Gareth John Morgan, Allison N Nau, Sherry Wong, Brian H Spencer, Yun Shen, Axin Hua, Matthew J Bullard, Vaishali Sanchorawala, Tatiana Prokaeva
{"title":"An updated AL-Base reveals ranked enrichment of immunoglobulin light chain variable genes in AL amyloidosis","authors":"Gareth John Morgan, Allison N Nau, Sherry Wong, Brian H Spencer, Yun Shen, Axin Hua, Matthew J Bullard, Vaishali Sanchorawala, Tatiana Prokaeva","doi":"10.1101/2024.09.11.612490","DOIUrl":null,"url":null,"abstract":"Background: Each monoclonal antibody light chain associated with AL amyloidosis has a unique sequence. Defining how these sequences lead to amyloid deposition could facilitate faster diagnosis and lead to new treatments. Methods: Light chain sequences are collected in the Boston University AL-Base repository. Monoclonal sequences from AL amyloidosis, multiple myeloma and the healthy polyclonal immune repertoire were compared to identify differences in precursor gene use, mutation frequency and physicochemical properties. Results: AL-Base now contains 2,193 monoclonal light chain sequences from plasma cell dyscrasias. Sixteen germline precursor genes were enriched in AL amyloidosis, relative to multiple myeloma and the polyclonal repertoire. Two genes, IGKV1-16 and IGLV1-36, were infrequently observed but highly enriched in AL amyloidosis. The number of mutations varied widely between light chains. AL-associated κ light chains harbored significantly more mutations compared to multiple myeloma and polyclonal sequences, whereas AL-associated λ light chains had fewer mutations. Machine learning tools designed to predict amyloid propensity were less accurate for new sequences than their original training data.\nConclusions: Rarely-observed light chain variable genes may carry a high risk of AL amyloidosis. New approaches are needed to define sequence-associated risk factors for AL amyloidosis. AL-Base is a foundational resource for such studies.","PeriodicalId":501307,"journal":{"name":"bioRxiv - Bioinformatics","volume":"15 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"bioRxiv - Bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.09.11.612490","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Each monoclonal antibody light chain associated with AL amyloidosis has a unique sequence. Defining how these sequences lead to amyloid deposition could facilitate faster diagnosis and lead to new treatments. Methods: Light chain sequences are collected in the Boston University AL-Base repository. Monoclonal sequences from AL amyloidosis, multiple myeloma and the healthy polyclonal immune repertoire were compared to identify differences in precursor gene use, mutation frequency and physicochemical properties. Results: AL-Base now contains 2,193 monoclonal light chain sequences from plasma cell dyscrasias. Sixteen germline precursor genes were enriched in AL amyloidosis, relative to multiple myeloma and the polyclonal repertoire. Two genes, IGKV1-16 and IGLV1-36, were infrequently observed but highly enriched in AL amyloidosis. The number of mutations varied widely between light chains. AL-associated κ light chains harbored significantly more mutations compared to multiple myeloma and polyclonal sequences, whereas AL-associated λ light chains had fewer mutations. Machine learning tools designed to predict amyloid propensity were less accurate for new sequences than their original training data.
Conclusions: Rarely-observed light chain variable genes may carry a high risk of AL amyloidosis. New approaches are needed to define sequence-associated risk factors for AL amyloidosis. AL-Base is a foundational resource for such studies.
背景:与 AL 淀粉样变性相关的每种单克隆抗体轻链都有独特的序列。确定这些序列是如何导致淀粉样蛋白沉积的,有助于更快地诊断并找到新的治疗方法。方法:波士顿大学 AL-Base 资料库收集了轻链序列。比较了 AL 淀粉样变性、多发性骨髓瘤和健康多克隆免疫复合物的单克隆序列,以确定前体基因使用、突变频率和理化性质的差异。结果:AL-Base目前包含2193个来自浆细胞性疾病的单克隆轻链序列。相对于多发性骨髓瘤和多克隆序列,16个种系前体基因在AL淀粉样变性中富集。IGKV1-16和IGLV1-36这两个基因在AL淀粉样变性病中并不常见,但却高度富集。不同轻链的突变数量差异很大。与多发性骨髓瘤和多克隆序列相比,AL相关的κ轻链突变明显较多,而AL相关的λ轻链突变较少。旨在预测淀粉样蛋白倾向的机器学习工具对新序列的准确性低于其原始训练数据:结论:罕见的轻链变异基因可能具有高发AL淀粉样变性病的风险。结论:罕见的轻链可变基因可能具有高风险,需要采用新方法来确定与序列相关的 AL 淀粉样变性风险因素。AL-Base 是此类研究的基础资源。