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Abstract 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.

Abstract Background Pathway analysis is one of the later stage data analysis steps essential in interpreting high-throughput gene expression data. We propose a set of algorithms which given gene expression data can recognize which portion of sub-pathways are actively utilized in the biological system being studied. The degree of activation is measured by conditional probability of the input expression data based on the Bayesian Network model constructed from the topological pathway. Results We demonstrate the effectiveness of our pathway analysis method by conducting two case studies. The first one applies our method to a well-studied temporal microarray data set for the cell cycle using the KEGG Cell Cycle pathway. Our method closely reproduces the biological claims associated with the data sets, but unlike the original work ours can produce how pathway routes interact with each other above and beyond merely identifying which pathway routes are involved in the process. The second study applies the method to the p53 mutation microarray data to perform a comparative study. Conclusions We show that our method achieves comparable performance against all other pathway analysis systems included in this study in identifying p53 altered pathways. Our method could pave a new way of carrying out next generation pathway analysis.

Abstract Background Gastrointestinal mucosal injury (mucositis), commonly affecting the oral cavity, is a clinically significant yet incompletely understood complication of cancer chemotherapy. Although antineoplastic cytotoxicity constitutes the primary injury trigger, the interaction of oral microbial commensals with mucosal tissues could modify the response. It is not clear, however, whether chemotherapy and its associated treatments affect oral microbial communities disrupting the homeostatic balance between resident microorganisms and the adjacent mucosa and if such alterations are associated with mucositis. To gain knowledge on the pathophysiology of oral mucositis, 49 subjects receiving 5-fluorouracil (5-FU) or doxorubicin-based chemotherapy were evaluated longitudinally during one cycle, assessing clinical outcomes, bacterial and fungal oral microbiome changes, and epithelial transcriptome responses. As a control for microbiome stability, 30 non-cancer subjects were longitudinally assessed. Through complementary in vitro assays, we also evaluated the antibacterial potential of 5-FU on oral microorganisms and the interaction of commensals with oral epithelial tissues. Results Oral mucositis severity was associated with 5-FU, increased salivary flow, and higher oral granulocyte counts. The oral bacteriome was disrupted during chemotherapy and while antibiotic and acid inhibitor intake contributed to these changes, bacteriome disruptions were also correlated with antineoplastics and independently and strongly associated with oral mucositis severity. Mucositis-associated bacteriome shifts included depletion of common health-associated commensals from the genera Streptococcus, Actinomyces, Gemella, Granulicatella, and Veillonella and enrichment of Gram-negative bacteria such as Fusobacterium nucleatum and Prevotella oris. Shifts could not be explained by a direct antibacterial effect of 5-FU, but rather resembled the inflammation-associated dysbiotic shifts seen in other oral conditions. Epithelial transcriptional responses during chemotherapy included upregulation of genes involved in innate immunity and apoptosis. Using a multilayer epithelial construct, we show mucositis-associated dysbiotic shifts may contribute to aggravate mucosal damage since the mucositis-depleted Streptococcus salivarius was tolerated as a commensal, while the mucositis-enriched F. nucleatum displayed pro-inflammatory and pro-apoptotic capacity. Conclusions Altogether, our work reveals that chemotherapy-induced oral mucositis is associated with bacterial dysbiosis and demonstrates the potential for dysbiotic shifts to aggravate antineoplastic-induced epithelial injury. These findings suggest that control of oral bacterial dysbiosis could represent a novel preventive approach to ameliorate oral mucositis.

Abstract Comprehensive and accurate identification of structural variations (SVs) from next generation sequencing data remains a major challenge. We develop FusorSV, which uses a data mining approach to assess performance and merge callsets from an ensemble of SV-calling algorithms. It includes a fusion model built using analysis of 27 deep-coverage human genomes from the 1000 Genomes Project. We identify 843 novel SV calls that were not reported by the 1000 Genomes Project for these 27 samples. Experimental validation of a subset of these calls yields a validation rate of 86.7%. FusorSV is available at https://github.com/TheJacksonLaboratory/SVE.