A multivariate approach to the integration of multi-omics datasets

Background: To leverage the potential of multi-omics studies, exploratory data analysis methods that provide systematic integration and comparison of multiple layers of omics information are required. We describe multiple co-inertia analysis (MCIA), an exploratory data analysis method that identifies co-relationships between multiple high dimensional datasets. Based on a covariance optimization criterion, MCIA simultaneously projects several datasets into the same dimensional space, transforming diverse sets of features onto the same scale, to extract the most variant from each dataset and facilitate biological interpretation and pathway analysis. Results: We demonstrate integration of multiple layers of information using MCIA, applied to two typical “omics” research scenarios. The integration of transcriptome and proteome profiles of cells in the NCI-60 cancer cell line panel revealed distinct, complementary features, which together increased the coverage and power of pathway analysis. Our analysis highlighted the importance of the leukemia extravasation signaling pathway in leukemia that was not highly ranked in the analysis of any individual dataset. Secondly, we compared transcriptome profiles of high grade serous ovarian tumors that were obtained, on two different microarray platforms and next generation RNA-sequencing, to identify the most informative platform and extract robust biomarkers of molecular subtypes. We discovered that the variance of RNA-sequencing data processed using RPKM had greater variance than that with MapSplice and RSEM. We provided novel markers highly associated to tumor molecular subtype combined from four data platforms. MCIA is implemented and available in the R/Bioconductor “omicade4” package. Conclusion: We believe MCIA is an attractive method for data integration and visualization of several datasets of multi-omics features observed on the same set of individuals. The method is not dependent on feature annotation, and thus it can extract important features even when there are not present across all datasets. MCIA provides simple graphical representations for the identification of relationships between large datasets

Quantitative proteome profiling of human myoma and myometrium tissue reveals kinase expression signatures with potential for therapeutic intervention

Uterine leiomyomas are benign tumors affecting a large proportion of the female population. Despite the very high prevalence, the molecular basis for understanding the onset and development of the disease are still poorly understood. In this study, we profiled the proteomes and kinomes of leiomyoma as well as myometrium samples from patients to a depth of >7000 proteins including 200 kinases. Statistical analysis identified a number of molecular signatures distinguishing healthy from diseased tissue. Among these, nine kinases (ADCK4, CDK5, CSNK2B, DDR1, EPHB1, MAP2K2, PRKCB, PRKG1, and RPS6KA5) representing a number of cellular signaling pathways showed particularly strong discrimination potential. Preliminary statistical analysis by receiver operator characteristics plots revealed very good performance for individual kinases (area under the curve, AUC of 0.70-0.94) as well as binary combinations thereof (AUC 0.70-1.00) that might be used to assess the activity of signaling pathways in myomas. Of note, the receptor tyrosine kinase DDR1 holds future potential as a drug target owing to its strong links to collagen signaling and the excessive formation of extracellular matrix typical for leiomyomas in humans

Quantitative proteome profiling of human myoma and myometrium tissue reveals kinase expression signatures with potential for therapeutic intervention

Uterine leiomyomas are benign tumors affecting a large proportion of the female population. Despite the very high prevalence, the molecular basis for understanding the onset and development of the disease are still poorly understood. In this study, we profiled the proteomes and kinomes of leiomyoma as well as myometrium samples from patients to a depth of >7000 proteins including 200 kinases. Statistical analysis identified a number of molecular signatures distinguishing healthy from diseased tissue. Among these, nine kinases (ADCK4, CDK5, CSNK2B, DDR1, EPHB1, MAP2K2, PRKCB, PRKG1, and RPS6KA5) representing a number of cellular signaling pathways showed particularly strong discrimination potential. Preliminary statistical analysis by receiver operator characteristics plots revealed very good performance for individual kinases (area under the curve, AUC of 0.70-0.94) as well as binary combinations thereof (AUC 0.70-1.00) that might be used to assess the activity of signaling pathways in myomas. Of note, the receptor tyrosine kinase DDR1 holds future potential as a drug target owing to its strong links to collagen signaling and the excessive formation of extracellular matrix typical for leiomyomas in humans

Sulfite Reductase Defines a Newly Discovered Bottleneck for Assimilatory Sulfate Reduction and Is Essential for Growth and Development in Arabidopsis thaliana[C][W]

This work examines role of sulfite reductase (SiR) in assimilatory reduction of inorganic sulfate to sulfide. Reduced sulfite reductase activity results in growth retardation and severe perturbations of sulfur, nitrogen, and carbon metabolism, demonstrating that, surprisingly, SiR plays a role in controlling flux in the assimilatory sulfate reduction pathway