AbstractIn-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab).

摘要深入的多组学表型分析为复杂的生理过程及其病理学提供了分子见解。在这里，我们报告了将18种不同的深度分子表型（组学-）技术应用于391名多种族糖尿病卡塔尔糖尿病代谢组学研究（QMDiab）参与者的尿液，血液和唾液样本。

Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations.

使用6304个具有1221345个遗传变异的定量分子性状，470837个DNA CpG位点的甲基化以及57000个转录本的基因表达，我们确定（1）平台内部分相关性，（2）平台间最佳相关性，以及（3）基因组，表观基因组，转录组和全基因组关联。

Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays “The Molecular Human”. We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data.

结合生物流体中>34000个具有统计学意义的性状-性状链接的分子网络，我们的研究描绘了“分子人类”。我们描述了每个组学在表型（年龄，性别，BMI和糖尿病状态），平台互补性以及多组学数据的固有相关结构中解释的差异。

Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to “The Molecular Human” (http://comics.metabolomix.com), providing interactive data exploration and hypotheses generation possibilities..

此外，我们构建了糖尿病亚型的多分子网络。最后，我们为“分子人类”生成了一个开放访问的web界面(http://comics.metabolomix.com)，提供交互式数据探索和假设生成的可能性。。

IntroductionThe quote “Learn how to see. Realize that everything connects to everything else” by Leonardo Da Vinci becomes substantive in the context of high-throughput deep molecular phenotyping technologies that enable the measurement of hundreds or even thousands of quantitative readouts of the genome, transcriptome, proteome, metabolome, and glycome as well as related intermediate omics layers, such as the epigenome, and microRNA-ome1.

引言莱昂纳多·达芬奇（LeonardoDavinci）的名言“学会看。认识到一切都与其他一切联系在一起”在高通量深度分子表型技术的背景下变得具有实质性意义，该技术能够测量数百甚至数千个基因组，转录组，蛋白质组，代谢组和糖组以及相关的中间组学层（如表观基因组和microRNA-ome1）的定量读数。

Integrated into a single study, these readouts simultaneously can provide complementary insights into the molecular interactions that define the physiological and pathophysiological processes in the human body.Indeed, molecular processes have been monitored in human biofluids through the integration of various omics approaches, including genomics, methylation, transcriptomics, proteomics, and metabolomics2,3,4,5,6,7,8,9,10,11.

将这些读数整合到一项研究中，可以同时提供对定义人体生理和病理生理过程的分子相互作用的补充见解。事实上，通过整合各种组学方法，包括基因组学，甲基化，转录组学，蛋白质组学和代谢组学，已经在人类生物流体中监测了分子过程2,3,4,5,6,7,8,9,10,11。

However, studies that deploy a broader range of omics techniques tend to have a smaller sample size, typically involving 1–36 individuals. For instance, these studies investigate dynamic changes in diverse molecular components in response to factors such as viral infection10, spaceflight8, as well as extensive exercise9.

然而，部署更广泛的组学技术的研究往往样本量较小，通常涉及1-36个人。例如，这些研究调查了不同分子成分对病毒感染10，太空飞行8以及广泛锻炼9等因素的动态变化。

In contrast, larger cohort studies (≥100 individuals) tend to focus on a more limited spectrum of omics measurements2,3,4,6. For example, the impact of lifestyle changes was monitored at the molecular level in processes related to obesity, diabetes, liver function, or cardiovascular disease using genomics, proteomics, and metabolomics3.

相比之下，较大的队列研究（≥100个人）倾向于关注更有限的组学测量范围2,3,4,6。例如，使用基因组学，蛋白质组学和代谢组学，在与肥胖，糖尿病，肝功能或心血管疾病相关的过程中，在分子水平上监测生活方式改变的影响3。

Similarly, proteomics and metabolomics were deployed to determine molecular signatures associated with schizophrenia2, while metabolomics and lipidomics were used for studying HIV infection6. The limited array of omics approaches was also used in a very large pop.

同样，蛋白质组学和代谢组学被用来确定与精神分裂症相关的分子特征，而代谢组学和脂质组学被用于研究HIV感染6。有限的组学方法也被用于非常大的流行音乐中。

RNA extraction

RNA提取

The miRNAs were isolated from 200 µL EDTA-plasma sample using the miRNeasy serum/plasma kit (Qiagen) following the manufacturer’s instructions. Briefly, the samples were lyzed using QIAzol Lysis Reagent and spiked with 3.5 μl miRNeasy Serum/Plasma Spike-In Control included in the kit. The chloroform was added, samples were mixed and the centrifuged.

按照制造商的说明，使用miRNeasy血清/血浆试剂盒（Qiagen）从200μlEDTA血浆样品中分离miRNA。简而言之，使用QIAzol裂解试剂裂解样品，并加入试剂盒中包含的3.5μlmiRNeasy血清/血浆加标对照。加入氯仿，混合样品并离心。

The obtained after centrifugation upper aqueous phase was transferred into the fresh tube, mixed with 1.5 volume of 100% ethanol, and transferred into an RNeasy MinElute spin column in a 2 ml collection tube, provided in the kit. The samples were centrifuged, the flow-throw was removed, and RWT buffer provided with the kit was added onto the RNeasy MinElute spin column.

将离心后获得的上层水相转移到新鲜管中，与1.5体积的100%乙醇混合，并转移到试剂盒中提供的2ml收集管中的RNeasy MinElute旋转柱中。将样品离心，除去流程，并将试剂盒提供的RWT缓冲液添加到RNeasy MinElute旋转柱上。

The samples were centrifuged, the flow-throw was discarded, RPE buffer, provided with the kit, was added onto the RNeasy MinElute spin column. The samples were centrifuged and flow-throw was removed. The 80% ethanol prepared in RNaze-free water was placed onto the MinElute spin column, the samples were centrifuged until the spin column membrane dried.

将样品离心，弃去流量，将试剂盒提供的RPE缓冲液添加到RNeasy MinElute旋转柱上。将样品离心并除去流动。将在不含RNaze的水中制备的80%乙醇置于MinElute旋转柱上，离心样品直至旋转柱膜干燥。

The MinElute spin column was placed in fresh collection tube and the total RNA including miRNA was eluted with 14 μl RNase-free water..

将MinElute旋转柱置于新鲜的收集管中，用14μl不含RNase的水洗脱包括miRNA在内的总RNA。。

miRNA profiling

miRNA分析

Prior the profiling, the isolated RNA samples were reverse transcribed to cDNA using the Exiqon Universal cDNA Synthesis Kit II (Exiqon Inc., MA, USA) according with the manufacturer instruction. Briefly, 2 μL of total RNA (5 ng/μL) were used for cDNA synthesis. All processes were conducted in 384 well plate format.

在分析之前，根据制造商的说明，使用Exiqon Universal cDNA Synthesis Kit II（Exiqon Inc.，MA，USA）将分离的RNA样品逆转录为cDNA。简而言之，将2μL总RNA（5ng/μL）用于cDNA合成。所有过程均以384孔板形式进行。

The quality and integrity of the synthesized cDNA was assessed using the miRNA QC PCR Panel (V4.M; Exiqon Inc.). Obtained cDNA was 50-fold diluted and mixed with 2x Exilent SYBR Green master mix (Exiqon Inc.), and ROX reference dye (4 μl/2 ml) (Thermo Fisher Scientific, MA, USA). The samples were loaded onto human serum/plasma focus miRNA PCR panels, and quantitative real-time PCR was performed using the QuantStudio 12 K Flex real-time PCR System (Applied Biosystems, CA, USA).

使用miRNA QC PCR面板（V4.M；Exiqon Inc.）评估合成的cDNA的质量和完整性。将获得的cDNA稀释50倍，并与2x流放SYBR Green master mix（Exiqon Inc.）和ROX参考染料（4μl/2ml）（Thermo Fisher Scientific，MA，USA）混合。将样品加载到人血清/血浆聚焦miRNA PCR板上，并使用QuantStudio 12K Flex实时PCR系统（Applied Biosystems，CA，USA）进行定量实时PCR。

The PCR data were processed using Exiqon GenEx qPCR analysis software (version 6). The inter-plate calibration was performed using the mean value of UniSp3 interplate calibrator. The samples with a high degree of hemolysis were identified after monitoring of calculated ΔCt between hsa-miR-23a-3p and hsa-miR-451a.

使用Exiqon GenEx qPCR分析软件（版本6）处理PCR数据。使用UniSp3 interplate校准器的平均值进行板间校准。在监测hsa-miR-23a-3p和hsa-miR-451a之间计算的ΔCt后，鉴定出高度溶血的样品。

The samples with ΔCt > 7 were removed from the analysis. Only microRNA assays with Ct ≤ 35, expressed in at least 60% of the samples were counted and the remaining samples were removed from the analysis. The global average of all expressed microRNAs with Ct < 35 was used to normalize individual assays.

从分析中除去ΔCt>7的样品。仅计数在至少60%的样品中表达的Ct≤35的microRNA测定，并从分析中除去剩余的样品。使用Ct<35的所有表达的microRNA的全球平均值来标准化单个测定。

Total of 169 miRNAs were profiled in 339 subjects..

在339名受试者中共分析了169个miRNA。。

Proteomics measurements using SOMAscan technologyThe EDTA-plasma samples were used for proteomics analysis based on SOMAscan assay (version 1.1) technology, which was conducted at the WCM-Q Proteomics Core15. The method employed protein-capture by Slow Offrate Modified Aptamers (SOMAmer)117. Briefly, undepleated EDTA-plasma was diluted and the following assay steps were performed: (1) Binding: analytes and SOMAmers, carrying a biotin moiety via a photocleavable linker were equilibrated; (2) Catch I: analyte/SOMAmer complexes were immobilized on streptavidin‐support, followed by washing steps to remove proteins not stably interacting with SOMAmers; (3) Cleave: release of analyte/SOMAmer complexes from streptavidin beads through exposure to long‐wave ultraviolet light resulting in linker cleavage; (4) Catch II: biotinylation of proteins in analyte/SOMAmer complexes and subsequent repeated immobilization on streptavidin support followed by washing steps to select against non‐specific analyte/SOMAmer complexes; (5) Elution: denaturation of analyte/SOMAmer complexes and SOMAmer release; (6) Quantification: hybridization to custom arrays of SOMAmer‐complementary oligonucleotides.

使用SOMAscan技术进行蛋白质组学测量EDTA血浆样品用于基于SOMAscan测定（版本1.1）技术的蛋白质组学分析，该技术在WCM-Q蛋白质组学核心15上进行。该方法采用慢速修饰适体（SOMAmer）117捕获蛋白质。简而言之，稀释未折叠的EDTA血浆并进行以下测定步骤：（1）结合：平衡通过光可裂解接头携带生物素部分的分析物和SOMAmers；（2） 捕获物I：将分析物/SOMAmer复合物固定在链霉亲和素载体上，然后进行洗涤步骤以去除与SOMAmers不稳定相互作用的蛋白质；（3） 裂解：通过暴露于长波紫外线导致接头裂解，从链霉亲和素珠中释放分析物/SOMAmer复合物；（4） 第二阶段：分析物/SOMAmer复合物中蛋白质的生物素化，随后在链霉亲和素载体上重复固定，然后进行洗涤步骤以选择非特异性分析物/SOMAmer复合物；（5） 洗脱：分析物/SOMAmer复合物的变性和SOMAmer释放；（6） 定量：与SOMAmer互补寡核苷酸的定制阵列杂交。

The primary data were submitted to Somalogic for normalization of raw intensities, across-batch calibration and steps of quality control. In total 1129 molecules were quantified in 356 samples.Proteomics measurements using Olink technologyHeparin-plasma samples were used for the proteomics measurements based on the Olink® technology (Olink Proteomics AB, Uppsala, Sweden) at the WCM-Q Proteomics Core.

主要数据已提交给Somalogic，以标准化原始强度，跨批次校准和质量控制步骤。在356个样品中总共定量了1129个分子。使用Olink技术进行蛋白质组学测量肝素血浆样品用于基于WCM-Q蛋白质组学核心的Olink®技术（Olink Proteomics AB，Uppsala，Sweden）的蛋白质组学测量。

The technology is based on a proximity extension assay (PEA)118, and enables for simultaneous analysis of 92 analytes in 1 µL of sample. We used two different Olink® panels, namely Cardiometabolic and Me.

该技术基于邻近延伸测定（PEA）118，能够同时分析1µL样品中的92种分析物。我们使用了两种不同的Olink®面板，即Cardiometabolic和Me。

Data availability

数据可用性

The source of data generated by each platform is provided on Figshare (https://doi.org/10.6084/m9.figshare.25975627.v2). The data can be downloaded as an excel file. Additionally, we are also providing data in.rda format for which we prepared R script that downloads the rda. data. The genetic data and methylation data access is not deposited because the informed consent given by the study participants does not cover posting of participant genotype and methylation data in public databases.

Figshare上提供了每个平台生成的数据源(https://doi.org/10.6084/m9.figshare.25975627.v2)。数据可以作为excel文件下载。此外，我们还提供了.rda格式的数据，为此我们准备了下载rda的R脚本。数据。遗传数据和甲基化数据访问未保存，因为研究参与者给出的知情同意书不包括在公共数据库中发布参与者基因型和甲基化数据。

Researcher affiliated with a research institution may request access to genetic data on an individual basis from the corresponding author (Karsten Suhre and Anna Halama, Weill Cornell Medicine—Qatar, Doha, Qatar). Access is subject to approval by the institutional research board of Weill Cornell Medicine—Qatar.

隶属于研究机构的研究人员可能会要求通讯作者（卡塔尔多哈威尔康奈尔医学公司的Karsten Suhre和Anna Halama）个人访问遗传数据。访问需要卡塔尔威尔康奈尔医学院机构研究委员会的批准。

The data sets deployed in this study were previously utilized as follows: Genomics data depicted as DNA14,15,16,20,137,138; Methylation data depicted as MET14,16; Transcriptomics data depicted as RNA139; Proteomics data measured on SOMA platform depicted as SOMA14,15,16,20,53,137,140; Glycomics data reflecting on total plasma N-glycosylation depicted as PGP14,53; Glycomics data reflecting on plasma IgG levels depicted as IgG14,138; Lipoproteomics data depicted as BRAIN14; The broad lipidomics data depicted as LD130; The targeted lipidomics data depicted as BM14,130; The untargeted metabolomics measured on HD4 platform depicted as HDF19,75,141; PM12,13,14,16,17,18,141,142; SM12,13,14,16,17,18,142; UM12,13,14,16,17,18,142; and CM16.

本研究中部署的数据集以前使用如下：基因组学数据描述为DNA14,15,16,20137138；甲基化数据描述为MET14,16；转录组学数据描述为RNA139；在SOMA平台上测量的蛋白质组学数据描述为SOMA14,15,16,20,53137140；反映总血浆N-糖基化的糖组学数据描述为PGP14,53；反映血浆IgG水平的糖组学数据描述为IgG14138；脂质蛋白质组学数据描述为大脑14；广泛的脂质组学数据描述为LD130；目标脂质组学数据描述为BM14130；；PM12,13,14,16,17,18141142；SM12,13,14,16,17,18142；UM12,13,14,16,17,18142；和CM16。

Transcriptomics data covering microRNA depicted as miRNA, proteomics data measured on OLINK platform depicted as OLINK, and Glycomics data reflecting on plasma IgA levels depicted as IgA were not published before..

转录组学数据涵盖了被描述为miRNA的microRNA，在被描述为OLINK的OLINK平台上测量的蛋白质组学数据，以及反映被描述为IgA的血浆IgA水平的糖组学数据。。

Code availability

代码可用性

We are also providing access to the source code used to generate COmics Server. The source code and a docker image could be accessed via GitHub at https://github.com/karstensuhre/comics and referenced using https://doi.org/10.5281/zenodo.11487725.

我们还提供对用于生成漫画服务器的源代码的访问。源代码和docker图像可以通过GitHub访问https://github.com/karstensuhre/comics并使用引用https://doi.org/10.5281/zenodo.11487725.

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Download referencesAcknowledgementsWe are grateful to all participants of QMDiab for providing their time and blood, and to the late Prof. Mohammed M. El-Din Selim for enabling the sample collection at Hamad Medical Corporation, Doha, Qatar. K.S. is supported by the Biomedical Research Program at Weill Cornell Medicine in Qatar, a program funded by the Qatar Foundation.

下载参考文献致谢我们感谢QMDiab的所有参与者提供了他们的时间和血液，并感谢已故的Mohammed M.El Din Selim教授能够在卡塔尔多哈的Hamad Medical Corporation进行样本采集。K、 美国得到了卡塔尔威尔康奈尔医学院生物医学研究项目的支持，该项目由卡塔尔基金会资助。

K.S. is also supported by the Qatar National Research Fund (QNRF) grant NPRP11C-0115-180010 and ARG01-0420-23000. A.H. is supported by the Qatar National Research Fund (QNRF) grant NPRP12S-0205-190042 and NPRP11S-0122-180359. The statements made herein are solely the responsibility of the authors.Author informationAuthors and AffiliationsBioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, QatarAnna Halama, Shaza Zaghlool, Gaurav Thareja, Sara Kader, Nisha Stephan & Karsten SuhreDepartment of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USAAnna Halama, Shaza Zaghlool, Gaurav Thareja, Sara Kader, Marjonneke Mook-Kanamori, Nisha Stephan, Jan Krumsiek & Karsten SuhreQatar Genome Program, Qatar Foundation, Qatar Science and Technology Park, Innovation Center, Doha, QatarWadha Al MuftahDepartment of Genetic Medicine, Weill Cornell Medicine, Doha, QatarWadha Al Muftah & Joel A.

K、 美国也得到了卡塔尔国家研究基金会（QNRF）资助NPRP11C-0115-180010和ARG01-0420-23000的支持。A、 H.得到卡塔尔国家研究基金（QNRF）资助NPRP12S-0205-190042和NPRP11S-0122-180359的支持。。作者信息作者和附属机构卡塔尔威尔康奈尔医学院信息学核心，教育城，多哈，卡塔尔哈拉玛，沙扎扎格卢尔，戈拉夫·塔雷亚，萨拉·卡德尔，尼莎·斯蒂芬和卡斯滕·苏赫勒生理学和生物物理学系，威尔康奈尔医学，纽约，纽约，乌萨纳·哈拉玛，沙扎扎格卢尔，戈拉夫·塔雷亚，萨拉·卡德尔，马尔乔内克·穆克·卡纳莫里，尼莎·斯蒂芬，扬·克鲁姆西克和卡斯滕·苏赫勒卡塔尔基因组计划，卡塔尔基金会，卡塔尔科技园，创新中心，卡塔尔多哈卡塔尔多哈威尔·康奈尔医学院遗传医学系。

MalekProteomics Core, Weill Cornell Medicine-Qatar, Education City, Doha, QatarHina Sarwath & Frank SchmidtGenomics Core, Weill Cornell Medicine-Qatar, Education City, Doha, QatarYasmin Ali Mohamoud & Joel A. MalekGerman Centre for Cardiovascular Research, Partner Site Greifswald, University Medicine Greifswald, Greifswald, GermanySabine Ameling, Nele Friedrich & Uwe VölkerDepartment of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Med.

MalekProteomics Core，Weill Cornell Medicine卡塔尔，教育城，多哈，卡塔尔Sarwath＆Frank SchmidtGenomics Core，Weill Cornell Medicine卡塔尔，教育城，多哈，卡塔尔，卡塔尔，Ali Mohamoud＆Joel A.MalekGerman心血管研究中心，合作伙伴网站Greifswald，大学医学Greifswald，Greifswald，GermanySabine Ameling，Nele Friedrich＆Uwe VölkerDepartment of Functional Genomics，Interfaculty Institute for Genetics and Functional Genomics，University Med。

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PubMed Google ScholarContributionsStudy design: A.H., K.S.; Sample collection: S.K., W.Al.M., M.M.-K., Conducted Experiments: H.S., Y.A.M., S.A., M.P.B., C.P., J.A., N.F., U.V., M.W., G.L., S.H.N.-S., J.A.M., S.A., J.G., D.M.-K., F.S., Data analysis: K.S., S.Z.; Data interpretation: A.H., K.S.; Provided Materials: A.H., N.S., G.T., S.K., W.Al.M., M.M.-K., J.K., J.M.S.; Manuscript writing: A.H, K.S.; Manuscript editing: A.H., K.S., J.M.S., F.S.

PubMed谷歌学术贡献研究设计：A.H.，K.S。；样本采集：S.K.，W.Al.M.，M.M.-K.，进行实验：H.S.，Y.A.M.，S.A.，M.P.B.，C.P.，J.A.，N.F.，U.V.，M.W.，G.L.，S.H.N.-S.，J.A.M.，S.A.，J.G.，D.M.-K.，F.S.，数据分析：K.S.，S.Z。；数据解释：A.H.，K.S。；提供的材料：A.H.，N.S.，G.T.，S.K.，W.Al.M.，M.M.-K.，J.K.，J.M.S。；手稿写作：A.H，K.S。；手稿编辑：A.H.，K.S.，J.M.S.，F.S。

All authors contributed to the critically reviewed manuscript.Corresponding authorsCorrespondence to.

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Competing interests

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The authors declare the following conflicts of interest: M.P.B. and G.L. are working for or have stakes in Genos Ltd., a private company specialized in glycomics analyses. All the other authors declare no competing interests.

作者声明存在以下利益冲突：M.P.B.和G.L.正在为Genos Ltd.工作或拥有股份，Genos Ltd.是一家专门从事糖组学分析的私营公司。所有其他作者都声明没有利益冲突。

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Nature Communications thanks Jessica Lasky-Su, and the other, anonymous, reviewers for their contribution to the peer review of this work. A peer review file is available.

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Reprints and permissionsAbout this articleCite this articleHalama, A., Zaghlool, S., Thareja, G. et al. A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes.

转载和许可本文引用本文Halama，A.，Zaghlool，S.，Thareja，G。等人，将多组学与人群特征和糖尿病亚型联系起来的分子人类路线图。

Nat Commun 15, 7111 (2024). https://doi.org/10.1038/s41467-024-51134-xDownload citationReceived: 01 August 2023Accepted: 26 July 2024Published: 19 August 2024DOI: https://doi.org/10.1038/s41467-024-51134-xShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

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GlycomicsLipidomicsMetabolomicsProteomicsType 2 diabetes

糖组学、脂肪组学、代谢组学、蛋白质组学、2型糖尿病

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