Trends in the molecular epidemiology and population genetics of emerging Sporothrix species.
Trends in the molecular epidemiology and population genetics of emerging Sporothrix species.
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新发孢子丝菌的分子流行病学和群体遗传学趋势。
DOI：
10.1016/j.simyco.2021.100129
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发表时间：
2021-09
影响因子：
16.5
通讯作者：
Rodrigues AM
中科院分区：
文献类型：
--
作者：
de Carvalho JA;Beale MA;Hagen F;Fisher MC;Kano R;Bonifaz A;Toriello C;Negroni R;Rego RSM;Gremião IDF;Pereira SA;de Camargo ZP;Rodrigues AM
孢子丝菌属（Ophiostomatales）包括对人类和其它哺乳动物以及环境真菌致病的物种。分子生物学的发展改变了我们对孢子丝菌的流行病学、宿主相关性和毒力的认识。孢子虫病的经典病原体申克孢子丝菌（Sporothrix schenckii）现在包括与巴西孢子丝菌（S. brasiliensis）、球形孢子丝菌（S. globosa）和卢氏孢子丝菌（S. luriei）嵌套在一个临床分支中的几个种。为了更精确地了解孢子丝菌种群内和种群间的爆发动态、结构和遗传变异起源，我们应用了三组具有歧视性的AFLP标记（#3 EcoRI-GA/MseI-TT、#5 EcoRI-GA/MseI-AG和#6 EcoRI-TA/MseI-AA）和交配型分析，对跨越主要流行区的大量人类、动物和环境分离株进行了分析。从188个样品中共扩增出451个多态位点，多态性信息含量较高（PIC = 0.1765-0.2253），标记指数（MI = 0.0001-0.0002），有效复用比（E = 15.1720-23.5591），分辨力（Rp = 26.1075-40.2795），鉴别力（D = 0.9766-0.9879）、期望杂合度（H = 0.1957-0.2588）和平均杂合度（Havp = 0.000007- 0.00009），证明AFLP标记对孢子丝菌属物种鉴定的有效性。使用程序结构的分析表明，三个遗传聚类匹配S. brasiliensis（人口1），S. schenckii（人口2），S. globosa（人口3），与所有人口之间的混合模式的存在。AMOVA揭示了高度结构化的聚类（PhiPT = 0.458-0.484，P < 0.0001），在群体内（46- 48%）和群体间（52- 54%）具有大致相等的遗传变异性。异宗配合是唯一的交配策略，MAT 1 -1和MAT 1 -2独特型在申克氏藻中的分布不存在显著的偏态（1：1）（χ2 = 2.522; P = 0.1122），支持随机交配。与此相反，发现S. globosa的偏态分布（χ2 = 9.529; P = 0.0020），以MAT 1 -1菌株为主，巴西链球菌的区域差异突出，里约热内卢以MAT 1 -2菌株为主（χ2 = 14.222; P = 0.0002）和伯南布哥（χ2 = 7.364; P = 0.0067），相比之下，南里奥格兰德MAT 1 -1的患病率更高（χ2 = 7.364; P = 0.0067）。流行病学趋势揭示了由于巴西链球菌通过奠基者效应引起的猫传播的孢子虫病的地理扩展。这些数据支持里约热内卢是导致这种疾病传播到巴西其他地区的起源中心。我们能够从分子数据中重建正在发生的疫情的来源、传播和演变，为旨在减缓疾病进展的决策提供高质量的信息。其他用途包括监测，快速诊断，病例连接和指导获得适当的抗真菌治疗。
Sporothrix (Ophiostomatales) comprises species that are pathogenic to humans and other mammals as well as environmental fungi. Developments in molecular phylogeny have changed our perceptions about the epidemiology, host-association, and virulence of Sporothrix. The classical agent of sporotrichosis, Sporothrix schenckii, now comprises several species nested in a clinical clade with S. brasiliensis, S. globosa, and S. luriei. To gain a more precise view of outbreaks dynamics, structure, and origin of genetic variation within and among populations of Sporothrix, we applied three sets of discriminatory AFLP markers (#3 EcoRI-GA/MseI-TT, #5 EcoRI-GA/MseI-AG, and #6 EcoRI-TA/MseI-AA) and mating-type analysis to a large collection of human, animal and environmental isolates spanning the major endemic areas. A total of 451 polymorphic loci were amplified in vitro from 188 samples, and revealed high polymorphism information content (PIC = 0.1765–0.2253), marker index (MI = 0.0001–0.0002), effective multiplex ratio (E = 15.1720–23.5591), resolving power (Rp = 26.1075–40.2795), discriminating power (D = 0.9766–0.9879), expected heterozygosity (H = 0.1957–0.2588), and mean heterozygosity (Havp = 0.000007–0.000009), demonstrating the effectiveness of AFLP markers to speciate Sporothrix. Analysis using the program structure indicated three genetic clusters matching S. brasiliensis (population 1), S. schenckii (population 2), and S. globosa (population 3), with the presence of patterns of admixture amongst all populations. AMOVA revealed highly structured clusters (PhiPT = 0.458–0.484, P < 0.0001), with roughly equivalent genetic variability within (46–48 %) and between (52–54 %) populations. Heterothallism was the exclusive mating strategy, and the distributions of MAT1-1 or MAT1-2 idiomorphs were not significantly skewed (1:1 ratio) for S. schenckii (χ2 = 2.522; P = 0.1122), supporting random mating. In contrast, skewed distributions were found for S. globosa (χ2 = 9.529; P = 0.0020) with a predominance of MAT1-1 isolates, and regional differences were highlighted for S. brasiliensis with the overwhelming occurrence of MAT1-2 in Rio de Janeiro (χ2 = 14.222; P = 0.0002) and Pernambuco (χ2 = 7.364; P = 0.0067), in comparison to a higher prevalence of MAT1-1 in the Rio Grande do Sul (χ2 = 7.364; P = 0.0067). Epidemiological trends reveal the geographic expansion of cat-transmitted sporotrichosis due to S. brasiliensis via founder effect. These data support Rio de Janeiro as the centre of origin that has led to the spread of this disease to other regions in Brazil. Our ability to reconstruct the source, spread, and evolution of the ongoing outbreaks from molecular data provides high-quality information for decision-making aimed at mitigating the progression of the disease. Other uses include surveillance, rapid diagnosis, case connectivity, and guiding access to appropriate antifungal treatment.
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