Skip to main content

Use of cervicovaginal fluid for the identification of biomarkers for pathologies of the female genital tract


Cervicovaginal fluid has an important function in the homeostasis and immunity of the lower female genital tract. Analysis of the cervicovaginal fluid proteome may therefore yield important information about the pathogenesis of numerous gynecological pathologies. Additionally, cervicovaginal fluid has great potential as a source of biomarkers for these conditions.

This review provides a detailed discussion about the human cervicovaginal proteome and the proteomics studies performed to characterize this biological fluid. Furthermore, infection-correlated pathological conditions of the female genital tract are discussed for which cervicovaginal fluid has been used in order to identify potential biomarkers. Recent years, numerous studies have analyzed cervicovaginal fluid samples utilizing antibody-based technologies, such as ELISA or Western blotting, to identify biomarkers for preterm birth, premature preterm rupture of membranes, bacterial vaginosis and cervical cancer. The present article will discuss the importance of proteomic technologies as alternative techniques to gain additional meaningful information about these conditions. In addition, the review focuses on recent proteomic studies on cervicovaginal fluid samples for the identification of potential biomarkers. We conclude that the use of proteomic technology for analysis of human cervicovaginal fluid samples is promising and may lead to the discovery of new biomarkers which can improve disease prevention and therapy development.

Female genital tract physiology

The female genital tract is characterized by a unique immunologic micro-environment. It is essential for human reproduction that the immune system of the female genital tract is modulated correctly as it needs to tolerate the presence of sperm and fertilized oocytes without starting an immune reaction. Nevertheless, a strong immune system in the genital tract is very important to protect the interior organs and the fetus or embryo against the large pathogenic stress [1]. This obligation of a dual immune system, one that tolerates and one that reacts efficiently, is a biological challenge. Failure to successfully accomplish this challenge can lead to specific gynecopathological conditions which eventually may result in severe complications.

The lower female genital tract (vagina and ectocervix) is lined with a protective non-keratinized stratified squamous epithelium, whereas the higher genital tract (uterus, oviducts and ovaria) is lined with a columnar epithelium connected with tight junctions. The mucosa creates a physical barrier for invading microorganisms due to the production of a glycocalyx (i.e. a hydrophilic glycoprotein layer) which allows for hydration of the mucosa which hinders the adherence of bacteria on epithelial cells. Nevertheless, when bacteria are able to reach the mucosa and adhere to it, they will be removed by cell shedding [18].

The mucosa of the lower female genital tract is covered by numerous commensal bacteria which form an important protective factor. The normal vaginal bacterial flora consists predominantly of Lactobacillus spp. (e.g. Lactobacillus jenensii, Lactobacillus crispatus, Lactobacillus iners) but (facultative) anaerobic species such as Gardnerella vaginalis are also present although in much lower concentrations. Glycogen from exfoliated cells is first metabolized to glucose by epithelial cells which is then used by the vaginal flora to produce lactic acid, thereby maintaining a low vaginal pH (3.5-4.7). Also, some Lactobacillus spp. (e.g. Lactobacillus crispatus, Lactobacillus rhamnosus and Lactobacillus acidophilus) produce H2O2 at concentrations lethal for exterior microorganisms. Additionally, commensals compete with nonresident bacteria for available nutrients and some bacteria produce broad-spectrum antimicrobial peptides (i.e. bacteriocins). Together, these factors exert a selective antimicrobial activity which inhibits the growth of nonresident bacteria without affecting the survival of the commensal flora [1, 2, 7, 911].

The presence of adaptive immunity mechanisms in the female genital tract is known, although detailed information is scarce. Langherhans (dendritic) cells, which reside in the genital mucosa, and epithelial cells are able to present antigens to T lymphocytes and thus can elicit an adaptive immune response [3, 12]. Also, plasma cells are localized nearby submucosal glands and secrete, after activation, immunoglobulins (primarily IgG and secretory IgA) into the cervicovaginal fluid (CVF; see below). These immunoglobulins can recruit and activate other immune cells, hinder bacterial adherence and opsonize pathogens. Additionally, mucosal-associated lymphoreticular tissue (MALT), which consists mainly of T-lymphocytes and monocytes/macrophages, is located in the lamina propria of the cervix [2].

Another vital element of the immune system of the female genital tract is CVF. CVF is made of (i) vulvar secretions from sebaceous, sweat, Bartholins and Skene glands, (ii) plasma transudate through the vaginal wall, (iii) exfoliated cells, (iv) bacterial products, (v) cervical mucus (vi) endometrial and oviductal fluids and (vii) secretions from vaginal immune cells. The latter three are influenced by sex steroid hormones e.g. during the menstrual cycle and pregnancy [13, 14]. CVF consists mainly of water and contains many different factors such as cholesterol, lipids, mucin, carbohydrates, amino acids, proteins and inorganic ions. It covers the lower female genital tract and hydrates the mucosa, creating a physical barrier for microbial invasion.

Many studies have used CVF as samples for the analysis of proteins and found correlations between different expression profiles and specific pathologies. It is therefore not surprising that during the last 5 years several comprehensive proteomics studies have been performed on CVF in order to catalogue the CVF proteome and to look for potential biomarkers.

Cervicovaginal fluid as potential biomarker source

Over the last 10 years, the potential of using body fluid for the identification of diagnostic or prognostic biomarkers has been recognized [15]. This is also the case for CVF, where the low cost and ease of sample collection, circumvention of the risk associated with biopsies and the possibility of observing high numbers of patients using multiple samples are clear advantages of this body fluid [1618]. Four different methods are frequently applied for the collection of CVF: 1) cervicovaginal washings (i.e. the cervicovagina is rinsed with washing buffer after which the fluid is collected), 2) cervicovaginal swabs (i.e. swabs or brushes are applied to the mucosal wall and rotated to collect the CVF), 3) cervicovaginal wicks (i.e. wicks such as tampons, strips or sponges are inserted in the female genital tract which absorb cervicovaginal secretions) and 4) diaphragm-like devices (i.e. cups placed over the cervix which collect cervical fluid). Few studies have been performed on the reliability and validity of the CVF collection methods. Snowhite et al. [19] compared the wicking method with cervicovaginal washings and found that cervicovaginal washings are the superior sample collection method for cytokine assays in terms of reproducibility. Another research group demonstrated that cervicovaginal washings are better samples as compared to swabs for assessment of HIV1 RNA in cervicovaginal secretions [20]. Lastly, collection of samples using cups has the advantage that ready-to-use samples are obtained. Because the secretions are not absorbed (cfr. swabs and wicks), no sample recovery steps need to be undertaken and protein loss is minimized. In addition, the cervical secretions are not diluted in a washing buffer during sample collection (cfr. cervicovaginal washings). However, because the cups are placed over the cervix, this sample collection method has the disadvantage that only cervical secretions and no vaginal secretions are collected.

These examples indicate that the sample collection method may influence results. Therefore, well defined and standardized sampling methods may help avoiding misinterpretation of results and decrease the technical variation of the experimental setup.

So far, plasma/serum is the most analyzed body fluid compared to other body fluids such as cerebrospinal fluid, urine, saliva, bronchoalveolar lavage fluid, nipple aspirate fluid, tear fluid and amniotic fluid [18]. A great advantage of plasma/serum is that these samples contain a plethora of potential biomarkers since this body fluid is in contact with nearly all organ systems. Nevertheless, some important disadvantages are inherent to the use of plasma/serum. Due to the presence of plasma/serum in the entire body, biomarkers often lack discriminatory power for a particular organ system and thus are not specific enough. Also, since the volume of plasma/serum is substantial, potential biomarkers are strongly diluted, resulting in lowered sensitivity. Moreover, it is well known that serum and plasma protein concentrations have a large dynamic range which often implies extensive sample preparation and separation steps. All together, these disadvantages result in a large inter- and intravariability of plasma/serum samples [16].

Other body fluids such as cerebrospinal fluid, urine or CVF are less subjected to these restraints. For instance, it can be expected that biomarkers found in CVF, being a "proximal" fluid, show more specificity and sensitivity for gynecopathological conditions as compared to blood [21]. This is due to 1) the relatively small volume/organ ratio of CVF, thereby increasing the possibility that potential biomarkers are less diluted, thus increasing their sensitivity and 2) CVF is a body fluid that is specific for the female genital tract and hence the CVF proteome will be related to conditions of these organs [16, 18, 21, 22]. CVF is therefore an interesting body fluid to use for the development of biomarkers for gynecological pathologies. One must keep in mind however that many factors may significantly influence the protein composition of the CVF. For example, secretions of the genital tract are notably influenced by varying levels and ratios of estrogens and progesterone during the menstrual cycle [23] resulting in variable amounts of protein yield (for instance, we measured between 0.58 mg and 13.37 mg in cervicovaginal washings). Therefore it is practically impossible to quantify the normal amount of total protein content in healthy CVF. Although one can expect some changes in the amount of protein content for pathological conditions, these are often unnoticeable due to the large normal fluctuations. Because of this variation, it is often difficult to distinguish real biomarkers from natural occurring biophysiological variations [16, 24]. For example, analysis of microbicide induced cytokine deregulation is often hindered by the menstrual cycle [4, 2527], pathological conditions, sexual intercourse [28], age [29], and the use of contraceptives [3033] or hormonal substitution therapies. If one disregards these effects, they will inevitably lead to biased or confounded results. The use of experimental designs of high-quality and well determined and clinically validated criteria are required to detect real product induced cytokine changes from background levels [34].

The use of body fluid as biomarker source implies the application of proteomic methodologies, because body fluids do not contain a corresponding genome or transcriptome and therefore gene expression cannot be analyzed [17]. Most studies on the identification of potential biomarkers are restricted to the use of antibody dependent techniques (e.g. ELISA or Western blotting). Thus far, only a limited number of large proteomic studies have been performed on CVF and the majority of these studies focused on the characterization of the CVF proteome. Only during the last few years quantitative methods were used to detect differences in protein profiles between particular conditions [14, 22, 3545].

Proteomics techniques for the characterization of the human cervicovaginal proteome

Generally, a proteomic biomarker study can be divided in several steps which are critical to obtain clinically and statistically significant results. (i) The characterization and analysis of the proteome, which also includes analysis of variation, is a first essential stage followed by (ii) differential analysis of the proteome, including statistical analysis to identify potential biomarkers (discovery phase). In a next phase (iii) the potential markers need to be validated on other independent cohorts (validation phase) after which (iv) the implementation possibilities of the validated markers in a clinical environment can be investigated [16, 18]. Only the last 5 years, studies started to investigate the human CVF proteome to characterize the protein composition and to detect protein expression differences between specific patient conditions (table 1) using comprehensive antibody-independent proteomics techniques.

Table 1 Overview of primarily qualitative proteomic studies performed on human CVF and cervical mucus.

Venkataraman et al. [38] published one of the first human CVF proteomics papers and demonstrated that human CVF may play a critical role in the host defense against HIV. The authors showed that CVF contained intrinsic anti-HIV activity from which the greater part was found in the cationic protein/peptide fraction. This fraction was analyzed using two-dimensional (native acid urea-PAGE followed by Tricine SDS-page) gel electrophoresis after which spots were identified using MALDI-TOF/TOF tandem mass spectrometry. The authors identified 18 polypeptides including known antimicrobial peptides/proteins such as calgranulin A/B, lysozyme, human neutrophil peptide (HNP) 1, cathepsin G and several histones [2]. However, none of these cationic polypeptides alone had potential anti-HIV activity. In addition, cationic fraction-depleted CVF did not contain anti-HIV activity but this activity was completely restored after adding the isolated cationic fraction. This indicates that the anti-HIV activity is almost entirely dependent on the CVF cationic fraction and probably is the result of a synergistic combination of different molecules. Also, it can be expected that, apart from the 18 proteins identified in this study, the cationic fraction from CVF is constituted of other proteins and peptides (such as elafin, see later) which could also play a role in the anti-HIV activity from CVF.

Di Quinzio et al. [40] used full human CVF instead of a particular fraction for their proteomics analysis. Cervicovaginal swabs from five pregnant women were collected and separated using 2D (isoelectric focusing (IEF) followed by SDS-PAGE) gel electrophoresis. Subsequently, protein spots common to all five gels were tryptically digested and identified using MALDI-TOF mass spectrometry. Alternatively, the samples were first separated using capillary reversed phase (RP)-HPLC followed by electrospray ion trap tandem mass spectrometry. The authors identified 15 different proteins common to the five samples. These proteins function in different areas such as blood transport, structural integrity, fatty acid metabolism, calcium binding, inflammation, proteinase inhibition and oxidative stress defense [40]. The group further developed their proteomics platform to allow the analysis of differences in the CVF proteome caused by normal labour and pregnancy. In a first study, the authors compared the 12-29kDa CVF proteome fraction from pregnant women at 26-30 days before labour onset, 1-3 days before onset and during natural term labour. They identified seven different proteins (cystatin-A, interleukin-1 receptor antagonist, glutathione S-transferase P, peroxiredoxin-2, thioredoxin, copper-zinc superoxide dismutase, and epidermal fatty-acid binding protein) [39]. In a follow-up study, interleukin-1 receptor antagonist (IL-1RA), was validated and shown to decrease in concentration during the course of the pregnancy. Also, the concentrations of IL-1RA were 6-fold lower in case of preterm premature rupture of membranes (see below), which indicates that IL-1RA functions in the remodeling of the fetal membranes [46]. Furthermore, the group used the same techniques to analyze the 25-45 kDa human CVF proteome fraction and compared expression profiles between pregnant women at 14-17 days, 7-10 days and 0-3 days before natural labour and during labour. The authors found temporal changes in the expression of five proteins (serpin B3, serpin B1, annexin A3, collagen α2 type IV and albumin) which correlate with natural occurring term labour [47]. Recently, the same research group showed that 1) expression of α-enolase was significantly increased during spontaneous labour at term [48] and 2) parturition results in increased oxidative stress. This last phenomenon was reflected in the CVF since the total antioxidant capacity of CVF decreased drastically during labour, largely due to a decline in copper zinc superoxide dismutase and thioredoxin-4 CVF concentrations. The combination of total antioxidant capacity of CVF and copper zinc superoxide dismutase concentration showed high specificity and sensitivity for the prediction of labour within three days [49]. These studies are good examples of how proteomics may yield important information about complex biological phenomena such as pregnancy.

Although the studies from Venkataraman et al. [38] and Di Quinzio et al. [40] were among the first to use proteomics techniques to mine the CVF proteome, their analyses were restricted to analysis of CVF proteome subfractions. In contrast, Dasari et al. [42] performed the first large comprehensive analysis of the CVF proteome. The authors used 2D (strong cation exchange (SCX) followed by RP)-HPLC or SDS-PAGE followed by RP-HPLC as sample separation methods. CVF proteins or peptides were identified using ESI-Q/TOF tandem mass spectrometry combined with several search engines. The study primarily aimed to identify CVF proteins and 150 different proteins were detected of which the majority functioned either as immune and defense-related or as metabolic proteins. The authors also compared serum and amniotic fluid protein sets with the proteins identified in the study. 77 proteins were unique to CVF, whereas 56 proteins were also found in serum and 17 in amniotic fluid. Additionally, they found that there is a significant degree of complementarity between the experimental setups as well as between the different search engines (Sequest, X!Tandem and OpenSea) used for the identification of CVF proteins. For example, only 38% of the spectra were identified by all three search engines, indicating that implementation of different search algorithms can result in a significant increase in number of identifications. Finally, the authors emphasized the need for biological and technical replicates in order to identify as many proteins as possible from the CVF proteome [42].

The same research group published the first comparative quantitative proteomics study on CVF. Pereira et al. [43] used 2D (IEF followed by SDS-PAGE) differential gel electrophoresis (2D-DIGE) to analyze differences in CVF protein expression levels between women with preterm-labour, spontaneous preterm birth patients and controls. Alternatively, they performed also 2D (SCX followed by RP) HPLC followed by ESI-Q/TOF to identify present proteins and to quantify protein expression semiquantitatively using spectral counting. During these experiments, the authors identified 205 proteins. Of those, 28 (2D-LC-MS/MS analysis) and 17 (2D-DIGE) proteins showed statistically significant changes between preterm labour patients, patients with spontaneous preterm birth and controls which can be considered as potential biomarkers [43]. This study is further discussed in more detail below.

Tang et al. [45] also performed a characterization of the CVF proteome using 2D (IEF followed by SDS-PAGE) gel electrophoresis and MALDI-TOF/TOF analysis for the identification of CVF proteins. 59 different proteins were thus identified. The authors showed the presence of polymorphonuclear cells (e.g. neutrophils and eosinophils) in CVF and demonstrated that these immune cells could be activated and secreted proteins in the lower genital tract. Additionally, they showed that CVF contains a large fraction (47%) of proteins which are also found in plasma, which could be indicative for the permeability status of genital tract mucosa. Since the permeability is often increased in case of cervicovaginal infection, these plasma proteins could eventually be used together with secreted proteins from activated neutrophils and eosinophils (e.g. lactoferrin, myeloperoxidase, human neutrophil lipocalin and eosinophilic cationic protein) as markers for cervicovaginal inflammation [45].

The proteomics study which resulted in the largest set of protein identifications was performed by Shaw et al. [44]. The authors were able to identify 685 proteins from human CVF by means of 2D (SCX followed by RP)-HPLC and SDS-PAGE followed by RP-HPLC. Tandem mass spectrometric analysis was executed using an ESI-linear ion trap after which proteins were identified and functionally classified. Due to the complementary nature of the techniques and the incorporation of several technical replicates the authors were able to identify a large number of CVF proteins. Also, a less stringent identification methodology was used in this study, which may also account for this high number. Additionally, the authors focused on the kallikrein family (i.e. family of 15 secreted serine proteases with tryptic or chymotryptic activity) and confirmed the presence of different kallikreins (kallikrein 6, 7, 10, 11, 12 and 13) in CVF by ELISA [44]. Further research on this topic by the same authors revealed that kallikreins may play a physiologically active role in the desquamation of vaginal epithelial cells and activation of cervicovaginal antimicrobial proteins. The authors also hypothesized that cervicovaginal kallikrein expression may result in pathological conditions (e.g. PPROM) [50, 51].

Klein et al. [14] performed proteome analyses on CVF samples from pregnant women who had symptoms of pending preterm birth. The authors identified 40, mainly high abundant, proteins using a shotgun proteomics approach using nanoscale RP-HPLC coupled to an ESI-ion trap mass spectrometer and showed that this method yields reproducible and reliable results. Additionally, they analyzed CVF using 2D-PAGE and demonstrated that the 2D-PAGE profiles are very similar for different samples, but that the spot intensities showed a certain degree of intersample variation.

Our group [22] also used proteomics technology to characterize the CVF proteome. During a colposcopy, the cervicovagina is rinsed with a 5% acetic acid solution in order to make HPV infected lesions visible. Normally, this washing fluid is discarded but was used by our group to perform CVF proteomics studies. Using C4-RP-HPLC fractionation on the protein level followed by C18-RP-HPLC on the peptide level, and MALDI-TOF/TOF mass spectrometry, we were able to identify 339 proteins. During these experiments, we also determined the technical variability of the platform. Three technical replicates were analyzed and 68% of the different proteins identified, were detected in all three replicates. Analysis of the chromatographic separations showed a high degree of reproducibility (coefficient of variation was 0.56%). This led to the conclusion that the largest contribution to the technical variation is made by the mass spectrometric analysis. Also, using an in-house build relational database, we compared the largest proteomics studies on CVF and determined a set of proteins which are identified in the vast majority of the studies, independent of patient physiology or used analytical methods. This overlapping protein set was the first step towards the delineation of the CVF core proteome (see below) [22].

Finally, two studies have been performed on cervical mucus [36, 37]. Because cervical mucus is part of the CVF (see above), these studies can be seen as an analysis of a subfraction of the CVF proteome. Andersch-Björkamn et al. [36] used 1D-PAGE and SDS-agarose composite gel electrophoresis to separate proteins and subsequently performed RP-nano-HPLC peptide separations. Peptides were identified using ESI-FTICR tandem mass spectrometry. The authors identified 178 proteins from cervical mucus. Furthermore, they analyzed protein and mucin composition and the mucin O-glycosylation of the cervical mucus at different time points during the menstrual cycle. The results showed that cervical mucus was similar before and after ovulation, but differed at the time of ovulation. This observation was also seen in alterations of the O-glycosylation of the mucus. The physiological significance of these changes is not yet understood [36]. Panicker et al. [37] recently performed a study on the human cervical mucus proteome. The authors used 2D-PAGE and 1D-PAGE followed by nanocapillary RP-HPLC. Tandem mass spectrometry was performed using an ESI-Q-TOF to characterize the cervical mucus proteome. Immunodepletion of albumin and immunoglobulins did not result in a significant improvement. Although the authors noticed an overlap between the different experimental setups, each platform yielded a certain amount of unique proteins, indicating the importance of using different techniques in order to maximize proteome coverage. The authors also characterized the phosphorylation and glycosylation of cervical mucus proteins and found 14 proteins which were posttranslationally modified [37].

The studies described above primarily aimed to catalogue the CVF proteome. Therefore, in what follows, some upcoming facts of the CVF proteome will be reviewed in more detail. Beside these studies, four proteomic studies on CVF have been performed which focused on the detection of differences in protein expression profiles due to HIV resistance and pending preterm birth [35, 41, 52, 53]. These quantitative studies are discussed further below.

The cervicovaginal proteome: numbers and facts

852 different proteins were identified in diverse antibody-independent proteomics studies on human CVF and cervical mucus (listed in additional file 1) [14, 22, 3638, 40, 4245]. Also, during our latest experiments we were able to identify an additional 256 CVF proteins (unpublished data), making it a total of 1108 different CVF proteins which are identified thus far (listed in additional file 2). 252 and 257 proteins were also found in respectively plasma [54, 55] and amniotic fluid [5658] (shown in additional file 2). 733 proteins from the CVF proteome were not identified in plasma or amniotic fluid.

Additionally, 223 proteins were identified in cervical mucus [36, 37]. Of those, 51 proteins were uniquely identified in cervical mucus (additional file 2) while the greater part (172 proteins) was also identified in studies on CVF [14, 22, 38, 40, 4245]. This was to be expected because cervical mucus is a subfraction of CVF. This suggests that analysis of subfractions of the CVF proteome may still yield new identifications [22].

Functional classification (Figure 1A) of the CVF proteome shows a great diversity of biological roles of which "protein metabolism and modification" and "immunity and defense" are the largest categories (respectively 17% and 10%). Classification based upon cellular localization (Figure 1B) shows that most proteins are present in the cytoplasm or in the extracellular region (respectively 21% and 20%)

Figure 1

Functional classification (A) and classification according to cellular localization (B) of all identified CVF proteins. All proteins identified in the proteomics studies listed in table 1 were classified according to several general functional and cellular localization terms.

Figure 2 gives a schematic overview of the proteome structure of the CVF. Although exceptions and deviations from this generalized scheme are most likely, it may help in explaining the difficulties encountered during CVF biomarker discovery. Roughly, a proteome consists of a plethora of proteins which are present in either higher concentrations (hence easier to identify) or lower concentrations (usually difficult to identify). The difference between the highest and the lowest protein concentration in a proteome is described as the dynamic range, which is specific for a particular sample and important to take into account when performing comprehensive proteomics studies. Indeed, the higher the dynamic range of the sample, the more difficult it is to describe its proteome as complete as possible. Implementation of different separation dimensions, optimization of the techniques and sample preparation may increase the sensitivity and resolving power of the proteomics platform, thus allowing the detection of more proteins.

Figure 2

Schematic overview of the CVF proteome composition. The CVF proteome can be roughly divided into "higher" and "lower" abundant proteins. The proteins from these categories can be further subdivided based on their function and cellular localization. The term "characteristic" is used here for proteins with a function and cellular localization which is representative for the functions of CVF. "Non-characteristic" is used for proteins which presumably do not have a measurable or known significant biological function in this body fluid. For each category, a general indication of the percentage immunological and extracellular is given. Proteins from the subcategories subsequently construct the "core" and "variable" proteome.

Because CVF is an extracellular fluid with an important role in the innate immunity, it can be expected that physiological relevant proteins are extracellular, immunological molecules. We call these "characteristic" proteins since, based on their function and localization, they represent the fundamental characteristics of CVF. Both high and low abundant CVF proteins may fulfill a physiological function in the cervicovagina. For example, we found that the most abundant protein in CVF is S100A9 [22] which forms with S100A8 the calprotectin heterodimer. Calprotectin is a potent cervicovaginal antimicrobial peptide and inhibits bacterial growth by sequestration of zinc. Its importance is also reflected in the numerous physiological and pathological processes (e.g. cervical cancer, pregnancy and labour) for which correlations have been found with differences in calprotectin expression levels [59]. Examples of low abundant physiological relevant proteins are β-defensins or histones. In response to infectious stimuli, histones can create neutrophil extracellular traps (NET) (i.e. protrusions made of a DNA backbone in which histones, proteases and antimicrobial peptides/proteins are embedded) which are efficient in trapping and eliminating pathogens [6062].

However, a substantial fraction of high and low abundant proteins may also originate from aberrant processes. For instance, sampling of CVF may cause disruption of the epithelial cell layer (e.g. when using brushes or tampons) and the continuous shedding of the epithelial cell layer may also bring in dead cells. These processes introduce many intracellular proteins (e.g. metabolic or cytoskeletal proteins) in the CVF which have no significant biological function in this body fluid. We therefore call these proteins "non-characteristic" since their function and localization do not match the CVF related characteristics.

In general, it can be assumed that due to the extracellular nature of the CVF and its importance in the innate immunity, the characteristic proteins will contain larger amounts of extracellular and immunological fractions as compared to the non-characteristic proteins.

Another part of the CVF is made of non-characteristic low abundant proteins of which the concentration shows large intra- and interindividual variations. These concern often intracellular proteins which take part in metabolic processes (e.g. glycolysis).

Although the higher abundant characteristic proteins may contain very interesting biomarkers, potential biomarkers usually reside in the lower abundant part of the proteome. Therefore, one of the main challenges is the isolation of the biomarker from the numerous surrounding background proteins. How can these two be distinguished? Since background proteins are usually sporadically and randomly detected, they show no correlation with a particular physiological or pathological condition. In contrast, there must be a clear and significant association between alterations in the concentration of a biomarker and a specific condition or pathology.

Taking all of the above into account, the CVF proteome can roughly be divided into two subfractions: the core and the variable proteome. The core proteome consists of proteins which are identified in most proteomics studies and therefore mainly represents high abundant (characteristic and non-characteristic) proteins, although characteristic low abundant proteins are also present. Hence, we expect the fraction of characteristic proteins to be high in the core proteome. We recently described a set of 136 proteins which are frequently identified, independent of patient physiology or used analytical methods. This subproteome has an increased extracellular immunological protein fraction [22], indicating that it is more relevant for CVF (as compared to the complete set of overlapping and non-overlapping proteins). It can therefore be considered as a first version of the core proteome. In contrast, the variable proteome contains proteins which are infrequently identified and often represent non-characteristic low abundant proteins. Therefore, the extracellular and immunological fraction is decreased in this part.

This subdivision may explain some dissimilarities seen between the different proteomics studies on CVF. The fractions of immunological and extracellular proteins are lower in the 'larger' studies of Shaw et al. [44] and Zegels et al. [22] (respectively 11.72% and 12.57%) as compared to the 'smaller' studies from Pereira et al. [43] and Dasari et al. [42] (respectively 18.95% and 19.61%). The same holds for the extracellular fraction [22]. In a recent experiment, we identified 706 proteins from which 267 proteins were new identifications (unpublished results). Only 5.33% of these proteins fell into the "immunity and defense" functional category, which is much lower than was found in any other study (see above).

A general trend is noticeable in proteomic analysis of CVF towards the identification of increasing quantities of different proteins using comprehensive techniques. This tendency was facilitated by the drastic technological evolution which proteomics has undergone the last 5 to 10 years. The development of sensitive, high resolution mass spectrometers with fast scan rates allows the coverage of dynamic ranges up to six orders of magnitude. The dynamic range of the experimental setup was further enhanced by improving HPLC separation prior to mass spectrometric analysis (e.g. increased sensitivity and reproducibility and use of columns with higher peak capacity). Moreover, progress has been made in multidimensional separations in order to further increase the peak capacity. All together, the technological developments in proteomics resulted in analytical platforms which can now cover larger parts of the proteome [63]. This is also reflected in the studies on the CVF proteome where increasing numbers of proteins were identified with the contemporary techniques (Figure 3).

Figure 3

Comparison of the total number of proteins versus the number of extracellular and immunological proteins. For each study listed in table 1 the number of extracellular (red line), immunological (green line) proteins and total number of identifications (blue line) were determined. The obtained results were plotted progressively from the study with the lowest number of identifications to the study with highest number of identifications.

It is the common opinion that by digging deeper into the CVF proteome, more potential biomarkers could be isolated and multi-protein expression profiles can thus be analyzed. With this reasoning however, one must take into account some inherent disadvantages of large proteomic studies. In Figure 3, the total number of proteins identified and the numbers of immunological or extracellular proteins (which are characteristic for the CVF proteome) in the different studies are plotted. From this, it is clear that while the total number of identified proteins increases exponentially, the immunological and extracellular fraction only increases linearly. This graph indicates therefore that the majority of newly obtained identifications have no known function or their function in the CVF is questionable or not biologically relevant. We therefore hypothesize that although large number of proteins are identified in the recent sensitive large proteomics studies, the vast majority of newly identified proteins are most likely part of the variable proteome which consist mainly of non-characteristic proteins. Thus by digging deeper into the CVF proteome, the challenge of identifying specific biomarkers among this large amount of non-characteristic proteins increases significantly. Optimization of sample collection methods to minimize cell lysis may be a step forward to reduce the amount of non-characteristic proteins. On the other hand, epithelial cell shedding is an essential process in the removal of pathogens and many other factors (e.g. hormonal regulation, non-oral contraceptives) will always cause cell disruption and lysis, prior to sample collection.

Cervicovaginal fluid biomarkers with potential clinical applications

The lower genital tract is exposed to a large microbial pressure and forms an important entry gate for a wide variety of pathogens such as Candida spp., Trichomonas vaginalis, human immunodeficiency virus (HIV) and human papillomavirus (HPV). In contrast, the upper female genital tract is believed to be sterile under normal circumstances. Nevertheless, pathogens such as Neisseria gonorrhoeae and Chlamydia trachomatis can occasionally ascend from the lower to the higher genital tract and can cause pathogenic conditions such as pelvic inflammatory disease (PID). The clinical impact of these infections may not be underestimated since they can lead to serious conditions such as preterm birth, increased susceptibility to sexually transmitted infections (STI), infertility and cancer [1, 2]. Therefore, early diagnosis is essential for the successful treatment of the underlying pathology and to prevent irreversible complications. For this reason, there is a high demand for new diagnostic tools, such as biomarkers, which allow for better and earlier treatment of gynecological pathologies [17].

In the following paragraphs some important infection-correlated pathologies are discussed for which potential biomarkers were discovered using human CVF samples. Table 2 gives an overview of these potential biomarkers.

Table 2 Overview of potential CVF protein/peptide biomarkers for different diseases, conditions or statuses of the female genital tract.

Human papillomavirus infection and cervical cancer

HPV is one of the most common sexually transmitted infections. It is estimated that 70-80% of the sexually active female population will acquire HPV before the age of 50 [64] and 80% of the HPV-infected patients will spontaneously clear the virus within 2 years [65, 66]. HPV is associated with high morbidity and mortality worldwide, since it can cause precancerous lesions and cervical cancer. The latter is the second most observed cancer in women and is caused for nearly 100% by HPV-infection [67]. There are more than 150 HPV types and almost 40 of them infect the anogenital region. HPV can be divided in low and high risk HPV. The most common low-risk types are type 6 and 11. The most common high-risk types are 16, 18, 45, 31 and 33). Cervical cancer is mainly caused by an infection with a high risk type [68]. Infection of squamous or glandular epithelial cells with HPV causes morphological alterations which lead to the formation of squamous or glandular intra-epithelial lesions. The severity of this precancerous condition depends on the extent, the site as well as on the depth of the infected epithelial layer [67, 69].

Because of the high prevalence, the severe consequences and the lack of a good treatment of advanced cervical carcinoma, the emphasis of managing this condition lies on prevention and diagnosis in a stadium as early as possible. The recently introduced prophylactic HPV vaccination will prevent about 98% of infections with HPV 16 and 18 but will also in part, due to so called cross protection, prevent HPV 31 (89%), HPV 33 (82%), HPV 45 (100%) and any high risk HPV type 16/18/31/33/35/39/45/51/52/56/58/59/66/68 (70%). As such, the HPV vaccination will prevent about 70 - 80% of all cervical cancers [70, 71]. Recently, clinical trials started with a nine-valent HPV vaccine which could potentially prevent 90% of all cervical cancers but information on this topic is still scarce. Therefore, it is still needed to screen women between the ages of 25 and 65 on a frequent basis by means of a Pap test (liquid based cytology) preferable with a type specific HPV testing. Screening using the classical Pap test has led to a considerable reduction of the mortality, yet there are still some notable shortcomings. The current screening is (1) labour intensive, (2) difficult to automatize, (3) vulnerable to inter- and intra-observer variability and (4) is not very sensitive [65, 72, 73]. Moreover research has demonstrated that despite intensive screening campaigns, the coverage in most Western countries stays low (e.g. 59% in Belgium) [74]. Testing for the presence of HPV DNA or RNA will increase the sensitivity, but it cannot predict progression to intra-epithelial lesions and eventually cervical cancer.

Taking all the above into account, this implicates that alternative screening methods (e.g. based upon protein biomarkers present in CVF) with both a higher sensitivity and specificity are needed. The identification of a highly sensitive and specific protein biomarker from CVF would mean a substantial improvement both on medical and social-economical field because an accurate biomarker for the detection of cervical neoplasia will result undoubtedly in a more precise and efficient screening which correlates with a further reduction of precancerous morbidity and cervical cancer morbidity and mortality [75]. Additionally, proteomic analysis of CVF could lead to biomarkers for the evaluation of patients after treatment [76] or to prognostic biomarkers which could predict the evolution from precancerous low-grade squamous intra-epithelial lesions (LSIL) to either high-grade squamous intra-epithelial lesions (HSIL) or clearance of the lesions. These prognostic biomarkers would benefit the screening method as the number of follow-up patients could be reduced significantly (up to 80%). Also, identification of proteins involved in the clearance of the virus could be of major importance for the future development of new antiviral therapies.

However, only few studies are performed on human CVF for the identification of such biomarkers as most large proteomic studies were restricted to cervical cancer tissue [7782]. Studies which used CVF as samples for their analysis did not use antibody-independent proteomics techniques, but mainly ELISA or Western blotting, and were therefore restricted to the analysis of a small number of proteins taking part in the immune response against HPV. In table 1, several proteins are listed which are associated with HPV infection, cervical cancer and cancer prognosis and progression. Nevertheless, these proteins are primarily correlated with the prognosis of the disease but do not allow for a sensitive and/or specific prediction of healthy women which are at high risk. It is our opinion that such potential biomarkers have the highest chance to be discovered in a proteome wide approach, using proteomics techniques on a large number of samples.


More than 25 million people have already died because of HIV since the discovery of the virus in 1981, making it one of the most disastrous epidemics in human history. In 2008, 33.4 million people were living with HIV and 2.7 million people got infected by the virus [83, 84], mainly due to heterosexual HIV transmission [8486].

One of the largest problems remains the absence of an effective therapy. Also, despite the enormous efforts made, the development of a prophylactic AIDS-vaccine will likely not be available in the recent years to come [87]. This leads to an increased importance for microbicides (i.e. chemical entities that can prevent or reduce transmission infections) which can be applied locally (e.g. vaginally or rectally) as a potential alternative approach in HIV prevention [9, 88, 89]. However, the efficacy trials often gave rise to unsatisfactory results like enhanced HIV-transmission, toxicity and immune activation [41, 90]. To overcome these problems, new powerful microbicides need to be identified which preferentially occur naturally and act under these in vivo circumstances against sexually transmitted HIV infections [41].

Analysis of samples obtained from exposed seronegative individuals (ESNs) may yield such new, therapeutically relevant microbicides. ESNs are individuals (<5% of the population) from high-risk cohorts that remain IgG-seronegative despite frequent HIV-exposure [91] and therefore show a certain degree of in vivo HIV-resistance. ESNs are found between commercial sex workers, hemophiliacs receiving HIV-contaminated blood, healthcare workers, children from HIV-infected mothers, intravenous drug users or they are the seronegative partner in a discordant couple [9294].

Some biological factors are already associated with the ESN-status. Those include innate host factors like mutations of chemokine receptors (e.g. CCR5-Δ32), the upregulation of chemokines such as RANTES, MIP-1α and -1β due to genetic polymorphisms, specific human leukocyte antigen haplotypes (e.g. HLA-B*27 and HLA-B*57), natural killer cell activity regulated by killer Ig like receptor/HLA interaction, the presence of autoantibodies and/or alloantibodies and more efficient immunological effects after toll like receptor stimulation. Also, particular properties of the acquired immune system of the host show correlation with the ESN-status such as cytotoxic and helper T lymphocyte responses against HIV epitopes, humoral immune responses with the production of neutralizing anti-HIV antibodies and the presence of soluble inhibitory factors [9299].

Generally, none of these associations alone can be held fully responsible for the observed HIV-resistance, and many unknown factors are yet to be discovered [41]. Current studies speculate that ESNs inhibit HIV-infection in a very early phase of viral transmission [100, 101]. HIV-resistance may for example result from mechanisms present at the viral entry gate, which often is the mucosa of the female genital tract. Among these mechanism, proteins or peptides present in CVF of ESNs may play an important role [41].

Several studies on female ESNs have used CVF as choice of sample for analysis. It has been described that cervicovaginal lavages from ESNs sometimes contain gp41 and p24 HIV antigens, suggesting that there was an HIV exposure, but no seroconversion could be detected [102]. Anti-HIV IgA and IgG antibodies were detected in CVF obtained from heterosexual ESN women [103113]. Additionally, it has been shown that levels of the HIV suppressive β-chemokine RANTES levels were significantly higher in CVF from ESNs [100, 114]. Moreover, CVF contains proteins/peptides with intrinsic anti-HIV activity such as defensins, lactoferrin, lysozyme, cathelicidin and SLPI [22, 92, 115, 116]. The proteomic study from Venkataraman et al. on CVF showed that the cationic fraction of CVF has natural anti-HIV activity. The authors speculated that this activity is the result from a complex synergism between different proteins in CVF [38]. It is possible that differences in protein expression patterns of those anti-HIV proteins/peptides may result in the observed HIV-resistance of some ESN cohorts. However, conventional techniques such as ELISA or Western blotting are incapable to analyze complex differential patterns from multiple proteins. Therefore, proteomics studies may aid in the elucidation of these factors.

Indeed, recently, Burgener et al. [35] studied HIV-resistance in ESN persons and used 2D-DIGE as a quantitative proteomics technique to compare protein expression patterns from ESNs with those from control groups. The authors identified 16 differentially expressed proteins with different biological functionalities such as protease inhibitors (e.g. serpin B3/B4/B13/B1 and cystatin A) and proteins with an immunological role (e.g. protein S100A7 and complement component 3). Iqbal et al. [41] used surface enhanced laser desorption ionization (SELDI)-TOF mass spectrometry for the analysis of the CVF proteome of a large population of ESNs and control groups. The authors demonstrated that elafin/trappin-2 is significantly upregulated in ESNs [41]. Elafin/trappin-2 is an antiprotease from the whey acidic protein (WAP) family, of which SLPI is also a member, that can be found in CVF (see above). It functions as an anti-inflammatory protein by counteracting proteases secreted by neutrophils. Due to the cationic nature of elafin/trappin-2 it can destabilize bacterial cell walls or viral envelopes, thus exerting a certain degree of antimicrobial activity [117]. At about the same time, elafin/trappin-2 was identified as a new anti-HIV factor which directly interacted with the virus for its inhibition, thus making it an interesting protein for further investigation [118]. Hence, both studies confirm each other, thereby increasing the possibility that elafin/trappin-2 effectively plays a significant role in HIV-resistance. Surprisingly, this protein was not among the differentially expressed proteins in the study of Burgener et al. [35] where a different technique was used (2D-PAGE vs. SELDI). These results indicate once again that different proteomics techniques complement each other and are necessary to uncover further other CVF proteins correlating with in vivo HIV-resistance. Moreover the use of samples obtained from dissimilar ethnological cohorts may yield different results as it becomes more evident that several different mechanisms may lead to HIV-resistance.

Bacterial vaginosis

Bacterial vaginosis (BV) is a frequently observed condition by which the normal vaginal flora is disrupted. The prevalence of this pathology in Europe is 5-30%. Other geographical regions, especially those with a lower socio-economic status like the sub-Saharan part of Africa, show higher prevalences (over 50%) [119121].

BV is a non-inflammatory condition, although it is often classified as a vaginitis, characterized by the loss of hydrogen peroxide forming Lactobacillus spp. (e.g. L. crispatus, L. acidophilus, L. rhamnosus) and by an increased growth of anaerobic species (e.g. Atopobium vaginae, Mobiluncus spp., Prevotella spp.). Also, whereas a healthy vaginal flora contains only few different and predominantly Lactobacillus species, the vaginal flora correlated with BV is polymicrobial in nature [119]. The question remains whether the loss of the Lactobacillus spp. is due to the introduction of a yet unknown factor which results in the passive growth of the anaerobic species or, vice versa, whether there is a massive influx of anaerobic species which eradicate the lactobacilli [122, 123]. Although BV is not classified as a sexually transmitted infection, the condition has several features indicating that it may be caused by an exogenous source [124]. Nevertheless, the etiology of BV is not yet well understood.

BV can lead to severe health problems and early diagnosis can prevent serious complications. A significant correlation exists between the presence of BV and preterm birth and it has been shown that treatment of BV may reduce the risk on preterm birth [119, 120, 124, 125]. BV has also been linked with PID, miscarriage [124] and infertility [126] increased susceptibility for HIV and HPV [124, 127132], herpes simplex virus type 2 [133135] and other lower genital tract pathogens [120].

The mechanism by which BV leads to those pathological conditions is not well known. It has been suggested that increased susceptibility to HIV and genital tract infections may be attributed to the production of succinate by anaerobes, which can inhibit neutrophilic function [136]. Also, some anaerobes produce sialydases which effect immune cells (e.g. inhibition of phagocytes) [137] and have mucinase activity [138]. Additionally, Ureaplasma urealyticum can produce proteases which can cleave IgA [139]. It has also been shown that CVF from some seropositive and seronegative women with BV has the capability to enhance HIV replication in vitro. This activity has been attributed to a certain soluble and protease sensitive HIV-inducing factor (HIF) which has not been identified yet. The general opinion hypothesizes that HIF is a mixture of products derived from BV associated bacteria on the one hand and host factors such as certain cytokines (e.g. myeloid-related protein 8) on the other hand [140, 141].

No large proteomics studies have been performed on CVF from women with BV, but several ELISA experiments were executed. The results show that BV CVF samples are deficient in antimicrobial activity due to lower expression levels of several antimicrobial peptides such as SLPI, defensins, lysozyme and lactoferrin [142147]. Furthermore, many studies also detected differences in expression levels of some proinflammatory cytokines correlated with BV (see table 1). These studies suggest that BV leads to the disruption of the normal innate and adaptive immune response and induces an inflammatory environment in the lower genital tract which can lead to increased susceptibility for HIV and other genital tract infections [120, 128, 148]. However, since only antibody dependent studies were performed, analysis was limited to a few well characterized proteins. Nevertheless, little is known about expression differences of multiple proteins, which may include new or unexpected ones. Differential analysis of the CVF proteome using proteomics approaches, may yield more information about the perturbation of the female genital tract immunity due to BV and further clarify the etiology of the disease.

Preterm Birth

As mentioned before, conditions such as bacterial vaginosis and other infections of the female genital tract are the main cause of preterm birth. Other risk factors include multiple pregnancy or uterine contractions of cervical incompetence. Nevertheless, a significant part of the observed preterm births are idiopathic, indicating that the etiology of preterm birth is still not well understood [149151].

Preterm birth, defined by the WHO as birth occurring before 37 completed weeks of gestation, is a major cause of neonatal morbidity and mortality [152, 153]. Recent reports estimate the incidence of preterm birth 9.6% worldwide, with the highest rates in Africa and North America (respectively 11.9% and 10.6%) and the lowest in Europe (6.2%). The impact of preterm birth on the health of the neonate can be very severe on short-term (e.g. respiratory stress syndrome, brain injury) as well as on long-term (e.g. chronic lung disease, sensory deficits, learning disabilities) thus affecting the child's development and the physical and psychological health of the infant's environment [153155].

Preterm birth is difficult to treat, therapy is often inefficient because the underlying cause is seldom determined and the diagnosis is frequently made too late [154, 156159].

Due to the clinical importance of preterm birth, prevention strategies need more attention in the management of this condition [157, 160]. Currently this is achieved by assessing the risk of the occurrence of preterm birth using several parameters or markers. History of preceding preterm birth is the most important risk factor and may relate to cervical anatomical problems. In addition, presence of any of the causal factors mentioned before drastically increases the chance on preterm birth [149, 161]. CVF fetal fibronectin (FFN) concentrations are also correlated with the risk for preterm labour/birth, but quantification of CVF FFN alone has not enough discriminatory power [162168]. Combination of this test with cervical ultrasound measurements however showed better correlations [166, 169172]. Several other markers have been correlated with the risk on preterm birth/labour, all of which are summed in table 1. Nevertheless, low specificity and precision are inherent to these primary predictors.

Therefore, new markers need to be identified which allow for more precise and specific risk assessment [149, 157]. Besides, these markers may give additional information about the etiology and/or pathophysiology of this condition which can be used in the development of alternative therapies [43].

As mentioned before, Pereira et al. [43] identified potential biomarkers for preterm birth/labour in a proteomics study. These included fibronectin, several S100 proteins (calgranulin A, B and C), acute-phase reaction proteins (annexin A3, α-1-antitrypsin and α-1-acid glycoprotein) and proteins involved in cellular organization and motility (e.g. profilin-1, thymosin β-4, rho GDI 2).

Recently, the same research group looked for biomarkers for intra-amniotic infection, which is an important risk factor for preterm birth and may lead to severe neonatal pathologies. 170 CVF samples were classified in three different groups: intra-amniotic infection and preterm birth; no intra-amniotic but preterm birth; no intra-amniotic infection and preterm labour. The samples underwent 2D-LC (SCX/RP) combined with ESI-QTOF mass spectrometry. Quantification and comparison of protein expression levels between the three groups was achieved by spectral counting. The authors found 26 potential biomarkers for intra-amniotic infection (statistical significant expression differences), which included acute phase reactants (e.g. α1-antitrypsin), immune modulators (e.g. lysozyme and α-defensin), amniotic fluid proteins (e.g. haptoglobin) and extracellular matrix-signaling proteins (e.g fibronectin). 13 of those were confirmed as potential biomarkers for intra-amniotic infection in immunoassays performed on the 170 samples. Using four of these proteins (α1-acid-glycoprotein, insulin-like growth factor binding protein-1, calgranulin C and cystatin A) in a logistic regression model, a fairly accurate differentiation between patients with intra-amniotic infection and those without intra-amniotic infection could be obtained. Although further clinical validation of these potential biomarkers is required, these results may lead to the development of new diagnostic assays for intra-amniotic infection. Also, the authors showed that the etiology of preterm birth caused by intra-amniotic infection is different than preterm birth without infection [52].

Shah et al. [53] used the stable isotope labeling by amino acids in cell culture (SILAC) technique to label proteins from an endocervical and vaginal cell-line (respectively End1 and Vk2). After the labeling process, a labeled cellular secretome (i.e. a "standard secretome") from vaginal and endocervical cells could be purified. CVF proteins can then be relatively quantified in test samples in a precise and accurate way by means of this "standard secretome" and multiple reaction monitoring (MRM). This technique is called stable isotope labeling proteome (SILAP). Normally, MRM requires the addition of labeled synthetic proteotypic peptides, which are often very expensive, but the use of SILAP has the advantage that the labeled "standard secretome" can be used instead. Nevertheless, since the concentration of the peptides in the "standard secretome" is not accurately known, it is not possible to absolutely quantify proteins in the test samples. A set of 15 candidate CVF biomarkers were thus relatively quantified and expression profiles were compared between healthy controls and preterm birth cases. This resulted in three proteins which were significantly upregulated (desmoplakin isoform 1, stratifin and thrombospondin), but the possible role of these proteins in the pathogenesis of preterm birth is not yet known [53].

Preterm premature rupture of membranes

Many studies have been performed on preterm premature rupture of membranes (PPROM) which is an important risk factor of preterm birth (see above). PPROM is defined as "rupture of the fetal membranes prior to onset of labour in a patient who has a gestational age of less than 37 weeks" and has an incidence of 3-5% of all pregnancies [173178]. A number of CVF proteins and peptides are associated with this condition and are listed in table 1. Also other non-protein markers are associated with PPROM such as lactate [173, 179, 180], urea [181] and creatinine [181183]. However, no differential proteomics studies have been performed focusing on this condition. Nevertheless, it can be expected that, as is the case with the preterm birth in general, the use of proteomic technology may result in new insights in the etiology of PPROM and the isolation of clinical applicable biomarkers.

Amnisure® International LLC (Cambridge, MA) has developed the FDA approved AmniSure® ROM test for the diagnosis of PPROM. This simple immunoassay identifies placental α-microglobulin-1 which is abundant in amniotic fluid (2000-25000 ng/ml), but almost undetectable in cervicovaginal secretions (5-25 ng/ml). Only in the case of PPROM, higher concentrations are detectable in CVF. Analysis of clinical application possibilities of the test has shown a sensitivity of 99% and a specificity of 88-100% [184, 185]. This test is a good example of the potential of CVF samples as biomarker sources and their applicability in clinical diagnosis. Although the test is still under investigation, the clinical use of such non-invasive test may eventually replace traditional diagnostic methods [177].

Classical approaches versus new techniques

The vast majority of potential CVF biomarkers listed in table 1 was identified and validated using antibody-dependent classical approaches such as Western blotting or ELISA. Only the last few years, the use of proteomics as a new alternative technique for biomarker discovery has gained more interest due to the evolution of techniques such as chromatography and mass spectrometry from low reproducible and highly specialized methods to more user friendly, robust systems. The use of comprehensive proteome characterization and analysis has the great advantage that a huge amount of proteins (at least those which fall in the dynamic range of the applied techniques) are under consideration as potential biomarkers. Other techniques such as ELISA require the use of antibodies, which may restrict investigation to those biomarkers for which antibodies are available and purchased. Additionally, antibody-based techniques are sometimes incapable of discriminating between different subtle protein forms, whereas mass spectrometry can solve this problem. For example, it has been published that two α-defensins (HNP 1 and 2) are very sensitive and specific biomarkers for intra-amniotic infection. However, using ELISA, it is not possible to discriminate between HNP1 and 2 due to high sequence similarity. In contrast, due to the mass difference between HNP1 and 2, mass spectrometry can differentiate between both forms [186, 187].

Nevertheless, this does not imply that proteomics is superior to antibody dependent methods in biomarker discovery experiments and makes them completely obsolete. Indeed, antibody-based validation is often required to confirm proteomics results. Also, as can be seen in table 1, a large part of the biomarkers identified in non-proteomics studies, are not identified in any of the CVF proteome characterization studies mentioned above. Therefore, techniques such as Western blotting or ELISA are rather complementary then inferior to proteomics.


CVF has great potential as a source for biomarkers for pathologies of the female genital tract. Many details about the complex physiology of the female genital tract, even under normal circumstances, are yet unknown and only few proteomics studies have been executed on this topic [39, 46, 47]. Nevertheless, CVF may yield a wealth of information about the "normal" functioning of the female genital tract and proteomic analysis may improve our understanding of micro-environmental changes due to the influence of multifaceted conditions such as menstrual cycle, age or pregnancy.

Despite the progress made in the discovery phase (table 1), not many of the potential biomarkers find their way towards a clinical application. Only the AmniSure® ROM test for the diagnosis of PPROM, based upon the detection of placental α-microglobulin-1, has been further validated in clinical trials and is FDA approved. The problem in the validation of biomarkers and the subsequent development of biomarker based diagnostic tests, often lies in the transition from discovery to validation phase. Frequently, too little effort has been made in setting up statistical relevant experiments and important aspects such as sample size, inter- and intraindividual and technical variabilities are often underestimated or not well defined. This inevitably leads to results which lack statistical power and to potential biomarkers which generally do not survive further validation [153, 178, 188, 189]. In order to obtain statistically significant and practically useable highly specific and sensitive CVF biomarkers, several steps must be undertaken. 1) It is essential to analyze and characterize the CVF proteome precisely to be able to define the physiological environment in which experiments are conducted [18]. 2) Also, the inter- and intraindividual variability must be determined, which is critical in order to determine the number of samples needed to obtain statistically relevant results and to distinguish real biomarkers from natural occurring biophysiological variations. So far, although quantitative and comparative experiments were performed on CVF, not much attention has been given to the contribution of biological variation [16, 24]. A good understanding of the effect of factors affecting the proteome composition (e.g. age, use of contraceptives, menstrual cycle, etc.) is therefore essential and should be taken into account during sample collection. Additionally, the choice of control groups in the experimental setup can have a drastic effect on the eventual results. For example, some markers (e.g. fibronectin, C-reactive protein, prolactin) for preterm birth are often found in preterm premature rupture of membranes (table 1). Nevertheless, markers for preterm birth which were found to be highly specific, may be in fact low specific if PPROM patients were included in the control group.

Up to now, most studies using CVF as clinical samples, used antibody dependent techniques such as ELISA and Western blotting, although these are only capable to provide very limited amounts of information so that important data are inevitably missed. Nevertheless, many of these studies improved our understanding of different pathologies and conditions. However, the use of proteomic technology has the advantage to analyze many different proteins at the same time and can yield large amounts of information. We strongly believe that quantitative and comparative proteomic analysis of the CVF proteome will yield new complementary insights and information about the biophysiological nature of the female genital tract which may lead to further progression in our understanding of gynecological pathologies. All together, this may result in a better management of these diseases and thus, hopefully, will reduce associated morbidity and mortality for women, mothers and children.



2D differential gel electrophoresis


bacterial vaginosis


cervicovaginal fluid


electrospray ionization


exposed seronegative individuals


fetal fibronectin


fourier transform ion cyclotron resonance


HIV-inducing factor


human immunodeficiency virus


human leukocyte antigen


human neutrophil peptide


human papillomavirus


high-grade squamous intra-epithelial lesions


isoelectric focusing


interleukin-1 receptor antagonist


liquid chromatography


low-grade squamous intra-epithelial lesions


matrix assisted laser desorption ionization


macrophage inhibitory protein


multiple reaction monitoring


mass spectrometry


not determined


neutrophil extracellular traps


polyacrylamide gel electrophoresis


pelvic inflammatory disease


preterm premature rupture of membranes




regulated on activation, normal T-cell expressed and secreted


reversed phase


strong cation exchange


surface enhanced laser desorption ionization


stable isotope labeling by amino acids in cell culture


stable isotope labeling proteome


secretory leukocyte protease inhibitor


sexually transmitted infection




whey acidic protein


  1. 1.

    Quayle AJ: The innate and early immune response to pathogen challenge in the female genital tract and the pivotal role of epithelial cells. J Reprod Immunol 2002, 57: 61–79. 10.1016/S0165-0378(02)00019-0

    CAS  PubMed  Google Scholar 

  2. 2.

    Cole AM: Innate host defense of human vaginal and cervical mucosae. Curr Top Microbiol Immunol 2006, 306: 199–230. full_text

    CAS  PubMed  Google Scholar 

  3. 3.

    Iijima N, Thompson JM, Iwasaki A: Dendritic cells and macrophages in the genitourinary tract. Mucosal Immunol 2008, 1: 451–459. 10.1038/mi.2008.57

    CAS  PubMed Central  PubMed  Google Scholar 

  4. 4.

    Ochiel DO, Fahey JV, Ghosh M, Haddad SN, Wira CR: Innate Immunity in the Female Reproductive Tract: Role of Sex Hormones in Regulating Uterine Epithelial Cell Protection Against Pathogens. Curr Womens Health Rev 2008, 4: 102–117. 10.2174/157340408784246395

    CAS  PubMed Central  PubMed  Google Scholar 

  5. 5.

    Eschenbach DA, Thwin SS, Patton DL, Hooton TM, Stapleton AE, Agnew K, Winter C, Meier A, Stamm WE: Influence of the normal menstrual cycle on vaginal tissue, discharge, and microflora. Clin Infect Dis 2000, 30: 901–907. 10.1086/313818

    CAS  PubMed  Google Scholar 

  6. 6.

    Kistner RW: Physiology of the vagina. In The human vagina. Edited by: Hafez ES, Evans TN. Amsterdam: Elsevier/North-Holland Biomedical Press; 1978:109–120.

    Google Scholar 

  7. 7.

    Valore EV, Park CH, Igreti SL, Ganz T: Antimicrobial components of vaginal fluid. Am J Obstet Gynecol 2002, 187: 561–568. 10.1067/mob.2002.125280

    CAS  PubMed  Google Scholar 

  8. 8.

    Wagner G, Levin RJ: Vaginal fluid. In The human vagina. Volume 2. Edited by: Hafez ES, Evans TN. Amsterdam: Elsevier/North-Holland Biomedical Press; 1978:121–137.

    Google Scholar 

  9. 9.

    Rohan LC, Sassi AB: Vaginal drug delivery systems for HIV prevention. AAPS J 2009, 11: 78–87. 10.1208/s12248-009-9082-7

    CAS  PubMed Central  PubMed  Google Scholar 

  10. 10.

    Hillier SL: Normal vaginal flora. In Sexually transmitted diseases. 3rd edition. Edited by: Holmes KK, Sparling PF, Mardh P, Lemon SM, Stamm WE, Piot P et al. McGraw-Hill, New York; 1999:191–204.

    Google Scholar 

  11. 11.

    Stanek R, Gain RE, Glover DD, Larsen B: High performance ion exclusion chromatographic characterization of the vaginal organic acids in women with bacterial vaginosis. Biomed Chromatogr 1992, 6: 231–235. 10.1002/bmc.1130060506

    CAS  PubMed  Google Scholar 

  12. 12.

    Wira CR, Grant-Tschudy KS, Crane-Godreau MA: Epithelial cells in the female reproductive tract: a central role as sentinels of immune protection. Am J Reprod Immunol 2005, 53: 65–76. 10.1111/j.1600-0897.2004.00248.x

    CAS  PubMed  Google Scholar 

  13. 13.

    Huggins GR, Preti G: Vaginal odors and secretions. Clin Obstet Gynecol 1981, 24: 355–377. 10.1097/00003081-198106000-00005

    CAS  PubMed  Google Scholar 

  14. 14.

    Klein LL, Jonscher KR, Heerwagen MJ, Gibbs RS, McManaman JL: Shotgun proteomic analysis of vaginal fluid from women in late pregnancy. Reprod Sci 2008, 15: 263–273. 10.1177/1933719107311189

    CAS  PubMed  Google Scholar 

  15. 15.

    Thomas PD, Campbell MJ, Kejariwal A, Mi H, Karlak B, Daverman R, Diemer K, Muruganujan A, Narechania A: PANTHER: a library of protein families and subfamilies indexed by function. Genome Res 2003, 13: 2129–2141. 10.1101/gr.772403

    CAS  PubMed Central  PubMed  Google Scholar 

  16. 16.

    Good DM, Thongboonkerd V, Novak J, Bascands JL, Schanstra JP, Coon JJ, Dominiczak A, Mischak H: Body fluid proteomics for biomarker discovery: lessons from the past hold the key to success in the future. J Proteome Res 2007, 6: 4549–4555. 10.1021/pr070529w

    CAS  PubMed  Google Scholar 

  17. 17.

    Veenstra TD, Conrads TP, Hood BL, Avellino AM, Ellenbogen RG, Morrison RS: Biomarkers: mining the biofluid proteome. Mol Cell Proteomics 2005, 4: 409–418. 10.1074/mcp.M500006-MCP200

    CAS  PubMed  Google Scholar 

  18. 18.

    Hu S, Loo JA, Wong DT: Human body fluid proteome analysis. Proteomics 2006, 6: 6326–6353. 10.1002/pmic.200600284

    CAS  PubMed Central  PubMed  Google Scholar 

  19. 19.

    Snowhite IV, Jones WE, Dumestre J, Dunlap K, Braly PS, Hagensee ME: Comparative analysis of methods for collection and measurement of cytokines and immunoglobulins in cervical and vaginal secretions of HIV and HPV infected women. J Immunol Methods 2002, 263: 85–95. 10.1016/S0022-1759(02)00038-8

    CAS  PubMed  Google Scholar 

  20. 20.

    Andreoletti L, Gresenguet G, Chomont N, Matta M, Quiniou Y, Si-Mohamed A, Belec L: Comparison of washing and swabbing procedures for collecting genital fluids to assess shedding of human immunodeficiency virus type 1 (HIV-1) RNA in asymptomatic HIV-1-infected women. J Clin Microbiol 2003, 41: 449–452. 10.1128/JCM.41.1.449-452.2003

    PubMed Central  PubMed  Google Scholar 

  21. 21.

    Schmidt A, Aebersold R: High-accuracy proteome maps of human body fluids. Genome Biol 2006, 7: 242. 10.1186/gb-2006-7-11-242

    PubMed Central  PubMed  Google Scholar 

  22. 22.

    Zegels G, Van Raemdonck GA, Coen EP, Tjalma WA, Van Ostade XW: Comprehensive proteomic analysis of human cervical-vaginal fluid using colposcopy samples. Proteome Sci 2009, 7: 17. 10.1186/1477-5956-7-17

    PubMed Central  PubMed  Google Scholar 

  23. 23.

    Huggins GR, Preti G: Vaginal odors and secretions. Clin Obstet Gynecol 1981, 24: 355–377. 10.1097/00003081-198106000-00005

    CAS  PubMed  Google Scholar 

  24. 24.

    Schrader M, Selle H: The process chain for peptidomic biomarker discovery. Dis Markers 2006, 22: 27–37.

    CAS  PubMed Central  PubMed  Google Scholar 

  25. 25.

    Imai T, Kurachi H, Adachi K, Adachi H, Yoshimoto Y, Homma H, Tadokoro C, Takeda S, Yamaguchi M, Sakata M, et al.: Changes in epidermal growth factor receptor and the levels of its ligands during menstrual cycle in human endometrium. Biol Reprod 1995, 52: 928–938. 10.1095/biolreprod52.4.928

    CAS  PubMed  Google Scholar 

  26. 26.

    Moriyama A, Shimoya K, Ogata I, Kimura T, Nakamura T, Wada H, Ohashi K, Azuma C, Saji F, Murata Y: Secretory leukocyte protease inhibitor (SLPI) concentrations in cervical mucus of women with normal menstrual cycle. Mol Hum Reprod 1999, 5: 656–661. 10.1093/molehr/5.7.656

    CAS  PubMed  Google Scholar 

  27. 27.

    Wira CR, Fahey JV: A new strategy to understand how HIV infects women: identification of a window of vulnerability during the menstrual cycle. AIDS 2008, 22: 1909–1917. 10.1097/QAD.0b013e3283060ea4

    PubMed Central  PubMed  Google Scholar 

  28. 28.

    Kelly RW, Carr GG, Critchley HO: A cytokine switch induced by human seminal plasma: an immune modulation with implications for sexually transmitted disease. Hum Reprod 1997, 12: 677–681. 10.1093/humrep/12.4.677

    CAS  PubMed  Google Scholar 

  29. 29.

    Shimoya K, Zhang Q, Temma K, Kimura T, Tsujie T, Tsutsui T, Wasada K, Kanzaki T, Koyama M, Murata Y: Secretory leukocyte protease inhibitor levels in cervicovaginal secretion of elderly women. Maturitas 2006, 54: 141–148. 10.1016/j.maturitas.2004.02.019

    CAS  PubMed  Google Scholar 

  30. 30.

    Cherpes TL, Marrazzo JM, Cosentino LA, Meyn LA, Murray PJ, Hillier SL: Hormonal contraceptive use modulates the local inflammatory response to bacterial vaginosis. Sex Transm Infect 2008, 84: 57–61. 10.1136/sti.2007.026625

    CAS  PubMed  Google Scholar 

  31. 31.

    Giraldo P, Neuer A, Ribeiro-Filho A, Linhares I, Witkin SS: Detection of the human 70-kD and 60-kD heat shock proteins in the vagina: relation to microbial flora, vaginal pH, and method of contraception. Infect Dis Obstet Gynecol 1999, 7: 23–25. 10.1002/(SICI)1098-0997(1999)7:1/2<23::AID-IDOG5>3.0.CO;2-W

    CAS  PubMed Central  PubMed  Google Scholar 

  32. 32.

    Ildgruben A, Sjoberg I, Hammarstrom ML, Backstrom T: Steroid receptor expression in vaginal epithelium of healthy fertile women and influences of hormonal contraceptive usage. Contraception 2005, 72: 383–392. 10.1016/j.contraception.2005.05.018

    CAS  PubMed  Google Scholar 

  33. 33.

    Ildgruben AK, Sjoberg IM, Hammarstrom ML: Influence of hormonal contraceptives on the immune cells and thickness of human vaginal epithelium. Obstet Gynecol 2003, 102: 571–582. 10.1016/S0029-7844(03)00618-5

    CAS  PubMed  Google Scholar 

  34. 34.

    Fichorova RN: Guiding the vaginal microbicide trials with biomarkers of inflammation. J Acquir Immune Defic Syndr 2004,37(Suppl 3):S184-S193.

    CAS  PubMed Central  PubMed  Google Scholar 

  35. 35.

    Burgener A, Boutilier J, Wachihi C, Kimani J, Carpenter M, Westmacott G, Cheng K, Ball TB, Plummer F: Identification of differentially expressed proteins in the cervical mucosa of HIV-1-resistant sex workers. J Proteome Res 2008, 7: 4446–4454. 10.1021/pr800406r

    CAS  PubMed  Google Scholar 

  36. 36.

    Andersch-Bjorkman Y, Thomsson KA, Holmen Larsson JM, Ekerhovd E, Hansson GC: Large scale identification of proteins, mucins, and their O-glycosylation in the endocervical mucus during the menstrual cycle. Mol Cell Proteomics 2007, 6: 708–716. 10.1074/mcp.M600439-MCP200

    PubMed  Google Scholar 

  37. 37.

    Panicker G, Ye Y, Wang D, Unger ER: Characterization of the Human Cervical Mucous Proteome. Clin Proteomics 2010, 6: 18–28. 10.1007/s12014-010-9042-3

    CAS  PubMed Central  PubMed  Google Scholar 

  38. 38.

    Venkataraman N, Cole AL, Svoboda P, Pohl J, Cole AM: Cationic polypeptides are required for anti-HIV-1 activity of human vaginal fluid. J Immunol 2005, 175: 7560–7567.

    CAS  PubMed  Google Scholar 

  39. 39.

    Di Quinzio MK, Georgiou HM, Holdsworth-Carson SJ, Ayhan M, Heng YJ, Walker SP, Rice GE, Permezel M: Proteomic analysis of human cervico-vaginal fluid displays differential protein expression in association with labor onset at term. J Proteome Res 2008, 7: 1916–1921. 10.1021/pr7006413

    CAS  PubMed  Google Scholar 

  40. 40.

    Di Quinzio MK, Oliva K, Holdsworth SJ, Ayhan M, Walker SP, Rice GE, Georgiou HM, Permezel M: Proteomic analysis and characterisation of human cervico-vaginal fluid proteins. Aust N Z J Obstet Gynaecol 2007, 47: 9–15. 10.1111/j.1479-828X.2006.00671.x

    PubMed  Google Scholar 

  41. 41.

    Iqbal SM, Ball TB, Levinson P, Maranan L, Jaoko W, Wachihi C, Pak BJ, Podust VN, Broliden K, Hirbod T, et al.: Elevated elafin/trappin-2 in the female genital tract is associated with protection against HIV acquisition. AIDS 2009, 23: 1669–1677. 10.1097/QAD.0b013e32832ea643

    CAS  PubMed  Google Scholar 

  42. 42.

    Dasari S, Pereira L, Reddy AP, Michaels JE, Lu X, Jacob T, Thomas A, Rodland M, Roberts CT Jr, Gravett MG, et al.: Comprehensive proteomic analysis of human cervical-vaginal fluid. J Proteome Res 2007, 6: 1258–1268. 10.1021/pr0605419

    CAS  PubMed  Google Scholar 

  43. 43.

    Pereira L, Reddy AP, Jacob T, Thomas A, Schneider KA, Dasari S, Lapidus JA, Lu X, Rodland M, Roberts CT Jr, et al.: Identification of novel protein biomarkers of preterm birth in human cervical-vaginal fluid. J Proteome Res 2007, 6: 1269–1276. 10.1021/pr0605421

    CAS  PubMed  Google Scholar 

  44. 44.

    Shaw JL, Smith CR, Diamandis EP: Proteomic analysis of human cervico-vaginal fluid. J Proteome Res 2007, 6: 2859–2865. 10.1021/pr0701658

    CAS  PubMed  Google Scholar 

  45. 45.

    Tang LJ, De SF, Odreman F, Venge P, Piva C, Guaschino S, Garcia RC: Proteomic analysis of human cervical-vaginal fluids. J Proteome Res 2007, 6: 2874–2883. 10.1021/pr0700899

    CAS  PubMed  Google Scholar 

  46. 46.

    Heng YJ, Di Quinzio MK, Permezel M, Rice GE, Georgiou HM: Interleukin-1 receptor antagonist in human cervicovaginal fluid in term pregnancy and labor. Am J Obstet Gynecol 2008, 199: 656–657. 10.1016/j.ajog.2008.06.011

    PubMed  Google Scholar 

  47. 47.

    Heng YJ, Di Quinzio MK, Permezel M, Ayhan M, Rice GE, Georgiou HM: Temporal proteomic analysis of human cervicovaginal fluid with impending term labor. J Proteome Res 2010, 9: 1344–1350. 10.1021/pr900892f

    CAS  PubMed  Google Scholar 

  48. 48.

    Dellios NL, Lappas M, Young IR, Palliser HK, Hirst JJ, Oliva K, Ayhan M, Rice GE: Increased expression of alpha-enolase in cervico-vaginal fluid during labour. Eur J Obstet Gynecol Reprod Biol 2010, 153: 16–22. 10.1016/j.ejogrb.2010.06.014

    CAS  PubMed  Google Scholar 

  49. 49.

    Heng YJ, Di Quinzio MK, Permezel M, Rice GE, Georgiou HM: Temporal expression of antioxidants in human cervicovaginal fluid associated with spontaneous labor. Antioxid Redox Signal 2010, 13: 951–957. 10.1089/ars.2010.3122

    CAS  PubMed  Google Scholar 

  50. 50.

    Shaw JL, Petraki C, Watson C, Bocking A, Diamandis EP: Role of tissue kallikrein-related peptidases in cervical mucus remodeling and host defense. Biol Chem 2008, 389: 1513–1522. 10.1515/BC.2008.171

    CAS  PubMed  Google Scholar 

  51. 51.

    Shaw JL, Diamandis EP: A potential role for tissue kallikrein-related peptidases in human cervico-vaginal physiology. Biol Chem 2008, 389: 681–688. 10.1515/BC.2008.069

    CAS  PubMed  Google Scholar 

  52. 52.

    Hitti J, Lapidus JA, Lu X, Reddy AP, Jacob T, Dasari S, Eschenbach DA, Gravett MG, Nagalla SR: Noninvasive diagnosis of intraamniotic infection: proteomic biomarkers in vaginal fluid. Am J Obstet Gynecol 2010, 203: 32.e1–32.e8. 10.1016/j.ajog.2010.03.037

    CAS  Google Scholar 

  53. 53.

    Shah SJ, Yu KH, Sangar V, Parry SI, Blair IA: Identification and quantification of preterm birth biomarkers in human cervicovaginal fluid by liquid chromatography/tandem mass spectrometry. J Proteome Res 2009, 8: 2407–2417. 10.1021/pr8010342

    CAS  PubMed Central  PubMed  Google Scholar 

  54. 54.

    Anderson NL, Polanski M, Pieper R, Gatlin T, Tirumalai RS, Conrads TP, Veenstra TD, Adkins JN, Pounds JG, Fagan R, et al.: The human plasma proteome: a nonredundant list developed by combination of four separate sources. Mol Cell Proteomics 2004, 3: 311–326. 10.1074/mcp.M300127-MCP200

    CAS  PubMed  Google Scholar 

  55. 55.

    States DJ, Omenn GS, Blackwell TW, Fermin D, Eng J, Speicher DW, Hanash SM: Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study. Nat Biotechnol 2006, 24: 333–338. 10.1038/nbt1183

    CAS  PubMed  Google Scholar 

  56. 56.

    Michaels JE, Dasari S, Pereira L, Reddy AP, Lapidus JA, Lu X, Jacob T, Thomas A, Rodland M, Roberts CT Jr, et al.: Comprehensive proteomic analysis of the human amniotic fluid proteome: gestational age-dependent changes. J Proteome Res 2007, 6: 1277–1285. 10.1021/pr060543t

    CAS  PubMed  Google Scholar 

  57. 57.

    Michel PE, Crettaz D, Morier P, Heller M, Gallot D, Tissot JD, Reymond F, Rossier JS: Proteome analysis of human plasma and amniotic fluid by Off-Gel isoelectric focusing followed by nano-LC-MS/MS. Electrophoresis 2006, 27: 1169–1181. 10.1002/elps.200500680

    CAS  PubMed  Google Scholar 

  58. 58.

    Park SJ, Yoon WG, Song JS, Jung HS, Kim CJ, Oh SY, Yoon BH, Jung G, Kim HJ, Nirasawa T: Proteome analysis of human amnion and amniotic fluid by two-dimensional electrophoresis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Proteomics 2006, 6: 349–363. 10.1002/pmic.200500084

    CAS  PubMed  Google Scholar 

  59. 59.

    Kostakis ID, Cholidou KG, Kallianidis K, Perrea D, Antsaklis A: The role of calprotectin in obstetrics and gynecology. Eur J Obstet Gynecol Reprod Biol 2010, 151: 3–9. 10.1016/j.ejogrb.2010.03.006

    CAS  PubMed  Google Scholar 

  60. 60.

    Brinkmann V, Reichard U, Goosmann C, Fauler B, Uhlemann Y, Weiss DS, Weinrauch Y, Zychlinsky A: Neutrophil extracellular traps kill bacteria. Science 2004, 303: 1532–1535. 10.1126/science.1092385

    CAS  PubMed  Google Scholar 

  61. 61.

    von Kockritz-Blickwede M, Nizet V: Innate immunity turned inside-out: antimicrobial defense by phagocyte extracellular traps. J Mol Med 2009, 87: 775–783. 10.1007/s00109-009-0481-0

    PubMed Central  PubMed  Google Scholar 

  62. 62.

    Logters T, Margraf S, Altrichter J, Cinatl J, Mitzner S, Windolf J, Scholz M: The clinical value of neutrophil extracellular traps. Med Microbiol Immunol 2009, 198: 211–219. 10.1007/s00430-009-0121-x

    PubMed  Google Scholar 

  63. 63.

    Yates JR, Ruse CI, Nakorchevsky A: Proteomics by mass spectrometry: approaches, advances, and applications. Annu Rev Biomed Eng 2009, 11: 49–79. 10.1146/annurev-bioeng-061008-124934

    CAS  PubMed  Google Scholar 

  64. 64.

    Ault KA: Epidemiology and natural history of human papillomavirus infections in the female genital tract. Infect Dis Obstet Gynecol 2006,2006(Suppl):40470.

    PubMed Central  PubMed  Google Scholar 

  65. 65.

    Azar KK, Tani M, Yasuda H, Sakai A, Inoue M, Sasagawa T: Increased secretion patterns of interleukin-10 and tumor necrosis factor-alpha in cervical squamous intraepithelial lesions. Hum Pathol 2004, 35: 1376–1384. 10.1016/j.humpath.2004.08.012

    CAS  PubMed  Google Scholar 

  66. 66.

    Baldwin P, Laskey R, Coleman N: Translational approaches to improving cervical screening. Nat Rev Cancer 2003, 3: 217–226. 10.1038/nrc1010

    CAS  PubMed  Google Scholar 

  67. 67.

    Woodman CB, Collins SI, Young LS: The natural history of cervical HPV infection: unresolved issues. Nat Rev Cancer 2007, 7: 11–22. 10.1038/nrc2050

    CAS  PubMed  Google Scholar 

  68. 68.

    Castellsague X: Natural history and epidemiology of HPV infection and cervical cancer. Gynecol Oncol 2008, 110: S4-S7. 10.1016/j.ygyno.2008.07.045

    PubMed  Google Scholar 

  69. 69.

    Snijders PJ, Steenbergen RD, Heideman DA, Meijer CJ: HPV-mediated cervical carcinogenesis: concepts and clinical implications. J Pathol 2006, 208: 152–164. 10.1002/path.1866

    CAS  PubMed  Google Scholar 

  70. 70.

    Einstein MH, Schiller JT, Viscidi RP, Strickler HD, Coursaget P, Tan T, Halsey N, Jenkins D: Clinician's guide to human papillomavirus immunology: knowns and unknowns. Lancet Infect Dis 2009, 9: 347–356. 10.1016/S1473-3099(09)70108-2

    CAS  PubMed  Google Scholar 

  71. 71.

    Lepique AP, Rabachini T, Villa LL: HPV vaccination: the beginning of the end of cervical cancer? - A Review. Mem Inst Oswaldo Cruz 2009, 104: 1–10. 10.1590/S0074-02762009000100001

    PubMed  Google Scholar 

  72. 72.

    Goldie SJ, Kuhn L, Denny L, Pollack A, Wright TC: Policy analysis of cervical cancer screening strategies in low-resource settings: clinical benefits and cost-effectiveness. JAMA 2001, 285: 3107–3115. 10.1001/jama.285.24.3107

    CAS  PubMed  Google Scholar 

  73. 73.

    Petry KU, Menton S, Menton M, van Loenen-Frosch F, de Carvalho GH, Holz B, Schopp B, Garbrecht-Buettner S, Davies P, Boehmer G, et al.: Inclusion of HPV testing in routine cervical cancer screening for women above 29 years in Germany: results for 8466 patients. Br J Cancer 2003, 88: 1570–1577. 10.1038/sj.bjc.6600918

    PubMed Central  PubMed  Google Scholar 

  74. 74.

    Arbyn M, Simoens C, Van OH, Foidart JM, Goffin F, Simon P, Fabri V: Analysis of 13 million individual patient records pertaining to Pap smears, colposcopies, biopsies and surgery on the uterine cervix (Belgium, 1996–2000). Prev Med 2009, 48: 438–443. 10.1016/j.ypmed.2009.02.021

    PubMed  Google Scholar 

  75. 75.

    Dehn D, Torkko KC, Shroyer KR: Human papillomavirus testing and molecular markers of cervical dysplasia and carcinoma. Cancer 2007, 111: 1–14. 10.1002/cncr.22425

    CAS  PubMed  Google Scholar 

  76. 76.

    Choi YP, Kang S, Hong S, Xie X, Cho NH: Proteomic analysis of progressive factors in uterine cervical cancer. Proteomics 2005, 5: 1481–1493. 10.1002/pmic.200401021

    CAS  PubMed  Google Scholar 

  77. 77.

    Bae SM, Lee CH, Cho YL, Nam KH, Kim YW, Kim CK, Han BD, Lee YJ, Chun HJ, Ahn WS: Two-dimensional gel analysis of protein expression profile in squamous cervical cancer patients. Gynecol Oncol 2005, 99: 26–35. 10.1016/j.ygyno.2005.05.041

    CAS  PubMed  Google Scholar 

  78. 78.

    Wong YF, Cheung TH, Lo KW, Wang VW, Chan CS, Ng TB, Chung TK, Mok SC: Protein profiling of cervical cancer by protein-biochips: proteomic scoring to discriminate cervical cancer from normal cervix. Cancer Lett 2004, 211: 227–234. 10.1016/j.canlet.2004.02.014

    CAS  PubMed  Google Scholar 

  79. 79.

    Zhu X, Lv J, Yu L, Zhu X, Wu J, Zou S, Jiang S: Proteomic identification of differentially-expressed proteins in squamous cervical cancer. Gynecol Oncol 2009, 112: 248–256. 10.1016/j.ygyno.2008.09.045

    CAS  PubMed  Google Scholar 

  80. 80.

    Bae SM, Min HJ, Ding GH, Kwak SY, Cho YL, Nam KH, Park CH, Kim YW, Kim CK, Han BD, et al.: Protein Expression Profile using Two-Dimensional Gel Analysis in Squamous Cervical Cancer Patients. Cancer Res Treat 2006, 38: 99–107. 10.4143/crt.2006.38.2.99

    PubMed Central  PubMed  Google Scholar 

  81. 81.

    Brown DR, Bryan JT: Abnormalities of cornified cell envelopes isolated from human papillomavirus type 11-infected genital epithelium. Virology 2000, 271: 65–70. 10.1006/viro.2000.0317

    CAS  PubMed  Google Scholar 

  82. 82.

    Lehr E, Hohl D, Huber M, Brown D: Infection with Human Papillomavirus alters expression of the small proline rich proteins 2 and 3. J Med Virol 2004, 72: 478–483. 10.1002/jmv.20011

    CAS  PubMed  Google Scholar 

  83. 83.

    UNAIDS: AIDS epidemic update december 2005. Geneva; 2005.

    Google Scholar 

  84. 84.

    UNAIDS: 2009 AIDS epidemic update. Geneva: UNAIDS; 2009.

    Google Scholar 

  85. 85.

    Kilmarx PH: Global epidemiology of HIV. Curr Opin HIV AIDS 2009, 4: 240–246. 10.1097/COH.0b013e32832c06db

    PubMed  Google Scholar 

  86. 86.

    UNAIDS: 2008 report on the global aids epidemic. Geneva: UNAIDS; 20098

  87. 87.

    Will there be an HIV vaccine in the next decade? Nat Med 2007, 13: 518–519.

  88. 88.

    Cutler B, Justman J: Vaginal microbicides and the prevention of HIV transmission. Lancet Infect Dis 2008, 8: 685–697. 10.1016/S1473-3099(08)70254-8

    CAS  PubMed Central  PubMed  Google Scholar 

  89. 89.

    Moscicki AB: Vaginal microbicides: where are we and where are we going? J Infect Chemother 2008, 14: 337–341. 10.1007/s10156-008-0630-3

    CAS  PubMed  Google Scholar 

  90. 90.

    Grant RM, Hamer D, Hope T, Johnston R, Lange J, Lederman MM, Lieberman J, Miller CJ, Moore JP, Mosier DE, et al.: Whither or wither microbicides? Science 2008, 321: 532–534. 10.1126/science.1160355

    CAS  PubMed Central  PubMed  Google Scholar 

  91. 91.

    Rowland-Jones SL, McMichael A: Immune responses in HIV-exposed seronegatives: have they repelled the virus? Curr Opin Immunol 1995, 7: 448–455. 10.1016/0952-7915(95)80087-5

    CAS  PubMed  Google Scholar 

  92. 92.

    Hirbod T, Broliden K: Mucosal immune responses in the genital tract of HIV-1-exposed uninfected women. J Intern Med 2007, 262: 44–58. 10.1111/j.1365-2796.2007.01822.x

    CAS  PubMed  Google Scholar 

  93. 93.

    Kulkarni PS, Butera ST, Duerr AC: Resistance to HIV-1 infection: lessons learned from studies of highly exposed persistently seronegative (HEPS) individuals. AIDS Rev 2003, 5: 87–103.

    PubMed  Google Scholar 

  94. 94.

    Shacklett BL: Understanding the "lucky few": the conundrum of HIV-exposed, seronegative individuals. Curr HIV/AIDS Rep 2006, 3: 26–31. 10.1007/s11904-006-0005-2

    PubMed  Google Scholar 

  95. 95.

    Arenzana-Seisdedos F, Parmentier M: Genetics of resistance to HIV infection: Role of co-receptors and co-receptor ligands. Semin Immunol 2006, 18: 387–403. 10.1016/j.smim.2006.07.007

    CAS  PubMed  Google Scholar 

  96. 96.

    Biasin M, Piacentini L, Lo CS, Naddeo V, Pierotti P, Borelli M, Trabattoni D, Mazzotta F, Shearer GM, Clerici M: TLR activation pathways in HIV-1-exposed seronegative individuals. J Immunol 2010, 184: 2710–2717. 10.4049/jimmunol.0902463

    CAS  PubMed  Google Scholar 

  97. 97.

    Jennes W, Verheyden S, Demanet C, Adje-Toure CA, Vuylsteke B, Nkengasong JN, Kestens L: Cutting edge: resistance to HIV-1 infection among African female sex workers is associated with inhibitory KIR in the absence of their HLA ligands. J Immunol 2006, 177: 6588–6592.

    CAS  PubMed  Google Scholar 

  98. 98.

    Marmor M, Hertzmark K, Thomas SM, Halkitis PN, Vogler M: Resistance to HIV infection. J Urban Health 2006, 83: 5–17. 10.1007/s11524-005-9003-8

    CAS  PubMed Central  PubMed  Google Scholar 

  99. 99.

    Miyazawa M, Lopalco L, Mazzotta F, Lo CS, Veas F, Clerici M: The 'immunologic advantage' of HIV-exposed seronegative individuals. AIDS 2009, 23: 161–175. 10.1097/QAD.0b013e3283196a80

    PubMed  Google Scholar 

  100. 100.

    Belec L, Ghys PD, Hocini H, Nkengasong JN, Tranchot-Diallo J, Diallo MO, Ettiegne-Traore V, Maurice C, Becquart P, Matta M, et al.: Cervicovaginal secretory antibodies to human immunodeficiency virus type 1 (HIV-1) that block viral transcytosis through tight epithelial barriers in highly exposed HIV-1-seronegative African women. J Infect Dis 2001, 184: 1412–1422. 10.1086/324375

    CAS  PubMed  Google Scholar 

  101. 101.

    Soderlund J, Hirbod T, Smed-Sorensen A, Johansson U, Kimani J, Plummer F, Spetz AL, Andersson J, Kaul R, Broliden K: Plasma and mucosal fluid from HIV type 1-infected patients but not from HIV type 1-exposed uninfected subjects prevent HIV type 1-exposed DC from infecting other target cells. AIDS Res Hum Retroviruses 2007, 23: 101–106. 10.1089/aid.2005.0104

    PubMed  Google Scholar 

  102. 102.

    Basu J, Romney SL, Angeletti RH, Vermund SH, Nieves E, Kadish AS, Mikhail MS, Orr GA: Human immunodeficiency virus (HIV) antigens and RNA in HIV-seronegative women with cervical intraepithelial neoplasia. AIDS Res Hum Retroviruses 2009, 25: 249–259. 10.1089/aid.2008.0096

    CAS  PubMed  Google Scholar 

  103. 103.

    Archibald DW, Hebert CC, Alger LS, Johsnon JP: Detection of HIV-specific antibodies in saliva and cervical secretions. Vaccine Res 1992, 1: 215–219.

    Google Scholar 

  104. 104.

    Belec L, Tevi-Benissan C, Gresenguet G, Meillet D, Pillot J: HIV-1 antibody serum negativity with vaginal secretions positivity. Lancet 1994, 343: 1046–1047. 10.1016/S0140-6736(94)90168-6

    CAS  PubMed  Google Scholar 

  105. 105.

    Belec L, Gresenguet G, Dragon MA, Meillet D, Pillot J: Detection of antibodies to human immunodeficiency virus in vaginal secretions by immunoglobulin G antibody capture enzyme-linked immunosorbent assay: application to detection of seminal antibodies after sexual intercourse. J Clin Microbiol 1994, 32: 1249–1255.

    CAS  PubMed Central  PubMed  Google Scholar 

  106. 106.

    Beyrer C, Artenstein AW, Rugpao S, Stephens H, VanCott TC, Robb ML, Rinkaew M, Birx DL, Khamboonruang C, Zimmerman PA, et al.: Epidemiologic and biologic characterization of a cohort of human immunodeficiency virus type 1 highly exposed, persistently seronegative female sex workers in northern Thailand. Chiang Mai HEPS Working Group. J Infect Dis 1999, 179: 59–67. 10.1086/314556

    CAS  PubMed  Google Scholar 

  107. 107.

    Broliden K, Hinkula J, Devito C, Kiama P, Kimani J, Trabbatoni D, Bwayo JJ, Clerici M, Plummer F, Kaul R: Functional HIV-1 specific IgA antibodies in HIV-1 exposed, persistently IgG seronegative female sex workers. Immunol Lett 2001, 79: 29–36. 10.1016/S0165-2478(01)00263-2

    CAS  PubMed  Google Scholar 

  108. 108.

    Buchacz K, Parekh BS, Padian NS, van der Straten A, Phillips S, Jonte J, Holmberg SD: HIV-specific IgG in cervicovaginal secretions of exposed HIV-uninfected female sexual partners of HIV-infected men. AIDS Res Hum Retroviruses 2001, 17: 1689–1693. 10.1089/08892220152741388

    CAS  PubMed  Google Scholar 

  109. 109.

    Devito C, Hinkula J, Kaul R, Lopalco L, Bwayo JJ, Plummer F, Clerici M, Broliden K: Mucosal and plasma IgA from HIV-exposed seronegative individuals neutralize a primary HIV-1 isolate. AIDS 2000, 14: 1917–1920. 10.1097/00002030-200009080-00006

    CAS  PubMed  Google Scholar 

  110. 110.

    Ghys PD, Belec L, Diallo MO, Ettiegne-Traore V, Becquart P, Maurice C, Nkengasong JN, Coulibaly IM, Greenberg AE, Laga M, et al.: Cervicovaginal anti-HIV antibodies in HIV-seronegative female sex workers in Abidjan, Cote d'Ivoire. AIDS 2000, 14: 2603–2608. 10.1097/00002030-200011100-00025

    CAS  PubMed  Google Scholar 

  111. 111.

    Lopalco L, Barassi C, Pastori C, Longhi R, Burastero SE, Tambussi G, Mazzotta F, Lazzarin A, Clerici M, Siccardi AG: CCR5-reactive antibodies in seronegative partners of HIV-seropositive individuals down-modulate surface CCR5 in vivo and neutralize the infectivity of R5 strains of HIV-1 In vitro. J Immunol 2000, 164: 3426–3433.

    CAS  PubMed  Google Scholar 

  112. 112.

    Kaul R, Trabattoni D, Bwayo JJ, Arienti D, Zagliani A, Mwangi FM, Kariuki C, Ngugi EN, Macdonald KS, Ball TB, et al.: HIV-1-specific mucosal IgA in a cohort of HIV-1-resistant Kenyan sex workers. AIDS 1999, 13: 23–29. 10.1097/00002030-199901140-00004

    CAS  PubMed  Google Scholar 

  113. 113.

    Mazzoli S, Trabattoni D, Lo CS, Piconi S, Ble C, Meacci F, Ruzzante S, Salvi A, Semplici F, Longhi R, et al.: HIV-specific mucosal and cellular immunity in HIV-seronegative partners of HIV-seropositive individuals. Nat Med 1997, 3: 1250–1257. 10.1038/nm1197-1250

    CAS  PubMed  Google Scholar 

  114. 114.

    Iqbal SM, Ball TB, Kimani J, Kiama P, Thottingal P, Embree JE, Fowke KR, Plummer FA: Elevated T cell counts and RANTES expression in the genital mucosa of HIV-1-resistant Kenyan commercial sex workers. J Infect Dis 2005, 192: 728–738. 10.1086/432482

    CAS  PubMed  Google Scholar 

  115. 115.

    Cole AM, Cole AL: Antimicrobial polypeptides are key anti-HIV-1 effector molecules of cervicovaginal host defense. Am J Reprod Immunol 2008, 59: 27–34. 10.1111/j.1600-0897.2007.00561.x

    CAS  PubMed  Google Scholar 

  116. 116.

    Kazmi SH, Naglik JR, Sweet SP, Evans RW, O'Shea S, Banatvala JE, Challacombe SJ: Comparison of human immunodeficiency virus type 1-specific inhibitory activities in saliva and other human mucosal fluids. Clin Vaccine Immunol 2006, 13: 1111–1118. 10.1128/CDLI.00426-05

    CAS  PubMed Central  PubMed  Google Scholar 

  117. 117.

    Williams SE, Brown TI, Roghanian A, Sallenave JM: SLPI and elafin: one glove, many fingers. Clin Sci (Lond) 2006, 110: 21–35. 10.1042/CS20050115

    CAS  Google Scholar 

  118. 118.

    Ghosh M, Shen Z, Fahey JV, Cu-Uvin S, Mayer K, Wira CR: Trappin-2/Elafin: a novel innate anti-human immunodeficiency virus-1 molecule of the human female reproductive tract. Immunology 2009, 129: 207–219. 10.1111/j.1365-2567.2009.03165.x

    PubMed  Google Scholar 

  119. 119.

    Fredricks DN, Fiedler TL, Marrazzo JM: Molecular identification of bacteria associated with bacterial vaginosis. N Engl J Med 2005, 353: 1899–1911. 10.1056/NEJMoa043802

    CAS  PubMed  Google Scholar 

  120. 120.

    St John E, Mares D, Spear GT: Bacterial vaginosis and host immunity. Curr HIV/AIDS Rep 2007, 4: 22–28. 10.1007/s11904-007-0004-y

    PubMed  Google Scholar 

  121. 121.

    Nyirjesy P: Vulvovaginal candidiasis and bacterial vaginosis. Infect Dis Clin North Am 2008, 22: 637–52. vi 10.1016/j.idc.2008.05.002

    PubMed  Google Scholar 

  122. 122.

    Hill GB: The microbiology of bacterial vaginosis. Am J Obstet Gynecol 1993, 169: 450–454.

    CAS  PubMed  Google Scholar 

  123. 123.

    Livengood CH: Bacterial vaginosis: an overview for 2009. Rev Obstet Gynecol 2009, 2: 28–37.

    PubMed Central  PubMed  Google Scholar 

  124. 124.

    Larsson PG, Bergstrom M, Forsum U, Jacobsson B, Strand A, Wolner-Hanssen P: Bacterial vaginosis. Transmission, role in genital tract infection and pregnancy outcome: an enigma. APMIS 2005, 113: 233–245. 10.1111/j.1600-0463.2005.apm_01.x

    CAS  PubMed  Google Scholar 

  125. 125.

    Donders GG, Van CK, Bellen G, Reybrouck R, Van den Bosch T, Riphagen I, Van LS: Predictive value for preterm birth of abnormal vaginal flora, bacterial vaginosis and aerobic vaginitis during the first trimester of pregnancy. BJOG 2009, 116: 1315–1324. 10.1111/j.1471-0528.2009.02237.x

    CAS  PubMed  Google Scholar 

  126. 126.

    Mania-Pramanik J, Kerkar SC, Salvi VS: Bacterial vaginosis: a cause of infertility? Int J STD AIDS 2009, 20: 778–781. 10.1258/ijsa.2009.009193

    CAS  PubMed  Google Scholar 

  127. 127.

    Atashili J, Poole C, Ndumbe PM, Adimora AA, Smith JS: Bacterial vaginosis and HIV acquisition: a meta-analysis of published studies. AIDS 2008, 22: 1493–1501. 10.1097/QAD.0b013e3283021a37

    PubMed Central  PubMed  Google Scholar 

  128. 128.

    Cohen CR, Duerr A, Pruithithada N, Rugpao S, Hillier S, Garcia P, Nelson K: Bacterial vaginosis and HIV seroprevalence among female commercial sex workers in Chiang Mai, Thailand. AIDS 1995, 9: 1093–1097. 10.1097/00002030-199509000-00017

    CAS  PubMed  Google Scholar 

  129. 129.

    Martin HL, Richardson BA, Nyange PM, Lavreys L, Hillier SL, Chohan B, Mandaliya K, Ndinya-Achola JO, Bwayo J, Kreiss J: Vaginal lactobacilli, microbial flora, and risk of human immunodeficiency virus type 1 and sexually transmitted disease acquisition. J Infect Dis 1999, 180: 1863–1868. 10.1086/315127

    CAS  PubMed  Google Scholar 

  130. 130.

    Sewankambo N, Gray RH, Wawer MJ, Paxton L, McNaim D, Wabwire-Mangen F, Serwadda D, Li C, Kiwanuka N, Hillier SL, et al.: HIV-1 infection associated with abnormal vaginal flora morphology and bacterial vaginosis. Lancet 1997, 350: 546–550. 10.1016/S0140-6736(97)01063-5

    CAS  PubMed  Google Scholar 

  131. 131.

    Taha TE, Hoover DR, Dallabetta GA, Kumwenda NI, Mtimavalye LA, Yang LP, Liomba GN, Broadhead RL, Chiphangwi JD, Miotti PG: Bacterial vaginosis and disturbances of vaginal flora: association with increased acquisition of HIV. AIDS 1998, 12: 1699–1706. 10.1097/00002030-199813000-00019

    CAS  PubMed  Google Scholar 

  132. 132.

    Taha TE, Gray RH, Kumwenda NI, Hoover DR, Mtimavalye LA, Liomba GN, Chiphangwi JD, Dallabetta GA, Miotti PG: HIV infection and disturbances of vaginal flora during pregnancy. J Acquir Immune Defic Syndr Hum Retrovirol 1999, 20: 52–59.

    CAS  PubMed  Google Scholar 

  133. 133.

    Cherpes TL, Melan MA, Kant JA, Cosentino LA, Meyn LA, Hillier SL: Genital tract shedding of herpes simplex virus type 2 in women: effects of hormonal contraception, bacterial vaginosis, and vaginal group B Streptococcus colonization. Clin Infect Dis 2005, 40: 1422–1428. 10.1086/429622

    PubMed  Google Scholar 

  134. 134.

    Kaul R, Nagelkerke NJ, Kimani J, Ngugi E, Bwayo JJ, Macdonald KS, Rebbaprgada A, Fonck K, Temmerman M, Ronald AR, et al.: Prevalent herpes simplex virus type 2 infection is associated with altered vaginal flora and an increased susceptibility to multiple sexually transmitted infections. J Infect Dis 2007, 196: 1692–1697. 10.1086/522006

    PubMed  Google Scholar 

  135. 135.

    Nagot N, Ouedraogo A, Defer MC, Vallo R, Mayaud P, Van de Perre P: Association between bacterial vaginosis and Herpes simplex virus type-2 infection: implications for HIV acquisition studies. Sex Transm Infect 2007, 83: 365–368. 10.1136/sti.2007.024794

    PubMed Central  PubMed  Google Scholar 

  136. 136.

    Rotstein OD, Pruett TL, Fiegel VD, Nelson RD, Simmons RL: Succinic acid, a metabolic by-product of Bacteroides species, inhibits polymorphonuclear leukocyte function. Infect Immun 1985, 48: 402–408.

    CAS  PubMed Central  PubMed  Google Scholar 

  137. 137.

    Hillier SL: The vaginal microbial ecosystem and resistance to HIV. AIDS Res Hum Retroviruses 1998,14(Suppl 1):S17-S21.

    PubMed  Google Scholar 

  138. 138.

    Wiggins R, Hicks SJ, Soothill PW, Millar MR, Corfield AP: Mucinases and sialidases: their role in the pathogenesis of sexually transmitted infections in the female genital tract. Sex Transm Infect 2001, 77: 402–408. 10.1136/sti.77.6.402

    CAS  PubMed Central  PubMed  Google Scholar 

  139. 139.

    Robertson JA, Stemler ME, Stemke GW: Immunoglobulin A protease activity of Ureaplasma urealyticum. J Clin Microbiol 1984, 19: 255–258.

    CAS  PubMed Central  PubMed  Google Scholar 

  140. 140.

    Cohn JA, Hashemi FB, Camarca M, Kong F, Xu J, Beckner SK, Kovacs AA, Reichelderfer PS, Spear GT: HIV-inducing factor in cervicovaginal secretions is associated with bacterial vaginosis in HIV-1-infected women. J Acquir Immune Defic Syndr 2005, 39: 340–346. 10.1097/01.qai.0000146599.47925.e0

    PubMed Central  PubMed  Google Scholar 

  141. 141.

    Olinger GG, Hashemi FB, Sha BE, Spear GT: Association of indicators of bacterial vaginosis with a female genital tract factor that induces expression of HIV-1. AIDS 1999, 13: 1905–1912. 10.1097/00002030-199910010-00013

    CAS  PubMed  Google Scholar 

  142. 142.

    Balu RB, Savitz DA, Ananth CV, Hartmann KE, Miller WC, Thorp JM, Heine RP: Bacterial vaginosis, vaginal fluid neutrophil defensins, and preterm birth. Obstet Gynecol 2003, 101: 862–868. 10.1016/S0029-7844(03)00042-5

    CAS  PubMed  Google Scholar 

  143. 143.

    Novak RM, Donoval BA, Graham PJ, Boksa LA, Spear G, Hershow RC, Chen HY, Landay A: Cervicovaginal levels of lactoferrin, secretory leukocyte protease inhibitor, and RANTES and the effects of coexisting vaginoses in human immunodeficiency virus (HIV)-seronegative women with a high risk of heterosexual acquisition of HIV infection. Clin Vaccine Immunol 2007, 14: 1102–1107. 10.1128/CVI.00386-06

    CAS  PubMed Central  PubMed  Google Scholar 

  144. 144.

    Rein MF, Shih LM, Miller JR, Guerrant RL: Use of a lactoferrin assay in the differential diagnosis of female genital tract infections and implications for the pathophysiology of bacterial vaginosis. Sex Transm Dis 1996, 23: 517–521. 10.1097/00007435-199611000-00014

    CAS  PubMed  Google Scholar 

  145. 145.

    Valore EV, Wiley DJ, Ganz T: Reversible deficiency of antimicrobial polypeptides in bacterial vaginosis. Infect Immun 2006, 74: 5693–5702. 10.1128/IAI.00524-06

    CAS  PubMed Central  PubMed  Google Scholar 

  146. 146.

    Wiesenfeld HC, Heine RP, Krohn MA, Hillier SL, Amortegui AA, Nicolazzo M, Sweet RL: Association between elevated neutrophil defensin levels and endometritis. J Infect Dis 2002, 186: 792–797. 10.1086/342417

    CAS  PubMed  Google Scholar 

  147. 147.

    Xu J, Holzman CB, Arvidson CG, Chung H, Goepfert AR: Midpregnancy vaginal fluid defensins, bacterial vaginosis, and risk of preterm delivery. Obstet Gynecol 2008, 112: 524–531. 10.1097/AOG.0b013e318184209b

    PubMed Central  PubMed  Google Scholar 

  148. 148.

    Spear GT, St JE, Zariffard MR: Bacterial vaginosis and human immunodeficiency virus infection. AIDS Res Ther 2007, 4: 25. 10.1186/1742-6405-4-25

    PubMed Central  PubMed  Google Scholar 

  149. 149.

    Goffinet F: Primary predictors of preterm labour. BJOG 2005,112(Suppl 1):38–47.

    PubMed  Google Scholar 

  150. 150.

    Holst D, Garnier Y: Preterm birth and inflammation-The role of genetic polymorphisms. Eur J Obstet Gynecol Reprod Biol 2008, 141: 3–9. 10.1016/j.ejogrb.2008.07.020

    CAS  PubMed  Google Scholar 

  151. 151.

    Varner MW, Esplin MS: Current understanding of genetic factors in preterm birth. BJOG 2005,112(Suppl 1):28–31.

    CAS  PubMed  Google Scholar 

  152. 152.

    World Health Organization: WHO: recommended definitions, terminology and format for statistical tables related to the perinatal period and use of a new certificate for cause of perinatal deaths. Modifications recommended by FIGO as amended October 14, 1976. Acta Obstet Gynecol Scand 1977, 56: 247–253.

    Google Scholar 

  153. 153.

    Beck S, Wojdyla D, Say L, Betran AP, Merialdi M, Requejo JH, Rubens C, Menon R, Van Look PFA: The worldwide incidence of preterm birth: a WHO systematic review of maternal mortality and morbidity. Bulletin of the World Health Organization 2009.

    Google Scholar 

  154. 154.

    Dodd JM, Flenady VJ, Cincotta R, Crowther CA: Progesterone for the prevention of preterm birth: a systematic review. Obstet Gynecol 2008, 112: 127–134. 10.1097/AOG.0b013e31817d0262

    CAS  PubMed  Google Scholar 

  155. 155.

    Steer P: The epidemiology of preterm labour. BJOG 2005,112(Suppl 1):1–3.

    PubMed  Google Scholar 

  156. 156.

    Berkman ND, Thorp JM Jr, Lohr KN, Carey TS, Hartmann KE, Gavin NI, Hasselblad V, Idicula AE: Tocolytic treatment for the management of preterm labor: a review of the evidence. Am J Obstet Gynecol 2003, 188: 1648–1659. 10.1067/mob.2003.356

    CAS  PubMed  Google Scholar 

  157. 157.

    Goffinet F, Maillard F, Fulla Y, Cabrol D: Biochemical markers (without markers of infection) of the risk of preterm delivery. Implications for clinical practice. Eur J Obstet Gynecol Reprod Biol 2001, 94: 59–68. 10.1016/S0301-2115(00)00317-1

    CAS  PubMed  Google Scholar 

  158. 158.

    Northen AT, Norman GS, Anderson K, Moseley L, Divito M, Cotroneo M, Swain M, Bousleiman S, Johnson F, Dorman K, et al.: Follow-up of children exposed in utero to 17 alpha-hydroxyprogesterone caproate compared with placebo. Obstet Gynecol 2007, 110: 865–872. 10.1097/01.AOG.0000281348.51499.bc

    PubMed  Google Scholar 

  159. 159.

    Mercer BM, Merlino AA: Magnesium sulfate for preterm labor and preterm birth. Obstet Gynecol 2009, 114: 650–668. 10.1097/AOG.0b013e3181b48336

    CAS  PubMed  Google Scholar 

  160. 160.

    Spong CY: Prediction and prevention of recurrent spontaneous preterm birth. Obstet Gynecol 2007, 110: 405–415. 10.1097/01.AOG.0000275287.08520.4a

    PubMed  Google Scholar 

  161. 161.

    Esplin MS, O'Brien E, Fraser A, Kerber RA, Clark E, Simonsen SE, Holmgren C, Mineau GP, Varner MW: Estimating recurrence of spontaneous preterm delivery. Obstet Gynecol 2008, 112: 516–523. 10.1097/AOG.0b013e318184181a

    PubMed  Google Scholar 

  162. 162.

    Coleman MA, McCowan LM, Pattison NS, Mitchell M: Fetal fibronectin detection in preterm labor: evaluation of a prototype bedside dipstick technique and cervical assessment. Am J Obstet Gynecol 1998, 179: 1553–1558. 10.1016/S0002-9378(98)70023-7

    CAS  PubMed  Google Scholar 

  163. 163.

    Swamy GK, Simhan HN, Gammill HS, Heine RP: Clinical utility of fetal fibronectin for predicting preterm birth. J Reprod Med 2005, 50: 851–856.

    CAS  PubMed  Google Scholar 

  164. 164.

    Yoneda S, Sakai M, Sasaki Y, Shiozaki A, Hidaka T, Saito S: Interleukin-8 and glucose in amniotic fluid, fetal fibronectin in vaginal secretions and preterm labor index based on clinical variables are optimal predictive markers for preterm delivery in patients with intact membranes. J Obstet Gynaecol Res 2007, 33: 38–44. 10.1111/j.1447-0756.2007.00474.x

    CAS  PubMed  Google Scholar 

  165. 165.

    Krupa FG, Faltin D, Cecatti JG, Surita FG, Souza JP: Predictors of preterm birth. Int J Gynaecol Obstet 2006, 94: 5–11. 10.1016/j.ijgo.2006.03.022

    CAS  PubMed  Google Scholar 

  166. 166.

    Leitich H: Secondary predictors of preterm labour. BJOG 2005,112(Suppl 1):48–50.

    PubMed  Google Scholar 

  167. 167.

    Owen P, Scott A: Can fetal fibronectin testing improve the management of preterm labour? Clin Exp Obstet Gynecol 1997, 24: 19–22. 10.1016/S0889-8545(05)70287-4

    CAS  PubMed  Google Scholar 

  168. 168.

    Parker J, Bell R, Brennecke S: Fetal fibronectin in the cervicovaginal fluid of women with threatened preterm labour as a predictor of delivery before 34 weeks' gestation. Aust N Z J Obstet Gynaecol 1995, 35: 257–261. 10.1111/j.1479-828X.1995.tb01976.x

    CAS  PubMed  Google Scholar 

  169. 169.

    Goldenberg RL, Iams JD, Mercer BM, Meis PJ, Moawad AH, Copper RL, Das A, Thom E, Johnson F, McNellis D, et al.: The preterm prediction study: the value of new vs standard risk factors in predicting early and all spontaneous preterm births. NICHD MFMU Network. Am J Public Health 1998, 88: 233–238. 10.2105/AJPH.88.2.233

    CAS  PubMed Central  PubMed  Google Scholar 

  170. 170.

    Berghella V, Baxter JK, Hendrix NW: Cervical assessment by ultrasound for preventing preterm delivery. Cochrane Database Syst Rev 2009, CD007235.

    Google Scholar 

  171. 171.

    Donders GG, Van CC, Bellen G, Reybrouck R, Van den Bosch T, Riphagen I, Van LS: Association between abnormal vaginal flora and cervical length as risk factors for preterm birth. Ultrasound Obstet Gynecol 2010, in press.

    Google Scholar 

  172. 172.

    Iams JD, Goldenberg RL, Meis PJ, Mercer BM, Moawad A, Das A, Thom E, McNellis D, Copper RL, Johnson F, et al.: The length of the cervix and the risk of spontaneous premature delivery. National Institute of Child Health and Human Development Maternal Fetal Medicine Unit Network. N Engl J Med 1996, 334: 567–572. 10.1056/NEJM199602293340904

    CAS  PubMed  Google Scholar 

  173. 173.

    Wiberg-Itzel E, Pettersson H, Cnattingius S, Nordstrom L: Prediction of time to spontaneous onset of labour with lactate concentration in vaginal fluid in women with suspected preterm prelabour rupture of the membranes. BJOG 2009, 116: 62–66. 10.1111/j.1471-0528.2008.02017.x

    CAS  PubMed  Google Scholar 

  174. 174.

    Anum EA, Hill LD, Pandya A, Strauss JF III: Connective tissue and related disorders and preterm birth: clues to genes contributing to prematurity. Placenta 2009, 30: 207–215. 10.1016/j.placenta.2008.12.007

    CAS  PubMed Central  PubMed  Google Scholar 

  175. 175.

    ACOG Practice Bulletin No. 80: premature rupture of membranes. Clinical management guidelines for obstetrician-gynecologists Obstet Gynecol 2007, 109: 1007–1019. 10.1097/01.AOG.0000263888.69178.1f

  176. 176.

    Simhan HN, Canavan TP: Preterm premature rupture of membranes: diagnosis, evaluation and management strategies. BJOG 2005,112(Suppl 1):32–37.

    PubMed  Google Scholar 

  177. 177.

    Caughey AB, Robinson JN, Norwitz ER: Contemporary diagnosis and management of preterm premature rupture of membranes. Rev Obstet Gynecol 2008, 1: 11–22.

    PubMed Central  PubMed  Google Scholar 

  178. 178.

    Esim E, Turan C, Unal O, Dansuk R, Cengizglu B: Diagnosis of premature rupture of membranes by identification of beta-HCG in vaginal washing fluid. Eur J Obstet Gynecol Reprod Biol 2003, 107: 37–40. 10.1016/S0301-2115(02)00277-4

    CAS  PubMed  Google Scholar 

  179. 179.

    Wiberg-Itzel E, Cnattingius S, Nordstrom L: Lactate determination in vaginal fluids: a new method in the diagnosis of prelabour rupture of membranes. BJOG 2005, 112: 754–758. 10.1111/j.1471-0528.2004.00521.x

    PubMed  Google Scholar 

  180. 180.

    Wiberg-Itzel E, Pettersson H, Cnattingius S, Nordstrom L: Association between lactate in vaginal fluid and time to spontaneous onset of labour for women with suspected prelabour rupture of the membranes. BJOG 2006, 113: 1426–1430. 10.1111/j.1471-0528.2006.01088.x

    CAS  PubMed  Google Scholar 

  181. 181.

    Kafali H, Oksuzler C: Vaginal fluid urea and creatinine in diagnosis of premature rupture of membranes. Arch Gynecol Obstet 2007, 275: 157–160. 10.1007/s00404-006-0240-1

    CAS  PubMed  Google Scholar 

  182. 182.

    Gurbuz A, Karateke A, Kabaca C: Vaginal fluid creatinine in premature rupture of membranes. Int J Gynaecol Obstet 2004, 85: 270–271. 10.1016/j.ijgo.2003.09.012

    CAS  PubMed  Google Scholar 

  183. 183.

    Li HY, Chang TS: Vaginal fluid creatinine, human chorionic gonadotropin and alpha-fetoprotein levels for detecting premature rupture of membranes. Zhonghua Yi Xue Za Zhi (Taipei) 2000, 63: 686–690.

    CAS  Google Scholar 

  184. 184.

    Lee SE, Park JS, Norwitz ER, Kim KW, Park HS, Jun JK: Measurement of placental alpha-microglobulin-1 in cervicovaginal discharge to diagnose rupture of membranes. Obstet Gynecol 2007, 109: 634–640. 10.1097/01.AOG.0000252706.46734.0a

    CAS  PubMed  Google Scholar 

  185. 185.

    Cousins LM, Smok DP, Lovett SM, Poeltler DM: AmniSure placental alpha microglobulin-1 rapid immunoassay versus standard diagnostic methods for detection of rupture of membranes. Am J Perinatol 2005, 22: 317–320. 10.1055/s-2005-870896

    PubMed  Google Scholar 

  186. 186.

    Buhimschi CS, Baumbusch MA, Campbell KH, Dulay AT, Buhimschi IA: Insight into innate immunity of the uterine cervix as a host defense mechanism against infection and preterm birth. Exp Rev Obstet Gynecol 2009, 4: 9–15. 10.1586/17474108.4.1.9

    CAS  Google Scholar 

  187. 187.

    Buhimschi IA, Buhimschi CS, Weiner CP, Kimura T, Hamar BD, Sfakianaki AK, Norwitz ER, Funai EF, Ratner E: Proteomic but not enzyme-linked immunosorbent assay technology detects amniotic fluid monomeric calgranulins from their complexed calprotectin form. Clin Diagn Lab Immunol 2005, 12: 837–844.

    CAS  PubMed Central  PubMed  Google Scholar 

  188. 188.

    Meyer RE, Thompson SJ, Addy CL, Garrison CZ, Best RG: Maternal serum placental alkaline phosphatase level and risk for preterm delivery. Am J Obstet Gynecol 1995, 173: 181–186. 10.1016/0002-9378(95)90187-6

    CAS  PubMed  Google Scholar 

  189. 189.

    Moawad AH, Goldenberg RL, Mercer B, Meis PJ, Iams JD, Das A, Caritis SN, Miodovnik M, Menard MK, Thurnau GR, et al.: The Preterm Prediction Study: the value of serum alkaline phosphatase, alpha-fetoprotein, plasma corticotropin-releasing hormone, and other serum markers for the prediction of spontaneous preterm birth. Am J Obstet Gynecol 2002, 186: 990–996. 10.1067/mob.2002.121727

    CAS  PubMed  Google Scholar 

  190. 190.

    Fey MC, Beal MW: Role of human papilloma virus testing in cervical cancer prevention. J Midwifery Womens Health 2004, 49: 4–13. 10.1016/j.jmwh.2003.10.002

    PubMed  Google Scholar 

  191. 191.

    Tjiong MY, van d, ten Kate FJ, Tjong AHS, ter SJ, Burger MP, Out TA: Increased IL-6 and IL-8 levels in cervicovaginal secretions of patients with cervical cancer. Gynecol Oncol 1999, 73: 285–291. 10.1006/gyno.1999.5358

    CAS  PubMed  Google Scholar 

  192. 192.

    Tjiong MY, van d, ter Schegget JS, Burger MP, ten Kate FW, Out TA: Cytokines in cervicovaginal washing fluid from patients with cervical neoplasia. Cytokine 2001, 14: 357–360. 10.1006/cyto.2001.0909

    CAS  PubMed  Google Scholar 

  193. 193.

    Lee DW, Kim YT, Kim SW, Kim S, Kim JH, Kang MH, Hwang JH, Lim J: Expression of interleukin-5 and tumor necrosis factor alpha in cervical carcinoma. Clin Vaccine Immunol 2009, 16: 959–961. 10.1128/CVI.00262-08

    CAS  PubMed Central  PubMed  Google Scholar 

  194. 194.

    Tjiong MY, Schegget JT, Tjiong AHS, Out TA, van d, Burger MP, Struyk L: IgG antibodies against human papillomavirus type 16 E7 proteins in cervicovaginal washing fluid from patients with cervical neoplasia. Int J Gynecol Cancer 2000, 10: 296–304. 10.1046/j.1525-1438.2000.010004296.x

    PubMed  Google Scholar 

  195. 195.

    Tjiong MY, Zumbach K, Schegget JT, van d, Out TA, Pawlita M, Struyk L: Antibodies against human papillomavirus type 16 and 18 E6 and E7 proteins in cervicovaginal washings and serum of patients with cervical neoplasia. Viral Immunol 2001, 14: 415–424. 10.1089/08828240152716655

    CAS  PubMed  Google Scholar 

  196. 196.

    Wang Z, Hansson BG, Forslund O, Dillner L, Sapp M, Schiller JT, Bjerre B, Dillner J: Cervical mucus antibodies against human papillomavirus type 16, 18, and 33 capsids in relation to presence of viral DNA. J Clin Microbiol 1996, 34: 3056–3062.

    CAS  PubMed Central  PubMed  Google Scholar 

  197. 197.

    Hernandez DE, Cohen A, Fisher D, Correnti M, Harner R: Antibody levels against galactosyl (alpha1 -->3) galactose epitopes in cervical mucus from patients with human papillomavirus infection. Gynecol Oncol 2002, 84: 374–377. 10.1006/gyno.2001.6516

    CAS  PubMed  Google Scholar 

  198. 198.

    Alvarez-Olmos MI, Barousse MM, Rajan L, Van Der Pol BJ, Fortenberry D, Orr D, Fidel PL Jr: Vaginal lactobacilli in adolescents: presence and relationship to local and systemic immunity, and to bacterial vaginosis. Sex Transm Dis 2004, 31: 393–400. 10.1097/01.OLQ.0000130454.83883.E9

    PubMed  Google Scholar 

  199. 199.

    Basso B, Gimenez F, Lopez C: IL-1beta, IL-6 and IL-8 levels in gyneco-obstetric infections. Infect Dis Obstet Gynecol 2005, 13: 207–211.

    CAS  PubMed Central  PubMed  Google Scholar 

  200. 200.

    Cauci S, Driussi S, Guaschino S, Isola M, Quadrifoglio F: Correlation of local interleukin-1beta levels with specific IgA response against Gardnerella vaginalis cytolysin in women with bacterial vaginosis. Am J Reprod Immunol 2002, 47: 257–264. 10.1034/j.1600-0897.2002.01096.x

    PubMed  Google Scholar 

  201. 201.

    Cauci S, Guaschino S, De AD, Driussi S, De SD, Penacchioni P, Quadrifoglio F: Interrelationships of interleukin-8 with interleukin-1beta and neutrophils in vaginal fluid of healthy and bacterial vaginosis positive women. Mol Hum Reprod 2003, 9: 53–58. 10.1093/molehr/gag003

    CAS  PubMed  Google Scholar 

  202. 202.

    Genc MR, Vardhana S, Delaney ML, Onderdonk A, Tuomala R, Norwitz E, Witkin SS: Relationship between a toll-like receptor-4 gene polymorphism, bacterial vaginosis-related flora and vaginal cytokine responses in pregnant women. Eur J Obstet Gynecol Reprod Biol 2004, 116: 152–156. 10.1016/j.ejogrb.2004.02.010

    CAS  PubMed  Google Scholar 

  203. 203.

    Genc MR, Karasahin E, Onderdonk AB, Bongiovanni AM, Delaney ML, Witkin SS: Association between vaginal 70-kd heat shock protein, interleukin-1 receptor antagonist, and microbial flora in mid trimester pregnant women. Am J Obstet Gynecol 2005, 192: 916–921. 10.1016/j.ajog.2004.09.023

    CAS  PubMed  Google Scholar 

  204. 204.

    Hedges SR, Barrientes F, Desmond RA, Schwebke JR: Local and systemic cytokine levels in relation to changes in vaginal flora. J Infect Dis 2006, 193: 556–562. 10.1086/499824

    CAS  PubMed  Google Scholar 

  205. 205.

    Imseis HM, Greig PC, Livengood CH, Shunior E, Durda P, Erikson M: Characterization of the inflammatory cytokines in the vagina during pregnancy and labor and with bacterial vaginosis. J Soc Gynecol Investig 1997, 4: 90–94. 10.1016/S1071-5576(97)00003-8

    CAS  PubMed  Google Scholar 

  206. 206.

    Platz-Christensen JJ, Mattsby-Baltzer I, Thomsen P, Wiqvist N: Endotoxin and interleukin-1 alpha in the cervical mucus and vaginal fluid of pregnant women with bacterial vaginosis. Am J Obstet Gynecol 1993, 169: 1161–1166.

    CAS  PubMed  Google Scholar 

  207. 207.

    Spandorfer SD, Neuer A, Giraldo PC, Rosenwaks Z, Witkin SS: Relationship of abnormal vaginal flora, proinflammatory cytokines and idiopathic infertility in women undergoing IVF. J Reprod Med 2001, 46: 806–810.

    CAS  PubMed  Google Scholar 

  208. 208.

    Sturm-Ramirez K, Gaye-Diallo A, Eisen G, Mboup S, Kanki PJ: High levels of tumor necrosis factor-alpha and interleukin-1beta in bacterial vaginosis may increase susceptibility to human immunodeficiency virus. J Infect Dis 2000, 182: 467–473. 10.1086/315713

    CAS  PubMed  Google Scholar 

  209. 209.

    Losikoff P, Fichorova R, Snyder B, Rodriguez I, Cu-Uvin S, Harwell J, Mayer KH: Genital tract interleukin-8 but not interleukin-1beta or interleukin-6 concentration is associated with bacterial vaginosis and its clearance in HIV-infected and HIV-uninfected women. Infect Dis Obstet Gynecol 2007, 2007: 92307.

    PubMed Central  PubMed  Google Scholar 

  210. 210.

    Tavares-Murta BM, de Resende AD, Cunha FQ, Murta EF: Local profile of cytokines and nitric oxide in patients with bacterial vaginosis and cervical intraepithelial neoplasia. Eur J Obstet Gynecol Reprod Biol 2008, 138: 93–99. 10.1016/j.ejogrb.2007.06.015

    CAS  PubMed  Google Scholar 

  211. 211.

    Wennerholm UB, Holm B, Mattsby-Baltzer I, Nielsen T, Platz-Christensen JJ, Sundell G, Hagberg H: Interleukin-1alpha, interleukin-6 and interleukin-8 in cervico/vaginal secretion for screening of preterm birth in twin gestation. Acta Obstet Gynecol Scand 1998, 77: 508–514. 10.1080/j.1600-0412.1998.770507.x

    CAS  PubMed  Google Scholar 

  212. 212.

    Goldenberg RL, Goepfert AR, Ramsey PS: Biochemical markers for the prediction of preterm birth. Am J Obstet Gynecol 2005, 192: S36-S46. 10.1016/j.ajog.2005.02.015

    CAS  PubMed  Google Scholar 

  213. 213.

    Friese K: The role of infection in preterm labour. BJOG 2003,110(Suppl 20):52–54.

    CAS  PubMed  Google Scholar 

  214. 214.

    Rizzo G, Capponi A, Rinaldo D, Tedeschi D, Arduini D, Romanini C: Interleukin-6 concentrations in cervical secretions identify microbial invasion of the amniotic cavity in patients with preterm labor and intact membranes. Am J Obstet Gynecol 1996, 175: 812–817. 10.1016/S0002-9378(96)80004-4

    CAS  PubMed  Google Scholar 

  215. 215.

    Discacciati MG, Simoes JA, Silva MG, Marconi C, Brolazo E, Costa ML, Cecatti JG: Microbiological characteristics and inflammatory cytokines associated with preterm labor. Arch Gynecol Obstet 2010, in press.

    Google Scholar 

  216. 216.

    Kurkinen-Raty M, Ruokonen A, Vuopala S, Koskela M, Rutanen EM, Karkkainen T, Jouppila P: Combination of cervical interleukin-6 and -8, phosphorylated insulin-like growth factor-binding protein-1 and transvaginal cervical ultrasonography in assessment of the risk of preterm birth. BJOG 2001, 108: 875–881. 10.1016/S0306-5456(00)00199-6

    CAS  PubMed  Google Scholar 

  217. 217.

    Goepfert AR, Goldenberg RL, Andrews WW, Hauth JC, Mercer B, Iams J, Meis P, Moawad A, Thom E, VanDorsten JP, et al.: The Preterm Prediction Study: association between cervical interleukin 6 concentration and spontaneous preterm birth. National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network. Am J Obstet Gynecol 2001, 184: 483–488. 10.1067/mob.2001.109653

    CAS  PubMed  Google Scholar 

  218. 218.

    Sakai M, Sasaki Y, Yoneda S, Kasahara T, Arai T, Okada M, Hosokawa H, Kato K, Soeda Y, Saito S: Elevated interleukin-8 in cervical mucus as an indicator for treatment to prevent premature birth and preterm, pre-labor rupture of membranes: a prospective study. Am J Reprod Immunol 2004, 51: 220–225. 10.1111/j.1600-0897.2004.00145.x

    PubMed  Google Scholar 

  219. 219.

    Sakai M, Ishiyama A, Tabata M, Sasaki Y, Yoneda S, Shiozaki A, Saito S: Relationship between cervical mucus interleukin-8 concentrations and vaginal bacteria in pregnancy. Am J Reprod Immunol 2004, 52: 106–112. 10.1111/j.1600-0897.2004.00203.x

    PubMed  Google Scholar 

  220. 220.

    Tanaka Y, Narahara H, Takai N, Yoshimatsu J, Anai T, Miyakawa I: Interleukin-1beta and interleukin-8 in cervicovaginal fluid during pregnancy. Am J Obstet Gynecol 1998, 179: 644–649. 10.1016/S0002-9378(98)70058-4

    CAS  PubMed  Google Scholar 

  221. 221.

    Arinami Y, Hasegawa I, Takakuwa K, Tanaka K: Prediction of preterm delivery by combined use of simple clinical tests. J Matern Fetal Med 1999, 8: 70–73. 10.1002/(SICI)1520-6661(199903/04)8:2<70::AID-MFM8>3.0.CO;2-L

    CAS  PubMed  Google Scholar 

  222. 222.

    Kanayama N, Terao T: The relationship between granulocyte elastase-like activity of cervical mucus and cervical maturation. Acta Obstet Gynecol Scand 1991, 70: 29–34. 10.3109/00016349109006174

    CAS  PubMed  Google Scholar 

  223. 223.

    Jotterand AD, Caubel P, Guillaumin D, Augereau F, Chitrit Y, Boulanger MC: Predictive value of cervical-vaginal prolactin in the evaluation of premature labor risk. J Gynecol Obstet Biol Reprod (Paris) 1997, 26: 95–99.

    CAS  Google Scholar 

  224. 224.

    Andrews WW, Tsao J, Goldenberg RL, Hauth JC, Mercer B, Iams J, Meis P, Moawad A, Das A, Van Dorsten PJ, et al.: The preterm prediction study: failure of midtrimester cervical sialidase level elevation to predict subsequent spontaneous preterm birth. Am J Obstet Gynecol 1999, 180: 1151–1154. 10.1016/S0002-9378(99)70609-5

    CAS  PubMed  Google Scholar 

  225. 225.

    Jacobsson B, Holst RM, Wennerholm UB, Andersson B, Lilja H, Hagberg H: Monocyte chemotactic protein-1 in cervical and amniotic fluid: relationship to microbial invasion of the amniotic cavity, intra-amniotic inflammation, and preterm delivery. Am J Obstet Gynecol 2003, 189: 1161–1167. 10.1067/S0002-9378(03)00594-5

    CAS  PubMed  Google Scholar 

  226. 226.

    Kekki M, Kurki T, Karkkainen T, Hiilesmaa V, Paavonen J, Rutanen EM: Insulin-like growth factor-binding protein-1 in cervical secretion as a predictor of preterm delivery. Acta Obstet Gynecol Scand 2001, 80: 546–551. 10.1080/j.1600-0412.2001.080006546.x

    CAS  PubMed  Google Scholar 

  227. 227.

    Lembet A, Eroglu D, Ergin T, Kuscu E, Zeyneloglu H, Batioglu S, Haberal A: New rapid bed-side test to predict preterm delivery: phosphorylated insulin-like growth factor binding protein-1 in cervical secretions. Acta Obstet Gynecol Scand 2002, 81: 706–712.

    PubMed  Google Scholar 

  228. 228.

    Goldenberg RL, Andrews WW, Guerrant RL, Newman M, Mercer B, Iams J, Meis P, Moawad A, Das A, VanDorsten JP, et al.: The preterm prediction study: cervical lactoferrin concentration, other markers of lower genital tract infection, and preterm birth. National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network. Am J Obstet Gynecol 2000, 182: 631–635. 10.1067/mob.2000.104211

    CAS  PubMed  Google Scholar 

  229. 229.

    Bernstein PS, Stern R, Lin N, Furgiuele J, Karmen A, Comerford-Freda M, Chazotte C: Beta-human chorionic gonadotropin in cervicovaginal secretions as a predictor of preterm delivery. Am J Obstet Gynecol 1998, 179: 870–873. 10.1016/S0002-9378(98)70180-2

    CAS  PubMed  Google Scholar 

  230. 230.

    Rengaraj G, Guleria K, Suneja A, Gambhir JK: Human chorionic gonadotropin in cervicovaginal secretions as a predictor of preterm birth. Gynecol Obstet Invest 2009, 67: 202–207. 10.1159/000189917

    CAS  PubMed  Google Scholar 

  231. 231.

    Shahin M, Raslan H: Comparative study of three amniotic fluid markers in premature rupture of membranes: prolactin, beta subunit of human chorionic gonadotropin, and alpha-fetoprotein. Gynecol Obstet Invest 2007, 63: 195–199. 10.1159/000097844

    CAS  PubMed  Google Scholar 

  232. 232.

    Carranza LS, Negrete LM, Quinzanos FC, Leanos MA: Usefulness of the qualitative detection of hCG in cervicovaginal lavage for the diagnosis of premature rupture of membranes. Ginecol Obstet Mex 2009, 77: 142–146.

    Google Scholar 

  233. 233.

    Cooper AL, Vermillion ST, Soper DE: Qualitative human chorionicgonadotropin testing of cervicovaginal washings for the detection of preterm premature rupture of membranes. Am J Obstet Gynecol 2004, 191: 593–596. 10.1016/j.ajog.2004.06.076

    CAS  PubMed  Google Scholar 

  234. 234.

    Mangano B, Diani F, Faccini G, Zatti N, Zardini E: Proposal of a new test for the diagnosis of PROM based on the determination of hCG in the washing fluid of the posterior vaginal fornix. Minerva Ginecol 2000, 52: 185–188.

    CAS  PubMed  Google Scholar 

  235. 235.

    Kim YH, Park YW, Kwon HS, Kwon JY, Kim BJ: Vaginal fluid beta-human chorionic gonadotropin level in the diagnosis of premature rupture of membranes. Acta Obstet Gynecol Scand 2005, 84: 802–805.

    PubMed  Google Scholar 

  236. 236.

    Anai T, Tanaka Y, Hirota Y, Miyakawa I: Vaginal fluid hCG levels for detecting premature rupture of membranes. Obstet Gynecol 1997, 89: 261–264. 10.1016/S0029-7844(96)00448-6

    CAS  PubMed  Google Scholar 

  237. 237.

    Erdemoglu E, Mungan T: Significance of detecting insulin-like growth factor binding protein-1 in cervicovaginal secretions: comparison with nitrazine test and amniotic fluid volume assessment. Acta Obstet Gynecol Scand 2004, 83: 622–626.

    PubMed  Google Scholar 

  238. 238.

    Akercan F, Cirpan T, Kazandi M, Terek MC, Mgoyi L, Ozkinay E: The value of the insulin-like growth factor binding protein-1 in the cervical-vaginal secretion detected by immunochromatographic dipstick test in the prediction of delivery in women with clinically unconfirmed preterm premature rupture of membranes. Eur J Obstet Gynecol Reprod Biol 2005, 121: 159–163. 10.1016/j.ejogrb.2004.12.006

    CAS  PubMed  Google Scholar 

  239. 239.

    Akercan F, Kazandi M, Sendag F, Cirpan T, Mgoyi L, Terek MC, Sagol S: Value of cervical phosphorylated insulinlike growth factor binding protein-1 in the prediction of preterm labor. J Reprod Med 2004, 49: 368–372.

    CAS  PubMed  Google Scholar 

  240. 240.

    Gaucherand P, Salle B, Sergeant P, Guibaud S, Brun J, Bizollon CA, Rudigoz RC: Comparative study of three vaginal markers of the premature rupture of membranes. Insulin like growth factor binding protein 1 diamine-oxidase pH. Acta Obstet Gynecol Scand 1997, 76: 536–540. 10.3109/00016349709024579

    CAS  PubMed  Google Scholar 

  241. 241.

    Jeurgens-Borst AJ, Bekkers RL, Sporken JM, van den Berg PP: Use of insulin like growth factor binding protein-1 in the diagnosis of ruptured fetal membranes. Eur J Obstet Gynecol Reprod Biol 2002, 102: 11–14. 10.1016/S0301-2115(01)00560-7

    CAS  PubMed  Google Scholar 

  242. 242.

    Kubota T, Takeuchi H: Evaluation of insulin-like growth factor binding protein-1 as a diagnostic tool for rupture of the membranes. J Obstet Gynaecol Res 1998, 24: 411–417. 10.1111/j.1447-0756.1998.tb00116.x

    CAS  PubMed  Google Scholar 

  243. 243.

    Lockwood CJ, Wein R, Chien D, Ghidini A, Alvarez M, Berkowitz RL: Fetal membrane rupture is associated with the presence of insulin-like growth factor-binding protein-1 in vaginal secretions. Am J Obstet Gynecol 1994, 171: 146–150.

    CAS  PubMed  Google Scholar 

  244. 244.

    Martinez de TB, Boulvain M, Dumps P, Bischof P, Meisser A, Irion O: Can we improve the diagnosis of rupture of membranes? The value of insulin-like growth factor binding protein-1. BJOG 2006, 113: 1096–1099. 10.1111/j.1471-0528.2006.01028.x

    Google Scholar 

  245. 245.

    Rutanen EM, Karkkainen TH, Lehtovirta J, Uotila JT, Hinkula MK, Hartikainen AL: Evaluation of a rapid strip test for insulin-like growth factor binding protein-1 in the diagnosis of ruptured fetal membranes. Clin Chim Acta 1996, 253: 91–101. 10.1016/0009-8981(96)80001-E

    CAS  PubMed  Google Scholar 

  246. 246.

    Rutanen EM, Pekonen F, Karkkainen T: Measurement of insulin-like growth factor binding protein-1 in cervical/vaginal secretions: comparison with the ROM-check Membrane Immunoassay in the diagnosis of ruptured fetal membranes. Clin Chim Acta 1993, 214: 73–81. 10.1016/0009-8981(93)90304-M

    CAS  PubMed  Google Scholar 

  247. 247.

    Broe D, Van DJ, Cowley D, Vacca A, Voreteliac V, Maquire D, Ellis V: Detection of premature rupture of membranes by measuring diamine oxidase in vaginal fluid: false-negative results caused by obstetric antiseptic creams. Clin Chem 1992, 38: 784.

    CAS  PubMed  Google Scholar 

  248. 248.

    De Meeus JB, Sima OB, Bascou V, Magnin G: Biological diagnosis of premature rupture of membranes: respective values of diamine oxidase activity compared to vaginal fluid pH (Amnicator). J Gynecol Obstet Biol Reprod (Paris) 1997, 26: 730–733.

    CAS  Google Scholar 

  249. 249.

    Gahl WA, Kozina TJ, Fuhrmann DD, Vale AM: Diamine oxidase in the diagnosis of ruptured fetal membranes. Obstet Gynecol 1982, 60: 297–304.

    CAS  PubMed  Google Scholar 

  250. 250.

    Gaucherand P, Guibaud S, Awada A, Rudigoz RC: Comparative study of three amniotic fluid markers in premature rupture of membranes: fetal fibronectin, alpha-fetoprotein, diamino-oxydase. Acta Obstet Gynecol Scand 1995, 74: 118–121. 10.3109/00016349509008918

    CAS  PubMed  Google Scholar 

  251. 251.

    Wishart MM, Jenkins DT, Knott ML: Measurement of diamine oxidase activity in vaginal fluid -- an aid to diagnosis of ruptured fetal membranes. Aust N Z J Obstet Gynaecol 1979, 19: 23–24. 10.1111/j.1479-828X.1979.tb01345.x

    CAS  PubMed  Google Scholar 

  252. 252.

    Ogino M, Hiyamuta S, Takatsuji-Okawa M, Tomooka Y, Minoura S: Establishment of a prediction method for premature rupture of membranes in term pregnancy using active ceruloplasmin in cervicovaginal secretion as a clinical marker. J Obstet Gynaecol Res 2005, 31: 421–426. 10.1111/j.1447-0756.2005.00313.x

    CAS  PubMed  Google Scholar 

  253. 253.

    Lockwood CJ, Senyei AE, Dische MR, Casal D, Shah KD, Thung SN, Jones L, Deligdisch L, Garite TJ: Fetal fibronectin in cervical and vaginal secretions as a predictor of preterm delivery. N Engl J Med 1991, 325: 669–674. 10.1056/NEJM199109053251001

    CAS  PubMed  Google Scholar 

  254. 254.

    Di Naro E, Ghezzi F, Raio L, Romano F, Mueller MD, McDougall J, Cicinelli E: C-reactive protein in vaginal fluid of patients with preterm premature rupture of membranes. Acta Obstet Gynecol Scand 2003, 82: 1072–1079. 10.1046/j.1600-0412.2003.00223.x

    PubMed  Google Scholar 

  255. 255.

    Gaucherand P, Guibaud S, Rudigoz RC, Wong A: Diagnosis of premature rupture of the membranes by the identification of alpha-feto-protein in vaginal secretions. Acta Obstet Gynecol Scand 1994, 73: 456–459. 10.3109/00016349409013430

    CAS  PubMed  Google Scholar 

  256. 256.

    Kishida T, Hirao A, Matsuura T, Katamine T, Yamada H, Sagawa T, Fujimoto S: Diagnosis of premature rupture of membranes with an improved alpha-fetoprotein monoclonal antibody kit. Clin Chem 1995, 41: 1500–1503.

    CAS  PubMed  Google Scholar 

  257. 257.

    Kishida T, Yamada H, Negishi H, Sagawa T, Makinoda S, Fujimoto S: Diagnosis of preterm premature rupture of the membranes using a newly developed AFP monoclonal antibody test kit. Eur J Obstet Gynecol Reprod Biol 1995, 58: 67–72. 10.1016/0028-2243(94)01973-B

    CAS  PubMed  Google Scholar 

  258. 258.

    Kishida T, Yamada H, Negishi H, Sagawa T, Makinoda S, Fujimoto S: Diagnosis of premature rupture of the membranes in preterm patients, using an improved AFP kit: comparison with ROM-check and/or nitrazine test. Eur J Obstet Gynecol Reprod Biol 1996, 69: 77–82. 10.1016/0301-2115(95)02519-7

    CAS  PubMed  Google Scholar 

  259. 259.

    Ni CY, Jia WX, Yi WM, Feng LH, Yu LZ: Practicability of using vaginal fluid markers in detecting premature rupture of membranes. Ann Clin Biochem 2003, 40: 542–545. 10.1258/000456303322326461

    PubMed  Google Scholar 

  260. 260.

    Buyukbayrak EE, Turan C, Unal O, Dansuk R, Cengizoglu B: Diagnostic power of the vaginal washing-fluid prolactin assay as an alternative method for the diagnosis of premature rupture of membranes. J Matern Fetal Neonatal Med 2004, 15: 120–125. 10.1080/14767050410001659842

    CAS  PubMed  Google Scholar 

  261. 261.

    Huber JF, Bischof P, Extermann P, Beguin F, Herrmann WL: Are vaginal fluid concentrations of prolactin, alpha-fetoprotein and human placental lactogen useful for diagnosing ruptured membranes? Br J Obstet Gynaecol 1983, 90: 1183–1185.

    CAS  PubMed  Google Scholar 

  262. 262.

    Koninckx PR, Trappeniers H, Van Assche FA: Prolactin concentration in vaginal fluid: a new method for diagnosing ruptured membranes. Br J Obstet Gynaecol 1981, 88: 607–610.

    CAS  PubMed  Google Scholar 

  263. 263.

    Phocas I, Sarandakou A, Kontoravdis A, Chryssicopoulos A, Zourlas PA: Vaginal fluid prolactin: a reliable marker for the diagnosis of prematurely ruptured membranes. Comparison with vaginal fluid alpha-fetoprotein and placental lactogen. Eur J Obstet Gynecol Reprod Biol 1989, 31: 133–141. 10.1016/0028-2243(89)90174-3

    CAS  PubMed  Google Scholar 

Download references


GZ is supported by a doctoral fellowship of the Research Foundation-Flanders (FWO). GAAVR is funded by a doctoral fellowship of the Agency for Innovation by Science and Technology in Flanders (IWT).

Author information



Corresponding author

Correspondence to Xaveer WM Van Ostade.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

GZ was responsible for the research and the writing of the manuscript. GAAVR and WAAT wrote a significant part about proteomics and HPV infection, provided advice and reviewed the manuscript. XWMVO was responsible for the supervision and thorough review of the manuscript. All authors read and approved the final manuscript.

Electronic supplementary material

Overview of all identifications obtained in different proteomic studies on human CVF

Additional file 1: . (PDF 235 KB)

List of proteins identified from CVF and cervical mucus

Additional file 2: . Swissprot accession number, the protein name, cellular localization and the different biological functions of the protein are given. Yellow highlighted proteins were also identified in human amniotic fluid, green highlighted proteins were identified in human serum and orange highlighted proteins were identified in serum as well as in amniotic fluid. Proteins marked with * are uniquely identified in cervical mucus and not in any other study on human CVF. (PDF 2 MB)

Authors’ original submitted files for images

Below are the links to the authors’ original submitted files for images.

Authors’ original file for figure 1

Authors’ original file for figure 2

Authors’ original file for figure 3

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Zegels, G., Van Raemdonck, G.A., Tjalma, W.A. et al. Use of cervicovaginal fluid for the identification of biomarkers for pathologies of the female genital tract. Proteome Sci 8, 63 (2010).

Download citation


  • Human Immunodeficiency Virus
  • Cervical Cancer
  • Preterm Birth
  • Bacterial Vaginosis
  • Female Genital Tract