Fetal developmental disorders and pregnancy complications pose critical public health challenges. Yet our ability to monitor prenatal development and neonatal health has not fully entered the era of genomic medicine. Recent technological advances have allowed us to monitor global gene expression in the living human fetus or newborn, raising the possibility of creating revolutionary early diagnostics for a range of developmental disorders. Yet in order to realize this vision, we need to overcome two bioinformatics challenges. First, state-of-the-art interpretation of gene expression data requires information about how genes work together as systems. However, existing systems/pathway annotation is focused predominantly on adult disorders and is inappropriate for the analysis of fetal gene expression. Second, traditional methods for designing diagnostics from gene expression data require many examples of each sample type to be recognized. For rare diseases that cannot currently be detected in utero, however, the collection of a large number of fetal expression profiles representing each disorder is impractical. Our proposal addresses these challenges via two specific aims. First, we will develop a method for discovering new, statistically-validated gene sets from expression data. We will use this method, along with literature mining, to develop new systems biology annotation specific to this developmental stage. Second, we will develop and validate a new computational paradigm for sample classification that can identify and determine the nature of rare abnormal developmental profiles. Our methods, though motivated by the study of human development, can be applied to any medical area where existing functional annotation is inadequate or the collection of typical classification data is infeasible.

Public Health Relevance

This project will enable the development of new diagnostics for monitoring maternal/fetal and neonatal health, which will in turn help reduce the huge public health costs of pregnancy complications and developmental disorders. This proposal will also develop new computational methodologies for interpreting gene expression data that can be applied to other fields in medicine and biology. Thus, our work will have a broad impact on the ability to translate genomic data into clinically-relevant diagnostics and treatments across the full range of human health.

Agency
National Institute of Health (NIH)
Institute
Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD)
Type
Research Project (R01)
Project #
1R01HD058880-01
Application #
7507028
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Oster-Granite, Mary Lou
Project Start
2008-12-02
Project End
2012-11-30
Budget Start
2008-12-02
Budget End
2009-11-30
Support Year
1
Fiscal Year
2009
Total Cost
$322,622
Indirect Cost
Name
Tufts University
Department
Biostatistics & Other Math Sci
Type
Schools of Engineering
DUNS #
073134835
City
Medford
State
MA
Country
United States
Zip Code
02155
Aziz, Nadine M; Guedj, Faycal; Pennings, Jeroen L A et al. (2018) Lifespan analysis of brain development, gene expression and behavioral phenotypes in the Ts1Cje, Ts65Dn and Dp(16)1/Yey mouse models of Down syndrome. Dis Model Mech 11:
Noto, Keith; Majidi, Saeed; Edlow, Andrea G et al. (2015) CSAX: Characterizing Systematic Anomalies in eXpression Data. J Comput Biol 22:402-13
Guedj, Faycal; Pennings, Jeroen L A; Ferres, Millie A et al. (2015) The fetal brain transcriptome and neonatal behavioral phenotype in the Ts1Cje mouse model of Down syndrome. Am J Med Genet A 167A:1993-2008
Edlow, Andrea G; Slonim, Donna K; Wick, Heather C et al. (2015) The pathway not taken: understanding 'omics data in the perinatal context. Am J Obstet Gynecol 213:59.e1-59.e172
Massingham, Lauren J; Johnson, Kirby L; Scholl, Thomas M et al. (2014) Amniotic fluid RNA gene expression profiling provides insights into the phenotype of Turner syndrome. Hum Genet 133:1075-82
Park, Jisoo; Wick, Heather C; Kee, Daniel E et al. (2014) Finding novel molecular connections between developmental processes and disease. PLoS Comput Biol 10:e1003578
Wick, Heather C; Drabkin, Harold; Ngu, Huy et al. (2014) DFLAT: functional annotation for human development. BMC Bioinformatics 15:45
Edlow, Andrea G; Vora, Neeta L; Hui, Lisa et al. (2014) Maternal obesity affects fetal neurodevelopmental and metabolic gene expression: a pilot study. PLoS One 9:e88661
Cao, Mengfei; Zhang, Hao; Park, Jisoo et al. (2013) Going the distance for protein function prediction: a new distance metric for protein interaction networks. PLoS One 8:e76339
Hui, Lisa; Wick, Heather C; Edlow, Andrea G et al. (2013) Global gene expression analysis of term amniotic fluid cell-free fetal RNA. Obstet Gynecol 121:1248-54

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