Since relevant biochemical changes are known to begin at the choriodecidual interface some weeks before actual clinical onset of labor, we hypothesized that the preterm choriodecidua may display gene and protein expression patterns specific to preterm labor. Transcriptomic (microarray) and proteomic (two-dimensional gel electrophoresis (2DGE)) profiling methodologies were utilized to compare changes in choriodecidual tissue collected from women who delivered before 35 weeks of gestation following spontaneous preterm labor (n=12) and gestation-matched non-laboring controls (n=7). Additionally, 2DGE was used to compare differences in protein expression during term and preterm labor and to construct a choriodecidual proteome map. Overall, expressed transcripts and proteins indicated active tissue remodelling independent of labor status and an association with inflammatory processes during labor. Spontaneous, infection-induced and abruption-associated preterm deliveries were each defined by distinct transcriptional profiles. Proteins osteoglycin and PGRMC2 were upregulated during term and preterm labor while galectin 1, annexin 3, annexin 5 and PDI were upregulated only during preterm labor suggesting a probable association with the underlying pathology. Together, these results represent novel data that warrant further investigations to elucidate plausible causal relationships of these molecules with spontaneous preterm delivery.

Elucidation of underlying cellular and molecular mechanisms is pivotal to the comprehension of biological systems. The successful progression of processes such as pregnancy and parturition depends on the complex interactions between numerous biological molecules especially within the uterine microenvironment. The tissue-and stage-specific expression of these biomolecules is intricately linked to and modulated by several endogenous and exogenous factors. Malfunctions may manifest as pregnancy disorders such as preterm labour, pre-eclampsia and fetal growth restriction that are major contributors to maternal and perinatal morbidity and mortality. Despite the immense amount of information available, our understanding of several aspects of these physiological processes remains incomplete. This translates into significant difficulties in the timely diagnosis and effective treatment of pregnancy-related complications. However, the emergence of powerful mass spectrometry-based proteomic techniques capable of identifying and characterizing multiple proteins simultaneously has added a new dimension to the field of biomedical research. Application of these high throughput methodologies with more conventional techniques in pregnancy-related research has begun to provide a novel perspective on the biochemical blueprint of pregnancy and its related disorders. Further, by enabling the identification of proteins specific to a disease process, proteomics is likely to contribute, not only to the comprehension of the underlying pathophysiologies, but also to the clinical diagnosis of multifactorial pregnancy disorders. Although the application of this technology to pregnancy research is in its infancy, characterization of the cellular proteome, unearthing of functional networks and the identification of disease biomarkers can be expected to significantly improve maternal healthcare in the future.

The complex interplay of many cell types and the temporal heterogeneity of pancreatic islet composition obscure the direct role of resident alpha and beta cells in the development of Type 1 diabetes. Therefore, in addition to studying islets isolated from non-obese diabetic mice, we analyzed homogeneous cell populations of murine alpha (αTC-1) and beta (NIT-1) cell lines to understand the role and differential survival of these two predominant islet cell populations. A total of 56 proteins in NIT-1 cells and 50 in αTC-1 cells were differentially expressed when exposed to proinflammatory cytokines. The major difference in the protein expression between cytokine-treated NIT-1 and αTC-1 cells was free radical scavenging enzymes. A similar observation was made in cytokine-treated whole islets, where a comprehensive analysis of subcellular fractions revealed that 438 unique proteins were differentially expressed under inflammatory conditions. Our data indicate that beta cells are relatively susceptible to ER and oxidative stress and reveal key pathways that are dysregulated in beta cells during cytokine exposure. Additionally, in the islets, inflammation also leads to enhanced antigen presentation, which completes a three-way insult on beta cells, rendering them targets of infiltrating T lymphocytes.

Maternal and perinatal morbidity and mortality rates are significantly higher in pregnancies complicated by preterm labor, pre-eclampsia and fetal growth restriction. Decades of research have not translated into a clear understanding of the underlying pathophysiologies or effective identification of women who are at high risk of developing these complications. Often the severity of these diseases does not correlate with the clinical symptoms, and current diagnostic methods are unable to accurately predict the conditions prior to clinical presentation. Though several potential markers have been proposed for each of these disorders, to date none have proven clinical utility. Emerging proteomic technology is only beginning to be employed in pregnancy research. A comprehensive analysis of gestational tissues can be expected to contribute to the elucidation of the complex molecular mechanisms of pregnancy and related complications. Comparison of the expression profiles of normal and pathogenic tissues and biofluids may also highlight novel candidate marker proteins that have so far remained undetected. More interestingly, rapidly evolving technologies using sophisticated bioinformatic tools are demonstrating their potential in disease diagnostics by using overall protein profiles to detect diseases. The clinical significance of these methodological advances is enormous. Early diagnosis together with improved understanding of underlying molecular mechanisms can enhance outcomes and increase effective management and therapeutic options.