Systems biology approaches to identify developmental bases for lung diseases

Abstract

A greater understanding of the regulatory processes contributing to lung development could be helpful to identify strategies to ameliorate morbidity and mortality in premature infants and to identify individuals at risk for congenital and/or chronic lung diseases. Over the past decade, genomics technologies have enabled the production of rich gene expression databases providing information for all genes across developmental time or in diseased tissue. These data sets facilitate systems biology approaches for identifying underlying biological modules and programs contributing to the complex processes of normal development and those that may be associated with disease states. The next decade will undoubtedly see rapid and significant advances in redefining both lung development and disease at the systems level.

Figure 1

Systems biology approaches in lung development. Systems biology involves integration of “classical” data collection methods, including genetic, environmental, experimental, and clinical data, with various “omics” approaches through both human subjects and animal models. The “omics” data from different sources can be integrated through various computational informatics approaches. The ultimate goal of systems biology is to understand the underlying mechanisms and pathways involved in complex biological processes including development and disease.

Figure 2

Discovery of SERPINE2 as a candidate gene for COPD susceptibility. “Systems biology” using a combination of genetic linkage, microarray gene expression and genetic association studies were used to identify SERPINE2 as a candidate susceptibility gene for COPD. LOD, logarithm of odds score. FEV₁, Forced Expiratory Volume in 1 second. RV, Residual Volume. ERV, Expiratory Reserve Volume. IRV, Inspiratory Reserve Volume. In the pedigree chart, square represents male, circle represents female, and triangles represent offspring. White indicates unaffected and black indicates diseased subjects. In the lower right of the figure, red indicates females and blue indicates male.

Figure 3

Developmental data alters disease mechanism prediction. (A) Pathway analysis identified a cluster of 31 genes, from among 159 genes significantly affected in BPD, which were related to IGF1. (B) Integrating normal human lung developmental data into the analysis, shifts CDK1 from a peripheral to a central node in this BPD-related pathway.

Figure 4

Defining lung development as molecular phases. Lung development is divided into five distinct histological stages that occur during specific time-periods. Alternately, lung development may be defined as a set of non-distinct molecular phases (Mol Phase MX), each occurring in waves and composed of overlapping gene sets (GS). For instance, in this example, the pseudoglandular stage includes 2 molecular phases (M1, M2). These phases are composed of 3 common gene sets (GS1-3), but differ by the contributions of gene set (GS4).