A proteomic clock of human pregnancy
A proteomic clock of human pregnancy
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DOI：
10.1016/j.ajog.2017.12.208
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发表时间：
2018-03-01
影响因子：
9.8
通讯作者：
Angst, Martin S.
中科院分区：
文献类型：
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作者：
Aghaeepour, Nima;Lehallier, Benoit;Angst, Martin S.
背景技术背景：早期发现妊娠相关病理的适应不良过程是可取的，因为它将使有针对性的干预措施之前的临床表现。血浆蛋白的定量分析在用于检测正常妊娠偏差的分子方法中具有突出的特征。然而，充分预测妊娠相关结果的蛋白质组特征的推导一直具有挑战性。阻碍这种努力的一个重要障碍是在测定技术的局限性，这阻止了广泛的检查血浆proteome.Objective：最近提供的一个高度多路复用的平台，同时测量1310血浆蛋白质打开了大门，一个更探索性的方法。本研究的主要目的是检查在足月妊娠期间收集的血浆分析是否允许鉴定一组紧密跟踪胎龄的蛋白质。在足月妊娠期间建立精确定时的血浆蛋白质组学变化是识别由胎儿和母体适应不良引起的常规模式偏离的关键步骤。第二个目的是深入了解所确定的蛋白质的功能属性，并将这些属性与相关的免疫学变化联系起来。研究设计：孕妇参加了这项纵向研究。在随后的2组21名（训练队列）和10名（验证队列）妇女中，在第一个（7 - 14周），第二个（15 - 20周）和第三个（24 - 32周）妊娠期和产后6周收集特定的血液标本，用于使用高度多重的基于适体的平台进行分析。采用弹性网络算法建立了预测孕龄的蛋白质组模型。使用自举程序和分段回归分析来提取预测胎龄所需的最少数量的蛋白质，而不影响预测能力。基因本体分析被应用于推断蛋白质组模型中包括的蛋白质之间的分子功能的富集。具有这种功能的蛋白质丰度的变化与预测妊娠期的免疫特征有关。结果：由74种蛋白质组成的独立验证模型强烈预测妊娠期（P = 3.8 × 10（-14），R = 0.97）。该模型可以减少到8种蛋白质，而不会失去其预测能力（P = 1.7 x 10（-3），R = 0.91）。排名前3位的蛋白质是磷脂酰肌醇蛋白聚糖3、绒毛膜促生长激素和颗粒蛋白。激活Janus激酶和信号转导和转录激活因子途径的蛋白质在蛋白质组模型中富集，绒毛膜促生长激素激素是排名靠前的蛋白质。绒毛膜促生长激素的abbandry强烈相关的信号转导和转录激活因子-5信号转导活性的CD4 T细胞，内源性细胞信号转导事件最预测孕龄。结论：结果表明，精确定时的变化，在血浆蛋白质组在足月妊娠反映蛋白质组时钟。重要的是，需要组合使用几种血浆蛋白进行准确预测。这种时钟令人兴奋的承诺是，偏离其规则的时间顺序分布可能有助于早期诊断妊娠相关的病理，并指出潜在的病理生理学。蛋白质组模型的功能分析产生了新的假设，chrononic生长催乳激素可能在怀孕期间至关重要的调节T细胞功能。
BACKGROUND: Early detection of maladaptive processes underlying pregnancy-related pathologies is desirable because it will enable targeted interventions ahead of clinical manifestations. The quantitative analysis of plasma proteins features prominently among molecular approaches used to detect deviations from normal pregnancy. However, derivation of proteomic signatures sufficiently predictive of pregnancy-related outcomes has been challenging. An important obstacle hindering such efforts were limitations in assay technology, which prevented the broad examination of the plasma proteome.OBJECTIVE: The recent availability of a highly multiplexed platform affording the simultaneous measurement of 1310 plasma proteins opens the door for a more explorative approach. The major aim of this study was to examine whether analysis of plasma collected during gestation of term pregnancy would allow identifying a set of proteins that tightly track gestational age. Establishing precisely timed plasma proteomic changes during term pregnancy is a critical step in identifying deviations from regular patterns caused by fetal and maternal maladaptations. A second aim was to gain insight into functional attributes of identified proteins and link such attributes to relevant immunological changes.STUDY DESIGN: Pregnant women participated in this longitudinal study. In 2 subsequent sets of 21 (training cohort) and 10 (validation cohort) women, specific blood specimens were collected during the first (7-14 weeks), second (15-20 weeks), and third (24-32 weeks) trimesters and 6 weeks postpartum for analysis with a highly multiplexed aptamer-based platform. An elastic net algorithm was applied to infer a proteomic model predicting gestational age. A bootstrapping procedure and piecewise regression analysis was used to extract the minimum number of proteins required for predicting gestational age without compromising predictive power. Gene ontology analysis was applied to infer enrichment of molecular functions among proteins included in the proteomic model. Changes in abundance of proteins with such functions were linked to immune features predictive of gestational age at the time of sampling in pregnancies delivering at term.RESULTS: An independently validated model consisting of 74 proteins strongly predicted gestational age (P = 3.8 x 10(-14), R = 0.97). The model could be reduced to 8 proteins without losing its predictive power (P = 1.7 x 10(-3), R = 0.91). The 3 top ranked proteins were glypican 3, chorionic somatomammotropin hormone, and granulins. Proteins activating the Janus kinase and signal transducer and activator of transcription pathway were enriched in the proteomic model, chorionic somatomammotropin hormone being the top-ranked protein. Abundance of chorionic somatomammotropin hormone strongly correlated with signal transducer and activator of transcription-5 signaling activity in CD4 T cells, the endogenous cell-signaling event most predictive of gestational age.CONCLUSION: Results indicate that precisely timed changes in the plasma proteome during term pregnancy mirror a proteomic clock. Importantly, the combined use of several plasma proteins was required for accurate prediction. The exciting promise of such a clock is that deviations from its regular chronological profile may assist in the early diagnoses of pregnancy-related pathologies, and point to underlying pathophysiology. Functional analysis of the proteomic model generated the novel hypothesis that chrionic somatomammotropin hormone may critically regulate T-cell function during pregnancy.