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Comprehensive proteomic analysis of human cervical-vaginal fluid and identification of novel biomarkers for prediction of spontaneous preterm birth

      Objective

      To characterize the cervical-vaginal proteome in pregnancy and to identify novel protein biomarkers for positive prediction of spontaneous preterm birth (SPTB).

      Study design

      CVF samples were collected prospectively from 2005-2006 at Oregon Health & Science University from women in preterm labor (PTL) <37 weeks and gestational age matched asymptomatic controls. All subjects had IAI excluded by placental histopathology and/or amniocentesis. CVF samples were analyzed using fluorescence two-dimensional differential in-gel electrophoresis (2D-DIGE), multidimensional liquid chromatography tandem mass spectrometry (2D-LC-MS/MS) and label-free quantification (spectral counting). Pair-wise comparison was performed using χ2 goodness-of-fit tests. Significance for each protein was determined after adjusting for multiple comparisons via the false-discovery rate (FDR) method. Western blots for specific targets were used to confirm differential expression.

      Results

      Of 60 subjects, 23 (54%) had SPTB < 37 weeks, 15 had PTL but delivered at term, 22 were asymptomatic controls. Comprehensive proteomic analysis of CVF revealed 206 unique proteins. Major functional categories in the CVF proteome were metabolism (33%) and immune response-related (22%) proteins. Label-free quantification identified 23 proteins in CVF, which exhibited significant differences in pair-wise comparisons and 16 proteins in progressive comparisons from asymptomatic to PTL to SPTB groups. Potential biomarkers included calcium modulators (Calgranulins, annexins, S100 calcium-binding protein A7), acute phase reactants (α-1-antitrypsin, α-1-acid glycoprotein, serotransferrin, haptoglobin) extracellular matrix proteins (fibronectin, epidermal fatty acid binding protein), and abundant proteins in amniotic fluid (IGFBP1 and vitamin D binding protein).

      Conclusion

      This dataset provides a foundation for evaluation of CVF protein biomarkers in pregnancy. Further characterization and quantification of SPTB markers in a larger cohort could provide the basis for non-invasive prediction of preterm birth.