Constructing a full, multiple-layer interactome for SARS-CoV-2 in the context of lung disease: Linking the virus with human genes and microbes.
Constructing a full, multiple-layer interactome for SARS-CoV-2 in the context of lung disease: Linking the virus with human genes and microbes.
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DOI：
10.1371/journal.pcbi.1011222
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发表时间：
2023-07
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
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由 SARS-CoV-2 病毒引起的 COVID-19 大流行已导致全球数百万人死亡。该疾病具有多种表现，其严重程度和长期结果各不相同。先前的努力通过揭示病毒感染的机制，有助于制定有效的治疗和预防策略。我们现在知道 SARS-CoV-2 感染生命周期中发生的所有直接蛋白质-蛋白质相互作用，但超越这些已知的相互作用，全面了解 SARS-CoV-2 感染的“完整相互作用组”至关重要，其中包括人类 microRNA (miRNA)、其他人类蛋白质编码基因和外源微生物。这可能有助于开发治疗 COVID-19 的新药、区分长期 COVID 的细微差别，以及识别 SARS-CoV-2 感染器官的组织病理学特征。为了构建完整的相互作用组，我们开发了一种基于潜在狄利克雷分配的统计建模方法，称为 MLCrosstalk（多层串扰）。 MLCrosstalk 整合了多个来源的数据，包括微生物、人类蛋白质编码基因、miRNA 和人类蛋白质-蛋白质相互作用。它根据患者样本中相似的共现模式构建“主题”，将 SARS-CoV-2 与基因和微生物分组。我们利用这些主题来推断 SARS-CoV-2 与蛋白质编码基因、miRNA 和微生物之间的联系。然后，我们使用网络传播来细化这些初始联系，将它们置于更大的网络和路径结构框架内。使用 MLCrosstalk，我们鉴定了 IL1 加工和 VEGFA-VEGFR2 通路中与 SARS-CoV-2 相关的基因。我们还发现 Rothia mucilaginosa 和 Prevotella melaninogenica 与 SARS-CoV-2 丰度呈正相关和负相关，单细胞测序数据分析证实了这一发现。我们的研究旨在了解 SARS-CoV-2 感染的完整相互作用组并开发针对 COVID-19 的新疗法。使用称为 MLCrosstalk 的统计建模方法，我们确定了 SARS-CoV-2、人类基因、miRNA 和微生物之间的联系。我们的研究结果表明，IL1 加工和 VEGFA-VEGFR2 途径中的某些人类基因与 SARS-CoV-2 相关，并且 Rothia mucilaginosa 和 Prevotella melaninogenica 的丰度分别与 SARS-CoV-2 丰度呈正相关和负相关。我们的工作提供了一种独特的方法来分析病毒和各种成分之间的相互作用，有可能改进我们治疗和预防 COVID-19 的策略。
The COVID-19 pandemic caused by the SARS-CoV-2 virus has resulted in millions of deaths worldwide. The disease presents with various manifestations that can vary in severity and long-term outcomes. Previous efforts have contributed to the development of effective strategies for treatment and prevention by uncovering the mechanism of viral infection. We now know all the direct protein–protein interactions that occur during the lifecycle of SARS-CoV-2 infection, but it is critical to move beyond these known interactions to a comprehensive understanding of the “full interactome” of SARS-CoV-2 infection, which incorporates human microRNAs (miRNAs), additional human protein-coding genes, and exogenous microbes. Potentially, this will help in developing new drugs to treat COVID-19, differentiating the nuances of long COVID, and identifying histopathological signatures in SARS-CoV-2-infected organs. To construct the full interactome, we developed a statistical modeling approach called MLCrosstalk (multiple-layer crosstalk) based on latent Dirichlet allocation. MLCrosstalk integrates data from multiple sources, including microbes, human protein-coding genes, miRNAs, and human protein–protein interactions. It constructs "topics" that group SARS-CoV-2 with genes and microbes based on similar patterns of co-occurrence across patient samples. We use these topics to infer linkages between SARS-CoV-2 and protein-coding genes, miRNAs, and microbes. We then refine these initial linkages using network propagation to contextualize them within a larger framework of network and pathway structures. Using MLCrosstalk, we identified genes in the IL1-processing and VEGFA–VEGFR2 pathways that are linked to SARS-CoV-2. We also found that Rothia mucilaginosa and Prevotella melaninogenica are positively and negatively correlated with SARS-CoV-2 abundance, a finding corroborated by analysis of single-cell sequencing data. Our research aimed to understand the full interactome of SARS-CoV-2 infection and develop new treatments for COVID-19. Using a statistical modeling approach called MLCrosstalk, we identified linkages between SARS-CoV-2, human genes, miRNAs, and microbes. Our findings suggest that certain human genes in the IL1-processing and VEGFA–VEGFR2 pathways are linked to SARS-CoV-2, and that the abundance of Rothia mucilaginosa and Prevotella melaninogenica is positively and negatively correlated with SARS-CoV-2 abundance, respectively. Our work offers a unique approach to analyzing the interactions between the virus and various components, with the potential to improve our strategies for treating and preventing COVID-19.
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DOI：
10.1038/s41579-022-00846-2
发表时间：
2023-03
期刊：
Nature reviews. Microbiology
影响因子：
--
作者：
通讯作者：
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影响因子：
3.7
作者：
Lim YW;Schmieder R;Haynes M;Furlan M;Matthews TD;Whiteson K;Poole SJ;Hayes CS;Low DA;Maughan H;Edwards R;Conrad D;Rohwer F
通讯作者：
Rohwer F
DOI：
10.1126/science.abe9403
发表时间：
2020-12-04
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Gordon DE;Hiatt J;Bouhaddou M;Rezelj VV;Ulferts S;Braberg H;Jureka AS;Obernier K;Guo JZ;Batra J;Kaake RM;Weckstein AR;Owens TW;Gupta M;Pourmal S;Titus EW;Cakir M;Soucheray M;McGregor M;Cakir Z;Jang G;O'Meara MJ;Tummino TA;Zhang Z;Foussard H;Rojc A;Zhou Y;Kuchenov D;Hüttenhain R;Xu J;Eckhardt M;Swaney DL;Fabius JM;Ummadi M;Tutuncuoglu B;Rathore U;Modak M;Haas P;Haas KM;Naing ZZC;Pulido EH;Shi Y;Barrio-Hernandez I;Memon D;Petsalaki E;Dunham A;Marrero MC;Burke D;Koh C;Vallet T;Silvas JA;Azumaya CM;Billesbølle C;Brilot AF;Campbell MG;Diallo A;Dickinson MS;Diwanji D;Herrera N;Hoppe N;Kratochvil HT;Liu Y;Merz GE;Moritz M;Nguyen HC;Nowotny C;Puchades C;Rizo AN;Schulze-Gahmen U;Smith AM;Sun M;Young ID;Zhao J;Asarnow D;Biel J;Bowen A;Braxton JR;Chen J;Chio CM;Chio US;Deshpande I;Doan L;Faust B;Flores S;Jin M;Kim K;Lam VL;Li F;Li J;Li YL;Li Y;Liu X;Lo M;Lopez KE;Melo AA;Moss FR 3rd;Nguyen P;Paulino J;Pawar KI;Peters JK;Pospiech TH Jr;Safari M;Sangwan S;Schaefer K;Thomas PV;Thwin AC;Trenker R;Tse E;Tsui TKM;Wang F;Whitis N;Yu Z;Zhang K;Zhang Y;Zhou F;Saltzberg D;QCRG Structural Biology Consortium;Hodder AJ;Shun-Shion AS;Williams DM;White KM;Rosales R;Kehrer T;Miorin L;Moreno E;Patel AH;Rihn S;Khalid MM;Vallejo-Gracia A;Fozouni P;Simoneau CR;Roth TL;Wu D;Karim MA;Ghoussaini M;Dunham I;Berardi F;Weigang S;Chazal M;Park J;Logue J;McGrath M;Weston S;Haupt R;Hastie CJ;Elliott M;Brown F;Burness KA;Reid E;Dorward M;Johnson C;Wilkinson SG;Geyer A;Giesel DM;Baillie C;Raggett S;Leech H;Toth R;Goodman N;Keough KC;Lind AL;Zoonomia Consortium;Klesh RJ;Hemphill KR;Carlson-Stevermer J;Oki J;Holden K;Maures T;Pollard KS;Sali A;Agard DA;Cheng Y;Fraser JS;Frost A;Jura N;Kortemme T;Manglik A;Southworth DR;Stroud RM;Alessi DR;Davies P;Frieman MB;Ideker T;Abate C;Jouvenet N;Kochs G;Shoichet B;Ott M;Palmarini M;Shokat KM;García-Sastre A;Rassen JA;Grosse R;Rosenberg OS;Verba KA;Basler CF;Vignuzzi M;Peden AA;Beltrao P;Krogan NJ
通讯作者：
Krogan NJ
宿主人类microRNA对SARS-COV-2基因组的高亲和力：硅分析中的一种。
影响因子：
5
作者：
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影响因子：
3
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Lim WS