Lectin activity of Pseudomonas aeruginosa vaccine candidates PSE17-1, PSE41-5 and PSE54

Discovery and development of COVID-19 vaccine from laboratory to clinic

The world has recently experienced one of the biggest and most severe public health disasters with severe acute respiratory syndrome coronavirus (SARS-CoV-2). SARS-CoV-2 is responsible for the coronavirus disease of 2019 (COVID-19) which is one of the most widespread and powerful infections affecting human lungs. Current figures show that the epidemic had reached 216 nations, where it had killed about 6,438,926 individuals and infected 590,405,710. WHO proclaimed the outbreak of the Ebola virus disease (EVD), in 2014 that killed hundreds of people in West Africa. The development of vaccines for SARS-CoV-2 becomes more difficult due to the viral mutation in its non-structural proteins (NSPs) especially NSP2 and NSP3, S protein, and RNA-dependent RNA polymerase (RdRp). Continuous monitoring of SARS-CoV-2, dynamics of the genomic sequence, and spike protein mutations are very important for the successful development of vaccines with good efficacy. Hence, the vaccine development for SARS-CoV-2 faces specific challenges starting from viral mutation. The requirement of long-term immunity development, safety, efficacy, stability, vaccine allocation, distribution, and finally, its cost is discussed in detail. Currently, 169 vaccines are in the clinical development stage, while 198 vaccines are in the preclinical development stage. The majority of these vaccines belong to the Ps-Protein subunit type which has 54, and the minor BacAg-SPV (Bacterial antigen-spore expression vector) type, at least 1 vaccination. The use of computational methods and models for vaccine development has revolutionized the traditional methods of vaccine development. Further, this updated review highlights the upcoming vaccine development strategies in response to the current pandemic and post-pandemic era, in the field of vaccine development.

Improving Protection to Prevent Bacterial Infections: Preliminary Applications of Reverse Vaccinology against the Main Cystic Fibrosis Pathogens

Reverse vaccinology is a powerful tool that was recently used to develop vaccines starting from a pathogen genome. Some bacterial infections have the necessity to be prevented then treated. For example, individuals with chronic pulmonary diseases, such as Cystic Fibrosis, are prone to develop infections and biofilms in the thick mucus that covers their lungs, mainly caused by Burkholderia cepacia complex, Haemophilus influenzae, Mycobacterium abscessus complex, Pseudomonas aeruginosa and Staphylococcus aureus. These infections are complicated to treat and prevention remains the best strategy. Despite the availability of vaccines against some strains of those pathogens, it is necessary to improve the immunization of people with Cystic Fibrosis against all of them. An effective approach is to develop a broad-spectrum vaccine to utilize proteins that are well conserved across different species. In this context, reverse vaccinology, a method based on computational analysis of the genome of various microorganisms, appears as one of the most promising tools for the identification of putative targets for broad-spectrum vaccine development. This review provides an overview of the vaccines that are under development by reverse vaccinology against the aforementioned pathogens, as well as the progress made so far.

Geraniol attenuates virulence factors by inhibiting quorum sensing of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a ubiquitous opportunistic pathogen that can cause severe respiratory tract infections. Geraniol, a chemical component of essential oils, has antimicrobial and anti-inflammatory activities, along with low toxicity. However, the effect and mechanism of geraniol against P. aeruginosa virulence factors are rarely studied. In this study, we investigated the quorum sensing (QS) inhibitory effects and mechanisms of geraniol against P. aeruginosa PAO1, using physiological and biochemical techniques, quantitative reverse transcription polymerase chain reaction, and transcriptomics. Geraniol slightly affected P. aeruginosa PAO1 growth, prolonged the lag phase, and delayed growth periods in a concentration-dependent manner. Geraniol inhibited three QS systems of P. aeruginosa, las, rhl, and pqs by suppressing the expression level of their key genes, including the three signal synthetase encoding genes of lasI, rhlI, and pqsABCDEH, and the corresponding signal receptor encoding genes of lasR, rhlR, and pqsR. Geraniol also suppressed certain virulence genes regulated by these three QS systems, including rhlABC, lasAB, lecAB, phzABMS, and pelABG, resulting in the attenuation of the related virulence factors, rhamnolipids, exoprotease LasA, elastase, lectin, pyocyanin, and biofilm. In conclusion, geraniol can suppress the virulence factors of P. aeruginosa PAO1 by inhibiting the three QS systems of las, rhl, and pqs. This study is significant for improving the treatment of bacterial infections caused by P. aeruginosa.

Pseudomonas aeruginosa: Recent Advances in Vaccine Development

Pseudomonas aeruginosa is an important opportunistic human pathogen. Using its arsenal of virulence factors and its intrinsic ability to adapt to new environments, P. aeruginosa causes a range of complicated acute and chronic infections in immunocompromised individuals. Of particular importance are burn wound infections, ventilator-associated pneumonia, and chronic infections in people with cystic fibrosis. Antibiotic resistance has rendered many of these infections challenging to treat and novel therapeutic strategies are limited. Multiple clinical studies using well-characterised virulence factors as vaccine antigens over the last 50 years have fallen short, resulting in no effective vaccination being available for clinical use. Nonetheless, progress has been made in preclinical research, namely, in the realms of antigen discovery, adjuvant use, and novel delivery systems. Herein, we briefly review the scope of P. aeruginosa clinical infections and its major important virulence factors.

LectinOracle: A Generalizable Deep Learning Model for Lectin-Glycan Binding Prediction

Ranging from bacterial cell adhesion over viral cell entry to human innate immunity, glycan-binding proteins or lectins are abound in nature. Widely used as staining and characterization reagents in cell biology and crucial for understanding the interactions in biological systems, lectins are a focal point of study in glycobiology. Yet the sheer breadth and depth of specificity for diverse oligosaccharide motifs has made studying lectins a largely piecemeal approach, with few options to generalize. Here, LectinOracle, a model combining transformer-based representations for proteins and graph convolutional neural networks for glycans to predict their interaction, is presented. Using a curated data set of 564,647 unique protein-glycan interactions, it is shown that LectinOracle predictions agree with literature-annotated specificities for a wide range of lectins. Using a range of specialized glycan arrays, it is shown that LectinOracle predictions generalize to new glycans and lectins, with qualitative and quantitative agreement with experimental data. It is further demonstrated that LectinOracle can be used to improve lectin classification, accelerate lectin directed evolution, predict epidemiological outcomes in the context of influenza virus, and analyze whole lectomes in host-microbe interactions. It is envisioned that the herein presented platform will advance both the study of lectins and their role in (glyco)biology.

Discovering mycobacterial lectins as potential drug targets and vaccine candidates for tuberculosis treatment: a theoretical approach

M. tuberculosis proliferates within the macrophages during infection and they are bounded by carbohydrates in the cell wall, called lectins. Despite their surface localization, the studies on exact functions of lectins are unexplored. Hence, in our study, using insilico approaches, 11 potential lectins of Mtb was explored as potential drug targets and vaccine candidates. Initially, a gene interaction network was constructed for the 11 potential lectins and identified its functional partners. A gene ontology analysis was also performed for the 11 mycobacterial lectins along with its functional partners and found most of the proteins are present in the extracellular region of the bacterium and belongs to the PE/PPE family of proteins. Further, molecular docking studies were performed for two of the potential lectins (Rv2075c and Rv1917c). A novel series of quinoxalinone and fucoidan derivatives have been made to dock against these selected lectins. Molecular docking study reveals that quinoxalinone derivatives showed better affinity against Rv2075c, whereas fucoidan derivatives have good binding affinity against Rv1917c. Moreover, the mycobacterial lectins can interact with the host and they are considered as potential vaccine candidates. Hence, immunoinformatics study was carried out for all the 11 potential lectins. B-cell and T-cell binding epitopes were predicted using insilico tools. Further, an immunodominant epitope ¹⁰⁶²SIPAIPLSVEV¹⁰⁷² of Rv1917c was identified, which was predicted to bind B-cell and most of the MHC alleles. Thus, the study has explored that mycobacterial lectins could be potentially used as drug targets and vaccine candidates for tuberculosis treatment.

Immunoinformatics design of multivalent chimeric vaccine for modulation of the immune system in Pseudomonas aeruginosa infection

Increasing in drug-resistant Pseudomonas aeruginosa and high mortality and morbidity rate have become a health challenge worldwide; therefore, developing the novel therapeutic strategies such as immunogenic vaccine candidate are required. Despite a substantial research effort, the future of immunization against P. aeruginosa due to failure in covering two separate stages of infection, and furthermore, inducing ineffective type of immune response, still remains controversial. In this study, immunoinformatics approach was utilized to design multivalent chimeric vaccine from both stages of infection containing Lectin, HIV TAT peptide, N-terminal fragment of exotoxin A and Epi8 of outer membrane protein F (OprF) with hydrophobic linkers which have a high density of B-cell, T Lymphocytes (HTL), T Lymphocytes (CTL), and IFN-γ epitopes. The physicochemical properties, antigenicity, and allergenicity for designed vaccine were analyzed. 3D model generation and refinement further validation of the final vaccine were followed by computational docking with molecular dynamics analyses that demonstrated high- affinity interaction between vaccine and TLR-4. Finally, designed vaccine was in silico cloned in pET22b. We have expected that the designed vaccine able to elucidate innate, humoral and cellular innate immune responses and control the interaction of P. aeruginosa with host and maybe overcome to P. aeruginosa vaccines drawback.

Computational tools for modern vaccine development

Vaccines play an essential role in controlling the rates of fatality and morbidity. Vaccines not only arrest the beginning of different diseases but also assign a gateway for its elimination and reduce toxicity. This review gives an overview of the possible uses of computational tools for vaccine design. Moreover, we have described the initiatives of utilizing the diverse computational resources by exploring the immunological databases for developing epitope-based vaccines, peptide-based drugs, and other resources of immunotherapeutics. Finally, the applications of multi-graft and multivalent scaffolding, codon optimization and antibodyomics tools in identifying and designing in silico vaccine candidates are described.