Ancient pathogen DNA in archaeological samples detected with a Microbial Detection Array.
Ancient pathogen DNA in archaeological samples detected with a Microbial Detection Array.
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DOI：
10.1038/srep04245
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发表时间：
2014-03-06
影响因子：
4.6
通讯作者：
Poinar HN
中科院分区：
文献类型：
--
作者：
Devault AM;McLoughlin K;Jaing C;Gardner S;Porter TM;Enk JM;Thissen J;Allen J;Borucki M;DeWitte SN;Dhody AN;Poinar HN
具有古病理学意义的古代人类遗骸通常含有高度降解的 DNA，其中致病类群通常是少数成分，使得基于序列的宏基因组表征成本高昂。微阵列可能为这些挑战提供潜在的解决方案，提供复杂样品中微生物多样性的快速、经济且信息丰富的快照，而无需与高通量测序相关的冗长分析和/或高成本。它们的多功能性对于现代临床标本来说已经很成熟，但尚未应用于古代遗骸。在这里，我们使用劳伦斯利弗莫尔微生物检测阵列 (LLMDA) 报告考古和历史人类遗骸的细菌谱。该阵列成功鉴定了先前验证的人类细菌病原体，包括 19 世纪肠道标本中的霍乱弧菌（霍乱）和中世纪牙齿中的鼠疫耶尔森氏菌（“黑死病”瘟疫），它们仅代表了各自 DNA 含量的微小部分（0.03% 和 0.08% 可对齐高通量鸟枪测序读数）。这表明 LLMDA 可以识别古代样本中的原发和/或共同感染的细菌病原体，从而作为一种快速且廉价的古病理学筛查工具来研究跨空间和时间的健康状况。
Ancient human remains of paleopathological interest typically contain highly degraded DNA in which pathogenic taxa are often minority components, making sequence-based metagenomic characterization costly. Microarrays may hold a potential solution to these challenges, offering a rapid, affordable, and highly informative snapshot of microbial diversity in complex samples without the lengthy analysis and/or high cost associated with high-throughput sequencing. Their versatility is well established for modern clinical specimens, but they have yet to be applied to ancient remains. Here we report bacterial profiles of archaeological and historical human remains using the Lawrence Livermore Microbial Detection Array (LLMDA). The array successfully identified previously-verified bacterial human pathogens, including Vibrio cholerae (cholera) in a 19th century intestinal specimen and Yersinia pestis (“Black Death” plague) in a medieval tooth, which represented only minute fractions (0.03% and 0.08% alignable high-throughput shotgun sequencing reads) of their respective DNA content. This demonstrates that the LLMDA can identify primary and/or co-infecting bacterial pathogens in ancient samples, thereby serving as a rapid and inexpensive paleopathological screening tool to study health across both space and time.
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影响因子：
64.8
作者：
Bos, Kirsten I.;Schuenemann, Verena J.;Golding, G. Brian;Burbano, Hernan A.;Waglechner, Nicholas;Coombes, Brian K.;McPhee, Joseph B.;DeWitte, Sharon N.;Meyer, Matthias;Schmedes, Sarah;Wood, James;Earn, David J. D.;Herring, D. Ann;Bauer, Peter;Poinar, Hendrik N.;Krause, Johannes
通讯作者：
Krause, Johannes
影响因子：
3.7
作者：
Erlandsson, Lena;Rosenstierne, Maiken W.;Fomsgaard, Anders
通讯作者：
Fomsgaard, Anders
影响因子：
5.4
作者：
Victoria, Joseph G.;Wang, Chunlin;Delwart, Eric L.
通讯作者：
Delwart, Eric L.
影响因子：
3.7
作者：
Gonzalez JM;Portillo MC;Belda-Ferre P;Mira A
通讯作者：
Mira A
影响因子：
4.4
作者：
Gardner SN;Jaing CJ;McLoughlin KS;Slezak TR
通讯作者：
Slezak TR