Onohuean H, Agwu E, Nwodo U U
Biopharmaceutics Unit, Department of Pharmacology and Toxicology, School of Pharmacy, Kampala International University Western Campus, Ishaka, Bushenyi, Uganda.
SA-MRC Microbial Water Quality Monitoring Centre, University of Fort Hare, Alice 5700, South Africa.
Heliyon. 2022 Feb 3;8(2):e08845. doi: 10.1016/j.heliyon.2022.e08845. eCollection 2022 Feb.
Adequate comprehension of the genomics of microbial resistance to an antimicrobial agent will advance knowledge on the management of associated pathologies and public health safety. However, continued emergences and reemergence of pathogens, including species, hallmarks a potential knowledge gap. A clear understanding of the process and forecast of the next trend should be in place to nip in the bud, microbial acquisition of resistance to antibiotics. Therefore, this two-decade (1 January 2000 to 31 December 2019) systematic review and meta-analytical study articulated the prevalence and incidence of antibiotics resistance genes in species isolated from environmental samples. Articles from the Web of Science and PubMed electronic databases was engaged. Heterogeneity of the data and bias were analyzed with random effect model meta-analysis and funnel plot. A total of 1920 sp. were reported by the ten selected articles included in this study; out of which 32.39% of identified isolates displayed antimicrobial resistance and associated genes. The distribution of antibiotics resistance genes in sp., reported within six countries was 21% tetracycline (), and 20% sulphonamide () and β-lactamase () respectively. The quinolone, tetracycline and sulfonamide resistance genes showed 32.97% (95% CI 0.18-0.53) prevalence while chloramphenicol, macrolides and aminoglycoside resistance genes are expressed in percentages as 28.67% (95% CI 0.15-0.47) and β-lactamase resistance genes 27.93% (95% CI 0.11-0.56) respectively. The antibiotics resistance genes (-ARG) distribution depicts no regular trend or pattern from the analyzed data. Consequently, more studies would be required to articulate the structure of cohesion in the distribution of the resistance determinants in microbes.
充分理解微生物对抗菌剂耐药性的基因组学将推动对相关病理管理和公共卫生安全的认识。然而,包括某些物种在内的病原体不断出现和再次出现,标志着潜在的知识差距。应该清楚了解这一过程并预测下一个趋势,以便将微生物对抗生素的耐药性扼杀在萌芽状态。因此,这项为期二十年(2000年1月1日至2019年12月31日)的系统综述和荟萃分析研究阐明了从环境样本中分离出的某些物种中抗生素耐药基因的流行率和发病率。使用了来自科学网和PubMed电子数据库的文章。采用随机效应模型荟萃分析和漏斗图分析数据的异质性和偏差。本研究纳入的十篇选定文章共报道了1920个某物种;其中32.39%的已鉴定分离株显示出抗菌耐药性及相关基因。在六个国家报告的某物种中,抗生素耐药基因的分布分别为21%的四环素(某物种)、20%的磺胺类(某物种)和β-内酰胺酶(某物种)。喹诺酮、四环素和磺胺类耐药基因的流行率为32.97%(95%置信区间0.18 - 0.53),而氯霉素、大环内酯类和氨基糖苷类耐药基因的表达百分比分别为28.67%(95%置信区间0.15 - 0.47)和β-内酰胺酶耐药基因27.93%(95%置信区间0.11 - 0.56)。从分析数据来看,某抗生素耐药基因(-ARG)的分布没有呈现出规律的趋势或模式。因此,需要更多的研究来阐明微生物中耐药决定因素分布的凝聚结构。