Anton Aurore, Plinet Mathilde, Peyret Thomas, Cazaudarré Thomas, Pesant Stéphanie, Rouquet Yannick, Tricoteaux Marie-Andrée, Bernier Matthieu, Bayette Jérémy, Fournier Remi, Marguerettaz Mélanie, Rolland Pierre, Bayol Thibaud, Abbaoui Nadia, Berry Antoine, Iriart Xavier, Cassaing Sophie, Chauvin Pamela, Bernard Elodie, Fabre Richard, François Jean-Marie
Dendris SAS, 335 Rue du Chêne Vert, 31670 Labège, France.
Laboratoire Inovie-CBM, 31000 Toulouse, France.
Diagnostics (Basel). 2023 Nov 11;13(22):3430. doi: 10.3390/diagnostics13223430.
Dermatophytosis is a superficial fungal infection with an ever-increasing number of patients. Culture-based mycology remains the most commonly used diagnosis, but it takes around four weeks to identify the causative agent. Therefore, routine clinical laboratories need rapid, high throughput, and accurate species-specific analytical methods for diagnosis and therapeutic management. Based on these requirements, we investigated the feasibility of DendrisCHIP technology as an innovative molecular diagnostic method for the identification of a subset of 13 pathogens potentially responsible for dermatophytosis infections in clinical samples. This technology is based on DNA microarray, which potentially enables the detection and discrimination of several germs in a single sample. A major originality of DendrisCHIP technology is the use of a decision algorithm for probability presence or absence of pathogens based on machine learning methods. In this study, the diagnosis of dermatophyte infection was carried out on more than 284 isolates by conventional microbial culture and DendrisCHIPDP, which correspond to the DendrisCHIP carrying oligoprobes of the targeted pathogens implicated in dermatophytosis. While convergence ranging from 75 to 86% depending on the sampling procedure was obtained with both methods, the DendrisCHIPDP proved to identify more isolates with pathogens that escaped the culture method. These results were confirmed at 86% by a third method, which was either a specific RT-PCR or genome sequencing. In addition, diagnostic results with DendrisCHIPDP can be obtained within a day. This faster and more accurate identification of fungal pathogens with DendrisCHIPDP enables the clinician to quickly and successfully implement appropriate antifungal treatment to prevent the spread and elimination of dermatophyte infection. Taken together, these results demonstrate that this technology is a very promising method for routine diagnosis of dermatophytosis.
皮肤癣菌病是一种浅表真菌感染，患者数量不断增加。基于培养的真菌学检查仍然是最常用的诊断方法，但鉴定病原体大约需要四周时间。因此，常规临床实验室需要快速、高通量且准确的种特异性分析方法用于诊断和治疗管理。基于这些需求，我们研究了DendrisCHIP技术作为一种创新分子诊断方法用于鉴定临床样本中13种可能导致皮肤癣菌病感染的病原体子集的可行性。该技术基于DNA微阵列，有可能在单个样本中检测和区分多种病菌。DendrisCHIP技术的一个主要创新点是使用基于机器学习方法的决策算法来判断病原体存在或不存在的概率。在本研究中，通过传统微生物培养和DendrisCHIPDP（即携带与皮肤癣菌病相关的靶向病原体寡核苷酸探针的DendrisCHIP）对284株以上的分离株进行了皮肤癣菌感染的诊断。虽然两种方法根据采样程序的一致性在75%至86%之间，但DendrisCHIPDP证明能鉴定出更多用培养方法未能检测出病原体的分离株。第三种方法（特异性逆转录聚合酶链反应或基因组测序）以86%的比例证实了这些结果。此外，使用DendrisCHIPDP可在一天内获得诊断结果。通过DendrisCHIPDP更快、更准确地鉴定真菌病原体，使临床医生能够迅速且成功地实施适当的抗真菌治疗，以预防皮肤癣菌感染的传播和消除。综上所述，这些结果表明该技术是皮肤癣菌病常规诊断中非常有前景的方法。
