Systems Immunology Lab, Department of Biology, Humboldt University Berlin, and Research Center ImmunoSciences, Charité University Medicine Berlin, Berlin, Germany.
BMC Genomics. 2012 Feb 21;13:79. doi: 10.1186/1471-2164-13-79.
The importance of peptide microarrays as a tool for serological diagnostics has strongly increased over the last decade. However, interpretation of the binding signals is still hampered by our limited understanding of the technology. This is in particular true for arrays probed with antibody mixtures of unknown complexity, such as sera. To gain insight into how signals depend on peptide amino acid sequences, we probed random-sequence peptide microarrays with sera of healthy and infected mice. We analyzed the resulting antibody binding profiles with regression methods and formulated a minimal model to explain our findings.
Multivariate regression analysis relating peptide sequence to measured signals led to the definition of amino acid-associated weights. Although these weights do not contain information on amino acid position, they predict up to 40-50% of the binding profiles' variation. Mathematical modeling shows that this position-independent ansatz is only adequate for highly diverse random antibody mixtures which are not dominated by a few antibodies. Experimental results suggest that sera from healthy individuals correspond to that case, in contrast to sera of infected ones.
Our results indicate that position-independent amino acid-associated weights predict linear epitope binding of antibody mixtures only if the mixture is random, highly diverse, and contains no dominant antibodies. The discovered ensemble property is an important step towards an understanding of peptide-array serum-antibody binding profiles. It has implications for both serological diagnostics and B cell epitope mapping.
在过去十年中，肽微阵列作为血清诊断工具的重要性大大增加。然而，由于我们对该技术的理解有限，因此对结合信号的解释仍然受到阻碍。对于用复杂程度未知的抗体混合物（如血清）探测的阵列尤其如此。为了深入了解信号如何依赖于肽的氨基酸序列，我们用健康和感染的小鼠的血清探测随机序列肽微阵列。我们用回归方法分析了由此产生的抗体结合图谱，并提出了一个最小模型来解释我们的发现。
将肽序列与测量信号相关联的多元回归分析导致了与氨基酸相关的权重的定义。尽管这些权重不包含关于氨基酸位置的信息，但它们可以预测高达 40-50%的结合图谱的变化。数学模型表明，这种与位置无关的方法仅适用于高度多样化的随机抗体混合物，而不是由少数抗体主导的混合物。实验结果表明，健康个体的血清符合这种情况，而感染个体的血清则不符合。
我们的结果表明，位置无关的氨基酸相关权重仅在混合物是随机的、高度多样化的且不含主导抗体的情况下，才能预测抗体混合物的线性表位结合。所发现的集合属性是理解肽阵列血清-抗体结合图谱的重要一步。它对血清学诊断和 B 细胞表位作图都有影响。
