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Immune Dysregulation Disorders in the Bioinformatics Paradigm

机译：生物信息学范例的免疫失调障碍

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Immune dysregulation is an entity that consists of primary immunodeficiency disorders (PIDs), autoimmune diseases (adaptive immunity dysregulation), and autoinflammatory syndromes (innate immunity dysregulation). It reflects inadequate immune function, which leads to exaggerated chronic inflammatory responses and so tissue damage [1]. Immune dysregulation disorders are clinically and genetically heterogenous disorders. The clinical phenotypes derived from distinct genotype can overlap, and different disorders can share the same phenotype. Thus, in many cases, a precise diagnosis and effective management are difficult to achieve. Over the last decade, remarkable progress has been made particularly in finding disease-causing genes for a number of rare monogenic immune dysregulatory disorders. With the explosion of multi-omics technologies, such as genomics, proteomics, and transcriptomics, high throughput sequencing approaches have been widely applied in the immune dysregulation studies. This has led to increase awareness and a quantum jumb in the number of these monogenic immune dysregulation disorders, as well as pathways that underlie their pathogenesis. Moreover, this has facilitated the identification of new biomarkers that can help in early diagnosis, prognosis, spectacular novel therapeutic successes in the clinic and prediction of therapeutic responses [2]. Given the complexity of our immune system, diversity of immune dysregulation disorders, the heterogenous phenotypes, and advent of next-generation sequencing (NGS) data that is becoming increasingly available, putting all of these together and moving these big data to the clinic is becoming ever more critical. In a recent study by Stray-Pedersen and colleagues, the authors sought to investigate the ability of whole exome sequencing (WES) to detect disease-causing variants in patients with PIDs. They used computational copy number variant prediction pipelines. A likely molecular diagnosis was achieved in 40% of unrelated probands. Clinical diagnosis was revised in nearly half, and management was directly altered in nearly a quarter of families based on molecular findings [3]. This approach will lead to timely diagnosis, alter medical management; provide accurate information about recurrence risks for family planning, and may result in healthcare savings by ending diagnostic odysseys. Despite these many successes, there are some challenges and obstacles we face with the big data created in the last decade. We, as physicians, have to choose the right patient to do the genetic testing on to get the highest yield. This can be done with the focus on the phenotypes and clinical manifestations. These days, many phenotype databases, such as PHO have made this process easier. Another challenge is that clinical NGS technology has evolved rapidly, outpacing resources for generating guidelines, standards, and resources such as data storage. Bioinformatics and NGS-based precision medicine has mainly focused on cancer and remains under-utilized in rare diseases such as immune dysregulation disorders. Our goal is to encourage the use of immunoinformatics to accelerate immune system research. There have been several bio-tools to study Immune disorders, such as patients' registries, genomic databases, phenotype ontologies, and decision support systems. Genome-Phenome mapping can be very helpful in studying genotype-phenotype correlation and in determining the presence of gene variants in asymptomatic family members. In summary, with the recent breakthroughs in rare monogenic immune dysregulatory disorders, there have been renaissance in the field of immune dysregulation. Future clinical initiatives that incorporate NGS into medical care (NGS-based precision medicine), in addition to decision supporting systems, will help to shape the trajectory of broader incorporation. This will establish a firm foundation for investigating more complex immune disorders, and will facilitate the field
机译：免疫失调是由原发性免疫缺陷疾病（PID的），自身免疫性疾病（适应性免疫失调），和自身炎症综合征（先天免疫失调）的实体。它反映了免疫功能不足，从而导致慢性夸张炎症反应等组织损伤[1]。免疫失调症是临床和遗传异质性疾病。从不同基因型来源的临床表型可以重叠，和不同的疾病可以共享相同的表型。因此，在许多情况下，精确的诊断和有效的管理是很难实现的。在过去的十年中，显着的进展已经取得了特别是在寻找致病基因的数量罕见的单基因免疫dysregulatory疾病。随着多组学技术，如基因组学，蛋白组学，转录和的爆炸，高通量测序的方法已被广泛应用于免疫失调的研究。这导致增加这些单基因免疫失调疾病的数量，以及所依据其发病途径的认识和量子JUMB。此外，这种促进了新的生物标记物，可以在早期诊断，预后，在诊所壮观新的治疗成功和治疗反应[2]预测帮助鉴定。考虑到我们的免疫系统的复杂性，免疫失调疾病，异质表型和下一代测序的到来（NGS）的数据变得越来越可用，把所有这些结合在一起，并移动这些大数据诊所的多样性正在成为以往任何时候都更加重要。在最近的一项研究杂散彼得森和他的同事，作者试图探讨全外显子测序（WES）来检测致病变异的患者的PID的能力。他们使用的计算拷贝数变异预测管道。一个可能的分子诊断中先证者无关的40％来实现的。临床诊断进行了修订，在近一半，而管理是基于分子发现[3]在近四分之一家庭的直接改变。这种做法将导致及时诊断，涂改医疗管理;提供有关计划生育的复发风险获得准确的信息，并可以通过结束诊断奥德赛导致医疗储蓄。尽管有这么多的成功，也有我们在过去十年创造了大数据面临一些挑战和障碍。我们作为医生，必须选择合适的病人做，以获得最高产量的基因检测。这可以通过将重点放在表型和临床表现来完成。这些天来，许多表型数据库，如PHO使这个过程更容易。另一个挑战是，临床NGS技术的迅速发展，超过了资源生成准则，标准，以及诸如数据存储资源。生物信息学和基于NGS-精度医学已经主要集中在利用不足在罕见疾病癌症和遗体如免疫失调病症。我们的目标是鼓励使用免疫信息学，加快免疫系统的研究。已经有一些生物工具来研究免疫系统疾病，如病人的登记，基因组数据库，表型本体，以及决策支持系统。基因组Phenome映射可以是在研究基因型 - 表型相关性和在确定无症状家族成员基因变体的存在非常有帮助。综上所述，在罕见的单基因免疫dysregulatory失调近期的突破，出现了免疫失调领域的复兴。未来的临床举措，纳入到NGS医疗保健（基于NGS精药），除了决策支持系统，将有助于形成更广泛的整合的轨迹。这将建立一个坚实的基础，为研究更复杂的免疫功能紊乱，并促进该领域

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