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.
At a glance
There is a wealth of biological information available for chronic diseases of the lung (1). However, a critical gap in knowledge that may create challenges in development of therapeutic modalities for lung diseases is the lack of complete understanding of the development mechanisms of the normal pulmonary system and of how alterations therein contribute to disease pathophysiology. It is becoming increasingly clear that many respiratory diseases have their origin early in life and are influenced by developmental, as well as genetic and environmental, factors. A thorough understanding of lung development is needed to understand how the environmental and genetic factors affect the process.
Lung morphogenesis occurs both prenatally and postnatally, and is typically divided into five phases, with the final alveolar phase occurring principally after birth in humans and rodents. The initiation of lung formation as a bud off the lateral foregut endoderm (“embryonic” stage) occurs from 26 d postconception to upto 5 wk of gestation, and the corresponding duration in mouse is through embryonic day 9.5. Expansion of the bronchial tree, including formation of the bronchi and bronchioles, occurs during the pseudoglandular stage that runs from 5 to 16 wk of gestation in humans, with corresponding time period in mouse being embryonic days 14.5–16.5. The distal region of the lung, where gas exchange will ultimately occur, expands exponentially during the canalicular period, which spans the 16th to the 26th wk of gestation in humans, and embryonic days 16.5–17.5 in mice. In the saccular stage, from the 26th to the 36th wk in humans (and embryonic day 17.5 to postnatal day 5 in mice), the air spaces in the respiratory portion of the lung mature to include include surfactant- (type II) and nonsurfactant-producing (type I) pneumocytes. The final stage of lung development, termed alveolarization or alveogenesis, begins before birth in humans and extends through at least the first decade of life; however, this occurs entirely postnatally in mice. In this stage, there is a vast expansion of the surface area of the lung, such that the adult human lung has roughly the same surface area as a tennis court, and a substantial reorganization of the capillaries to facilitate gas exchange.
The lung is a complex three-dimensional organ whose functions depend on the formation and maintenance of dynamic interactions between multiple cell and tissue systems. These include the highly branched system of airway tubes and terminal alveolar sacs, a complex hierarchy of respiratory and nonrespiratory epithelial cells that lines these tubes and sacs, blood and lymphatic vessels, nerves, smooth muscle cells and fibroblasts, and cells of the immune system. Defects, not only in individual components, but in interactions among them, lead to significant respiratory disorders that affect neonates, infants, juveniles, and adults.
Systems biology has been defined as the study of the interactions between the components of biological systems and of how these interactions give rise to the function and behavior of these systems (2). However, in practical terms, systems biology still means different things to different people. It can be interpreted as the ability to obtain, integrate, and analyze complex data sets using interdisciplinary tools, from multiple platforms, for example, genomics, epigenetics, transcriptomics, proteomics, and metabolomics. The advantage it has over “classical” or traditional approaches is that it considers the organ as a whole and involves modeling of the entire system through integration of its various components. Once individual components accurately mimic the responses to specific stimuli, they can be integrated into a system that can be used to understand organ physiology, including development, disease progression, and therapeutic interventions, and to predict the molecular responses to biological perturbations (3). This is an ideal approach for obtaining a greater understanding of the complexity of the lung, its formation, and its development (Figure 1).
The application of comprehensive and unbiased functional genomics methods, complementing focused approaches that draw on decades of research, provide a more integrated approach to better understand developmental processes and regulatory networks. Over the past few years, there has been a great expansion of genomic data that has facilitated redefining lung development at the molecular level, provided a better understanding of the mechanisms of lung pathophysiology, and identified putative markers for early disease detection, diagnosis, and treatment. However, as compared with other disciplines, there has been a lag in implementing systems biology approaches in the pulmonary field, and it is more so evident in the area of lung development research. At a recent National Institutes of Health workshop, participants recommended, among other things, developing strategies for integration of systems biology approaches to decipher the mechanisms of developmental origins of lung diseases (4). An expansion of systems-level approaches to integrate multiple levels of molecular and functional information has great potential for discovery of underlying mechanisms occurring during the process of lung development. Because “omics” technologies are completely unsupervised, they have the potential to discover new and unsuspected links between processes and pathways during development.
Global Gene Expression of Lung Development
A greater understanding of the regulatory processes contributing to lung development could help ameliorate morbidity and mortality in premature infants and identify individuals at risk for congenital and/or chronic lung diseases. The development of high-throughput approaches to determine DNA sequences and mRNA abundance, and to simultaneously analyze large numbers of proteins, has facilitated impressive progress in this direction. Genomics technologies also have provided rich gene expression databases containing information for specific genes across development (Table 1).
The most commonly applied approach for high-throughput genome-wide technologies is gene expression microarrays. For more than a decade now, microarrays have proven useful in gaining novel insights for many human diseases. Microarray technology has revolutionized the search for disease biomarkers by simultaneous comparison of expression changes of thousands of genes. This has led to an increasing number of data sets being deposited in public databases, such as the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/), Array Express (http://www.ebi.ac.uk/arrayexpress/), and Stanford Microarray Database (http://smd.stanford.edu/). These data sets enable systems biology approaches for identifying biological modules contributing to complex processes. The rapid emergence of microarray technology in combination with information about sequences and function of genes provides a wealth of data in both human and animal models.
In the field of lung biology, most of the gene expression data sets have been two-class (e.g., case–control or treatment–control) comparison studies looking for markers of lung diseases. However, the application of expression profiling methods to study lung development has lagged behind. The first applications of high-throughput analysis methods to study global gene expression of the developing lung were published at the beginning of the new millennium. Golpon and colleagues in 2001 looked at the expression of Hox genes in normal and diseased (emphysema and pulmonary hypertension) human adult lungs and fetal (at 12 wk of gestation) and adult mouse lung tissue using Affymetrix human and murine microarrays (5). Using basic analytical approaches, they characterized differences in the pattern of HOX gene expression among fetal, adult, and diseased lung specimens. Similarly, Kaplan used cDNA arrays to identify developmentally regulated genes in the lungs of wild-type and transgenic mice with targeted hypomorphic disruption in the glucocorticoid receptor (GRhypo) gene at embryonic day 18 and postnatal day 1 (6). They identified changes in expression of 31 genes in GRhypo mice as compared with wild-type mice. Lin and Shannon used oligonucleotide arrays to compare the expression of 38,018 known genes and expressed sequence tags in the embryonic mouse (embryonic day 13.5) lung and trachea (7). They identified 204 genes, including novel and known lung-specific genes, as differentially expressed in lung tissue. One novel gene, melanoma inhibitory activity, was suggested to be a potential marker of lung epithelial differentiation. Liu and Hogan carried out genome-wide expression profiling in an effort to elucidate mechanisms of branching morphogenesis. This study compared gene expression in epithelial tissue from the tips of branching tubes with that of a more proximal region, in embryonic day 11.5 lung buds, identifying 20 genes as differentially expressed (8).
In all these studies, expression profiling served only as a supporting component of a larger study, focusing on only one individual time point. None of these studies had generated comprehensive microarray data spanning stages of development. Our group undertook a large-scale gene expression analysis of murine lung development in which we included all stages of lung development beginning at embryonic day 12 and continuing to adulthood (9). We applied various clustering approaches to segregate genes into groups in accordance to their developmental expression patterns. We identified genes encoding regulatory proteins with expression patterns highly correlated to those of extracellular matrix genes. This analysis revealed previously unknown associations among the expression patterns of genes that may have functional significance in this complex process. In a similar study, Bonner and colleagues used oligonucleotide microarrays to study developmental expression patterns across different lung development stages and showed various patterns associated with lung development and the temporal regulation of key regulatory pathways (10). They analyzed RNA from four samples at each stage of lung development using Affymetrix U74Av2 microarrays (Affymetrix, Santa Clara, CA) representing over 12,000 genes and expressed sequence tags. They identified 1,346 genes and expressed sequence tags as significantly different in at least one stage. Lü and colleagues used microarrays to characterize the transcriptomic profiles of proximal and distal regions of the mouse respiratory tract at embryonic day 11.5, when branching morphogenesis is initiating, to gain insights into the pathways that potentially distinguish the proximal nonbranching region from the distal branching region. This study identified 83 genes upregulated in branching regions and 128 upregulated in nonbranching regions (11).
One of the major limitations of all these studies exists in their experimental designs because most suffer from limited samples size or lack of replicates, which hampers the ability to accurately identify true changes. Another significant limitation has been the lack of corollary spatial information within the lung. Spatial information is essential for hypothesis development because localized gene expression is critical in development, morphogenesis, and differentiation. Because most of these studies have used lung tissues rather than isolated cell types, and the lung is a complex organ with a heterogeneous mixture of cell types, they may not capture cell-specific data. The complex anatomy of the lung generally makes identifying cell type–specific gene expression changes difficult and hence requires additional methods such as immunohistochemistry or laser capture microdissection. An interesting, possible exception is the study by Okubo and Hogan, who examined differential gene expression from RNA isolated from the caudal lobe (endoderm and mesoderm) of SftpC-CatCLef1 transgenic and wild-type embryo (embryonic day18.5) lungs using the Affymetrix MOE430 microarray (12). Statistical analysis of the array data identified a mixture of cells expressing marker genes characteristic of different cell types, indicating a shift in cell lineage commitments. Another exception is the study by O’Reilly and colleagues, who used genome-wide expression analysis of a genetically labeled subset of type II pneumocytes to identify novel epithelial mechanisms of innate immune responses to respiratory viral infection (13).
With the advent of RNA-Seq technology, a massively parallel sequencing approach to measuring gene expression at the RNA level, researchers are now capable of building on microarray data to provide additional insights into the transcriptome of normal and disease processes occurring in the lung. Hackett and colleagues have recently used RNA-Seq to characterize the transcriptome of small airway epithelial cells. They observed that in addition to genes previously known to be expressed by Clara cells, which are markers of the predominant airway epithelial cell type, genes characteristic of neuroendocrine cells were highly expressed as well (14).
In addition to studies investigating changes in expression at the mRNA level, changes in expression of microRNAs (miRNAs), protein, and their metabolites have been studied to a lesser extent. miRNAs are a family of small noncoding RNAs (21–25 nucleotides in length) found in almost all mammalian genomes. miRNA registry databases such as the Sanger Institute miRBase (http://www.mirbase.org/) contain annotations for all published miRNAs that were either experimentally validated for mature miRNA expression or computationally predicted for the corresponding hairpin structures (15). miRNAs play an important role in cell proliferation, differentiation, tumorigenesis, and organ development. miRNAs are estimated to be responsible for regulating the expression of at least a quarter of the human genome. Very few miRNA studies have assessed miRNA expression during lung development. Bhaskaran and colleagues used a custom miRNA microarray platform to profile the expression of miRNAs at different stages in rat lung development and identified 21 miRNAs that were significantly changed during this process (16). Yang and colleagues used a microarray that covered probes for more than 1,891 miRNAs to profile expression at three separate time points during rat lung development at embryonic days 16, 19, and 21. They identified 167 miRNAs as differentially expressed (81 upregulated and 86 downregulated) during rat lung development (17). Dong and colleagues used a crossplatform approach to study the regulation of miRNAs in mouse lung organogenesis (18). They used both miRNA and mRNA expression profiling across all recognized stages of lung development beginning at embryonic day 12 and continuing to adulthood. They analyzed the expression patterns of dynamically regulated miRNAs and mRNAs, and further correlated those with protein levels from an existing mass spectrometry–derived protein database for lung development.
There are very few studies that have assessed genome-wide expression at the protein or metabolomics levels. Cox and colleagues studied protein expression during mouse lung organogenesis from embryonic day 13.5 until adulthood using gel-free two-dimensional liquid chromatography coupled to shotgun tandem mass spectrometry (19). They correlated protein expression patterns with gene expression profiles obtained from a previously published microarray data set (9). Computational modeling of the proteomic profiles in conjunction with DNA microarray data identified groups of genes with statistically significant correlations in expression levels of proteins and transcripts during lung development.
Phosphoproteomics is a branch of proteomics that identifies and characterizes proteins containing a phosphate group as a posttranslational modification. As compared with expression analysis, phosphoproteomics provides information on change in phosphorylation status, which reflects a change in protein activity. Although phosphoproteomics expands the current knowledge about the numbers and types of phosphoproteins, its greatest promise is the rapid analysis of entire phosphorylation-based signaling networks (20). Application of phosphoproteomics in the pulmonary field is still in the development phase and has primarily been focused on in vitro studies of lung cancer (21,22,23). Giorgianni and colleagues have recently generated the phosphoproteomic profile of human bronchoalveolar lavage fluid, which characterized phosphorylation of several proteins involved in lung function and disease mechanisms (24).
Metabolomics is a global approach to understanding regulation of metabolic pathways and networks of a biologic system (25). Feihn and colleagues used gas chromatography–time of flight chromatograms on lungs of smoke-exposed pregnant rats and their fetuses to study altered metabolic phenotypes in developing lungs affected by cigarette smoke (26). Using multivariate statistics, they identified 46 metabolites that were differentially regulated in fetal lungs that were exposed prenatally to environmental tobacco smoke, indicating alterations in metabolic phenotypes of developing lungs due to cigarette smoke. Fetal lungs showed major downregulation for free fatty acids but only a few upregulations of metabolites such as ketone bodies and sugar phosphates.
High-throughput transcriptome profiling technologies have enabled recent comprehensive studies of the genome-wide expression patterns and global biological processes underlying organogenesis in animal models. However, corresponding developmental studies in humans are generally lacking. We have recently generated genome-wide expression profiling from human fetal lung tissue specimens to identify global transcriptomic features of the developing human lung (27). Our data set, composed of expression profiles from 38 samples representing 29 distinct time points and spanning the pseudoglandular and early canalicular stages of human lung development, represents an encyclopedia of gene expression data. Analysis of this brief developmental time interval using principal component analysis identified a set of 3,223 characteristic genes contributing to the changes in the developing lung transcriptome, including both known and novel markers; this set of genes is capable of defining the features of human lung development.
Development as Predictor of Disease
The “developmental origins of adult disease” hypothesis, often referred to as the “Barker hypothesis,” named after its leading proponent David Barker, states that adverse influences early in development, and particularly during intrauterine life, can result in long-term changes in physiology and metabolism, which result in increased disease risk in adulthood (28). Current genomic data support the concept that developmentally predominant genes and pathways are commonly associated with disease pathogenesis.
The idea of shared molecular mechanisms between tumorigenesis and organogenesis has been discussed since the latter part of the last century (29). Kho and colleagues looked at genome-wide expression data generated from human cerebellar brain tumors (medulloblastomas) and normal mouse cerebellar tissues collected from postnatal days 1–60 (30). They used principal component analysis to project the profiles of human medulloblastomas onto a normal mouse cerebellar development temporal series. They found that the human medulloblastomas had genomic profiles most similar to early-stage mouse cerebella, and normal human cerebella were more similar to adult mouse cerebella. This approach proved informative in the pulmonary system as well. Liu and colleagues compared genome-wide expression in lung cancer samples with gene expression in normal mice during development and found similarities between expression patterns in the lung cancer subtypes and the developing mouse lung (31). They observed that cancer prognosis in humans was correlated with lung maturity in mouse. When expression in the human cancer was more similar to that of mature mouse lung cells, the prognosis was better, and when it was similar to that of very immature mouse lung cells, prognosis was poor.
One of the initial applications of “systems biology” was integrated genomics such as combining genome-wide expression with genetic association data. We have reported the identification of multiple chronic obstructive pulmonary disease (COPD) susceptibility genes through the integration of human genetics with gene expression profiling of normal lung development and diseased lung tissue (32). Specifically, we used an integrative approach involving genetic linkage data, genome-wide association data, and gene expression profiling data to identify serine protease inhibitor E2 (SERPINE2) as a novel candidate susceptibility gene for COPD (33) (Figure 2). SERPINE2, an inhibitor of thrombin and plasmin, was known to promote extracellular matrix production and inhibit apoptosis; however, its role in the pulmonary system had not been previously explored. Analysis of microarray data exploring gene expression changes during embryonic lung development (9,10) showed SERPINE2 had highest expression during airspace morphogenesis. SERPINE2 is located within a region of chromosome 2 that had previously been defined as a linkage region for early-onset COPD. Analysis of genome-wide association data from a family-based population identified multiple SERPINE2 polymorphisms as being associated with COPD, an observation that was further replicated in a case–control population (34). Using genome-wide expression profile data from two independent populations (32,35), we observed that SERPINE2 expression was significantly correlated with measures of pulmonary function in human patients with COPD (36). We subsequently applied this approach of integrated genomics to identify iron-responsive element binding protein 2 (37), SOX5 (38), and FGF7 (39) as additional COPD susceptibility genes. Furthermore, we have looked at the developmental expression patterns of expression of COPD marker genes (32) and found 27 of those to be changing during early embryonic development (27), suggesting this disease process involves dysregulation of developmental pathways.
Other studies have used genome-wide expression data from both human and murine lung development in combination with genetic association data to identify asthma susceptibility genes. Although there was no significant overrepresentation of the asthma genes among genes differentially expressed during lung development, differential expression in more than 10 asthma candidate genes during development was observed (40). Similarly, Wnt signaling genes that were differentially expressed during fetal lung development were also associated with impaired lung function in asthmatic children (41).
We have recently studied genome-wide expression changes in lungs of patients with bronchopulmonary dysplasia, a chronic lung disease of the newborn with a strong developmental contribution. We identified genes and pathways that are involved in disease pathogenesis (42). One of the pathways found to be associated with gene expression changes in bronchopulmonary dysplasia is sonic hedgehog signaling, which has previously been linked to both lung development and COPD, a chronic lung disease of the aged (32,43). In all, 31 of 159 genes identified as dysregulated (~20%) could be linked through a single network with IGF1 as the central node (Figure 3a). However, when bronchopulmonary dysplasia gene expression analysis was limited to genes also involved in early embryonic lung development as identified by expression profiling (27), we observed that CDK1 became the central node, instead of IGF1 (Figure 3b). These data demonstrate that consideration of developmental processes can have a significant impact on disease gene discovery, both helping to identify novel genes and pathways, and modifying interpretations of disease-associated pathophysiological processes. These studies further indicate that regulatory mechanisms governing development of the mammalian lung consist of a complex set of discrete, yet overlapping pathways that, when altered, may potentially lead to the onset of chronic diseases either perinatally (bronchopulmonary dysplasia) or in the aged (COPD).
“Hubs and Spokes” Model of Genomes and Systems: State of Science
Tremendous efforts, primarily focused over the past two decades, have led to the identification of numerous critical transcription factors, secreted growth factors, and their receptors that play an essential role in proper lung formation. However, nearly all studies to date have involved linear dissection of a single molecule or pathway. A complete understanding of lung development and its pathophysiological perturbations will require integration of the complex mechanisms driving morphogenesis and cellular differentiation.
In an early application of systems biology to understand the mechanisms of asthma, Novershtern and colleagues compiled a gene expression database from five publicly available mouse microarray data sets consisting of 4,305 gene sets (44). Using this collection of genome-wide expression data sets, they generated a network of functional groups for asthma, dominated primarily by immune response classes. Whitsett and Matsuzaki used a combination of mouse genetics and functional genomics to integrate individual molecules involved in cellular differentiation and surfactant production into a “circuit” necessary to prepare the lung for the “transition to air breathing” (45). Similarly, Xu and colleagues employed a systems biology approach to generate a transcriptional network describing regulation of surfactant homeostasis in the lung. Instead of focusing on individual genes, they identified gene sets as regulatory hubs in networks of transcription factors (46). Even though this approach will not identify epigenetic, posttranscriptional, and gene–environmental interactions critical to gene regulation, it provides a systematic view and working model of a transcriptional network regulating the formation and metabolism of the pulmonary surfactant system.
Classic descriptions of mammalian lung development have focused on the transition through histomorphological stages. Although these stages, and their morphological correlates, are highly conserved across species, significant differences exist in their relative length and timing. As an example, birth occurs in the saccular stage in rodents, but in the alveolar stage in humans. As the rodent lung is not biochemically immature at birth (e.g., with respect to surfactant), this example highlights the differences in molecular and histological development. In fact, it has long been appreciated that discrete molecular processes occurring during lung development, such as branching morphogenesis or respiratory epithelial cell differentiation, are not bounded by histological stages. Therefore, it is rational to anticipate that lung development could be defined in terms of discrete molecular transitions analogous to histological stages (see Figure 4).
In previous work, we used unsupervised principal component analysis of genome-wide expression data to identify global transcriptomic features of mouse lung development (47). Gene expression variation was found to be associated with macroscopic biological features such as age and alveolar formation. In particular, we observed an overlying biological program, which accounted for much of the genome-wide variation in expression and which defined the distance in age of the lung from the day of birth. We termed this program the “time-to-birth” signature. We also identified groups of genes with expression patterns corresponding to the time-to-birth molecular signature. These analyses suggested the possibility of characterizing lung development in molecular terms, in addition to histological terms. We have also subsequently applied the same approach to genome-wide expression patterns of developing human lung from samples spanning the pseudoglandular and canalicular stages (estimated 53–154 d postconception) (27). We observed that global shifts in gene expression (e.g., molecular phases) during human lung development sometimes parallel histologically defined stages. However, as in the mouse, we identified novel, distinct molecular phases that did not correlate with histological stages. We conclude that molecular phases of lung development may correlate with appreciated histomorphological stages or novel substages or may cross boundaries of stages. These phases, in turn, are composed of overlapping (rather than unique) sets of genes (Figure 4). Reconsidering normal developmental processes at this level of resolution should improve our understanding of essential molecular events during both normal development and pathological derangements of the lung, and may provide further insights into critical windows of development wherein environmental exposures may lead to subsequent disease.
A greater understanding of the regulatory pathways controlling lung development is essential for attempts to identify individuals at increased risk for chronic lung disease (in the newborn, juvenile, or adult periods), to better define pathogenic mechanisms of disease, and to identify targets for therapeutic intervention. Likewise, attempts to identify lung disease biomarkers will benefit from a greater knowledge of physiological context, including dynamic gene expression levels during normal states, such as organ development. It is rational to hypothesize that a majority of disease-related genes and pathways are congruent with a physiological set that also contributes to organ formation. Current data support the concept that developmentally predominant genes and pathways are commonly associated with disease pathogenesis. To gain a deeper understanding of the functional and regulatory pathways that play critical roles during complex mechanisms of pulmonary organogenesis, integrative systems biology approaches can be applied to combine experimental techniques with genome-level information and computational methods (modeling and simulation) (48). Given the advances and availability of high-throughput technologies to define biological processes in incredible detail, significant advances in our understanding of lung development are feasible and achievable. Systems biology approaches promise to provide a much more complete map of the functioning and interaction of the signaling networks and groups of coexpressed genes that are involved in lung formation, which may give insights to explain critical aspects of disease pathogenesis. We have presented here a snapshot of applications of different systems-level approaches in exploring molecular mechanisms of lung development. Similar approaches are already being applied in pulmonary medicine to discover the bases of complex lung diseases and to overcome the limitations faced in development diagnostic markers and therapeutic targets (49).
Statement of Financial Support
This work was supported by the National Institutes of Health, Flight Attendant Medical Research Institute, American Lung Association, and the Francis Families Foundation.
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We thank Sorachai Srisuma and Alvin Kho for technical assistance in creating figures. We also thank our many additional colleagues and collaborators who have contributed to our understanding of the concepts of systems biology in lung development through their creative comments and discussions.