One important limitation in understanding preterm births or stillbirths is the application of traditional biological methodologies to the complex process of parturition. Traditional biology attempts to explain complex phenomena by the functional properties of individual components of a complex system, a method described as "naive reductionism" [92
]. Recent advances in highdimensional systems biology allow the characterization of complex processes through the global description of the components of a system and their interactions. New tools for systems biology research now exist:
• computational design (or computer assisted in-silico modeling) [93
• genomics (characterization of genetic potential)
• transcriptomics (characterization of gene expression)
• proteomics (characterization of the protein output of gene transcription and translation)
• metabolomics (characterization of the metabolic consequences of gene expression)
Collectively, these techniques have revolutionized biologic research in the last decade. A PubMed search reveals that over 39,000 articles have been published from 1998 through 2008 utilizing these techniques (search terms "functional genomics, transcriptome, proteomics, metabolomics). These techniques, however, have been only infrequently applied to pregnancy, with approximately
1,000 relevant articles published in the same time period (search term "+ pregnancy"). This disparity in utilization of these powerful research tools is increasing (Figure ) and represents a critical gap in the understanding of parturition and stillbirths.
Figure 3 A compilation of systems biology publications and proportion relating to pregnancy, semi-logarithmic scale. Data source: Data abstracted from PubMed, 1997-2008. Key abstracting words “functional genomics, transcriptomics, proteomics… alone (more ...)
Nonetheless, gene expression studies of the pregnant uterus have contributed to the understanding of parturition [94
] (Table ). The assessment of gene expression is typically measured by detection of mRNA copies produced by each gene in what are known as microarray assays. These studies differed in design, gene expression platforms, and the number of genes studied. Yet collectively, these studies demonstrate that labor is a highly complex biological process, potentially involving hundreds of genes. The number of differentially expressed genes discovered, in fact, usually depends directly upon the number sought (Table ). The most recent study, for example, using an Affymetrix platform to ascertain activity of over 12,000 genes found 110 genes that were up-regulated, and 29 that were down-regulated in association with labor [99
]. Regardless of technique, microarray analysis has led to a new understanding of labor as inherently an inflammatory process, even in the absence of infection [97
]. Approximately 25-30% of genes—with up-regulated expression in labor—code for inflammatory proteins including chemokines, cytokines, extracellular remodeling proteins, and apoptosis. Thus, these studies have contributed to the preliminary understanding of events governing myometrial activation prior to labor.
Compilation of studies comparing differential gene expression in human myometrium associated with labor
In addition to functional genomics, it is well established that the activity of many genes may be modified by SNPs that normally occur with varied frequencies across populations. More than 30 SNPs have been associated with increased or decreased risk of preterm birth or preterm premature rupture of membranes [75
]. Consistent with microarray data, the majority of the SNPs associated with preterm birth are in inflammatory, apoptotic, and tissue remodeling genes [75
] Thus, the application of systems biology, including functional genomics, transcriptomics, and SNP analysis, has contributed to a new paradigm: myometrial activation and the onset of myometrial stimulation is a highly complex genetically-controlled inflammatory process.
Proteomics has also made contributions in the understanding of abnormal parturition. Proteomics, a mass spectrometry-based technology, refers to a description of the protein complement of a system. Only 1-1.5% of the human genome codes for mRNA, and thus, leads to protein synthesis. These proteins, in turn, mediate cell-to-cell interactions in both health and disease. The major advantage of proteomics is that it most directly describes the functional output of a cell in both health and disease. Proteomics primarily aids biomarker discovery in the diagnosis of abnormal conditions of pregnancy that may contribute to prematurity. As noted by the March of Dimes, such diagnostic tools are urgently needed to facilitate timely intervention [100
]. Proteomic analysis of amniotic fluid and cervical-vaginal secretions has been utilized to discover novel biomarkers for intra-amniotic infection [101
], spontaneous preterm birth [75
], and preterm premature rupture of the membranes [104
]. Proteomic analysis of maternal urine has also identified several specific biomarkers for preeclampsia, an important cause of indicated preterm birth [75
]. Additionally, comprehensive proteomic analysis has been utilized to fully characterize the protein complement of amniotic fluid and of cervical-vaginal fluid. Michaels, et al., utilizing LS/LS-MS/MS, identified 219 proteins in amniotic fluid, including 96 that were unique to amniotic fluid [105
]. Similarly, Dasari, et al. identified a total of 150 unique proteins within cervical-vaginal fluid in pregnant women. Metabolism (32%) and immune response-related (22%) proteins were the major functional categories of proteins represented in the CVF proteome. A comparison of the CVF, serum, and amniotic fluid proteomes showed that 77 proteins are unique to CVF, while 56 and 17 CVF proteins also occur in serum and amniotic fluid, respectively [106
]. The differential expression of proteins identified by these and other studies will likely provide the basis for diagnostic tests for many adverse pregnancy conditions including preterm birth.
Unfortunately, none of these news tools for systems biology has been applied to stillbirth. It is likely that application of these techniques in a systems biological approach will lead to significant contributions in the understanding of preterm birth and stillbirth. Because of the ability to study many processes simultaneously, however, systems biology depends upon carefully defined clinical phenotypes to avoid errors of misclassification and upon carefully prepared samples to avoid technical errors. Defining and correctly classifying differing phenotypes, or etiologies, of preterm birth or stillbirth—and appropriate collection and storage of biologic samples—are therefore critical needs and unmet gaps in systems biology and in understanding parturition.