Predicting the targets of IRF8 and NFATc1 during osteoclast differentiation using the machine learning method framework cTAP.

BACKGROUND: Interferon regulatory factor-8 (IRF8) and nuclear factor-activated T cells c1 (NFATc1) are two transcription factors that have an important role in osteoclast differentiation. Thanks to ChIP-seq technology, scientists can now estimate potential genome-wide target genes of IRF8 and NFATc1. However, finding target genes that are consistently up-regulated or down-regulated across different studies is hard because it requires analysis of a large number of high-throughput expression studies from a comparable context. METHOD: We have developed a machine learning based method, called, Cohort-based TF target prediction system (cTAP) to overcome this problem. This method assumes that the pathway involving the transcription factors of interest is featured with multiple "functional groups" of marker genes pertaining to the concerned biological process. It uses two notions, Gene-Present Sufficiently (GP) and Gene-Absent Insufficiently (GA), in addition to log2 fold changes of differentially expressed genes for the prediction. Target prediction is made by applying multiple machine-learning models, which learn the patterns of GP and GA from log2 fold changes and four types of Z scores from the normalized cohort's gene expression data. The learned patterns are then associated with the putative transcription factor targets to identify genes that consistently exhibit Up/Down gene regulation patterns within the cohort. We applied this method to 11 publicly available GEO data sets related to osteoclastgenesis. RESULT: Our experiment identified a small number of Up/Down IRF8 and NFATc1 target genes as relevant to osteoclast differentiation. The machine learning models using GP and GA produced NFATc1 and IRF8 target genes different than simply using a log2 fold change alone. Our literature survey revealed that all predicted target genes have known roles in bone remodeling, specifically related to the immune system and osteoclast formation and functions, suggesting confidence and validity in our method. CONCLUSION: cTAP was motivated by recognizing that biologists tend to use Z score values present in data sets for the analysis. However, using cTAP effectively presupposes assembling a sizable cohort of gene expression data sets within a comparable context. As public gene expression data repositories grow, the need to use cohort-based analysis method like cTAP will become increasingly important.

Osterix-mCherry Expression Allows for Early Bone Detection in a Calvarial Defect Model.

The process of new bone formation following trauma requires the temporal recruitment of cells to the site, including mesenchymal stem cells, preosteoblasts, and osteoblasts, the latter of which deposit minerals. Hence, bone repair, a process that is assessed by the extent of mineralization within the defect, can take months before it is possible to determine if a treatment is successful. Here, a fluorescently tagged Osterix, an early key gene in the bone formation cascade, is used as a predictive measure of bone formation. Using a calvarial defect model in mice, the ability to noninvasively track the Osterix transcription factor in an Osterix-mCherry mouse model is evaluated as a measure for bone formation following treatment with recombinant human Bone-Morphogenetic-Protein 2 (rhBMP-2). Two distinct delivery materials are utilized, an injectable nanocomposite hydrogel and a collagen sponge, that afford distinct release kinetics and it is found that cherry-fluorescent protein can be detected as early as 2 weeks following treatment. Osterix intensity correlates with subsequent bone formation and hence can serve as a rapid screening tool for osteogenic drugs or for the evaluation and optimization of delivery platforms.