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MALDI-MS Profiling to Address Honey Bee Health Status under Bacterial Challenge through Computational Modeling.
Proteomics ( IF 3.4 ) Pub Date : 2019-11-11 , DOI: 10.1002/pmic.201900268
Karim Arafah 1 , Sébastien Nicolas Voisin 1 , Victor Masson 1, 2 , Cédric Alaux 3 , Yves Le Conte 3 , Michel Bocquet 4 , Philippe Bulet 1, 2
Affiliation  

Honey bees play a critical role in the maintenance of plant biodiversity and sustainability of food webs. In the past few decades, bees have been subjected to biotic and abiotic threats causing various colony disorders. Therefore, monitoring solutions to help beekeepers to improve bee health are necessary. Matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS) profiling has emerged within this decade as a powerful tool to identify in routine micro-organisms and is currently used in real-time clinical diagnosis. MALDI BeeTyping is developed to monitor significant hemolymph molecular changes in honey bees upon infection with a series of entomopathogenic Gram-positive and -negative bacteria. A Serratia marcescens strain isolated from one naturally infected honey bee collected from the field is also considered. A series of hemolymph molecular mass fingerprints is individually recorded and to the authors' knowledge, the first computational model harboring a predictive score of 97.92% and made of nine molecular signatures that discriminate and classify the honey bees' systemic response to the bacteria is built. Hence, the model is challenged by classifying a training set of hemolymphs and an overall recognition of 91.93% is obtained. Through this work, a novel, time and cost saving high-throughput strategy that addresses honey bee health on an individual scale is introduced.

中文翻译:

MALDI-MS分析通过计算建模解决细菌挑战下的蜜蜂健康状况。

蜜蜂在维持植物生物多样性和食物网的可持续性中起着至关重要的作用。在过去的几十年中,蜜蜂受到了生物和非生物的威胁,从而引起各种菌落失调。因此,有必要监测解决方案以帮助养蜂人改善蜜蜂的健康状况。基质辅助激光解吸电离质谱(MALDI-MS)谱图在此十年中已经出现,它是鉴定常规微生物的有力工具,目前用于实时临床诊断中。MALDI BeeTyping的开发是为了监测蜜蜂感染一系列致病性革兰氏阳性和阴性细菌后蜜蜂中明显的血淋巴分子变化。还考虑了从野外采集的一只自然感染的蜜蜂中分离出的粘质沙雷氏菌。分别记录了一系列的血淋巴分子质量指纹图谱,并且据作者所知,建立了第一个具有97.92%的预测分数并由9个分子特征组成的计算模型,以区分和分类蜜蜂对细菌的系统性反应。因此,通过对血淋巴的训练集进行分类来挑战模型,并获得91.93%的总体识别率。通过这项工作,提出了一种新颖的,节省时间和成本的高通量策略,该策略可解决单个规模的蜜蜂健康问题。建立了对细菌的系统性反应。因此,通过对血淋巴的训练集进行分类来挑战模型,并获得91.93%的总体识别率。通过这项工作,提出了一种新颖的,节省时间和成本的高通量策略,该策略可解决单个规模的蜜蜂健康问题。建立了对细菌的系统性反应。因此,通过对血淋巴的训练集进行分类来挑战模型,并获得91.93%的总体识别率。通过这项工作,提出了一种新颖的,节省时间和成本的高通量策略,该策略可解决单个规模的蜜蜂健康问题。
更新日期:2019-11-11
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