Threat Of Antibiotic Resistance Research Articles

Development of an effective meropenem/KPC-2 inhibitor combination to combat infections caused by carbapenem-resistant K. pneumoniae

The global public health threat of bacterial antibiotic resistance continues to escalate and necessitates the implementation of urgent measures to expand our arsenal of antimicrobial drugs. In this study, we identified a benzoxaborane compound, namely 5-chloro-1,3-dihydro-1-hydroxy-2,1-benzoxaborole (AN2178), which can effectively inhibit the catalytic activity of the Klebsiella pneumoniae carbapenemase (KPC-2) enzyme. The efficacy of AN2718 as an inhibitor for the KPC-2 enzyme was verified through various assays, including enzyme activity assays and isothermal titration calorimetry. Results of multiple biochemical assays, minimum inhibitory concentration assay, and time-killing assay also showed that binding of AN2718 to KPC-2 enabled the restoration of the bactericidal effect of meropenem. The survival rate of mice infected by carbapenem-resistant, high-virulence strains increased significantly upon treatment with this agent. Most importantly, the meropenem and AN2718 combination is effective on KPC-2 mutations such as KPC-33 that were clinically evolved and exhibited resistance to ceftazidime-avibactam upon the clinical uses of this drug for a couple of years. Comprehensive safety tests both in vitro and in vivo, such as cytotoxicity, haemolytic activity, and cytochrome P450 inhibition assays demonstrated that AN2718 was safe for clinical use. These promising data indicate that AN2718 has a high potential for being approved for the treatment of drug resistant bacterial infections, including those caused by the Ceftazidime-Avibactam resistant strains. To conclude, the compound AN2718 can be regarded as a valuable addition to the current antimicrobial armamentarium and a promising tool to combat antimicrobial resistance.

Significant reduction of the culturing time required for bacterial identification and antibiotic susceptibility determination by infrared spectroscopy.

Rapid testing of bacteria for antibiotic susceptibility is essential for effective treatment and curbing the emergence of multidrug-resistant bacteria. The misuse of antibiotics, coupled with the time-consuming classical testing methods, intensifies the threat of antibiotic resistance, a major global health concern. In this study, employing infrared spectroscopy-based machine learning techniques, we significantly shortened the time required for susceptibility testing to 10 hours, a significant improvement from the 24 hours in our previous studies as well as the conventional methods that typically take at least 48 hours. This remarkable reduction in turnaround time (from 48 hours to 10 hours), achieved by minimizing the culturing period, offers a game-changing advantage for clinical applications. Our study involves a dataset comprising 400 bacterial samples (200 E. coli, 100 Klebsiella pneumoniae, and 100 Pseudomonas aeruginosa) with an impressive 96% accuracy in the taxonomic classification at the species level and up to 82% accuracy in bacterial susceptibility to various antibiotics.

Longitudinal Analysis of Urban Stormwater Microbiome and Resistome from Watersheds with and without Green Infrastructure using Long-Read Sequencing

Since stormwater conveys a variety of contaminants into water bodies, green infrastructure (GI) is increasingly being adopted as an on-site treatment solution in addition to controlling peak flows. The purpose of this study was to identify differences in microbial water quality of stormwater in watersheds retrofitted with GI vs. those without GI. Considering stormwater is recently recognized as a contributor to the antibiotic resistance (AR) threat, another goal of this study was to characterize changes in the microbiome and collection of AR genes (resistome) of urban stormwater with season, rainfall characteristics, and fecal contamination. MinION long-read sequencing was used to analyze stormwater microbiome and resistome from watersheds with and without GI in Columbus, Ohio, United States, over 18 months. We characterized fecal contamination in stormwater via culturing Escherichia coli and with molecular microbial source tracking (MST) to identify sources of fecal contamination. Overall, season and storm event (rainfall) characteristics had the strongest relationships with changes in the stormwater microbiome and resistome. We found no significant differences in microbial water quality or the microbiome of stormwater in watersheds with and without GI implemented. However, there were differences between the communities of microorganisms hosting antibiotic resistance genes (ARGs) in stormwater from watersheds with and without GI, indicating the potential sensitivity of AR bacteria to treatment. Stormwater was contaminated with high concentrations of human-associated fecal bacterial genes, and the ARG host bacterial community had considerable similarities to human feces/wastewater. We also identified 15 potential pathogens hosting ARGs in these stormwater resistome, including vancomycin-resistant Enterococcus faecium (VRE) and multidrug-resistant Pseudomonas aeruginosa. In summary, urban stormwater is highly contaminated and has a great potential to spread AR and microbial hazards to nearby environments. This study presents the most comprehensive analysis of stormwater microbiome and resistome to date, which is crucial to understanding the potential microbial risk from this matrix. This information can be used to guide future public health policy, stormwater reuse programs, and urban runoff treatment initiatives.

STUDY OF ANTIBIOTIC RESISTANCE AT REGIONAL LEVEL

The introduction of antibiotics revolutionized medicine, dramatically reducing deaths and illnesses caused by infections. However, decades of overuse have accelerated the evolution of microorganisms, leading to a critical challenge: antibiotic resistance. This research aims to investigate the regional risk factors contributing to antibiotic resistance among the Poltava region population. Materials and methods. 100 patients of the 2nd Municipal Clinical Hospital and the Regional (Republican) Center for Emergency Medical Aid and Disaster Medicine of Poltava were included in the study. Data collection was carried out from November 2023 to March 2024. Patients were offered a series of 11 questions. Research results. This study has revealed the following antibiotic usage patterns among the residents of the Poltava region. It has been found that 22% of respondents are currently undergoing treatment, 8% in the last month, 36% in the last year, and 34% of the respondents tool the treatment for more than a year. When using antibiotics without a prescription, 30% of patients consulted a pharmacy, 52% trusted the Internet, 10% chose the cheapest medicine, and 8% followed advertisements. For subsequent illnesses, 76% of respondents used antibiotics they had taken before: amoxicillin (42%), ciprofloxacin (28%), azithromycin (22%), and chloramphenicol (8%). While 80% reported following prescribed antibiotic regimens, 20% interrupted treatment prematurely due to perceived improvement. Despite 84% acknowledging the importance of completing antibiotic courses, a concerning 16% were unaware of this crucial practice. Interestingly, 11% reported using antibiotics for preventive purposes (before getting sick). However, a positive trend emerged: 85% of respondents confirmed undergoing testing for bacterial susceptibility before receiving antibiotics. Only 9% reported amoxicillin as ineffective. When antibiotics were ineffective, 46% of respondents consulted a doctor, 24% took no action and waited for the antibiotic to take effect, 21% stopped one antibiotic and started another, and 14% preferred intramuscular injections and self-medication. Conclusion. A significant number of patients (48%) take antibacterial drugs without a doctor's prescription and 52% search for information on the Internet. 76% of patients use the same drug for a recurrent disease. Amoxicillin is the most often preferred (42%), a smaller number of patients came to the pharmacy for ciprofloxacin (28%), 22% of respondents preferred azithromycin. This study underscores the necessity for public health interventions to tackle antibiotic misuse and misconceptions in the Poltava region. Promoting responsible usage, emphasizing the importance of completing treatment courses, and discouraging self-medication are crucial steps in combating the growing threat of antibiotic resistance.

Antibiotic Loaded Phytosomes as a Way to Develop Innovative Lipid Formulations of Polyene Macrolides.

The threat of antibiotic resistance of fungal pathogens and the high toxicity of the most effective drugs, polyene macrolides, force us to look for new ways to develop innovative antifungal formulations. The aim of this study was to determine how the sterol, phospholipid, and flavonoid composition of liposomal forms of polyene antibiotics, and in particular, amphotericin B (AmB), affects their ability to increase the permeability of lipid bilayers that mimic the membranes of mammalian and fungal cells. To monitor the membrane permeability induced by various polyene-based lipid formulations, a calcein leakage assay and the electrophysiological technique based on planar lipid bilayers were used. The replacement of cholesterol with its biosynthetic precursor, 7-dehydrocholesterol, led to a decrease in the ability of AmB-loaded liposomes to permeabilize lipid bilayers mimicking mammalian cell membranes. The inclusion of plant flavonoid phloretin in AmB-loaded liposomes increased the ability of the formulation to disengage a fluorescent marker from lipid vesicles mimicking the membranes of target fungi. I-V characteristics of the fungal-like lipid bilayers treated with the AmB phytosomes were symmetric, demonstrating the functioning of double-length AmB pores and assuming a decrease in the antibiotic threshold concentration. The therapeutic window of polyene lipid formulations might be expanded by varying their sterol composition. Polyene-loaded phytosomes might be considered as the prototypes for innovative lipid antibiotic formulations.

PREVALENCE AND ANTIBIOTIC RESISTANCE PROFILES OF BACTERIAL ISOLATED FROM BURN WOUND INFECTIONS

Burn wound infections remain a sizeable assignment, especially because of the superiority of multidrug-resistant gram-negative organisms. The objective of this take a look at was to assess the superiority of bacterial infections in burn wounds, perceive the most not unusual bacterial species, and determine their antibiotic resistance profiles. A overall of eighty samples had been amassed from burn sufferers. Bacterial cultures were performed, and the isolated bacteria were identified using standard microbiological techniques. Antibiotic susceptibility testing was conducted using the disc diffusion method. In this study, 44 samples (73.3%) tested positive for bacterial growth. Pseudomonas sp. was the most commonly isolated bacteria (56%), followed by Staphylococcus aureus (22%) and others. Antibiotic resistance profiling revealed high resistance rates among the isolated bacteria, with Rifampicin showing the highest resistance rate (80%), followed by Cefotaxime (64%) and Amoxicillin (56%). The high prevalence of multidrug-resistant gram-negative organisms in burn wound infections underscores the urgent need for effective antibiotic management strategies. These findings highlight the importance of continuous surveillance of antibiotic resistance patterns and the development of tailored treatment protocols to mitigate the morbidity and mortality associated with burn wound infections. Additionally, there is a critical need for the development of new antimicrobial agents and infection control measures to combat the growing threat of antibiotic resistance in burn care settings.

Uncovering the growing burden of enteric fever: A molecular analysis of Salmonella Typhi antimicrobial resistance

Enteric fever, a persistent public health challenge in developing regions, is exacerbated by suboptimal socioeconomic conditions, contaminated water and food sources, and insufficient sanitation. This study delves into the antimicrobial susceptibility of Salmonella Typhi, uncovering the genetic underpinnings of its resistance. Analyzing 897 suspected cases, we identified a significant prevalence of typhoid fever, predominantly in males (58.3 %) and younger demographics. Alarmingly, our data reveals an escalation in resistance to both primary and secondary antibiotics, with cases of multi-drug resistant (MDR) and extensively drug-resistant (XDR) S. Typhi reaching 14.7 % and 43.4 %, respectively, in 2021. The Multiple Antibiotic Resistance (MAR) index exceeded 0.2 in over half of the isolates, signaling widespread antibiotic misuse. The study discerned 47 unique antibiotic resistance patterns and pinpointed carbapenem and macrolide antibiotics as the remaining effective treatments against XDR strains, underlining the critical need to preserve these drugs for severe cases. Molecular examinations identified blaTEM, blaSHV, and blaCTX-M genes in ceftriaxone-resistant strains, while qnrS was specific to ciprofloxacin-resistant variants. Notably, all examined strains exhibited a singular mutation in the gyrA gene, maintaining wild-type gyrB and parC genes. The erm(B) gene emerged as the primary determinant of azithromycin resistance. Furthermore, a distressing increase in resistance genes was observed over three years, with erm(B), blaTEM and qnrS showing significant upward trends. These findings are a clarion call for robust antimicrobial stewardship programs to curtail inappropriate antibiotic use and forestall the burgeoning threat of antibiotic resistance in S. Typhi.

Dual-channel fluorescent sensors based on chitosan-coated Mn-doped ZnS micromaterials to detect ampicillin

The global threat of antibiotic resistance has increased the importance of the detection of antibiotics. Conventional methods to detect antibiotics are time-consuming and require expensive specialized equipment. Here, we present a simple and rapid biosensor for detecting ampicillin, a commonly used antibiotic. Our method is based on the fluorescent properties of chitosan-coated Mn-doped ZnS micromaterials combined with the β-lactamase enzyme. The biosensors exhibited the highest sensitivity in a linear working range of 13.1–72.2 pM with a limit of detection of 8.24 pM in deionized water. In addition, due to the biological specificity of β-lactamase, the proposed sensors have demonstrated high selectivity over penicillin, tetracycline, and glucose through the enhancing and quenching effects at wavelengths of 510 nm and 614 nm, respectively. These proposed sensors also showed promising results when tested in various matrices, including tap water, bottled water, and milk. Our work reports for the first time the cost-effective (Mn:ZnS)Chitosan micromaterial was used for ampicillin detection. The results will facilitate the monitoring of antibiotics in clinical and environmental contexts.

Shigella Vaccines: The Continuing Unmet Challenge.

Shigellosis is a severe gastrointestinal disease that annually affects approximately 270 million individuals globally. It has particularly high morbidity and mortality in low-income regions; however, it is not confined to these regions and occurs in high-income nations when conditions allow. The ill effects of shigellosis are at their highest in children ages 2 to 5, with survivors often exhibiting impaired growth due to infection-induced malnutrition. The escalating threat of antibiotic resistance further amplifies shigellosis as a serious public health concern. This review explores Shigella pathology, with a primary focus on the status of Shigella vaccine candidates. These candidates include killed whole-cells, live attenuated organisms, LPS-based, and subunit vaccines. The strengths and weaknesses of each vaccination strategy are considered. The discussion includes potential Shigella immunogens, such as LPS, conserved T3SS proteins, outer membrane proteins, diverse animal models used in Shigella vaccine research, and innovative vaccine development approaches. Additionally, this review addresses ongoing challenges that necessitate action toward advancing effective Shigella prevention and control measures.

Emerging threats of high biofilm formation and antibiotic resistance in clinical methicillin-resistant Staphylococcus aureus (MRSA) isolates from Pakistan

ObjectivesThis multicenter study, conducted from a One Health perspective, aimed to comprehensively examine the prevalence of methicillin-resistant Staphylococcus aureus (MRSA) infections and their biofilm-forming capabilities in Pakistan. Phylogenetic analysis of the study isolates was also performed. MethodsA total of 150 MRSA isolates were screened from various clinical samples using Cefoxitin antibiotic discs. Genotypic confirmation was conducted through mecA, S. aureus-specific nuc, and 16S rRNA genes. Biofilm formation was assessed using Congo red agar (CRA) and slime layer quantification methods. The intercellular adhesion (ica) operon genes, specifically icaA and icaD, were detected. Phylogenetic analysis utilized the 16S rRNA sequences. Statistical associations between various parameters were determined using chi-square analysis. ResultsThe presence of the mecA gene was observed in 131 out of 150 isolates (87.3%). CRA identified 28% and 40% of isolates as strong and moderate biofilm producers, respectively, while 9.3% were classified as non-biofilm producers. The slime layer assay exhibited higher sensitivity, classifying only 4.7% of isolates as non-biofilm producers. Biofilm-forming genes icaA and icaD were detected in 85.3% and 86.7% of the isolates, respectively. Antibiotic resistance was more prevalent among biofilm-forming isolates, particularly against ciprofloxacin, levofloxacin, erythromycin, trimethoprim-sulfamethoxazole, and fosfomycin. Ceftaroline demonstrated efficacy irrespective of biofilm-forming abilities. Conversely, non-biofilm producers exhibited complete susceptibility to clarithromycin and tigecycline. ConclusionsClinical MRSA strains exhibit a substantial potential for biofilm formation, contributing to a resistant phenotype. Routine antibiotic testing in clinical settings that overlook the biofilm aspect may lead to the failure of empiric antibiotic therapy.

In silico and in vivo Investigations of the Immunoreactivity of Klebsiella pneumoniae OmpA Protein as a Vaccine Candidate.

The growing threat of antibiotic resistance and Klebsiella pneumoniae infection in healthcare settings highlights the urgent need for innovative solutions, such as vaccines, to address these challenges. This study sought to assess the potential of using K. pneumoniae OmpA as a vaccine candidate through both in silico and in vivo analyses. The study examined the OmpA protein sequence for subcellular localization, antigenicity, allergenicity, similarity to the human proteome, physicochemical properties, B-cell epitopes, MHC binding sites, tertiary structure predictions, molecular docking, and immune response simulations. The ompA gene was cloned into the pET-28a (+) vector, expressed, purified and confirmed using Western blotting analysis. IgG levels in the serum of the immunized mice were measured using ELISA with dilutions ranging from 1:100 to 1:6400, targeting rOmpA and K. pneumoniae ATCC 13883. The sensitivity and specificity of the ELISA method were also assessed. The bioinformatics analysis identified rOmpA as a promising vaccine candidate. The immunized group demonstrated significant production of specific total IgG antibodies against rOmpA and K. pneumoniae ATCC1 13883, as compared to the control group (p < 0.0001). The titers of antibodies produced in response to bacterial exposure did not show any significant difference when compared to the anti-rOmpA antibodies (p > 0.05). The ELISA test sensitivity was 1:3200, and the antibodies in the serum could accurately recognize K. pneumoniae cells. This study is a significant advancement in the development of a potential vaccine against K. pneumoniae that relies on OmpA. Nevertheless, additional experimental analyses are required.

The Prospect of Biomimetic Immune Cell Membrane-Coated Nanomedicines for Treatment of Serious Bacterial Infections and Sepsis.

Invasive bacterial infections and sepsis are persistent global health concerns, complicated further by the escalating threat of antibiotic resistance. Over the past 40 years, collaborative endeavors to improve the diagnosis and critical care of septic patients have improved outcomes, yet grappling with the intricate immune dysfunction underlying the septic condition remains a formidable challenge. Anti-inflammatory interventions that exhibited promise in murine models failed to manifest consistent survival benefits in clinical studies through recent decades. Novel therapeutic approaches that target bacterial virulence factors, for example with monoclonal antibodies, aim to thwart pathogen-driven damage and restore an advantage to the immune system. A pioneering technology addressing this challenge is biomimetic nanoparticles-a therapeutic platform featuring nanoscale particles enveloped in natural cell membranes. Borne from the quest for a durable drug delivery system, the original red blood cell-coated nanoparticles showcased a broad capacity to absorb bacterial and environmental toxins from serum. Tailoring the membrane coating to immune cell sources imparts unique characteristics to the nanoparticles suitable for broader application in infectious disease. Their capacity to bind both inflammatory signals and virulence factors assembles the most promising sepsis therapies into a singular, pathogen-agnostic therapeutic. This review explores the ongoing work on immune cell-coated nanoparticle therapeutics for infection and sepsis. SIGNIFICANCE STATEMENT: Invasive bacterial infections and sepsis are a major global health problem made worse by expanding antibiotic resistance, meaning better treatment options are urgently needed. Biomimetic cell-membrane-coated nanoparticles are an innovative therapeutic platform that deploys a multifaceted mechanism to action to neutralize microbial virulence factors, capture endotoxins, and bind excessive host proinflammatory cytokines, seeking to reduce host tissue injury, aid in microbial clearance, and improve patient outcomes.

Machine learning enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression

Peri-implantitis is a complex infectious disease that manifests as progressive loss of alveolar bone around the dental implants and hyper-inflammation associated with microbial dysbiosis. Using antibiotics in treating peri-implantitis is controversial because of antibiotic resistance threats, the non-selective suppression of pathogens and commensals within the microbial community, and potentially serious systemic sequelae. Therefore, conventional treatment for peri-implantitis comprises mechanical debridement by nonsurgical or surgical approaches with adjunct local microbicidal agents. Consequently, current treatment options may not prevent relapses, as the pathogens either remain unaffected or quickly re-emerge after treatment. Successful mitigation of disease progression in peri-implantitis requires a specific mode of treatment capable of targeting keystone pathogens and restoring bacterial community balance toward commensal species. Antimicrobial peptides (AMPs) hold promise as alternative therapeutics through their bacterial specificity and targeted inhibitory activity. However, peptide sequence space exhibits complex relationships such as sparse vector encoding of sequences, including combinatorial and discrete functions describing peptide antimicrobial activity. In this paper, we generated a transparent Machine Learning (ML) model that identifies sequence-function relationships based on rough set theory using simple summaries of the hydropathic features of AMPs. Comparing the hydropathic features of peptides according to their differential activity for different classes of bacteria empowered predictability of antimicrobial targeting. Enriching the sequence diversity by a genetic algorithm, we generated numerous candidate AMPs designed for selectively targeting pathogens and predicted their activity using classifying rough sets. Empirical growth inhibition data is iteratively fed back into our ML training to generate new peptides, resulting in increasingly more rigorous rules for which peptides match targeted inhibition levels for specific bacterial strains. The subsequent top scoring candidates were empirically tested for their inhibition against keystone and accessory peri-implantitis pathogens as well as an oral commensal bacterium. A novel peptide, VL-13, was confirmed to be selectively active against a keystone pathogen. Considering the continually increasing number of oral implants placed each year and the complexity of the disease progression, prevalence of peri-implant diseases continues to rise. Our approach offers transparent ML-enabled paths towards developing antimicrobial peptide-based therapies targeting the changes in the microbial communities that can beneficially impact disease progression.

Nosocomial infections: a look at the problem in the context of the global threat of antibiotic resistance (review)

Antibiotic resistance poses a serious threat to public health. The problem of nosocomial infections is inextricably linked with antibiotic resistance. Nosocomial infections in most cases are caused by the formation and spread of hospital strains that are resistant to antibacterial drugs. Nosocomial infections caused by multidrug-resistant microorganisms increase healthcare costs, lengthen hospital stays, and lead to extremely poor patient outcomes. The article systematizes data on the prevalence of nosocomial infections and examines the problem of antibiotic resistance of leading nosocomial pathogens. Approaches to monitoring and management of antibiotic resistance and epidemiological surveillance of nosocomial infections are analyzed. A promising area of research in public health is considered – osocomial infections associated with previous hospitalization.

Differentiating viral and bacterial infections: A machine learning model based on routine blood test values

The growing threat of antibiotic resistance necessitates accurate differentiation between bacterial and viral infections for proper antibiotic administration. In this study, a Virus vs. Bacteria machine learning model was developed to distinguish between these infection types using 16 routine blood test results, C-reactive protein concentration (CRP), biological sex, and age. With a dataset of 44,120 cases from a single medical center, the model achieved an accuracy of 82.2 %, a sensitivity of 79.7 %, a specificity of 84.5 %, a Brier score of 0.129, and an area under the ROC curve (AUC) of 0.905, outperforming a CRP-based decision rule. Notably, the machine learning model enhanced accuracy within the CRP range of 10–40 mg/L, a range where CRP alone is less informative. These results highlight the advantage of integrating multiple blood parameters in diagnostics. The "Virus vs. Bacteria" model paves the way for advanced diagnostic tools, leveraging machine learning to optimize infection management.

ApoE Mimetic Peptide COG1410 Kills Mycobacterium smegmatis via Directly Interfering ClpC's ATPase Activity.

Antimicrobial peptides (AMPs) hold promise as alternatives to combat bacterial infections, addressing the urgent global threat of antibiotic resistance. COG1410, a synthetic peptide derived from apolipoprotein E, has exhibited potent antimicrobial properties against various bacterial strains, including Mycobacterium smegmatis. However, our study reveals a previously unknown resistance mechanism developed by M. smegmatis against COG1410 involving ClpC. Upon subjecting M. smegmatis to serial passages in the presence of sub-MIC COG1410, resistance emerged. The comparative genomic analysis identified a point mutation in ClpC (S437P), situated within its middle domain, which led to high resistance to COG1410 without compromising bacterial fitness. Complementation of ClpC in mutant restored bacterial sensitivity. In-depth analyses, including transcriptomic profiling and in vitro assays, uncovered that COG1410 interferes with ClpC at both transcriptional and functional levels. COG1410 not only stimulated the ATPase activity of ClpC but also enhanced the proteolytic activity of Clp protease. SPR analysis confirmed that COG1410 directly binds with ClpC. Surprisingly, the identified S437P mutation did not impact their binding affinity. This study sheds light on a unique resistance mechanism against AMPs in mycobacteria, highlighting the pivotal role of ClpC in this process. Unraveling the interplay between COG1410 and ClpC enriches our understanding of AMP-bacterial interactions, offering potential insights for developing innovative strategies to combat antibiotic resistance.

Gene expression profile of Campylobacter jejuni in response to macrolide antibiotics.

Campylobacter jejuni is a foodborne pathogen that causes gastroenteritis in humans and has developed resistance to various antibiotics. The primary objective of this research was to examine the network of antibiotic resistance in C. jejuni. The study involved the wild and antibiotic-resistant strains placed in the presence and absence of antibiotics to review their gene expression profiles in response to ciprofloxacin via microarray. Differentially expressed genes (DEGs) analysis and Protein-Protein Interaction (PPI) Network studies were performed for these genes. The results showed that the resistance network of C. jejuni is modular, with different genes involved in bacterial motility, capsule synthesis, efflux, and amino acid and sugar synthesis. Antibiotic treatment resulted in the down-regulation of cluster genes related to translation, flagellum formation, and chemotaxis. In contrast, cluster genes involved in homeostasis, capsule formation, and cation efflux were up-regulated. The study also found that macrolide antibiotics inhibit the progression of C. jejuni infection by inactivating topoisomerase enzymes and increasing the activity of epimerase enzymes, trying to compensate for the effect of DNA twisting. Then, the bacterium limits the movement to conserve energy. Identifying the antibiotic resistance network in C. jejuni can aid in developing drugs to combat these bacteria. Genes involved in cell division, capsule formation, and substance transport may be potential targets for inhibitory drugs. Future research must be directed toward comprehending the underlying mechanisms contributing to the modularity of antibiotic resistance and developing strategies to disrupt and mitigate the growing threat of antibiotic resistance effectively.

Structural basis of DNA recognition of the Campylobacter jejuni CosR regulator.

Campylobacter jejuni has emerged as an antibiotic-resistant threat worldwide. CosR is an essential regulator for this bacterium and is important for Campylobacter adaptation to various stresses. Here, we describe the structural basis of CosR binding to target DNA as determined by cryo-electron microscopy and X-ray crystallography. Since CosR is a potential target for intervention, our studies may facilitate the development of novel therapeutics to combat C. jejuni infection.

Mechanism of Protease Resistance of D-Amino Acid Residue Containing Cationic Antimicrobial Heptapeptides.

Antimicrobial peptides (AMPs) have been an alternate promising class of therapeutics in combating global antibiotic resistance threat. However, the short half-life of AMPs, owing to protease degradability, is one of the major bottlenecks in its commercial success. In this study, we have developed all-D-amino acid containing small cationic peptides P4C and P5C, which are completely protease-resistant, noncytotoxic, nonhemolytic, and potent against the ESKAPE pathogens in comparison to their L analogues. MD simulations suggested marginal improvement in the peptide-binding affinity to the membrane-mimetic SDS micelle (∼ 1 kcal/mol) in response to L → D conversion, corroborating the marginal improvement in the antimicrobial activity. However, L → D chirality conversion severely compromised the peptide:protease (trypsin) binding affinity (≥10 kcal/mol). The relative distance between the scissile peptide carbonyl and the catalytic triad of the protease (H57, D102, and S195) was found to be significantly altered in the D-peptide:protease complex (inactive conformation) relative to the active L-peptide:protease complex. Thus, the poor binding affinity between D-peptides and the protease, resulting in the inactive complex formation, explained their experimentally observed proteolytic stability. This mechanistic insight might be extended to the proteolytic stability of the D-peptides in general and stimulate the rational design of protease-resistant AMPs.

Clostridioides difficile Infection: Diagnosis and Treatment Challenges.

Clostridioides difficile is the most important cause of healthcare-associated diarrhea in the United States. The high incidence and recurrence rates of C. difficile infection (CDI), associated with high morbidity and mortality, pose a public health challenge. Although antibiotics targeting C. difficile bacteria are the first treatment choice, antibiotics also disrupt the indigenous gut flora and, therefore, create an environment that is favorable for recurrent CDI. The challenge of treating CDI is further exacerbated by the rise of antibiotic-resistant strains of C. difficile, placing it among the top five most urgent antibiotic resistance threats in the USA. The evolution of antibiotic resistance in C. difficile involves the acquisition of new resistance mechanisms, which can be shared among various bacterial species and different C. difficile strains within clinical and community settings. This review provides a summary of commonly used diagnostic tests and antibiotic treatment strategies for CDI. In addition, it discusses antibiotic treatment and its resistance mechanisms. This review aims to enhance our current understanding and pinpoint knowledge gaps in antimicrobial resistance mechanisms in C. difficile, with an emphasis on CDI therapies.