Zhang Ningxin, Wheeler David, Truglio Mauro, Lazzarini Cristina, Upritchard Jenine, McKinney Wendy, Rogers Karen, Prigitano Anna, Tortorano Anna M, Cannon Richard D, Broadbent Roland S, Roberts Sally, Schmid Jan
Institute of Fundamental Sciences, Massey University, Palmerston North, New Zealand.
Nextgen Bioinformatic Services, Palmerston North, New Zealand.
Front Microbiol. 2018 Jun 5;9:1179. doi: 10.3389/fmicb.2018.01179. eCollection 2018.
The yeast is an important opportunistic human pathogen. For strain typing or drug susceptibility testing, a single colony recovered from a patient sample is normally used. This is insufficient when multiple strains are present at the site sampled. How often this is the case is unclear. Previous studies, confined to oral, vaginal and vulvar samples, have yielded conflicting results and have assessed too small a number of colonies per sample to reliably detect the presence of multiple strains. We developed a next-generation sequencing (NGS) modification of the highly discriminatory MLST (multilocus sequence typing) method, 100+1 NGS-MLST, for detection and typing of multiple strains in clinical samples. In 100+1 NGS-MLST, DNA is extracted from a pool of colonies from a patient sample and also from one of the colonies. MLST amplicons from both DNA preparations are analyzed by high-throughput sequencing. Using base call frequencies, our bespoke DALMATIONS software determines the MLST type of the single colony. If base call frequency differences between pool and single colony indicate the presence of an additional strain, the differences are used to computationally infer the second MLST type without the need for MLST of additional individual colonies. In mixes of previously typed pairs of strains, 100+1 NGS-MLST reliably detected a second strain. Inferred MLST types of second strains were always more similar to their real MLST types than to those of any of 59 other isolates (22 of 31 inferred types were identical to the real type). Using 100+1 NGS-MLST we found that 7/60 human samples, including three superficial candidiasis samples, contained two unrelated strains. In addition, at least one sample contained two highly similar variants of the same strain. The probability of samples containing unrelated strains appears to differ considerably between body sites. Our findings indicate the need for wider surveys to determine if, for some types of samples, routine testing for the presence of multiple strains is warranted. 100+1 NGS-MLST is effective for this purpose.
酵母是一种重要的人类机会致病菌。对于菌株分型或药敏试验,通常使用从患者样本中分离出的单个菌落。当采样部位存在多种菌株时,这种方法就不够了。这种情况出现的频率尚不清楚。以往的研究局限于口腔、阴道和外阴样本,结果相互矛盾,且每个样本评估的菌落数量过少,无法可靠地检测出多种菌株的存在。我们开发了一种对高分辨率多位点序列分型(MLST)方法的下一代测序(NGS)改进方法,即100 + 1 NGS - MLST,用于检测和分型临床样本中的多种菌株。在100 + 1 NGS - MLST中,从患者样本的菌落池中以及其中一个菌落中提取DNA。对两种DNA制剂的MLST扩增子进行高通量测序。使用碱基调用频率,我们定制的DALMATIONS软件确定单个菌落的MLST类型。如果菌落池和单个菌落之间的碱基调用频率差异表明存在额外的菌株,则利用这些差异通过计算推断出第二种MLST类型,而无需对额外的单个菌落进行MLST分析。在先前已分型的菌株对混合物中,100 + 1 NGS - MLST能够可靠地检测出第二种菌株。推断出的第二种菌株的MLST类型总是比其他59种分离株中的任何一种更接近其真实的MLST类型(31种推断类型中有22种与真实类型相同)。使用100 + 1 NGS - MLST,我们发现60份人类样本中有7份,包括3份浅表念珠菌病样本,含有两种不相关的菌株。此外,至少有一个样本包含同一菌株的两个高度相似的变体。不同身体部位的样本中含有不相关菌株的概率似乎有很大差异。我们的研究结果表明,需要进行更广泛的调查,以确定对于某些类型的样本,是否有必要常规检测多种菌株的存在。100 + 1 NGS - MLST在此方面是有效的。