An interactive database for the investigation of high-density peptide microarray guided interaction patterns and antivenom cross-reactivity

Snakebite envenoming is a major neglected tropical disease that affects millions of people every year. The only effective treatment against snakebite envenoming consists of unspecified cocktails of polyclonal antibodies purified from the plasma of immunized production animals. Currently, little data exists on the molecular interactions between venom toxin epitopes and antivenom antibody paratopes. To address this issue, high-density peptide microarray (hdpm) technology has recently been adapted to the field of toxinology. However, analysis of such valuable datasets requires expert understanding and, thus, complicates its broad application within the field. In the present study, we developed a user-friendly, and high-throughput web application named “Snake Toxin and Antivenom Binding Profiles” (STAB Profiles), to allow straight-forward analysis of hdpm datasets. To test our tool and evaluate its performance with a large dataset, we conducted hdpm assays using all African snake toxin protein sequences available in the UniProt database at the time of study design, together with eight commercial antivenoms in clinical use in Africa, thus representing the largest venom-antivenom dataset to date. Furthermore, we introduced a novel method for evaluating raw signals from a peptide microarray experiment and a data normalization protocol enabling intra-microarray and even inter-microarray chip comparisons. Finally, these data, alongside all the data from previous similar studies by Engmark et al., were preprocessed according to our newly developed protocol and made publicly available for download through the STAB Profiles web application (http://tropicalpharmacology.com/tools/stab-profiles/). With these data and our tool, we were able to gain key insights into toxin-antivenom interactions and were able to differentiate the ability of different antivenoms to interact with certain toxins of interest. The data, as well as the web application, we present in this article should be of significant value to the venom-antivenom research community. Knowledge gained from our current and future analyses of this dataset carry the potential to guide the improvement and optimization of current antivenoms for maximum patient benefit, as well as aid the development of next generation antivenoms.蛇咬伤中毒（Snakebite envenoming）是一种重大的被忽视热带病（neglected tropical disease），每年影响数百万人群。当前针对蛇咬伤中毒的唯一有效治疗手段，是从免疫生产动物血浆中纯化得到的未明确配比的多克隆抗体（polyclonal antibodies）混合制剂。目前，关于蛇毒毒素表位（venom toxin epitopes）与抗蛇毒血清抗体互补位（antivenom antibody paratopes）之间的分子相互作用的相关数据仍十分匮乏。为解决这一研究痛点，高密度肽微阵列（high-density peptide microarray, hdpm）技术近年已被应用于蛇毒毒理学（toxinology）研究领域。然而，此类高价值数据集的分析需要专业背景支撑，这限制了其在该领域的广泛应用。本研究开发了一款操作简便且高通量的网络应用程序——“蛇毒毒素与抗蛇毒血清结合谱”（Snake Toxin and Antivenom Binding Profiles, STAB Profiles），以实现高密度肽微阵列数据集的便捷分析。为验证该工具并基于大型数据集评估其性能，本研究在研究设计阶段，使用了UniProt数据库中收录的所有非洲蛇毒毒素蛋白序列，并结合8种非洲临床在用的商业抗蛇毒血清，开展了高密度肽微阵列实验，由此构建了迄今规模最大的蛇毒-抗蛇毒血清数据集。此外，本研究提出了一种全新的肽微阵列实验原始信号评估方法，以及一套可实现芯片内乃至芯片间信号比对的数据标准化流程（data normalization protocol）。最后，本研究将本次实验产生的数据，与Engmark等人此前同类研究的全部数据，按照本研究新开发的流程完成预处理，并通过STAB Profiles网络应用程序（http://tropicalpharmacology.com/tools/stab-profiles/）面向公众开放下载。借助该数据集与本工具，我们已获得关于毒素-抗蛇毒血清相互作用的关键见解，并能够区分不同抗蛇毒血清与特定目标毒素的结合能力差异。 本文所呈现的数据集与网络应用程序，对蛇毒-抗蛇毒血清研究领域具有重要价值。从本数据集当前及未来的分析中获得的认知，有望指导现有抗蛇毒血清的改良与优化，以最大化患者获益，同时亦可助力下一代抗蛇毒血清的开发。