Antibiotic resistance (AR) is the resistance mechanism pattern in bacteria that evolves over some time, thus protecting the bacteria against antibiotics. AR is due to bacterial evolution to make itself fit to changing environmental conditions in a quest for survival of the fittest. AR has emerged due to the misuse and overuse of antimicrobial drugs, and few antibiotics are now left to deal with these superbug infections. To combat AR, vaccination is an effective method, used either therapeutically or prophylactically. In the current study, an in silico approach was applied for the design of multi-epitope-based vaccines against Providencia rettgeri, a major cause of traveler’s diarrhea. A total of six proteins: fimbrial protein, flagellar hook protein (FlgE), flagellar basal body L-ring protein (FlgH), flagellar hook-basal body complex protein (FliE), flagellar basal body P-ring formation protein (FlgA), and Gram-negative pili assembly chaperone domain proteins, were considered as vaccine targets and were utilized for B- and T-cell epitope prediction. The predicted epitopes were assessed for allergenicity, antigenicity, virulence, toxicity, and solubility. Moreover, filtered epitopes were utilized in multi-epitope vaccine construction. The predicted epitopes were joined with each other through specific GPGPG linkers and were joined with cholera toxin B subunit adjuvant via another EAAAK linker in order to enhance the efficacy of the designed vaccine. Docking studies of the designed vaccine construct were performed with MHC-I (PDB ID: 1I1Y), MHC-II (1KG0), and TLR-4 (4G8A). Findings of the docking study were validated through molecular dynamic simulations, which confirmed that the designed vaccine showed strong interactions with the immune receptors, and that the epitopes were exposed to the host immune system for proper recognition and processing. Additionally, binding free energies were estimated, which highlighted both electrostatic energy and van der Waals forces to make the complexes stable. Briefly, findings of the current study are promising and may help experimental vaccinologists to formulate a novel multi-epitope vaccine against P. rettgeri.

BackgroundType 2 diabetes mellitus (T2DM) is a multifactorial disorder that leads to alterations in gene regulation. Long non-coding RNAs (lncRNAs) have become a major research topic as they are involved in metabolic disorders.MethodsThis study included a total of 400 study subjects; 200 were subjects with T2DM and 200 were healthy subjects. Extracted RNA was used to synthesize cDNA by quantitative real time. Serum analysis was carried out to determine differences in biochemical parameters. Recorded data were used to evaluate associations with expression of lncRNAs NF-kappaB interacting lncRNA (NKILA), nuclear enriched abundant transcript 1 (NEAT1), metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), and myocardial infarction-associated transcript (MIAT) in T2DM cases.ResultsCompared with healthy controls, patients with T2DM showed an overall increase in expression of lncRNAs NKILA, NEAT, MALAT1, and MIAT by 3.94-fold, 5.28-fold, 4.46-fold, and 6.35-fold, respectively. Among patients with T2DM, higher expression of lncRNA NKILA was associated with hypertension (p=0.001), smoking (p<0.0001), and alcoholism (p<0.0001). Altered NEAT1 expression was significantly associated with weight loss (p=0.04), fatigue (p=0.01), slow wound healing (p=0.002), blurred vision (p=0.008), loss of appetite (p=0.007), smoking (p<0.0001), and alcoholism (p<0.0001). Higher expression of lncRNA MALAT1 was significantly linked with weight loss (p=0.003), blurred vision (p=0.01), smoking (p<0.0001), and alcoholism (p<0.0001). Expression of lncRNA MIAT was associated with only blurred vision (p<0.0001), smoking (p<0.0001), and alcoholism (p<0.0001). Positive correlations of lncRNA NKILA with lncRNAs NEAT1 (r=0.42, p<0.0001), MALAT (r=0.36, p<0.0001) and MIAT (r=0.42, p<0.0001) were observed among patients with T2DM. Significant positive correlations of lncRNA NEAT with lncRNAs MALAT and MIAT were observed among patients with T2DM. A positive correlation between lncRNAs MALAT and MIAT was also observed among patients with T2DM.ConclusionIncreased circulating NKILA, NEAT1, MALAT, and MIAT expression in patients with T2DM, which is linked with poor patient outcomes and significantly linked with alcoholism and smoking, may influence the degree and severity of disease among patients with T2DM. These lncRNAs may contribute to the progression of T2DM disease or other related diabetes-related complications.

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The antimicrobial activity, minimum inhibitory concentration (MIC), and minimum bactericidal concentration (MBC) of TQ were determined using an agar well diffusion method and broth microdilution assays, and the synergistic effect was evaluated using antibiotics in parallel. The disruptive effect of TQ on bacterial cell membranes was determined using scanning electron microscopy. The antivirulence Original Article properties of TQ, which include adherence and biofilm formation, were also investigated using adherence and biofilm formation assays, respectively. Results: Thymoquinone demonstrated bactericidal efficacy against 4/14 bacterial strains, with MIC range of 1.04-8.3 μg/mL and and MBC range of 10.41-66.66 μg/ mL. Thymoquinone showed synergism against Klebsiella pneumoniae, Staphylococcus epidermidis (American Type Culture Collection 12228), Staphylococcus aureus, and Staphylococcus epidermidis in combination with the tested antibiotics. Thymoquinone inhibited bacterial adhesion by 39%-54%, 48%-68%, and 61%-81% at 0.5 × MIC, 1 × MIC, and 2 × MIC, respectively. The tested bacterial strains significantly inhibited biofilm formation after treatment with various concentrations of TQ for 24 and 48 hours. Conclusion: The combinatory effect of TQ with antimicrobials should be considered when developing new antimicrobial therapy regimens to overcome multidrugresistant.