Principle, application and challenges of development siRNA-based therapeutics against bacterial and viral infections: a comprehensive review.

While significant progress has been made in understanding and applying gene silencing mechanisms and the treatment of human diseases, there have been still several obstacles in therapeutic use. For the first time, ONPATTRO, as the first small interfering RNA (siRNA) based drug was invented in 2018 for treatment of hTTR with polyneuropathy. Additionally, four other siRNA based drugs naming Givosiran, Inclisiran, Lumasiran, and Vutrisiran have been approved by the US Food and Drug Administration and the European Medicines Agency for clinical use by hitherto. In this review, we have discussed the key and promising advances in the development of siRNA-based drugs in preclinical and clinical stages, the impact of these molecules in bacterial and viral infection diseases, delivery system issues, the impact of administration methods, limitations of siRNA application and how to overcome them and a glimpse into future developments.

Reduction of UreB and CagA expression level by siRNA construct in Helicobacter pylori strain SS1.

BACKGROUND: Two important virulence factors, urease and cagA, play an important role in Helicobacter pylori (H. pylori) gastric cancer. Aim of this study was to investigate the expression level and function of ureB and cagA using small interfering RNAs (siRNA). METHODS: SS1 strain of H. pylori was considered as host for natural transformation. siRNA designed for ureB and cagA genes were inserted in pGPU6/GFP/Neo siRNA plasmid vector to evaluate using phenotypic and genotypic approaches. Then, qPCR was performed for determining inhibition rate of ureB and cagA gene expression. RESULTS: The expression levels of siRNA-ureB and siRNA-cagA in the recombinant strain SS1 were reduced by about 5000 and 1000 fold, respectively, compared to the native H. pylori strain SS1. Also, preliminary evaluation of siRNA-ureB in vitro showed inhibition of urea enzyme activity. These data suggest that siRNA may be a powerful new tool for gene silencing in vitro, and for the development of RNAi-based anti-H. pylori therapies. CONCLUSION: Our results show that targeting ureB and cagA genes with siRNA seems to be a new strategy to inhibit urease enzyme activity, reduce inflammation and colonization rate.

Designing a novel multi-epitopes pan-vaccine against SARS-CoV-2 and seasonal influenza: in silico and immunoinformatics approach.

The merger of COVID-19 and seasonal influenza infections is considered a potentially serious threat to public health. These two viral agents can cause extensive and severe lower and upper respiratory tract infections with lung damage with host factors. Today, the development of vaccination has been shown to reduce the risk of hospitalization and mortality from the COVID-19 virus and influenza epidemics. Therefore, this study contributes to an immunoinformatics approach to producing a vaccine that can elicit strong and specific immune responses against COVID-19 and influenza A and B viruses. The NCBI, GISAID, and Uniprot databases were used to retrieve sequences. Linear B cell, Cytotoxic T lymphocyte, and Helper T lymphocyte epitopes were predicted using the online servers. Population coverage of MHC I epitopes worldwide for SARS-CoV-2, Influenza virus H3N2, H3N2, and Yamagata/Victoria were 99.93%, 68.67%, 68.38%, and 85.45%, respectively. Candidate epitopes were linked by GGGGS, GPGPG, and KK linkers. Different epitopes were permutated several times to form different peptides and then screened for antigenicity, allergenicity, and toxicity. The vaccine construct was analyzed for physicochemical properties, conformational B-cell epitopes, interaction with Toll-like receptors, and IFN-gamma-induced. Immune stimulation response of final construct was evaluated using C-IMMSIM. Eventually, the final construct sequence was codon-optimized for Escherichia coli K12 and Homo sapiens to design a multi-epitope vaccine and mRNA vaccine. In conclusion, due to the variable nature of SARS-CoV-2 and influenza proteins, the design of a multi-epitope vaccine can protect against all their standard variants, but laboratory validation is required.Communicated by Ramaswamy H. Sarma.

In silico designing and immunoinformatics analysis of a novel peptide vaccine against metallo-beta-lactamase (VIM and IMP) variants.

The rapid spread of acquired metallo-beta-lactamases (MBLs) among gram negative pathogens is becoming a global concern. Improper use of broad-spectrum antibiotics can trigger the colonization and spread of resistant strains which lead to increased mortality and significant economic loss. In the present study, diverse immunoinformatic approaches are applied to design a potential epitope-based vaccine against VIM and IMP MBLs. The amino acid sequences of VIM and IMP variants were retrieved from the GenBank database. ABCpred and BCPred online Web servers were used to analyze linear B cell epitopes, while IEDB was used to determine the dominant T cell epitopes. Sequence validation, allergenicity, toxicity and physiochemical analysis were performed using web servers. Seven sequences were identified for linear B cell dominant epitopes and 4 sequences were considered as dominant CD4+ T cell epitopes, and the predicted epitopes were joined by KK and GPGPG linkers. Stabilized multi-epitope protein structure was obtained using molecular dynamics simulation. Molecular docking showed that the designed vaccine exhibited sustainable and strong binding interactions with Toll-like receptor 4 (TLR4). Finally, codon adaptation and in silico cloning studies were performed to design an effective vaccine production strategy. Immune simulation significantly provided high levels of immunoglobulins, T helper cells, T-cytotoxic cells and INF-γ. Even though the introduced vaccine candidate demonstrates a very potent immunogenic potential, but wet-lab validation is required to further assessment of the effectiveness of this proposed vaccine candidate.

Global prevalence of Clostridioides difficile in 17,148 food samples from 2009 to 2019: a systematic review and meta-analysis.

BACKGROUND: Clostridioides (Clostridium) difficile is an important infectious pathogen, which causes mild-to-severe gastrointestinal infections by creating resistant spores and producing toxins. Spores contaminated foods might be one of the most significant transmission ways of C. difficile-associated infections. This systematic review and meta-analysis study were conducted to investigate the prevalence of C. difficile in food. METHODS: Articles that published the prevalence of C. difficile in food in PubMed, Web of Science, and Scopus databases were retrieved using selected keywords between January 2009 and December 2019. Finally, 17,148 food samples from 60 studies from 20 countries were evaluated. RESULTS: The overall prevalence of C. difficile in various foods was 6.3%. The highest and lowest levels of C. difficile contamination were detected to seafood (10.3%) and side dishes (0.8%), respectively. The prevalence of C. difficile was 4% in cooked food, 6.2% in cooked chicken and 10% in cooked seafood. CONCLUSIONS: There is still little known concerning the food-borne impact of C. difficile, but the reported contamination might pose a public health risk. Therefore, to improve the food safety and prevent contamination with C. difficile spores, it is necessary to observe hygienic issues during foods preparation, cooking and transfer.

Designing multi-epitope vaccine against important colorectal cancer (CRC) associated pathogens based on immunoinformatics approach.

BACKGROUND: It seems that several members of intestinal gut microbiota like Streptococcus bovis, Bacteroides fragilis, Helicobacter pylori, Fusobacterium nucleatum, Enterococcus faecalis, Escherichia coli, Peptostreptococcus anaerobius may be considered as the causative agents of Colorectal Cancer (CRC). The present study used bioinformatics and immunoinformatics approaches to design a potential epitope-based multi-epitope vaccine to prevent CRC with optimal population coverage. METHODS: In this study, ten amino acid sequences of CRC-related pathogens were retrieved from the NCBI database. Three ABCpred, BCPREDS and LBtope online servers were considered for B cells prediction and the IEDB server for T cells (CD4+ and CD8+) prediction. Then, validation, allergenicity, toxicity and physicochemical analysis of all sequences were performed using web servers. A total of three linkers, AAY, GPGPG, and KK were used to bind CTL, HTL and BCL epitopes, respectively. In addition, the final construct was subjected to disulfide engineering, molecular docking, immune simulation and codon adaptation to design an effective vaccine production strategy. RESULTS: A total of 19 sequences of different lengths for linear B-cell epitopes, 19 and 18 sequences were considered as epitopes of CD4+ T and CD8+ cells, respectively. The predicted epitopes were joined by appropriate linkers because they play an important role in producing an extended conformation and protein folding. The final multi-epitope construct and Toll-like receptor 4 (TLR4) were evaluated by molecular docking, which revealed stable and strong binding interactions. Immunity simulation of the vaccine showed significantly high levels of immunoglobulins, helper T cells, cytotoxic T cells and INF-γ. CONCLUSION: Finally, the results showed that the designed multi-epitope vaccine could serve as an excellent prophylactic candidate against CRC-associated pathogens, but in vitro and animal studies are needed to justify our findings for its use as a possible preventive measure.

Rapid Detection of vanA Resistance Gene from E. faecalis Clinical Isolates Using Duplex Loop-Mediated Isothermal Amplification and Triplex PCR Assay.

Today, the spread of vancomycin-resistant strains isolated from Enterococcus faecalis (E. faecalis) has become a major health concern worldwide. Therefore, it is essential to provide a rapid and sensitive assay for identifying vanA gene for timely and appropriate antimicrobial control of resistant enterococcal infections. For this purpose, a cross-sectional study was performed on different clinical specimens of enterococci from Imam Reza hospital, Kermanshah, Iran. The antimicrobial susceptibility testing was determined by disk diffusion and MIC methods. Triplex-PCR and duplex-LAMP assays were also used to identify vanA E. faecalis resistance gene isolates. The results of this study shown that out of 108 Enterococcus isolates, 86, 18, 2, 1, and one isolates of E. faecalis, E. faecium, E. avium, E. psudoavium, and E. raffinosus were identified, respectively. On the other hand, E. faecalis was confirmed in 87 and 88 isolates using duplex-LAMP and triplex PCR, respectively. The LAMP primer set designed in this study can reliably identify seven distinct regions of the vanA gene, and finally the sensitivity, specificity, and the positive and negative predictive values of LAMP assay were shown to be 94.19%, 72.73%, 76.19%, and 93.10%, respectively. In general, sample processing, isothermal reaction and result reporting were completed using the LAMP assay in 75 minutes. Our findings suggest that LAMP assay has been approved as an alternative to the vancomycin resistance Enterococcus genotype (vanA and vanB) compared to other methods and has the advantage of being rapid, time-consuming, and easy for diagnosis.

Airborne and aerosol pathogen transmission modeling of respiratory events in buildings: An overview of computational fluid dynamics.

Pathogen droplets released from respiratory events are the primary means of dispersion and transmission of the recent pandemic of COVID-19. Computational fluid dynamics (CFD) has been widely employed as a fast, reliable, and inexpensive technique to support decision-making and to envisage mitigatory protocols. Nonetheless, the airborne pathogen droplet CFD modeling encounters limitations due to the oversimplification of involved physics and the intensive computational demand. Moreover, uncertainties in the collected clinical data required to simulate airborne and aerosol transport such as droplets' initial velocities, tempo-spatial profiles, release angle, and size distributions are broadly reported in the literature. There is a noticeable inconsistency around these collected data amongst many reported studies. This study aims to review the capabilities and limitations associated with CFD modeling. Setting the CFD models needs experimental data of respiratory flows such as velocity, particle size, and number distribution. Therefore, this paper briefly reviews the experimental techniques used to measure the characteristics of airborne pathogen droplet transmissions together with their limitations and reported uncertainties. The relevant clinical data related to pathogen transmission needed for postprocessing of CFD data and translating them to safety measures are also reviewed. Eventually, the uncertainty and inconsistency of the existing clinical data available for airborne pathogen CFD analysis are scurtinized to pave a pathway toward future studies ensuing these identified gaps and limitations.

Immunometabolism in biofilm infection: lessons from cancer.

BACKGROUND: Biofilm is a community of bacteria embedded in an extracellular matrix, which can colonize different human cells and tissues and subvert the host immune reactions by preventing immune detection and polarizing the immune reactions towards an anti-inflammatory state, promoting the persistence of biofilm-embedded bacteria in the host. MAIN BODY OF THE MANUSCRIPT: It is now well established that the function of immune cells is ultimately mediated by cellular metabolism. The immune cells are stimulated to regulate their immune functions upon sensing danger signals. Recent studies have determined that immune cells often display distinct metabolic alterations that impair their immune responses when triggered. Such metabolic reprogramming and its physiological implications are well established in cancer situations. In bacterial infections, immuno-metabolic evaluations have primarily focused on macrophages and neutrophils in the planktonic growth mode. CONCLUSION: Based on differences in inflammatory reactions of macrophages and neutrophils in planktonic- versus biofilm-associated bacterial infections, studies must also consider the metabolic functions of immune cells against biofilm infections. The profound characterization of the metabolic and immune cell reactions could offer exciting novel targets for antibiofilm therapy.

CFD modeling of airborne pathogen transmission of COVID-19 in confined spaces under different ventilation strategies.

Airborne transmission is an important route of spread of viral diseases (e.g., COVID-19) inside the confined spaces. In this respect, computational fluid dynamics (CFD) emerged as a reliable and fast tool to understand the complex flow patterns in such spaces. Most of the recent studies, nonetheless, focused on the spatial distribution of airborne pathogens to identify the infection probability without considering the exposure time. This research proposes a framework to evaluate the infection probability related to both spatial and temporal parameters. A validated Eulerian-Lagrangian CFD model of exhaled droplets is first developed and then evaluated with an office case study impacted by different ventilation strategies (i.e., cross- (CV), single- (SV), mechanical- (MV) and no-ventilation (NV)). CFD results were analyzed in a bespoke code to calculate the tempo-spatial distribution of accumulated airborne pathogens. Furthermore, two indices of local and general infection risks were used to evaluate the infection probability of the ventilation scenarios. The results suggest that SV has the highest infection probability while SV and NO result in higher dispersions of airborne pathogens inside the room. Eventually, the time history of indices reveals that the efficiency of CV and MV can be poor in certain regions of the room.

A worldwide systematic review and meta-analysis of bacteria related to antibiotic-associated diarrhea in hospitalized patients.

INTRODUCTION: Antibiotic-associated diarrhea (AAD) is a major hospital problem and a common adverse effect of antibiotic treatment. The aim of this study was to investigate the prevalence of the most important bacteria that cause AAD in hospitalized patients. MATERIALS AND METHODS: PubMed, Web of Science and Scopus databases were searched using multiple relevant keywords and screening carried out based on inclusion/exclusion criteria from March 2001 to October 2021. The random-effects model was used to conduct the meta-analysis. RESULTS: Of the 7,377 identified articles, 56 met the inclusion criteria. Pooling all studies, the prevalence of Clostridioides (Clostridium) difficile, Clostridium perfringens, Klebsiella oxytoca, and Staphylococcus aureus as AAD-related bacteria among hospitalized patients were 19.6%, 14.9%, 27%, and 5.2%, respectively. The prevalence of all four bacteria was higher in Europe compared to other continents. The highest resistance of C. difficile was estimated to ciprofloxacin and the lowest resistances were reported to chloramphenicol, vancomycin, and metronidazole. There was no or little data on antibiotic resistance of other bacteria. CONCLUSIONS: The results of this study emphasize the need for a surveillance program, as well as timely public and hospital health measures in order to control and treat AAD infections.

In silico vaccine design and epitope mapping of New Delhi metallo-beta-lactamase (NDM): an immunoinformatics approach.

BACKGROUND: Antibiotic resistance is a global health crisis. The adage that "prevention is better than cure" is especially true regarding antibiotic resistance because the resistance appears and spreads much faster than the production of new antibiotics. Vaccination is an important strategy to fight infectious agents; however, this strategy has not attracted sufficient attention in antibiotic resistance prevention. New Delhi metallo-beta-lactamase (NDM) confers resistance to many beta-lactamases, including important carbapenems like imipenem. Our goal in this study is to use an immunoinformatics approach to develop a vaccine that can elicit strong and specific immune responses against NDMs that prevent the development of antibiotic-resistant bacteria. RESULTS: In this study, 2194 NDM sequences were aligned to obtain a conserved sequence. One continuous B cell epitope and three T cell CD4+ epitopes were selected from NDMs conserved sequence. Epitope conservancy for B cell and HLA-DR, HLA-DQ, and HLA-DP epitopes was 100.00%, 99.82%, 99.41%, and 99.86%, respectively, and population coverage of MHC II epitopes for the world was 99.91%. Permutation of the four epitope fragments resulted in 24 different peptides, of which 6 peptides were selected after toxicity, allergenicity, and antigenicity assessment. After primary vaccine design, only one vaccine sequence with the highest similarity with discontinuous B cell epitope in NDMs was selected. The final vaccine can bind to various Toll-like receptors (TLRs). The prediction implied that the vaccine would be stable with a good half-life. An immune simulation performed by the C-IMMSIM server predicted that two doses of vaccine injection can induce a strong immune response to NDMs. Finally, the GC-Content of the vaccine was designed very similar to E. coli K12. CONCLUSIONS: In this study, immunoinformatics strategies were used to design a vaccine against different NDM variants that could produce an effective immune response against this antibiotic-resistant factor.

An update review of globally reported SARS-CoV-2 vaccines in preclinical and clinical stages.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the rapidly spreading pandemic COVID-19 in the world. As an effective therapeutic strategy is not introduced yet and the rapid genetic variations in the virus, there is an emerging necessity to design, evaluate and apply effective new vaccines. An acceptable vaccine must elicit both humoral and cellular immune responses, must have the least side effects and the storage and transport systems should be available and affordable for all countries. These vaccines can be classified into different types: inactivated vaccines, live-attenuated virus vaccines, subunit vaccines, virus-like particles (VLPs), nucleic acid-based vaccines (DNA and RNA) and recombinant vector-based vaccines (replicating and non-replicating viral vector). According to the latest update of the WHO report on April 2nd, 2021, at least 85 vaccine candidates were being studied in clinical trial phases and 184 candidate vaccines were being evaluated in pre-clinical stages. In addition, studies have shown that other vaccines, including the Bacillus Calmette-Guérin (BCG) vaccine and the Plant-derived vaccine, may play a role in controlling pandemic COVID-19. Herein, we reviewed the different types of COVID-19 candidate vaccines that are currently being evaluated in preclinical and clinical trial phases along with advantages, disadvantages or adverse reactions, if any.

Identification of Hemolysine Genes and their Association with Antimicrobial Resistance Pattern among Clinical Isolates of Staphylococcus aureus in West of Iran.

BACKGROUND: Staphylococcus aureus is expressing a broad range of different hemolysins enhancing its ability to establish and maintain infection in humans. The aim of this study was to identify the types of hemolysins in different clinical isolates of S. aureus and their association with antibiotic resistance patterns. MATERIALS AND METHODS: In this cross-sectional and descriptive study, clinical isolates of S. aureus were collected from Hamedan's hospitals during an 11-month period from June 2016 to January 2017 and identified by using biochemical tests. To determine the antibiotic resistance pattern, disk diffusion method and minimum inhibitory concentration (MIC) were conducted. Genomic DNA was extracted using extraction kit. The polymerase chain reaction was done with specific primers for identification of hla, hlb, hld, and hld genes. RESULTS: Among a total of 389 clinical samples, 138 isolates (35.45%) of S. aureus were identified, which 87 isolates (63.04%) were cefoxitin MIC of >4 µg/ml and resistant to methicillin. The highest frequency of antibiotic resistance was observed against erythromycin in 108 isolates (78.26%) and penicillin in 133 isolates (96.37%) and the lowest resistance was against gatifloxacin in 50 isolates (36.23%) and Cefazolin in 11 isolates (97.7%). Furthermore, the hla, hlb, hld, and hlg genes were detected among 11 (7.97%), 7 (5.07%), 16 (11.59%), and 4 (2.89%) isolates, respectively. There was a significant relationship between the presence of alpha and delta hemolysin-encoding genes and the antibiotic resistance pattern of isolates (P < 0.05). CONCLUSION: The results exhibited that the association between the presence of the hemolysin genes and the antibiotic resistance pattern can be considered as a serious issue.