Understanding Human Autoimmunity

Review

Author: Romain Banchereau¹, Alma-Martina Cepika¹, Jacques Banchereau², and Virginia Pascual¹
Affiliation: ¹Baylor Institute for Immunology Research, Dallas, Texas 75204;
email: romain.banchereau@bswhealth.org,
almamartina.cepika@bswhealth.org,
virginia.pascual@bswhealth.org
²The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut 06030; email: jacques.banchereau@jax.org

Autoimmune diseases：
   Human autoimmune diseases result from complex interactions between genetic and environmental factors that build up over time until the appearance of clinical symptoms. Their common denominator is the loss of tolerance to self-antigens, which can be organ specific, as in type 1 diabetes (T1D), or widely expressed, as in systemic lupus erythematosus (SLE), leading to the appearance of autoantibodies and/or autoreactive T cells.
adaptive immunity以及innate immunity均参与了疾病的发生发展。
Autoinflammation：
   The term autoinflammation was coined to describe a group of diseases characterized by predominant activation of the innate immune system in the absence of detectable B or T cell autoreactivity. Autoinflammatory diseases manifest with fevers and multiorgan inflammation, as is the case in familial cryopyrinopathies¹. Within this spectrum, there are diseases with both autoimmune and autoinflammatory features, such as psoriasis, a disease featuring innate immunity dysregulation driven by autoreactive Th17 cells.

TRANSCRIPTOMICS TO UNRAVEL MECHANISMS OF INFLAMMATORY DISEASES

Interferonopathies

The term interferonopathy refers to diseases where elevated type I IFN levels and/or biological activity is found in biological specimens. These patients characteristically share a prominent blood IFN signature that can be used as a complementary biomarker.

IFN families

IFN families.png

I型IFN的生理功能包括：antiviral, antiproliferative及immunomodulatory，能够 trigger autoimmunity through the activation of B cells, T cells, and dendritic cells (DCs)².体外实验证明，IFN-α 能够增强活化的B向分泌免疫球蛋白的浆细胞分化³ 并能够上调单核细胞来源的树突状细胞(moDCs)表面的BAFF和APRIL表达 ⁴,以促进B细胞的存活. 另外，IFN-α也能够诱导单核细胞向moDCs细胞分化 ⁵, 上调T细胞共刺激分子的表达，如MHC class II, CD80, and CD86小鼠模型中，IFN-α启动的幼稚CD8+ T细胞活化增殖并获得相应的效应功能⁶，IFN-α刺激moDCs诱导幼稚T细胞分化为辅助T细胞⁷。
II型IFN主要来源于natural killer (NK) 和T cells.
III型IFN 来源于多种细胞系, 包括：plasmacytoid DCs (pDCs), regulatory T cells, macrophages, and hepatocytes.

Cellular sensors and cells involved in type I IFN production.

Picture from Type I Interferons in Autoimmune Disease,Annu. Rev. Pathol. Mech. Dis. 2019. 14:367–9
   通常I型干扰素与其他的促炎因子都是通过病原体来源核酸激活免疫细胞的模式识别受体(pattern-recognition receptors, PRRs)后所分泌的⁸。尽管感知病原体来源的核酸能够产生大量的抗菌免疫反应的基础，但是，对自身核酸的不恰当识别将会导致自身免疫性疾病。
   能够识别核酸的PRRs包括：TLRs的内体家族(TLR3, 7–9)以及胞质感受器。
   Sensing of nucleic acids through endosomal receptors and IFN production is a characteristic of the natural IFN-producing cells or pDCs, which secrete high amounts of these cytokines in response to viruses or self-antigens following ligation to TLR7 or TLR9. Viruses and endogenous nucleic acids can also enter pDCs through Fc receptors when bound by antibodies. Whether engagement of TLR7 or TLR9 results in the production of type I IFN or proinflammatory cytokines depends on the compartment in which these TLRs encounter their ligands.
   TLR9-mediated recognition of large DNA-containing immune complexes is dependent, however, on a distinct convergence of phagocytic and autophagic pathways involving autophagy-related proteins but not the conventional autophagic preinitiation complex. Overall, production of type I IFN is tightly regulated by several surface receptors in pDCs. Picture from Type I Interferons in Autoimmune Disease,Annu. Rev. Pathol. Mech. Dis. 2019. 14:367–9
   胞质核酸感受器包括:维甲酸诱导基因I样受体(retinoic acid–inducible gene 1 (RIG-I)-like receptors, RLRs) 识别胞质dsRNA and 5'pppRNA以及AIM2-like receptors，ALRs识别 胞质DNA.Cyclic GMP-AMP (cGAMP) synthase (cGAS)是近期发现的胞质PRR，其能够识别DNA并诱导产生cGAMP. cGAMP 结合并激活定位于线粒体相关内质网膜(mitochondria-associated endoplasmic reticulum membranes，MAMs)的 IFN 基因刺激因子(stimulator of IFN genes，STING). 除了STING, 线粒体抗病毒信号蛋白 (mitochondrial antiviral-signaling protein，MAVS) 也在这个区域富集,并在这个区域与RLRs RIG-I 和 MDA5进一步激活天然免疫信号^9-10。活化后的STING and/or MAVS导致并传导核内IRF3 与IRF7活化并激活 NF-κB通路.一旦核内IRF3, IRF7与NF-κB 亚单位将与特定 IFN-β promoter位点结合，也能够与ISGs亚基集合, 包括IRFs和PRRs.
   In humans, STING广泛表达于造血和非造血细胞，如endothelial细胞.在中心粒细胞中缺乏, 中心粒细胞具有独特胞质DNA受体库，如SOX2.

The IFN signature

  由不同的IFN家族成员诱导的转录子被称为IFN signature. Interferome数据库¹¹就报道了由不同细胞群体或单独的细胞与重组的IFN混合后(主要是IFN-α2, IFN-β, IFN-γ和IFN-λ家族成员)，通过microarray平台测序，检测到的>5000个转录读数相关基因。然而，这种方法仅对mRNA的变化进行了描述，并未对isoforms的可变剪切或者新的miRNAs和lncRNAs进行探索。
  三个IFN家族的signature高度重合，type I and type II IFN pathways can reinforce each other， as small amounts of type II IFN enhance signaling through TLRs leading to type I IFN secretion，and type I IFN signaling induces the expression of the IFN-γR, thus cross-amplifying downstream pathways.
  文中介绍了作者开发的描述IFN signature的方法：
一种减小血液样本转录数据维度的方法：a modular analytical framework.

A module framework to biologically interpret the blood transcriptome
a. 选择了大量的不同免疫条件的全血样本作为参考数据集
b. 提取共表达基因¹²
c. 结合 knowledge-based (e.g., pathway enrichment analysis)与data-driven (e.g., hierarchical clustering, module enrichment in isolated leukocyte populations)对相应模块进行注释。
d. Module fingerprints可根据疾病种类以及个体样本进行衍生，并且映射模块网络。颜色代表与对照的进行比较结果(red, up; blue, down),颜色的密度表示超过阈值的的部分。
e. modules同样可以被用来进行常规的基因集富集分析(e.g., GSEA, QuSAGE, Q-Gen)
   随后对modules的功能功能解释，两个因素驱动血液转录组module的差异性表达：细胞频数的改变 (e.g.,B cell, cytotoxic cell modules)以及细胞内的转录丰度改变(e.g., inflammation, IFN) 。这主要与IFN的通路相关(M1.2, M3.4以及M5.12).这三个模块基因在自身免疫以及感染性疾病中均显著上调。如下图所示：
The interferon (IFN) signature in systemic lupus erythematosus (SLE)
a. Unsupervised hierarchical clustering揭示~85%的SLE样本type I IFN signature上调。
b. SLE的module fingerprint：揭示三个IFN相关的modules上调。
c. 基因水平的网络图揭示IFN-inducible transcripts与疾病的活动度具有正相关。

Monogenic interferonopathies

   最早由2011年Yanick Crow¹³提出该概念并用于定义与孟德尔法则相关的IFN上调所引起的一组疾病。
   多数患者变现出血液或脑脊液中的I型IFN蛋白表达增加或/和活性增高。他们血液往往都表现出具有相似的IFN signature，这些IFN signature能够辅助疾病诊断以及作为潜在的依赖于突变相关通路治疗靶点。分散在这些通路的IFN signature功能被解释，也为理解核酸感知和信号传导如何调节I型IFN提供了基础。此外，患者的亲属偶尔携带此类突变且在没有临床症状的情况下显示出血液IFN signature，这增加了解释的复杂性。

单基因突变诱导的干扰素相关疾病

Sporadic interferonopathies

complex–mediated systemic autoimmunity

SLE

Other sporadic diseases with IFN signature

IL-1-Mediated Diseases

   IL-1β是天然免疫和获得性免疫的重要中介^14-15, IL-1β被翻译为需要被 caspase 1裂解的前体，caspase 1是一种在胞质中被炎症小体激活的酶. NLRP3或NLRC4等天然免疫sensors、上游信号分子如PSTPIP1、下游信号分子或受体拮抗剂如IL-1Ra的突变将导致IL-1的增加。除此之外， IL-1β也参与一些罕见的单基因疾病的病理过程。

单基因突变诱导的IL-1相关疾病
   IL-1家族受体的结构域与所有胞质内所有TLRs的结构域具有很高的同源性。因此，TLRs配体与IL-1家族成员触发的炎症后会产生非常相似的下游信号与高度重叠的转录表达谱。
complex–mediated systemic autoimmunity

Multiple Cytokines, One Disease: The Case of Psoriasis

APPLICATION OF TRANSCRIPTOMICS IN THE CLINIC

   转录组数据鉴定的相关gene signature能够被用于：(a) diagnose disease, (b) assess disease prognosis, (c) act as surrogate scores of clinical disease activity, and (d) monitor or predict response to treatment

Five applications of transcriptomics for the clinic.

Disease Diagnosis

   Fever发热, arthritis关节炎, arthralgia关节痛, fatigue疲劳, anemia贫血, leukopenia白细胞减少和抗核抗体是许多自身免疫性疾病以及病毒感染共同的临床表现。在没有典型症状的情况下，自身免疫性疾病的准确诊断极具挑战性，这在疾病发病前后是很常见的。基于转录组的特定gene signature可用于弥补当前诊断手段的不足。

• 幼年特发性关节炎( juvenile idiopathic arthritis,sJIA):12-gene PBMC signature能够准确的与其他发热疾病进行鉴别诊断¹⁶。
• IBD: colonic gene signatures与溃疡性结肠炎ulcerative colitis、Crohn病具有明显差异¹⁷， 并且12-gene PBMC signature enriched for immunoglobulins能够与两种疾病相鉴别¹⁸。
• RA与osteoarthritis¹⁹
• meta-analyses^20-21构建的自身免疫性疾病core以及specific gene signatures.

Disease Prognosis

   若通过Biomarkers能够预测临床结果，比如在易感人群中疾病进展、flares或者参与特殊组织或器官的进展对临床医生来说具有非常强的吸引力。例如相关研究²²
   这里有很多前瞻性实验被提及：

• T1D相关
  • DIPP (Type I Diabetes Prediction and Prevention) project：To identify and immunomonitor children at risk of T1D to unravel the pathomechanisms of T1D development.
  • Diabetes Autoimmunity Study in the Young (DAISY)：five-gene lymphocyte signature能够预测T1D的进展
  • BABYDIET study

Clinical Score Surrogates and Disease Activity Assessment

Monitoring Response to Treatment

Predicting Response to Treatment

Patient Stratification

Stratification of SLE patients through individual immunoprofiling

a. 每个患者在观察期内的基因共表达模块与模块/临床特征相关性矩阵
b. 与感兴趣的临床特征最相关的相关基因共表达模块选择
c. 根据免疫网络对这些患者进行分层并对这些患者进行基因测序和SNP分析
d. 根据不同的分层患者进行临床实验的设计

EMERGING APPLICATIONS OF TRANSCRIPTOMICS IN AUTOIMMUNITY

Repertoire Analysis

eQTLs

Isoforms

文献

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