用深长读RNA-seq绘制老年人额叶皮层医学相关RNA异构体多样性-动脉网

Mapping medically relevant RNA isoform diversity in the aged human frontal cortex with deep long-read RNA-seq

Nature 等信源发布 2024-05-22 21:03

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AbstractDetermining whether the RNA isoforms from medically relevant genes have distinct functions could facilitate direct targeting of RNA isoforms for disease treatment. Here, as a step toward this goal for neurological diseases, we sequenced 12 postmortem, aged human frontal cortices (6 Alzheimer disease cases and 6 controls; 50% female) using one Oxford Nanopore PromethION flow cell per sample.

摘要确定来自医学相关基因的RNA亚型是否具有不同的功能可以促进RNA亚型的直接靶向治疗疾病。在这里，作为实现神经系统疾病这一目标的一步，我们使用每个样品一个牛津纳米孔PromethION流通池对12个死后老年人额叶皮质（6例阿尔茨海默病病例和6例对照；50%女性）进行了测序。

We identified 1,917 medically relevant genes expressing multiple isoforms in the frontal cortex where 1,018 had multiple isoforms with different protein-coding sequences. Of these 1,018 genes, 57 are implicated in brain-related diseases including major depression, schizophrenia, Parkinson’s disease and Alzheimer disease.

我们鉴定了1917个在额叶皮层中表达多种同工型的医学相关基因，其中1018个具有不同蛋白质编码序列的多种同工型。在这1018个基因中，有57个与大脑相关疾病有关，包括抑郁症，精神分裂症，帕金森氏病和阿尔茨海默病。

Our study also uncovered 53 new RNA isoforms in medically relevant genes, including several where the new isoform was one of the most highly expressed for that gene. We also reported on five mitochondrially encoded, spliced RNA isoforms. We found 99 differentially expressed RNA isoforms between cases with Alzheimer disease and controls..

我们的研究还发现了医学相关基因中的53种新的RNA亚型，其中包括几种新的亚型是该基因表达最高的基因之一。我们还报道了五种线粒体编码的剪接RNA亚型。我们在阿尔茨海默病患者和对照组之间发现了99种差异表达的RNA亚型。。

MainHuman protein-coding genes average more than eight RNA isoforms, resulting in almost four distinct protein-coding sequences1,2. As a result of practical limitations in standard short-read sequencing technologies, researchers have historically been forced to collapse all isoforms into a single gene expression measurement, a major oversimplification of the underlying biology.

主要人类蛋白质编码基因平均超过八种RNA同种型，导致几乎四种不同的蛋白质编码序列1,2。由于标准短读测序技术的实际局限性，研究人员历史上被迫将所有同工型折叠成单个基因表达测量，这是对基础生物学的一个重大简化。

Many unique isoforms from a single gene body appear to have unique interactomes at the protein level3. Distinct functions for individual isoforms from a single gene body have already been demonstrated for a handful of genes4,5,6. Notably, isoforms can play entirely different, or even opposite, roles within a given cell; a classic example includes two well-studied BCL-X (BCL2L1) transcripts with opposite functions, where BCL-XL is anti-apoptotic and BCL-XS is pro-apoptotic6.

来自单个基因体的许多独特同工型似乎在蛋白质水平上具有独特的相互作用组3。对于少数基因4,5,6，已经证明了来自单个基因体的单个同种型的不同功能。值得注意的是，同工型可以在给定细胞内发挥完全不同甚至相反的作用；一个经典的例子包括两个经过充分研究的具有相反功能的BCL-X（BCL2L1）转录本，其中BCL-XL是抗凋亡的，BCL-XS是促凋亡的6。

Changes in the expression ratio between the BCL-X isoforms are implicated in cancer and are being studied as therapeutic targets7, demonstrating the importance of understanding individual RNA isoform function rather than treating them as a ‘single’ gene.Knowing which tissues and cell types express each isoform is an important first step in understanding isoform function.

BCL-X亚型之间表达比例的变化与癌症有关，正在作为治疗靶点进行研究7，这表明了解单个RNA亚型功能而不是将其视为“单一”基因的重要性。了解哪些组织和细胞类型表达每种同工型是理解同工型功能的重要第一步。

The limitations of using short-read sequencing for studying differential RNA isoform expression/usage8,9 include relying on heuristics to assemble and quantify isoforms10,11,12. As a result of these limitations, detailed analysis of individual isoforms has been limited to highly studied genes. In principle, long reads can sequence the entire isoforms directly12.

使用短读测序研究差异RNA同种型表达/用途8,9的局限性包括依赖启发式来组装和量化同种型10,11,12。由于这些限制，对单个同工型的详细分析仅限于高度研究的基因。原则上，长读可以直接对整个同工型进行测序12。

However, the imperfections of long-read data13 still require some heuristics to estimate the expression of each isoform13,14. Recent long-read RNA sequencing (RNA-seq) studies used targeted approaches .

然而，长读取数据13的缺陷仍然需要一些启发式方法来估计每个异构体的表达13,14。最近的长读RNA测序（RNA-seq）研究使用了靶向方法。

Data availability

数据可用性

Raw long-read RNA-seq data generated and utilized in the present study are publicly available in Synapse92: https://www.synapse.org/#!Synapse:syn52047893. Raw long-read RNA-seq data generated and utilized in the present study are also publicly available in NIH Sequence Read Archive (SRA) (accession no.

本研究中生成和使用的原始长读RNA-seq数据可在Synapse92中公开获得：https://www.synapse.org/#哦！突触：syn52047893。本研究中产生和使用的原始长读RNA-seq数据也可在NIH序列阅读档案（SRA）中公开获得（登录号：。

SRP456327)93 https://trace.ncbi.nlm.nih.gov/Traces/?view=study&acc=SRP456327. Output from long-read RNA-seq and proteomics pipelines, reference files and annotations are publicly available at94 https://doi.org/10.5281/zenodo.8180677. Long-read RNA-seq results from this article can be easily visualized through this web application: https://ebbertlab.com/brain_rna_isoform_seq.html.

SRP456327）93https://trace.ncbi.nlm.nih.gov/Traces/?view=study&acc=SRP456327.长读RNA-seq和蛋白质组学管道的输出，参考文件和注释可在94公开获得https://doi.org/10.5281/zenodo.8180677.本文的长读RNA-seq结果可以通过此web应用程序轻松可视化：https://ebbertlab.com/brain_rna_isoform_seq.html.

Raw cell-line deep proteomics data utilized in this article are publicly available at https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD024364. Raw brain proteomics data from round 2 of the ROSMAP TMT study are publicly available at https://www.synapse.org/#!Synapse:syn17015098. GTEx long-read RNA-seq data used for validation of our study results are available at https://anvil.terra.bio/#workspaces/anvil-datastorage/AnVIL_GTEx_V9_hg38.

本文中使用的原始细胞系深度蛋白质组学数据可在https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD024364.ROSMAP TMT研究第二轮的原始大脑蛋白质组学数据可在https://www.synapse.org/#哦！突触：syn17015098。用于验证我们研究结果的GTEx长读RNA-seq数据可在https://anvil.terra.bio/#workspaces/anvil-数据存储/AnVIL\u GTEx\u V9\u hg38。

ROSMAP short-read RNA-seq data used for validation of our study results are available at https://www.synapse.org/#!Synapse:syn21589959. CHM13 reference genome sequence can be found at https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/analysis_set/chm13v2.0.fa.gz. CHM13 reference GFF3 annotation can be found at https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/annotation/chm13.draft_v2.0.gene_annotation.gff3.

用于验证我们研究结果的ROSMAP短读RNA-seq数据可在https://www.synapse.org/#哦！突触：syn21589959。CHM13参考基因组序列可以在https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/analysis_set/chm13v2.0.fa.gz.CHM13参考GFF3注释可以在https://s3-us-west-2.amazonaws.com/human-pangenomics/T2T/CHM13/assemblies/annotation/chm13.draft_v2.0.gene_annotation.gff3.

The transcript annotation from Glinos et al.19 was retrieved from https://storage.googleapis.com/gtex_analysis_v9/long_read_data/flair_filter_transcripts.gtf.gz. The transcript annotation from Leung.

Glinos等人19的转录本注释是从https://storage.googleapis.com/gtex_analysis_v9/long_read_data/flair_filter_transcripts.gtf.gz.梁的成绩单注释。

Code availability

代码可用性

All code used in the manuscript is publicly available at https://github.com/UK-SBCoA-EbbertLab/brain_cDNA_discovery (ref. 95).

手稿中使用的所有代码均可在https://github.com/UK-SBCoA-EbbertLab/brain_cDNA_discovery（参考文献95）。

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A. et al. Ebbert Lab Nanopore PCS111 brain cDNA discovery (12 samples—AD vs controls). Zenodo https://doi.org/10.5281/zenodo.8180677 (2023).Heberle, B. A. et al. Brain cDNA Discovery. GitHub https://github.com/UK-SBCoA-EbbertLab/brain_cDNA_discovery (2023).Download referencesAcknowledgementsThis work was supported by: the National Institutes of Health (NIH; grant nos.

A、等人。Ebbert Lab Nanopore PCS111脑cDNA发现（12个样品AD与对照）。泽诺多https://doi.org/10.5281/zenodo.8180677（2023年）。Heberle，B.A.等人，《大脑cDNA发现》。GitHubhttps://github.com/UK-SBCoA-EbbertLab/brain_cDNA_discovery（2023年）。下载参考文献致谢这项工作得到了以下机构的支持：美国国立卫生研究院（NIH；grant nos。

R35GM138636, R01AG068331 to M.T.W.E. and 5R50CA243890 to S.G.); the BrightFocus Foundation (grant no. A2020161S to M.T.W.E.), Alzheimer’s Association (grant no. 2019-AARG-644082 to M.T.W.E.), PhRMA Foundation (grant no. RSGTMT17 to M.T.W.E.); the Ed and Ethel Moore Alzheimer’s Disease Research Program of Florida Department of Health (grant nos.

R35GM138636，M.T.W.E.的R01AG068331和S.G.的5R50CA243890）；BrightFocus基金会（授予M.T.W.E.的授权号A2020161S），阿尔茨海默病协会（授予M.T.W.E.的授权号2019-AARG-644082），PhRMA基金会（授予M.T.W.E.的授权号RSGTMT17）；佛罗里达州卫生部Ed和埃塞尔·摩尔阿尔茨海默病研究计划（批准号：。

8AZ10 and 9AZ08 to M.T.W.E. and 6AZ06 to J.D.F.); and the Muscular Dystrophy Association (to M.T.W.E.). We appreciate the contributions of the Sanders-Brown Center on Aging at the University of Kentucky. We are deeply grateful to the research participants and their families who made this research possible.

M.T.W.E.的8AZ10和9AZ08以及J.D.F.的6AZ06）；和肌肉萎缩症协会（致M.T.W.E.）。我们感谢肯塔基大学桑德斯·布朗老龄化中心的贡献。我们非常感谢使这项研究成为可能的研究参与者及其家人。

We thank S. L. Anderson from the University of Kentucky brain bank for preparing the brain samples used in the present study. We thank the University of Kentucky Center for Computational Sciences and Information Technology Services Research Computing for their support and use of the Morgan Compute Cluster and associated research computing resources.

我们感谢肯塔基大学脑库的S.L.Anderson准备本研究中使用的大脑样本。我们感谢肯塔基大学计算科学与信息技术服务研究计算中心对摩根计算集群和相关研究计算资源的支持和使用。

We thank Singularity Sylabs for providing support and extra cloud storage for our soft.

我们感谢Singularity Sylabs为我们的软件提供支持和额外的云存储。

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PubMed Google ScholarContributionsB.A.H., J.A.B. and M.T.W.E. developed and designed the study and wrote the paper. B.A.H., M.L.P., B.A.W., B.J.W., K.I.D., M.E.W., E.J.F. and A.S. performed all analyses. M.L.P. developed the RShiny app. K.I.D. embedded the RShiny app into ebbertlab.com.

PubMed谷歌学术贡献b。A、 H.，J.A.B.和M.T.W.E.开发并设计了这项研究并撰写了论文。B、 A.H.，M.L.P.，B.A.W.，B.J.W.，K.I.D.，M.E.W.，E.J.F.和A.S.进行了所有分析。M、 L.P.开发了RShiny应用程序。K、 ID.将RShiny应用程序嵌入ebbertlab.com。

J.A.B., K.A.N., L.A.G., G.A.F., P.H.D., S.G., E.G., R.W. and S.M.-E. helped generate sequencing and supporting data. N.T.S., E.J.F. and A.S. generated and advised on proteomics analyses. P.T.N. provided the invaluable brain samples and pathology. J.D.F., M.R. and J.B.M. provided important intellectual contributions.Corresponding authorCorrespondence to.

J、 A.B.，K.A.N.，L.A.G.，G.A.F.，P.H.D.，S.G.，E.G.，R.W.和S.M.-E.帮助生成测序和支持数据。N、 T.S.，E.J.F.和A.S.生成并建议进行蛋白质组学分析。P、 T.N.提供了宝贵的大脑样本和病理学。J、 D.F.，M.R.和J.B.M.提供了重要的智力贡献。对应作者对应。

Mark T. W. Ebbert.Ethics declarations

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Competing interests

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The authors declare no competing interests.

作者声明没有利益冲突。

Peer review

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Nature Biotechnology thanks Stefan Canzar, Sandra T. Cooper and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

自然生物技术感谢Stefan Canzar，Sandra T.Cooper和另一位匿名审稿人对这项工作的同行评议做出的贡献。

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 Basic sequencing metrics.AD = Alzheimer’s disease cases, CT = Cognitively unimpaired aged controls. a, Number of reads per sample after each step of the analysis.

Additional informationPublisher的注释Springer Nature在已发布地图和机构隶属关系中的管辖权主张方面保持中立。扩展数据扩展数据图1基本测序指标。AD=阿尔茨海默病病例，CT=认知未受损的老年对照。a、分析每个步骤后每个样品的读数。

All downstream analysis were done with Mapped pass reads with both primers and MAPQ > 10. b, N50 and median read length for Mapped pass reads with both primers and MAPQ > 10. c, Percentage of reads that are full-length or unique as determined by bambu. Full-length counts = reads containing all exon-exon boundaries (that is, intron chain) from its respective transcript.

所有下游分析均使用引物和MAPQ的定位pass读数进行 > 10.b，N50和使用引物和MAPQ进行映射pass读取的中值读取长度 > 10.c，由bambu确定的全长或唯一读数的百分比。全长计数=包含来自其各自转录本的所有外显子-外显子边界（即内含子链）的读数。

Unique counts = reads that were assigned to a single transcript. All boxplots from this panel come from n = 12 biologically independent samples. Male AD n = 3, Female AD n = 3, Male CT n = 3, Female CT n = 3. All boxplots in this panel follow this format: center line, median; box limits, upper and lower quartiles; whiskers, 1.5x interquartile range.Extended Data Fig.

唯一计数=分配给单个成绩单的读数。这个面板中的所有箱线图都来自n = 12个生物学独立的样本。男性AD n = 3，女性AD n = 3，男性CT n = 3，女性CT n = 3.该面板中的所有箱线图均遵循以下格式：中心线，中位数；盒子限制，上下四分位数；晶须，1.5倍四分位间距。扩展数据图。

2 Expression distribution and diversity for genes and transcripts.a, Number of genes and transcripts represented across median CPM threshold. Cutoff shown as the dotted line set at median CPM = 1. b, Distribution of log10 median CPM values for gene bodies, dotted line shows cutoff point of median CPM = 1.

2基因和转录本的表达分布和多样性。a，在中位CPM阈值上代表的基因和转录本的数量。截止值显示为中位数CPM的虚线 = 1.b，基因体的log10中值CPM值的分布，虚线显示中值CPM的截止点 = 1

c, Distribution of log10 median CPM values for gene bodies, dotted line shows cutoff point of median CPM = 1.Extended Data Fig. 3 Expression of different transcript biotypes on aged human frontal cortex tissue using long-read RNAseq data.a, Lineplot showing the number of transcripts from different biotypes expressed above different median CPM threshold in long-read RNAseq data from aged human d.

c、基因体的log10中位数CPM值分布，虚线显示中位数CPM的截止点 = 1。扩展数据图3使用长读RNAseq数据在老年人额叶皮层组织上表达不同转录物生物型。a，线图显示在来自老年人的长读RNAseq数据中在不同中值CPM阈值以上表达的不同生物型的转录物数量d。

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Reprints and permissionsAbout this articleCite this articleAguzzoli Heberle, B., Brandon, J.A., Page, M.L. et al. Mapping medically relevant RNA isoform diversity in the aged human frontal cortex with deep long-read RNA-seq.

转载和许可本文引用本文Aguzzoli-Heberle，B.，Brandon，J.A.，Page，M.L.等人用深度长读RNA-seq绘制老年人额叶皮层中与医学相关的RNA亚型多样性。

Nat Biotechnol (2024). https://doi.org/10.1038/s41587-024-02245-9Download citationReceived: 06 August 2023Accepted: 15 April 2024Published: 22 May 2024DOI: https://doi.org/10.1038/s41587-024-02245-9Share 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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