Mycobacterium abscessus pathogenesis identified by phenogenomic analyses.
Mycobacterium abscessus pathogenesis identified by phenogenomic analyses.
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DOI：
10.1038/s41564-022-01204-x
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发表时间：
2022-09
影响因子：
28.3
通讯作者：
中科院分区：
文献类型：
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作者：
对可用于确定治疗药物靶点和预测患者轨迹的毒力、耐药性和临床结果的遗传决定因素的无知，往往严重阻碍了对新出现和已确定病原体的医学和科学反应。以新出现的多药耐药细菌脓肿分枝杆菌为例，我们表明在表型基因组分析中结合高维表型分型和全基因组测序可以快速揭示可操作的系统级细菌病理生物学见解。通过对331株临床分离株的表型分析，我们发现了三种不同的分离株群，每种分离株都具有不同的毒力特征，并与不同的临床结果相关。我们将全基因组关联研究与全蛋白质组计算结构建模相结合，以确定可能的因果变异，并采用直接耦合分析来确定共同进化，从而潜在的上位性基因网络。然后，我们在体内使用基于crispr的沉默来验证我们的发现，并发现临床相关的脓分枝杆菌毒力因子，包括分泌系统，从而说明表型基因组学如何揭示新兴致病菌的关键途径。通过整合331个临床分离株的蛋白质组结构模型、GWAS分析和基因相互作用网络图谱，鉴定了脓肿分枝杆菌的抗生素耐药性和毒力因子。
The medical and scientific response to emerging and established pathogens is often severely hampered by ignorance of the genetic determinants of virulence, drug resistance and clinical outcomes that could be used to identify therapeutic drug targets and forecast patient trajectories. Taking the newly emergent multidrug-resistant bacteria Mycobacterium abscessus as an example, we show that combining high-dimensional phenotyping with whole-genome sequencing in a phenogenomic analysis can rapidly reveal actionable systems-level insights into bacterial pathobiology. Through phenotyping of 331 clinical isolates, we discovered three distinct clusters of isolates, each with different virulence traits and associated with a different clinical outcome. We combined genome-wide association studies with proteome-wide computational structural modelling to define likely causal variants, and employed direct coupling analysis to identify co-evolving, and therefore potentially epistatic, gene networks. We then used in vivo CRISPR-based silencing to validate our findings and discover clinically relevant M. abscessus virulence factors including a secretion system, thus illustrating how phenogenomics can reveal critical pathways within emerging pathogenic bacteria. Antibiotic resistance and virulence factors are identified in Mycobacterium abscessus by integrating proteome-wide structural modelling, GWAS analyses and mapping gene interaction networks for 331 clinical isolates.
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影响因子：
9.2
作者：
Clark, Rebecca I.;Woodcock, Katie J.;Geissmann, Frederic;Trouillet, Celine;Dionne, Marc S.
通讯作者：
Dionne, Marc S.
DOI：
10.1126/science.abb8699
发表时间：
2021-04-30
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bryant JM;Brown KP;Burbaud S;Everall I;Belardinelli JM;Rodriguez-Rincon D;Grogono DM;Peterson CM;Verma D;Evans IE;Ruis C;Weimann A;Arora D;Malhotra S;Bannerman B;Passemar C;Templeton K;MacGregor G;Jiwa K;Fisher AJ;Blundell TL;Ordway DJ;Jackson M;Parkhill J;Floto RA
通讯作者：
Floto RA
影响因子：
14.8
作者：
Gasperini M;Starita L;Shendure J
通讯作者：
Shendure J
影响因子：
10
作者：
Floto RA;Olivier KN;Saiman L;Daley CL;Herrmann JL;Nick JA;Noone PG;Bilton D;Corris P;Gibson RL;Hempstead SE;Koetz K;Sabadosa KA;Sermet-Gaudelus I;Smyth AR;van Ingen J;Wallace RJ;Winthrop KL;Marshall BC;Haworth CS;US Cystic Fibrosis Foundation and European Cystic Fibrosis Society
通讯作者：
US Cystic Fibrosis Foundation and European Cystic Fibrosis Society
影响因子：
24.3
作者：
Jhun, Byung Woo;Moon, Seong Mi;Koh, Won-Jung
通讯作者：
Koh, Won-Jung