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.

Neo-intline: integrated pipeline enables neoantigen design through the in-silico presentation of T-cell epitope.

Neoantigen vaccines are one of the most effective immunotherapies for personalized tumour treatment. The current immunogen design of neoantigen vaccines is usually based on whole-genome sequencing (WGS) and bioinformatics prediction that focuses on the prediction of binding affinity between peptide and MHC molecules, ignoring other peptide-presenting related steps. This may result in a gap between high prediction accuracy and relatively low clinical effectiveness. In this study, we designed an integrated in-silico pipeline, Neo-intline, which started from the SNPs and indels of the tumour samples to simulate the presentation process of peptides in-vivo through an integrated calculation model. Validation on the benchmark dataset of TESLA and clinically validated neoantigens illustrated that neo-intline could outperform current state-of-the-art tools on both sample level and melanoma level. Furthermore, by taking the mouse melanoma model as an example, we verified the effectiveness of 20 neoantigens, including 10 MHC-I and 10 MHC-II peptides. The in-vitro and in-vivo experiments showed that both peptides predicted by Neo-intline could recruit corresponding CD4+ T cells and CD8+ T cells to induce a T-cell-mediated cellular immune response. Moreover, although the therapeutic effect of neoantigen vaccines alone is not sufficient, combinations with other specific therapies, such as broad-spectrum immune-enhanced adjuvants of granulocyte-macrophage colony-stimulating factor (GM-CSF) and polyinosinic-polycytidylic acid (poly(I:C)), or immune checkpoint inhibitors, such as PD-1/PD-L1 antibodies, can illustrate significant anticancer effects on melanoma. Neo-intline can be used as a benchmark process for the design and screening of immunogenic targets for neoantigen vaccines.

The volatile anesthetic isoflurane differentially inhibits voltage-gated sodium channel currents between pyramidal and parvalbumin neurons in the prefrontal cortex.

Background: How volatile anesthetics work remains poorly understood. Modulations of synaptic neurotransmission are the direct cellular mechanisms of volatile anesthetics in the central nervous system. Volatile anesthetics such as isoflurane may reduce neuronal interaction by differentially inhibiting neurotransmission between GABAergic and glutamatergic synapses. Presynaptic voltage-dependent sodium channels (Nav), which are strictly coupled with synaptic vesicle exocytosis, are inhibited by volatile anesthetics and may contribute to the selectivity of isoflurane between GABAergic and glutamatergic synapses. However, it is still unknown how isoflurane at clinical concentrations differentially modulates Nav currents between excitatory and inhibitory neurons at the tissue level. Methods: In this study, an electrophysiological recording was applied in cortex slices to investigate the effects of isoflurane on Nav between parvalbumin (PV+) and pyramidal neurons in PV-cre-tdTomato and/or vglut2-cre-tdTomato mice. Results: Isoflurane at clinically relevant concentrations produced a hyperpolarizing shift in the voltage-dependent inactivation and slowed the recovery time from the fast inactivation in both cellular subtypes. Since the voltage of half-maximal inactivation was significantly depolarized in PV+ neurons compared to that of pyramidal neurons, isoflurane inhibited the peak Nav currents in pyramidal neurons more potently than those of PV+ neurons (35.95 ± 13.32% vs. 19.24 ± 16.04%, P = 0.036 by the Mann-Whitney test). Conclusions: Isoflurane differentially inhibits Nav currents between pyramidal and PV+ neurons in the prefrontal cortex, which may contribute to the preferential suppression of glutamate release over GABA release, resulting in the net depression of excitatory-inhibitory circuits in the prefrontal cortex.

Comparison of Performances among Four Bleeding-Prediction Scores in Elderly Cancer Patients with Venous Thromboembolism.

The performances of RIETE, VTE-BLEED, SWITCO65 + , and Hokusai-VTE scores for predicting major bleeding events in hospitalized elderly cancer patients with venous thromboembolism (VTE) have not been evaluated. This study validated the performances of these scoring systems in a cohort of elderly cancer patients with VTE. Between June 2015 and March 2021, a total of 408 cancer patients (aged ≥ 65 years) with acute VTE were consecutively enrolled. The overall rates of in-hospital major bleeding and clinically relevant bleeding (CRB) were 8.3% (34/408) and 11.8% (48/408), respectively. RIETE score could categorize patients with increasing rate of major bleeding and CRB into low-/intermediate- and high-risk categories (7.1 vs. 14.1%, p = 0.05 and 10.1 vs. 19.7%, p = 0.02, respectively). The discriminative power of the four scores for predicting major bleeding was poor to moderate, indicated by areas under the receiver operating characteristic curves (0.45 [95% confidence interval, CI: 0.35-0.55] for Hokusai-VTE, 0.54 [95% CI: 0.43-0.64] for SWITCO65 + , 0.58 [95% CI: 0.49-0.68] for VTE-BLEED, and 0.61 [95% CI: 0.51-0.71] for RIETE). RIETE score might be used to predict major bleeding in hospitalized elderly cancer patients with acute VTE.

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.

Univ-flu: A structure-based model of influenza A virus hemagglutinin for universal antigenic prediction.

The rapid mutations on hemagglutinin (HA) of influenza A virus (IAV) can lead to significant antigenic variance and consequent immune mismatch of vaccine strains. Thus, rapid antigenicity evaluation is highly desired. The subtype-specific antigenicity models have been widely used for common subtypes such as H1 and H3. However, the continuous emerging of new IAV subtypes requires the construction of universal antigenic prediction model which could be applied on multiple IAV subtypes, including the emerging or re-emerging ones. In this study, we presented Univ-Flu, series structure-based universal models for HA antigenicity prediction. Initially, the universal antigenic regions were derived on multiple subtypes. Then, a radial shell structure combined with amino acid indexes were introduced to generate the new three-dimensional structure based descriptors, which could characterize the comprehensive physical-chemical property changes between two HA variants within or across different subtypes. Further, by combining with Random Forest classifier and different training datasets, Univ-Flu could achieve high prediction performances on intra-subtype (average AUC of 0.939), inter-subtype (average AUC of 0.771), and universal-subtype (AUC of 0.978) prediction, through independent test. Results illustrated that the designed descriptor could provide accurate universal antigenic description. Finally, the application on high-throughput antigenic coverage prediction for circulating strains showed that the Univ-Flu could screen out virus strains with high cross-protective spectrum, which could provide in-silico reference for vaccine recommendation.

Exploring the Dynamic of Bacterial Communities in Manila Clam (Ruditapes philippinarum) During Refrigerated Storage.

Microorganism contamination is one of the most important factors affecting the spoilage and food safety of Manila clams. This study aimed to gain insights into bacterial composition and the dynamic change of bacterial communities on retailed Manila clam during refrigerated storage within the edible period. High-throughput sequencing was conducted to monitor the bacterial population with the prolongation of storage time of Day 0, Day 1, and Day 3. Result demonstrated that phyla of Proteobacteria, Actinobacteriota, Acidobacteriota, and Chloroflexi composed the majority of bacterial communities during the whole observation process. Furthermore, the increase of Proteobacteria showed a positive correlation with the storage time, whereas Acidobacteriota and Chloroflexi continued to decline in storage. For genus annotation, none of genus obtained dominant population in storage. From Day 0 to Day 1, the genera of Streptomyces, Bradyrhizobium, and Mycobacterium significantly increased; meanwhile, 12 genera significantly decreased. Compared with samples at Day 0, a total of 15 genera significantly decreased with the reduced proportion ranging from 0.50 to 4.40% at Day 3. At the end of the storage, the genus Crossiella became the most redundant population. Both the richness and diversity decreased at the start of storage at Day 1, and then slightly increased at Day 3 was observed. Based on the result in this study, strategy targeting the increased bacteria could be tested to improve the consumption quality and safety of refrigerated clam.

Insights into Adaptive Mechanisms of Extreme Acidophiles Based on Quorum Sensing/Quenching-Related Proteins.

Quorum sensing (QS) is a unique mechanism for microorganisms to coordinate their activities through intercellular communication, including four main types of autoinducer-1 (AI-1, namely, N-acyl homoserine lactone [AHL]), AI-2, AI-3, and diffusible signaling factor [DSF]) based on signaling molecules. Quorum quenching (QQ) enzymes can disrupt the QS phenomenon by inactivating signaling molecules. QS is proposed to regulate biofilm formation in extremely acidic environments, but the QS/QQ-related genomic features in most acidophilic bacteria are still largely unknown. Here, genome annotation of 83 acidophiles from the genera Acidithiobacillus, Leptospirillum, Sulfobacillus, and Acidiphilium altogether revealed the existence of AI-1, AI-3, DSF, and AhlD (AHL degradation enzyme). The conservative investigation indicated that some QS/QQ-related proteins harbored key residues or motifs, which were necessary for their activities. Phylogenetic analysis showed that LuxI/R (AI-1 synthase/receptor), QseE/F (two-component system of AI-3), and RpfC/G (two-component system of DSF) exhibited similar evolutionary patterns within each pair. Meanwhile, proteins clustered approximately according to the species taxonomy. The widespread Acidithiobacillus strains, especially A. ferrooxidans, processed AI-1, AI-3, and DSF systems as well as the AhlD enzyme, which were favorable for their mutual information exchange and collective regulation of gene expression. Some members of the Sulfobacillus and Acidiphilium without AHL production capacity contained the AhlD enzyme, which may evolve for niche competition, while DSF in Leptospirillum and Acidithiobacillus could potentially combine with the cyclic diguanylate (c-di-GMP) pathway for self-defense and niche protection. This work will shed light on our understanding of the extent of communication networks and adaptive evolution among acidophiles via QS/QQ coping with environmental changes. IMPORTANCE Understanding cell-cell communication QS is highly relevant for comprehending the regulatory and adaptive mechanisms among acidophiles in extremely acidic ecosystems. Previous studies focused on the existence and functionality of a single QS system in several acidophilic strains. Four representative genera were selected to decipher the distribution and role of QS and QQ integrated with the conservative and evolutionary analysis of related proteins. It was implicated that intra- or intersignaling circuits may work effectively based on different QS types to modulate biofilm formation and energy metabolism among acidophilic microbes. Some individuals could synthesize QQ enzymes for specific QS molecular inactivation to inhibit undesirable acidophile species. This study expanded our knowledge of the fundamental cognition and biological roles underlying the dynamical communication interactions among the coevolving acidophiles and provided a novel perspective for revealing their environmental adaptability.

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.

Association between radiotherapy and risk of death from cardiovascular diseases in lung and bronchus cancer.

Background: Radiotherapy plays an important role in the treatment of lung cancer. However, radiation-related deaths from cardiovascular disease (CVD) are a concern in these patients, and few studies have examined CVD-related death associated with lung cancer. We aimed to evaluate the risk of CVD-related death after radiotherapy in patients with lung and bronchus cancer. Methods: Data were extracted from the surveillance, epidemiology, and end results database. Propensity score matching (PSM) was applied to reduce possible bias between patients who received radiotherapy and those who did not. The Kaplan-Meier method was used to estimate cardiovascular-specific survival (CVSS), and the log-rank test was used to compare CVSS between the radiotherapy and no radiotherapy groups. Cox proportional hazards regression analysis was performed to estimate the hazard ratio (HR) of CVD-related death. Results: A total of 225,570 patients with lung and bronchus cancer were included, and 201,282 patients remained after PSM. Radiotherapy was identified as an independent risk factor for CVSS among patients with lung and bronchus cancer before PSM (HR: 1.18, P < 0.001) and after PSM (HR: 1.18, P < 0.001). Patients treated with radiotherapy had a significantly worse CVSS than those who did not receive radiotherapy before PSM (25-year CVSS: 49.9 vs. 56.4%, P = 0.002) and after PSM (25-year CVSS: 48.4 vs. 56.7%, P < 0.001). Radiotherapy was associated with more deaths from heart disease before PSM (81.9 vs. 77.2%, P < 0.001) and after PSM (83.0 vs. 78.7%, P < 0.001). Conclusion: Radiotherapy is associated with an increased risk of CVD-related death, especially death from heart disease, in patients with lung and bronchus cancer. More efforts are needed to monitor cardiovascular health after radiotherapy.

Revealing the Mutation Patterns of Drug-Resistant Reverse Transcriptase Variants of Human Immunodeficiency Virus through Proteochemometric Modeling.

Drug-resistant cases of human immunodeficiency virus (HIV) nucleoside reverse transcriptase inhibitors (NRTI) are constantly accumulating due to the frequent mutations of the reverse transcriptase (RT). Predicting the potential drug resistance of HIV-1 NRTIs could provide instructions for the proper clinical use of available drugs. In this study, a novel proteochemometric (PCM) model was constructed to predict the drug resistance between six NRTIs against different variants of RT. Forty-seven dominant mutation sites were screened using the whole protein of HIV-1 RT. Thereafter, the physicochemical properties of the dominant mutation sites can be derived to generate the protein descriptors of RT. Furthermore, by combining the molecular descriptors of NRTIs, PCM modeling can be constructed to predict the inhibition ability between RT variants and NRTIs. The results indicated that our PCM model could achieve a mean AUC value of 0.946 and a mean accuracy of 0.873 on the external validation set. Finally, based on PCM modeling, the importance of features was calculated to reveal the dominant amino acid distribution and mutation patterns on RT, to reflect the characteristics of drug-resistant sequences.

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.

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.

Raman spectroscopy combined with machine learning for rapid detection of food-borne pathogens at the single-cell level.

Rapid detection of food-borne pathogens in early food contamination is a permanent topic to ensure food safety and prevent public health problems. Raman spectroscopy, a label-free, highly sensitive and dependable technology has attracted more and more attention in the field of diagnosing food-borne pathogens in recent years. In the research, 15,890 single-cell Raman spectra of 23 common strains from 7 genera were obtained at the single cell level. Then, the nonlinear features of raw data were extracted by kernel principal component analysis, and the individual bacterial cell was evaluated and discriminated at the serotype level through the decision tree algorithm. The results demonstrated that the average correct rate of prediction on independent test set was 86.23 ± 0.92% when all strains were recognized by only one model, but there were high misjudgment rates for certain strains. Therefore, the four-level classification models were introduced, and the different hierarchies of the identification models achieved accuracies in the range of 87.1%-95.8%, which realized the efficient prediction of strains at the serotype level. In summary, Raman spectroscopy combined with machine learning based on fingerprint difference was a prospective strategy for the rapid diagnosis of pathogenic bacteria.

A Novel Design of Multi-epitope Vaccine Against Helicobacter pylori by Immunoinformatics Approach.

Helicobacter pylori (H. pylori) is a gram-negative spiral bacterium that caused infections in half of the world's population and had been identified as type I carcinogen by the World Health Organization. Compared with antibiotic treatment which could result in drug resistance, the vaccine therapy is becoming a promising immunotherapy option against H. pylori. Further, the multi-epitope vaccine could provoke a wider immune protection to control H. pylori infection. In this study, the in-silico immunogenicity calculations on 381 protein sequences of H. pylori were performed, and the immunogenicity of selected proteins with top-ranked score were tested. The B cell epitopes and T cell epitopes from three well performed proteins UreB, PLA1, and Omp6 were assembled into six constructs of multi-epitope vaccines with random orders. In order to select the optimal constructs, the stability of the vaccine structure and the exposure of B cell epitopes on the vaccine surface were evaluated based on structure prediction and solvent accessible surface area analysis. Finally Construct S1 was selected and molecular docking showed that it had the potential of binding TLR2, TLR4, and TLR9 to stimulate strong immune response. In particular, this study provides good suggestions for epitope assembly in the construction of multi-epitope vaccines and it may be helpful to control H. pylori infection in the future. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s10989-020-10148-x) contains supplementary material, which is available to authorized users.

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.