Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder
题目：自闭症、精神分裂症和躁郁症中的转录组范围上的==异构体水平==的失调
主要作者及第一单位：
Michael J. Gandal1,2,3,4,, Pan Zhang5,, Evi Hadjimichael6,7,8,9,, […] Chunyu Liu10,17,27, Lilia M.Iakoucheva5,, Dalila Pinto6,7,8,9,*, Daniel H. Geschwind1,2,3,4,
- 1Department of Psychiatry, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, 695 Charles E. Young Drive South, Los Angeles, CA 90095, USA.
发表期刊及时间：
Science 14 Dec 2018: Vol. 362, Issue 6420, eaat8127 DOI: 10.1126/science.aat8127
摘要：
Most genetic risk for psychiatric disease lies in regulatory regions, implicating pathogenic dysregulation of gene expression and splicing. However, comprehensive assessments of transcriptomic organization in diseased brains are limited. In this work, we integrated genotypes and RNA sequencing in brain samples from 1695 individuals with autism spectrum disorder (ASD), schizophrenia, and bipolar disorder, as well as controls. More than 25% of the transcriptome exhibits ==differential splicing==（差异剪接） or expression, with isoform-level changes capturing the largest disease effects and genetic enrichments. ==Coexpression networks== (共表达网络) isolate disease-specific neuronal alterations, as well as microglial, astrocyte, and interferon-response modules defining previously unidentified neural-immune mechanisms. We integrated genetic and genomic data to perform a transcriptome-wide association study, prioritizing disease loci likely mediated by ==cis effects== （顺式作用） on brain expression. This transcriptome-wide characterization of the molecular pathology across three major psychiatric disorders provides a comprehensive resource for ==mechanistic insight== (洞察机制) and therapeutic development.
大多数精神疾病的基因风险存在于调控区域，涉及基因表达和剪接的致病性失调。然而，患病者大脑转录组的全面评估是有限的。在这项工作中，我们整合了 1695 例自闭症、精神分裂和躁狂抑郁症患者和对照组的大脑样本的基因型和 RNA 测序。超过 25%的转录组表现出剪接或表达上的差异，亚型水平的变化捕获了最大的疾病影响和遗传富集。共表达网络分离出疾病特异性的神经元改变，以及定义先前不确定的神经元免疫机制的小胶质细胞、星形胶质细胞和干扰素反应模块。我们整合了遗传和基因组数据，进行了全转录组关联研究，优先考虑在大脑表达中可能由顺式作用调节的疾病位点。这个在三种主要的神经疾病的转录组范围分子病理学的特征，为洞察机制和治疗发展提供了全面的资源。
图表选析：
The PsychENCODE cross-disorder transcriptomic resource.
Human brain RNA-seq was integrated with ==genotypes== (基因型) across individuals with ASD, SCZ, BD, and controls, identifying pervasive dysregulation, including protein-coding, noncoding, splicing, and isoform-level changes. Systems-level and integrative genomic analyses prioritize previously unknown neurogenetic mechanisms and provide insight into the molecular neuropathology of these disorders.
PsychENCODE ：跨疾病转录组资源
被测试者中51个患自闭症，559个患精神分裂症，222个患双向情感障碍和936个正常人，他们的基因型以及大脑的RNA序列被整合分析并用以识别普遍性的失调（左侧图表12），包括蛋白编码、非编码、剪接和异构体水平的改变（右侧图表1234）。系统水平上和整合基因组分析优先考虑以前未知的神经遗传机制，并提供深入了解这些疾病的分子神经病理学。
Fig. 1 Gene and isoform expression dysregulation in brain samples from individuals with psychiatric disorders.
(A) ==DE==（DE: Differential Expression， 差异表达） effect size ==(|log2FC|)== （FC：Fold Change， 倍数变化）histograms are shown for protein-coding, ==lncRNA== （long non-coding RNA， 长非编码RNA）, and ==pseudogene==（假基因） biotypes up- or down-regulated ==(FDR < 0.05)== (FDR: false discovery rate ，错误发现率) in disease. Isoform-level changes ==(DTE; blue)== (DTE: differential transcript expression) show larger effect sizes than at the gene level ==(DGE; red)==（differential gene expression）, particularly for protein-coding biotypes in ASD and SCZ. (B) A literature-based comparison shows that the number of DE genes detected is dependent on study sample size for each disorder. (C) ==Venn diagrams==（韦恩图） depict overlap among up- or down-regulated genes and isoforms across disorders. (D) ==Gene ontology enrichments== （基因本体富集分析） are shown for differentially expressed genes or isoforms. The top five pathways are shown for each disorder. (E) Heatmap depicting cell type specificity of enrichment signals. Differentially expressed features show substantial enrichment for known ==CNS== （CNS: central nervous system ，中枢神经系统） cell type markers, defined at the gene level from single-cell RNA-seq. (F) Annotation of 944 ==ncRNAs==（ncRNA： non-coding RNA非编码RNA） DE in at least one disorder. From left to right: Sequence-based characterization of ncRNAs for measures of human selective constraint; brain developmental expression trajectories are similar across each disorder (colored lines represent mean trajectory across disorders); tissue specificity; and CNS cell type expression patterns.
图1. 精神病患者脑组织基因及异构体表达异常的研究
（A）DE 效应大小(|log2FC|)直方图显示蛋白质编码、lncRNA 和假基因生物型在疾病中上调或下调
(FDR<0.05)。异构体水平变化（DTE；蓝色）比基因水平（DGE；红色）显示出更大的效应大小，特
别是对于 ASD 和 SCZ 中的蛋白质编码生物型。（B）基于文献的比较表明，DE 基因的检测数量取决于每
个疾病的研究样本大小。（C）Venn 图描绘了跨疾病的上调或下调基因及异构体之间的重叠部分。（D）对差异表达的基因或异构体进行了基因本体的富集分析。显示了每种疾病的前五条通路都。（E）热力图描述富集信号的细胞类型特异性。差异表达的特征显示已知 CNS 细胞类型标记物大量富集，这些标记物是从单细胞 RNA-seq的基因水平进行定义的。（F）注释了944个ncRNA在至少一种疾病中差异表达。从左到右：基于序列的非编码RNA在人类选择性限制措施中的特征；大脑发育表达轨迹在每个疾病中是相似的（有色线代表障碍的平均轨迹）；组织特异性；和 CNS 细胞类型表达模式。
Fig. 3 Overlap and genetic enrichment among dysregulated transcriptomic features.
(A) Scatterplots demonstrate overlap among dysregulated transcriptomic features, summarized by their first principal component across subjects (R2 values; P < 0.05). ==PRS== (polygenic risk scores , 多基因风险评分) show greatest association with differential transcript signal in SCZ. (B) ==SNP== （SNP：Single* Nucleotide Polymorphism ，单核苷酸多态性） heritability in SCZ is enriched among multiple differentially expressed transcriptomic features, with down-regulated isoforms showing the most substantial association via stratified LD-score regression. (C**) Several individual genes and isoforms exhibit genome-wide significant associations with disease PRS. Plots are split by direction of association with increasing PRS. In ASD, most associations localize to the 17q21.31 locus, harboring a common inversion polymorphism.
图 3 异常的转录组特征之间的重叠和基因富集
（A）散点图显示失调的转录组学特征之间的重叠，通过它们在受试者中的第一主成分总结（R2值; P <0.05）。PRS（疾病的多基因风险评分）显示 其在SCZ 中与差异转录物信号有最大关联。（B）SCZ 中的 SNP 遗传多样性在多种差异表达的转录组特征中富集，其中下调的异构体通过分层 LD 分数回归显示最显着的关联。（C）几种单个基因和异构体在疾病 PRS 中表现出全基因组显著相关性。根据与 PRS 相关联的方向对图进行分割。在 ASD 中，大多数关联定位于 17q21.31 基因座，具有共同的染色体倒置多态性
Fig. 4 Transcriptome-wide association.
Results from a TWAS prioritize genes whose cis-regulated expression in brain is associated with disease. Plots show conditionally-independent TWAS prioritized genes, with lighter shades depicting marginal associations. The sign of TWAS z-scores indicates predicted direction of effect. Genes significantly up- or down-regulated in diseased brain are shown with arrows, indicating directionality. (A) In SCZ, 193 genes (164 outside of MHC) are prioritized at Bonferroni-corrected P < 0.05, including 107 genes with conditionally independent signals. Of these, 23 are also differentially expressed in SCZ brains with 11 in the same direction as predicted. (B) Seventeen genes are prioritized in BD, of which 15 are conditionally independent. (C) In ASD, a TWAS prioritizes 12 genes, of which 5 are conditionally independent.
图4 转录组范围的关联(分析结果)
TWAS的结果优先考虑那些与疾病相关的在脑中顺式调节表达的基因。上图显示了条件独立的TWAS优先考虑的基因，用较浅的阴影标示边缘关联。TWAS的z值表示效果的预测方向。用箭头表示在患者脑中显著上调或下调的基因，标示方向。（A）在精神分裂症中，在Bonferroni校正P<0.05的结果中，193个基因（在MHC外有164个）被优先选出来，包括107个具有条件独立信号的基因。其中，在SCZ脑中差异表达的23个基因中的11个与预测的方向相同。（B）在BD中有17个基因被放在前面，其中15个是条件独立的。（C）在ASD中，TWAS优先考虑了12个基因，其中5个是条件独立的。
Fig. 5 Gene and isoform coexpression networks capture shared and disease-specific cellular processes and interactions.
(A) Coexpression networks demonstrate pervasive dysregulation across psychiatric disorders. Hierarchical clustering shows that separate gene- and isoform-based networks are highly overlapping, with greater specificity conferred at the isoform level. Disease associations are shown for each module (==linear regression== （线性回归） β value, FDR < 0.05, –P* < 0.05). Module enrichments (FDR < 0.05) are shown for major CNS cell types. Enrichments are shown for GWAS results from SCZ , using stratified LD score regression (FDR < 0.05, –P < 0.05). (B) Coexpression modules capture specific cellular identities and biological pathways. Colored circles represent module DE effect size in disease, with red outlines representing GWAS enrichment in that disorder. Modules are organized and labeled based on CNS cell type and top gene ontology enrichments. (C) Examples of specific modules dysregulated across disorders, with the top 25 hub genes shown. Edges represent coexpression (Pearson correlation > 0.5) and known protein-protein interactions. Nodes are colored to represent disorders in which that gene is differentially expressed (*FDR < 0.05).
图 5 基因和异构体共表达网络捕获共享和疾病特有的细胞过程和相互作用。（A）共表达网络显示了精神障碍的普遍失调。分层聚类展示了分离的以基因和以异构体为基础的网络高度重叠，并且赋予异构体水平上更大的特异性。每个模块都显示出疾病的相关性（线性回归β值，FDR<0.05,-P<0.05）。展示了主要的中枢神经细胞类型的模块富集分析（FDR<0.05）。用分层的 LD 评分回归分析（FDR<0.05,-P<0.05）从精神分裂症（59）、双向情感障碍（97）和自闭症（38）患者所得的全转录组关联研究结果显示了富集。（B）共表达模块捕获特定的细胞特征和生物学路径。有色的圆形代表了疾病中模块 DE 影响大小，红色轮廓代表了相关疾病的全转录组关联研究的富集程度。基于中枢神经细胞类型对模块进行组织和标记，基因本体的多样性达到顶峰。（C）疾病中特定模块失调的例子，最核心的 25 个基因也展示了出来。边缘代表了共表达（Pearson 相关性>0.05）和已知的蛋白-蛋白相互作用。有色的节点代表了在疾病中有差异表达的基因（FDR<0.05）
Fig. 7 Distinct neural-immune trajectories in disease.
(A) Coexpression networks refine the neural-immune/inflammatory processes up-regulated in ASD, SCZ, and BD. Previous work has identified specific contributions to this signal from astrocyte and microglial populations (13, 19). Here, we identify additional contributions from distinct IFN-response and NFkB signaling modules. (B) Eigengene-disease associations are shown for each of four identified neural-immune module pairs. The astrocyte and IFN-response modules are up-regulated in ASD and SCZ. NFkB signaling is elevated across all three disorders. The microglial module is up-regulated in ASD and down-regulated in SCZ and BD. (C) Top hub genes for each module are shown, along with edges supported by coexpression (light gray; Pearson correlation > 0.5) and known protein-protein interactions (dark lines). Nodes follow the same coloring scheme as in Fig. 5C. Hubs in the astrocyte module (geneM3/isoM1) include several canonical, specific astrocyte markers, including SOX9, GJA1, SPON1, and NOTCH2. Microglial module hub genes include canonical, specific microglial markers, including AIF1, CSF1R, TYROBP, and TMEM119. The NFkB module includes many known downstream transcription factor targets (JAK3, STAT3, JUNB, and FOS) and upstream activators (IL1R1, nine TNF receptor superfamily members) of this pathway. (D) The top four GO enrichments are shown for each module. (E) Module enrichment for known cell type–specific marker genes, collated from sequencing studies of neural-immune cell types (98–102). (F) Module eigengene expression across age demonstrates distinct and dynamic neural-immune trajectories for each disorder.
图7 疾病中独特的神经-免疫轨迹
(A) 共表达网络完善了在ASD、SCZ和BD中上调的神经-免疫/炎症过程。之前的工作已经从星状胶质细胞和小神经胶质细胞群体中证实了对这个信号的特殊贡献（13，19）。这次我们验证了来自明显的干扰素应答和核转录因子信号模块的更多的贡献。（B）四个被鉴别的神经免疫模块对均分别显示了固有基因和疾病的联系。星形胶质细胞和干扰素应答模块在ASD和SCZ中上调。核转录因子信号在三种精神障碍中均上调。小神经胶质细胞模块在ASD中上调，在另外两种病中下调。（C）每个模块中最高的中心基因被展示出来，还有共表达支持的边界（浅灰色，皮尔森相关系数 > 0.5）和已知的蛋白-蛋白互作（黑色线）。节点遵循图5C中的配色组合。星形胶质细胞模块中的中枢（geneM3 / isoM1）包括几种经典的特异性星形胶质细胞标记物，包括SOX9，GJA1，SPON1和NOTCH2。小神经胶质细胞模块中的枢纽基因包括几种经典的特异性小神经胶质细胞标志物，包括AIF1, CSF1R, TYROBP和TMEM119。NFkB模型包括许多在这个通路中已知的下游转录因子靶点（JAK3, STAT3, JUNB和FOS）和上游激活物（IL1R1，九个TNF受体超家族成员）。（D）每个模块都会显示前四个GO富集通路。（E）已知细胞类型特异标志物基因的模块富集，从对神经免疫细胞类型（98-102）测序研究中搜集而来。（F）跨年龄的模块固有基因表达阐述了独特的、动态的每种疾病的神经免疫轨迹
Fig. 8 LncRNA annotation, ANK2 isoform switching, and microexon enrichment.
(A) FISH images demonstrate interneuron expression for two poorly annotated lincRNAs—LINC00643 and LINC01166—in area 9 of adult human prefrontal cortex. Sections were labeled with GAD1 probe (green) to indicate GABAergic neurons and lncRNA (magenta) probes for LINC00643 (left) or for LINC01166 (right). All sections were counterstained with DAPI (blue) to reveal cell nuclei. Lipofuscin autofluorescence is visible in both the green and red channels and appears orange. Scale bar, 10 μm. FISH was repeated at least twice on independent samples (table S9) (21), with similar results (see also fig. S16). (B) ANK2 isoforms ANK2-006 and ANK2-013 show significant DTU in SCZ and ASD, respectively (FDR < 0.05). (C) Exon structure of ANK2 highlighting (dashed lines) the ANK2-006 and ANK2-013 isoforms. (Inset) These isoforms have different protein domains and carry different microexons. ANK2-006 is affected by multiple ASD DNMs, while ANK2-013 could be entirely eliminated by a de novo CNV deletion in ASD. (D) Disease-specific coexpressed PPI network. Both ANK2-006 and ANK2-013 interact with NRCAM. The ASD-associated isoform ANK2-013has two additional interacting partners, SCN4B and TAF9. (E) As a class, switch isoforms are significantly enriched for microexon(s). In contrast, exons of average length are not enriched among switch isoforms. The y axis displays odds ratio on a log2 scale. P values are calculated using logistic regression and corrected for multiple comparisons. (F*) Enrichment of 64 genes with switch isoforms for: ASD risk loci (81); CHD8 targets (103); FMRP targets (33); mutationally constraint genes (104); syndromic and highly ranked (1 and 2) genes from SFARI Gene database; vulnerable ASD genes (105); genes with probability of loss-of-function intolerance (pLI) > 0.99 as reported by the Exome Aggregation Consortium (106); genes with likely-gene-disruption (LGD) or LGD plus missense de novo mutations (DNMs) found in patients with neurodevelopmental disorders (21).
图8LncRNA注释，ANK2同种型转换和微外显子富集。
A）FISH图像显示成人人类前额皮质区域9中两个注释不足的lincRNA-LINC00643和LINC01166-的中间神经元表达。用GAD1探针（绿色）标记切片以指示用于LINC00643（左）或用于LINC01166（右）的gama氨基丁酸能神经元和lncRNA（品红色）探针。所有切片用DAPI（蓝色）复染以显示细胞核。 Lipofuscin自发荧光在绿色和红色通道中均可见，并呈橙色。比例尺，10μm。在独立样品上重复FISH至少两次（表S9）（21），具有类似的结果（也参见图S16）。 （B）ANK2同种型ANK2-006和ANK2-013分别在SCZ和ASD中显示显着的DTU（* FDR <0.05）。 （C）ANK2的外显子结构突出显示（虚线）ANK2-006和ANK2-013同种型。 （插图）这些同种型具有不同的蛋白质结构域并携带不同的微外显子。 ANK2-006受多个ASD 新生突变的影响，而ANK2-013可以通过ASD中的从头拷贝数变异缺失完全消除。 （D）疾病特异性共表达的PPI网络。 ANK2-006和ANK2-013都与NRCAM相互作用。 ASD相关同种型ANK2-013具有两个额外的相互作用配偶体SCN4B和TAF9。 （E）作为一个类，开关同种型显着富集微外显子。相反，平均长度的外显子不会在开关同种型中富集。 y轴以log2标度显示优势比。使用逻辑回归计算P值并校正多重比较。 （F）用开关同种型富集64个基因：ASD风险基因座（81）; CHD8目标（103）; FMRP目标（33）;突变约束基因（104）;来自SFARI基因数据库的综合征和高度排名（1和2）基因;脆弱的ASD基因（105）; Exome Aggregation Consortium（106）报告的功能丧失不耐受概率（pLI）> 0.99的基因;神经发育障碍患者中发现LGD或LGD加新生突变的基因（21）。
翻译小组：
陈凯星、邓俊玮、王俊豪、黄敬潼、黄子亮、叶名琛、李碧琪、渠梦葳、郑凌伶
