZnO and SnO2, the ZnO@SnO2 sensor exhibits a higher response (138.9) to 50 ppm triethylamine (TEA) at 152°C, which is 19.56 times that of the pure ZnO sensor and 21.7 times that of the SnO2 sensor. It has a short response/recovery time (11/11 s), excellent selectivity and cycling stability. Compared with other volatile organic compounds or gases, it has higher selectivity for TEA detection.

Identification and evaluation of a panel of strong constitutive promoters in Listeria monocytogenes for improving the expression of foreign antigens.

Attenuated Listeria monocytogenes could be a potential vaccine vector for the immunotherapy of tumors or pathogens. However, the lack of reliable promoters has limited its ability to express foreign antigens. In the present study, 21 promoters were identified from Listeria monocytogenes through RNA-seq analysis under two pH conditions of pH 7.4 and pH 5.5. Based on the constructed fluorescence report system, 7 constitutive promoters exhibited higher strength than Phelp (1.8-fold to 5.4-fold), a previously reported strong promoter. Furthermore, the selected 5 constitutive promoters exhibited higher UreB production activity than Phelp (1.1-fold to 8.3-fold). Notably, a well-characterized constitutive promoter P18 was found with the highest activity of fluorescence intensity and UreB production. In summary, the study provides a panel of strong constitutive promoters for Listeria monocytogenes and offers a theoretical basis for mining constitutive promoters in other organisms. KEY POINTS: • Twenty-one promoters were identified from L. monocytogenes through RNA-seq. • Fluorescent tracer of L. monocytogenes (P18) was performed in vitro and in vivo. • A well-characterized constitutive promoter P18 could improve the expression level of a foreign antigen UreB in L. monocytogenes.

Development overview of Raman-activated cell sorting devoted to bacterial detection at single-cell level.

Understanding the metabolic interactions between bacteria in natural habitat at the single-cell level and the contribution of individual cell to their functions is essential for exploring the dark matter of uncultured bacteria. The combination of Raman-activated cell sorting (RACS) and single-cell Raman spectra (SCRS) with unique fingerprint characteristics makes it possible for research in the field of microbiology to enter the single cell era. This review presents an overview of current knowledge about the research progress of recognition and assessment of single bacterium cell based on RACS and further research perspectives. We first systematically summarize the label-free and non-destructive RACS strategies based on microfluidics, microdroplets, optical tweezers, and specially made substrates. The importance of RACS platforms in linking target cell genotype and phenotype is highlighted and the approaches mentioned in this paper for distinguishing single-cell phenotype include surface-enhanced Raman scattering (SERS), biomarkers, stable isotope probing (SIP), and machine learning. Finally, the prospects and challenges of RACS in exploring the world of unknown microorganisms are discussed. KEY POINTS: • Analysis of single bacteria is essential for further understanding of the microbiological world. • Raman-activated cell sorting (RACS) systems are significant protocol for characterizing phenotypes and genotypes of individual bacteria.

Immunological evaluation of virulence-deficient Listeria monocytogenes strains in C57BL/6 mice.

Attenuated Listeria monocytogenes (L. monocytogenes), which has unique advantages in presenting foreign antigens, was widely used in tumor immunotherapy research. As a live vaccine vector, attenuated L. monocytogenes was required to not only have certain invasiveness but also ensure safety, while the lack of different virulence factors may cause L. monocytogenes to show different safety and invasiveness. To evaluate the potential of virulence-deficient L. monocytogenes strains as a vaccine vector, four mutant strains EGD-eΔactA, EGD-eΔactA/inlB, EGD-eΔhly, and EGD-eΔprfA were used to infect C57BL/6 mice for determining related immune indexes. Compared with EGD-e, mutant strains showed significantly decreased invasion in C57BL/6 mice and caused relatively minor damage to spleen and liver. However, EGD-eΔactA and EGD-eΔactA/inlB were superior to EGD-eΔhly and EGD-eΔprfA in the comprehensive evaluation of inflammatory factor transcription level, immune cell differentiation and antibody level, which proved that they have a stronger adjuvant effect as a vaccine vector.

Cable-Like Core-Shell Mesoporous SnO2 Nanofibers by Single-Nozzle Electrospinning Phase Separation for Formaldehyde Sensing.

In this study, we have developed a simple and efficient single-nozzle electrospinning strategy involving the phase separation of polystyrene and poly(vinylpyrrolidone) to construct cable-like core-shell mesoporous SnO2 nanofibers. Compared with traditional multi-axial electrospinning approaches to the synthesis of core-shell nanofibers, the single-nozzle electrospinning process requires no complex multi-axial electrospinning setups or post-treatments, just drying and annealing after electrospinning. The obtained SnO2 nanofibers show promise as a sensing material for formaldehyde at low concentrations, the detection limit being about 1 ppm. Furthermore, the nanofibers exhibited good cycling stability and selectivity, with response and recovery times toward 10 ppm formaldehyde being approximately 18 and 196 s, respectively, at an operating temperature of 195 °C.

Hydrangea-like mesoporous WO3 nanoflowers with crystalline framework for 3-hydroxy-2-butanone sensing.

In this study, a simple and efficient strategy for the construction of hydrangea-like mesoporous WO3 nanoflowers templated using diblock copolymer PS119-PtBA129 was developed. The nanoflower shows good gas sensing properties, especially for 3-hydroxy-2-butanone (3H-2B), which is the signature metabolite of Listeria monocytogenes (L. monocytogenes). Therefore, the gas sensing of 3H-2B by hydrangea-like mesoporous WO3 nanoflowers can be used to detect L. monocytogenes. In the case of 25 ppm 3H-2B as target gas, the response (Ra/Rg) of the hydrangea-like mesoporous WO3 nanoflowers at 205 °C is 152, where Ra and Rg are the resistances of the sensing device in air and target gas, respectively, and the response and recovery times at 25 ppm are 25 s and 146 s, respectively. Schematic illustration of the formation of hydrangea-like mesoporous WO3 nanoflowers and its gas sensing implication.

SERS-based lateral flow assay combined with machine learning for highly sensitive quantitative analysis of Escherichia coli O157:H7.

In the present study, surface-enhanced Raman scattering-based lateral flow assay (SERS-LFA) strips were applied to promptly and sensitively detect Escherichia coli O157:H7 (E. coli O157:H7) to ensure food safety. The SERS nanotags were prepared by connecting peculiar monoclonal antibody (McAb) against E. coli O157:H7 directly onto the surfaces of gold-silver core-shell nanostructures loaded with two-layer Raman reporter molecules of 5,5'-dithiobis-(2-nitrobenzoic acid) (DTNB). The Raman signal intensity at 1335 cm-1 on the test line (T line) of SERS-LFA strips was detected in the wide range of 101-109 colony-forming units/mL (CFU/mL), and regression models based on machine learning were combined to accurately and quantitatively analyze E. coli O157:H7. The limit of detection (LOD) of the extreme gradient boosting regression (XGBR) based on the Raman signal intensity of DTNB was 6.94 × 101 CFU/mL for E. coli O157:H7, which was approximately four orders of magnitude lower than that of visual limits. In addition, although E. coli O157:H7 was spiked into the food matrices including milk and beef at an ultra-low dose of 10 CFU/mL, the SERS-LFA combined with XGBR was able to successfully explore E. coli O157:H7 from the mixture that was incubated for only 2 h, in which the recoveries were mainly distributed between 86.41 and 128.25%. In summary, these results demonstrated that the SERS-LFA had a significant potential as a powerful tool for the point-of-care testing (POCT) of E. coli O157:H7 in the early food contamination stage.

Antibiotic Resistance Patterns of Staphylococcus aureus Isolates from Retail Foods in Mainland China: A Meta-Analysis.

Foodborne Staphylococcus aureus, including methicillin-resistant S. aureus (MRSA), is increasingly threatening human health. Pooled prevalence rates of S. aureus contamination have been extensively studied in retail food products in mainland China, but data regarding antibiotic resistance rates of S. aureus remain scattered. This study was designed to collect researches published between 2007 and 2017 in mainland China and to evaluate the antibiotic resistance of S. aureus from retail foods using a meta-analytic approach. We systematically searched the China National Knowledge Infrastructure (CNKI) and Web of Science databases to identify peer-reviewed literature. A number of multilevel random-effects models were fitted to estimate mean occurrence rates of antibiotic-resistant S. aureus, and subgroup analyses were performed to compare antibiotic resistance rates of S. aureus throughout the years and among the methods to determine the antimicrobial susceptibility. Among the considered antibiotics, S. aureus showed the highest resistance rate to penicillin G (87%, 95% confidence interval [CI] 83-90%), followed by ampicillin (72%, 95% CI 62-81%) and erythromycin (41%, 95% CI 36-46%). MRSA showed the highest resistance rate to ampicillin (98%, 95% CI 89-100%), followed by oxacillin (97%, 95% CI 80-100%) and penicillin G (96%, 95% CI 89-99%). Multidrug resistance (MDR) of S. aureus was most frequently observed to three antibiotics (17%, 95% CI 12-22%), and MRSA showed the highest resistance rate to four antibiotics (24%, 95% CI 5-67%). Subgroup analyses results proved that sources of heterogeneity among studies were neither publication year nor detection method. In conclusion, the meta-analysis showed that ß-lactam antibiotics resistance of S. aureus and MRSA strains isolated from retail foods remained the most serious, and MDR of S. aureus and MRSA were also observed. Therefore, it is important to monitor the antibiotic resistance of S. aureus and MRSA in food chain, and food safety measures should be taken to reduce the transmission of this bacterium from foods to human beings.

The recent progress in proteochemometric modelling: focusing on target descriptors, cross-term descriptors and application scope.

As an extension of the conventional quantitative structure activity relationship models, proteochemometric (PCM) modelling is a computational method that can predict the bioactivity relations between multiple ligands and multiple targets. Traditional PCM modelling includes three essential elements: descriptors (including target descriptors, ligand descriptors and cross-term descriptors), bioactivity data and appropriate learning functions that link the descriptors to the bioactivity data. Since its appearance, PCM modelling has developed rapidly over the past decade by taking advantage of the progress of different descriptors and machine learning techniques, along with the increasing amounts of available bioactivity data. Specifically, the new emerging target descriptors and cross-term descriptors not only significantly increased the performance of PCM modelling but also expanded its application scope from traditional protein-ligand interaction to more abundant interactions, including protein-peptide, protein-DNA and even protein-protein interactions. In this review, target descriptors and cross-term descriptors, as well as the corresponding application scope, are intensively summarized. Additionally, we look forward to seeing PCM modelling extend into new application scopes, such as Target-Catalyst-Ligand systems, with the further development of descriptors, machine learning techniques and increasing amounts of available bioactivity data.

Attenuated Listeria monocytogenes protecting zebrafish (Danio rerio) against Vibrio species challenge.

Live attenuated bacteria is a promising candidate vector for the delivery of vaccines in clinic trials. In the field of aquaculture industry, live vector vaccine also could provide long-term and effective protection against fish bacterial diseases. In our previous work, we demonstrated attenuated Listeria monocytogenes (Lm) had the potential to be an aquaculture vaccine vector in cellular level and zebrafish model. To further investigate the potential application of attenuated Lm in aquaculture vaccines, the outer membrane protein K (OmpK) from Vibrio parahaemolyticus (V. parahaemolyticus), as a conservative protective antigen, was fused to a new antigen-delivery system, and introduced into double-gene attenuated Lm strain (EGDe-ΔactA/inlB, Lmdd) to get live-vector vaccine strain Lmdd-OmpK. The strain Lmdd-OmpK showed the stable secrete efficacy of OmpK and was tested the cross-protective immunity against Vibrio species. After intraperitoneal administration in zebrafish, Lmdd and Lmdd-OmpK strain both improved the survival rates of zebrafish infected by V. parahaemolyticus, Vibrio alginolyticus (V. alginolyticus) and Vibrio anguillarum (V. anguillarum), respectively. In summary, attenuated Lm is able to protect zebrafish against Vibrio species challenge, illustrating its potential value for further aquaculture vaccines development.

Ultrathin ZIF-67 nanosheets as a colorimetric biosensing platform for peroxidase-like catalysis.

In this work, we report a zeolitic imidazolate framework (ZIF-67) which could catalyze 3,3',5,5'-tetramethylbenzidine (TMB) to produce a yellow chromogenic reaction. ZIF-67 showed high peroxidase-like activity compared with copper-based metal-organic framework nanoparticles (Cu-MOF), zinc-based metal-organic framework nanoparticles (ZIF-8), and horseradish peroxidase (HPR). We discovered for the first time that the cobalt-based metal-organic framework nanoparticles possess intrinsic peroxidase-like activity without H2O2, which can be employed to quantitatively monitor the H2O2. Graphical abstract ᅟ.

SEPPA 2.0--more refined server to predict spatial epitope considering species of immune host and subcellular localization of protein antigen.

Spatial Epitope Prediction server for Protein Antigens (SEPPA) has received lots of feedback since being published in 2009. In this improved version, relative ASA preference of unit patch and consolidated amino acid index were added as further classification parameters in addition to unit-triangle propensity and clustering coefficient which were previously reported. Then logistic regression model was adopted instead of the previous simple additive one. Most importantly, subcellular localization of protein antigen and species of immune host were fully taken account to improve prediction. The result shows that AUC of 0.745 (5-fold cross-validation) is almost the baseline performance with no differentiation like all the other tools. Specifying subcellular localization of protein antigen and species of immune host will generally push the AUC up. Secretory protein immunized to mouse can push AUC to 0.823. In this version, the false positive rate has been largely decreased as well. As the first method which has considered the subcellular localization of protein antigen and species of immune host, SEPPA 2.0 shows obvious advantages over the other popular servers like SEPPA, PEPITO, DiscoTope-2, B-pred, Bpredictor and Epitopia in supporting more specific biological needs. SEPPA 2.0 can be accessed at http://badd.tongji.edu.cn/seppa/. Batch query is also supported.

Research on fatigue detection of flight trainees based on face EMF feature model combination with PSO-CNN algorithm.

Even though the capability of aircraft manufacturing has improved, human factors still play a pivotal role in flight accidents. For example, fatigue-related accidents are a common factor in human-led accidents. Hence, pilots' precise fatigue detections could help increase the flight safety of airplanes. The article suggests a model to recognize fatigue by implementing the convolutional neural network (CNN) by implementing flight trainees' face attributions. First, the flight trainees' face attributions are derived by a method called the land-air call process when the flight simulation is run. Then, sixty-eight points of face attributions are detected by employing the Dlib package. Fatigue attribution points were derived based on the face attribution points to construct a model called EMF to detect face fatigue. Finally, the proposed PSO-CNN algorithm is implemented to learn and train the dataset, and the network algorithm achieves a recognition ratio of 93.9% on the test set, which can efficiently pinpoint the flight trainees' fatigue level. Also, the reliability of the proposed algorithm is validated by comparing two machine learning models.