A PROTEOMICS COMPUTATIONAL APPROACH TO PRETERM DELIVERY

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): Preterm birth is a multifactorial and heterogeneous clinical entity that accounts for the birth of 4.5 million premature infants worldwide. Prematurity accounts for 75% of all infant mortality and 50% of long-term neurological handicaps, including blindness, deafness, developmental delay, cerebral palsy, and chronic lung disease. However, not all premature infants have a poor outcome. Correct and early identification of the pregnancy at risk for preterm delivery and poor neonatal outcome is critical for development of educated therapies. We hypothesize that women who are destined to deliver preterm express cervicovaginal biomarkers several weeks or months prior to their clinical presentation that can be reliably identified using proteomics tools coupled with novel mathematical algorithms for post-experimental data analysis. Our goals are to identify proteomic profiles that are predictive for pregnancy complications such as preterm labor or preterm premature rupture of the membranes (pPROM) and should pPROM occur of intra-amniotic inflammation. This approach may in the future guide clinical practice in preventing prematurity and hence adverse pregnancy outcome. To accomplish these objectives, three specific aims over five years will be pursued: (1) To identify reliable proteomic biomarkers for early diagnosis of impending PTD due to PTL or pPROM; (2) To identify cervicovaginal biomarkers that will reliably diagnose pPROM; (3) To identify a proteomic profile in the cervicovaginal secretions of women with pPROM that is predictive of intra-amniotic inflammation. The identification and classification in advance of patients destined to deliver preterm may outline subgroups of patients in whom trials of various interventions might be undertaken without the contamination of the trial with patients who will respond to placebo. Further, the new directions provided by future identification of multiple biomarkers are likely to lead to pathology specific treatments.