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The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., poses a significant global threat due to their heightened virulence and extensive antibiotic resistance. These pathogens contribute largely to the prevalence of nosocomial or hospital-acquired infections, resulting in high morbidity and mortality rates. To tackle this healthcare problem urgent measures are needed, including development of innovative vaccines and therapeutic strategies. Designing vaccines involves a complex and resource-intensive process of identifying protective antigens and potential vaccine candidates (PVCs) from pathogens. Reverse vaccinology (RV), an approach based on genomics, made this process more efficient by leveraging bioinformatics tools to identify potential vaccine candidates. In recent years, artificial intelligence and machine learning (ML) techniques has shown promise in enhancing the accuracy and efficiency of reverse vaccinology. This study introduces a supervised ML classification framework, to predict potential vaccine candidates specifically against ESKAPE pathogens. The model's training utilized biological and physicochemical properties from a dataset containing protective antigens and non-protective proteins of ESKAPE pathogens. Conventional autoencoders based strategy was employed for feature encoding and selection. During the training process, seven machine learning algorithms were trained and subjected to Stratified 5-fold Cross Validation. Random Forest and Logistic Regression exhibited best performance in various metrics including accuracy, precision, recall, WF1 score, and Area under the curve. An ensemble model was developed, to take collective strengths of both the algorithms. To assess efficacy of our final ensemble model, a high-quality benchmark dataset was employed. VacSol-ML(ESKAPE) demonstrated outstanding discrimination between protective vaccine candidates (PVCs) and non-protective antigens. VacSol-ML(ESKAPE), proves to be an invaluable tool in expediting vaccine development for these pathogens. Accessible to the public through both a web server and standalone version, it encourages collaborative research. The web-based and standalone tools are available at http://vacsolml.mgbio.tech/.

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Urinary tract infections (UTIs) are the second most prevalent bacterial infections and uropathogenic Escherichia coli (UPEC) stands among the primary causative agents of UTIs. The usage of antibiotics is the routine therapy being used in various countries to treat UTIs but becoming ineffective because of increasing antibiotic resistance among UPEC strains. Thus, there must be the development of some alternative treatment strategies such as vaccine development against UPEC. In the following study, pan-genomics along with reverse vaccinology approaches is used under the framework of bioinformatics for the identification of core putative vaccine candidates, employing 307 UPEC genomes (complete and draft), available publicly. A total of nine T-cell epitopes (derived from B-cells) of both MHC classes (I and II), were prioritized among three potential protein candidates. These epitopes were then docked together by using linkers (GPGPG and AAY) and an adjuvant (Cholera Toxin B) to form a poly-valent vaccine construct. The chimeric vaccine construct was undergone by molecular modelling, further refinement and energy minimization. We predicted positive results of the vaccine construct in immune simulations with significantly high levels of immune cells. The protein-protein docking analysis of vaccine construct with toll-like receptors predicted efficient binding, which was further validated by molecular dynamics simulation of vaccine construct with TLR-2 and TLR-4 at 120 ns, resulting in stable complexes' conformation throughout the simulation run. Overall, the vaccine construct demonstrated positive antigenic response. In future, this chimeric vaccine construct or the identified epitopes could be experimentally validated for the development of UPEC vaccines against UTIs.Communicated by Ramaswamy H. Sarma.

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Infectious diseases have been tremendously increasing as the organisms of even normal flora become opportunistic and cause an infection, and Escherichia coli (E. coli EQ101) is one of them. Urinary tract infections are caused by various microorganisms, but Escherichia coli is the primary cause of almost 70%-90% of all UTIs. It has multiple strains, possessing diverse virulence factors, contributing to its pathogenicity. Furthermore, these virulent strains also can cause overlapping pathogenesis by sharing resistance and virulence factors among each other. The current study is aimed at analyzing the genetic variants associated with multi-drug-resistant (MDR) E. coli using the whole genome sequencing platform. The study includes 100 uropathogenic Escherichia coli (UPEC) microorganisms obtained from urine samples out of which 44% were multi-drug-resistant (MDR) E. coli. Bacteria have been isolated and antimicrobial susceptibility test (AST) was determined by disk diffusion method on the Mueller-Hinton agar plate as recommended by the Clinical and Laboratory Standards Institute (CLSI) 2020, and one isolate has been selected which shows resistance to most of the antibiotics, and that isolate has been analyzed by whole genome sequencing (WGS), accompanied by data and phylogenetic analysis, respectively. Organisms were showing resistance against ampicillin (10 µg), cefixime (5 µg), ceftriaxone (30 µg), nalidixic acid (30 µg), ciprofloxacin (5 µg), and ofloxacin (5 µg) on antimicrobial susceptibility test. WGS were done on selected isolate which identified 25 virulence genes (air, astA, chuA, fyuA, gad, hra, iha, irp2, iss, iucC, iutA, kpsE, kpsMII_K1, lpfA, mchF, ompT, papA_F43, sat, senB, sitA, terC, traT, usp, vat, and yfcV) and seven housekeeping genes (adk, fumC, gyrB, icd, mdh, purA, and recA). Among resistance genes, seven genes (TolC, emrR, evgA, qacEdelta1, H-NS, cpxA, and mdtM) were identified to be involved in antibiotic efflux, three AMR genes (aadA5, mphA, and CTX-M-15) were involved in antibiotic inactivation, and two genes (sul1 and dfrA14) were found to be involved in antibiotic drug replacement. Our data identified antibiotic resistance and virulence genes of the isolate. We suggest further research work to establish region-based resistance profile in comparison with the global resistance pattern.

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Introduction: Salmonella enterica serovar Typhi (S. Typhi) is a major cause of morbidity and mortality in developing countries, contributing significantly to the global disease burden. Methods: In this study, S. Typhi strains were isolated from 100 patients exhibiting symptoms of typhoid fever at a tertiary care hospital in Pakistan. Antimicrobial testing of all isolates was performed to determine the sensitivity and resistance pattern. Three MDR strains, namely QS194, QS430, and QS468, were subjected to whole genome sequencing for genomic characterization. Results and Discussion: MLST analysis showed that QS194, belonged to ST19, which is commonly associated with Salmonella enterica serovar typhimurium. In contrast, QS430 and QS468, belonged to ST1, a sequence type frequently associated with S. Typhi. PlasmidFinder identified the presence of IncFIB(S) and IncFII(S) plasmids in QS194, while IncQ1 was found in QS468. No plasmid was detected in QS430. CARD-based analysis showed that the strains were largely resistant to a variety of antibiotics and disinfecting agents/antiseptics, including fluoroquinolones, cephalosporins, monobactams, cephamycins, penams, phenicols, tetracyclines, rifamycins, aminoglycosides, etc. The S. Typhi strains possessed various virulence factors, such as Vi antigen, Agf/Csg, Bcf, Fim, Pef, etc. The sequencing data indicated that the strains had antibiotic resistance determinants and shared common virulence factors. Pangenome analysis of the selected S. Typhi strains identified 13,237 genes, with 3,611 being core genes, 2,093 shell genes, and 7,533 cloud genes. Genome-based typing and horizontal gene transfer analysis revealed that the strains had different evolutionary origins and may have adapted to distinct environments or host organisms. These findings provide important insights into the genetic characteristics of S. Typhi strains and their potential association with various ecological niches and host organisms.

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Staphylococcus aureus (S. aureus) ST22 is considered a clinically important clone because an epidemic strain EMRSA-15 belongs to ST22, and several outbreaks of this clone have been documented worldwide. We performed genomic analysis of an S. aureus strain Lr2 ST22 from Pakistan and determined comparative analysis with other ST22 strains. The genomic data show that Lr2 belongs to spa-type t2986 and harbors staphylococcal cassette chromosome mec (SCCmec) type IVa(2B), one complete plasmid, and seven prophages or prophage-like elements. The strain harbors several prophage-associated virulence factors, including Panton-Valentine leukocidin (PVL) and toxic shock syndrome toxin (TSST). The single nucleotide polymorphism (SNPs)-based phylogenetic relationship inferred from whole genome and core genome revealed that strain Lr2 exhibits the nearest identities to a South African community-acquired methicillin-resistant S. aureus (CA-MRSA) ST22 strain and makes a separate clade with an Indian CA-MRSA ST22 strain. Although most ST22 strains carry blaZ, mecA, and mutations in gyrA, the Lr2 strain does not have the blaZ gene but, unlike other ST22 strains, carries the antibiotic resistance genes erm(C) and aac(6')-Ie-aph(2″)-Ia. Among ST22 strains analyzed, only the strain Lr2 possesses both PVL and TSST genes. The functional annotation of genes unique to Lr2 revealed that mobilome is the third-largest Cluster of Orthologous Genes (COGs) category, which encodes genes associated with prophages and transposons. This possibly makes methicillin-resistant S. aureus (MRSA) Lr2 ST22 strain highly virulent, and this study would improve the knowledge of MRSA ST22 strains in Pakistan. However, further studies are needed on a large collection of MRSA to comprehend the genomic epidemiology and evolution of this clone in Pakistan.

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The vaccine development strategies have evolved from using an entire organism as an immunogen to a single antigen and further towards an epitope. Since an epitope is a relatively tiny and immunologically relevant part of an antigen, it has the potential to stimulate more robust and specific immune responses while causing minimal adverse effects. As a result, the recent focus of vaccine development has been to develop multi-epitope vaccines that can target multiple virulence mechanisms. Accordingly, we designed multi-epitope vaccine candidates B (multi-B-cell epitope immunogen) and CTB-B (an adjuvant - cholera toxin subunit B (CTB) - attached to immunogen B) against S. aureus by employing immunoinformatics approaches. The designed vaccines are composed of B-cell epitope segments (20-mer) of the eight well-characterized S. aureus virulence factors, namely ClfB, FnbpA, Hla, IsdA, IsdB, LukE, SdrD, and SdrE connected in series. The designed vaccines were expressed, purified, and administered to C57BL/6 mice with Freund adjuvant to evaluate the immunogenicity and protective efficacy. The results revealed that the immunized mice showed high IgG titers for the immunogen, and the antibody titers increased significantly following the second immunization. However, the generated antibodies did not protect the mice from infection. The interaction of anti-B antibodies with source virulence factors showed that the generated antibodies have no binding affinity with any of the corresponding virulence factors. Our results demonstrate the limitation of the in silico designed B-cell multi-epitope vaccine and suggest that a protein domain carrying both linear and conformational B-cell epitopes might be a better choice for developing an effective multi-epitope vaccine against S. aureus.

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BACKGROUND: The coronavirus SARS-CoV-2 is a member of the Coronaviridae family that has caused a global public health emergency. Currently, there is no approved treatment or vaccine available against it. The current study aimed to cover the diversity of SARS-CoV-2 strains reported from all over the world and to design a broad-spectrum multi-epitope vaccine using an immunoinformatics approach. METHODS: For this purpose, all available complete genomes were retrieved from GISAID and NGDC followed by genome multiple alignments to develop a global consensus sequence to compare with the reference genome. Fortunately, comparative genomics and phylogeny revealed a significantly high level of conservation between the viral strains. All the Open Reading Frames (ORFs) of the reference sequence NC_045512.2 were subjected to epitope mapping using CTLpred and HLApred, respectively. The predicted CTL epitopes were then screened for antigenicity, immunogenicity and strong binding affinity with HLA superfamily alleles. HTL predicted epitopes were screened for antigenicity, interferon induction potential, overlapping B cell epitopes and strong HLA DR binding potential. The shortlisted epitopes were arranged into two multi-epitope sequences, Cov-I-Vac and Cov-II-Vac, and molecular docking was performed with Toll-Like Receptor 8 (TLR8). RESULTS: The designed multi-epitopes were found to be antigenic and non-allergenic. Both multi-epitopes were stable and predicted to be soluble in an Escherichia coli expression system. The molecular docking with TLR8 also demonstrated that they have a strong binding affinity and immunogenic potential. These in silico analyses suggest that the proposed multi-epitope vaccine can effectively evoke an immune response.