Gopikrishnan Mohanraj, Haryini Sree, C George Priya Doss
Department of Integrative Biology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India.
Department of Biomedical Sciences, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India.
J Basic Microbiol. 2024 May;64(5):e2300579. doi: 10.1002/jobm.202300579. Epub 2024 Feb 2.
In recent years, antibiotic therapy has encountered significant challenges due to the rapid emergence of multidrug resistance among bacteria responsible for life-threatening illnesses, creating uncertainty about the future management of infectious diseases. The escalation of antimicrobial resistance in the post-COVID era compared to the pre-COVID era has raised global concern. The prevalence of nosocomial-related infections, especially outbreaks of drug-resistant strains of Staphylococcus aureus, have been reported worldwide, with India being a notable hotspot for such occurrences. Various virulence factors and mutations characterize nosocomial infections involving S. aureus. The lack of proper alternative treatments leading to increased drug resistance emphasizes the need to investigate and examine recent research to combat future pandemics. In the current genomics era, the application of advanced technologies such as next-generation sequencing (NGS), machine learning (ML), and quantum computing (QC) for genomic analysis and resistance prediction has significantly increased the pace of diagnosing drug-resistant pathogens and insights into genetic intricacies. Despite prompt diagnosis, the elimination of drug-resistant infections remains unattainable in the absence of effective alternative therapies. Researchers are exploring various alternative therapeutic approaches, including phage therapy, antimicrobial peptides, photodynamic therapy, vaccines, host-directed therapies, and more. The proposed review mainly focuses on the resistance journey of S. aureus over the past decade, detailing its resistance mechanisms, prevalence in the subcontinent, innovations in rapid diagnosis of the drug-resistant strains, including the applicants of NGS and ML application along with QC, it helps to design alternative novel therapeutics approaches against S. aureus infection.
近年来，由于引发危及生命疾病的细菌中多重耐药性迅速出现，抗生素治疗面临重大挑战，这给传染病的未来管理带来了不确定性。与新冠疫情前的时代相比，新冠疫情后时代抗菌药物耐药性的升级引发了全球关注。医院相关感染的流行情况，尤其是耐甲氧西林金黄色葡萄球菌菌株的暴发，在全球范围内都有报道，印度是此类事件的一个显著热点地区。涉及金黄色葡萄球菌的医院感染具有多种毒力因子和突变特征。缺乏合适的替代治疗方法导致耐药性增加，这凸显了开展研究以应对未来大流行的必要性。在当前的基因组学时代，诸如下一代测序（NGS）、机器学习（ML）和量子计算（QC）等先进技术在基因组分析和耐药性预测中的应用，显著加快了耐药病原体的诊断速度，并加深了对基因复杂性的认识。尽管能够及时诊断，但在缺乏有效替代疗法的情况下，消除耐药性感染仍然无法实现。研究人员正在探索各种替代治疗方法，包括噬菌体疗法、抗菌肽、光动力疗法、疫苗、宿主导向疗法等等。本综述主要关注过去十年金黄色葡萄球菌的耐药历程，详细阐述其耐药机制、在该次大陆的流行情况、耐药菌株快速诊断方面的创新，包括NGS和ML应用以及QC的应用情况，这有助于设计针对金黄色葡萄球菌感染的新型替代治疗方法。
