We have recently presented in [1
] an approach to identify the so called emergent
properties of a biological system, i.e. properties that arise from the interaction of portions of a system. In particular, this method is based on the integration of translational (microarrays for mRNA gene expression) and post-translational (RT-PCR of miRNAs) data and applied to observations related to human brain tumors published in [2
]. Emergent properties are a well known concept in Systems Theory and are now becoming more common in Systems Biology [3
]. In general, the concept of emergent property relates to the fact that a system studied in its entirety shows features that cannot be captured when the system is observed through its (simplified) subsystems (Reductionist
approach). Applied to molecular biology, this corresponds to the observation that separate analyses of different aspects of a system (e.g., transcriptional and/or post-transcriptional mechanisms) lead to results that may not be concordant with analyses of the system as a whole. This may be due to underestimating or overlooking interactions among miRNAs and mRNAs. The identification of emergent properties can be done through the use of latent variables in multivariate statistics (in particular via the use of Factor Analysis, FA, [7
]). Latent variables are so-called hidden variables which are not evident in the original observed data, because they emerge from consideration of the covariance patterns when a large number of relevant variables are analyzed simultaneously.
Taking advantage of the parallelism existing between biological systems' emergent properties and latent variables, we have used the ability of latent variables to describe emergent properties, by applying multivariate analysis simultaneously to different parts of a biological system, and notably to transcriptional and post-transcriptional data. In practice, each latent variable (i.e. each factor) obtained from analyzing jointly the mRNA and miRNA data consists of a group of heterogeneous molecules (mRNAs, miRNAs). It is then the interaction
among molecules in the same group (i.e. factor) that defines an emergent
property. This was done on a dataset of 330 miRNAs and ~14,500 mRNAs that for our purposes were merged (in the joint analysis) into a single table (containing all molecular data and as many clinical indications of the tumor class as there are samples, twelve) [1
]. Conversely, traditional parallel
analyses imply that the two mRNA and miRNA data tables are studied separately, and that annotation results are jointly discussed only afterwards. Therefore, the association between miRNAs and mRNAs relies solely on manual curation, while our approach offers to researchers non-trivial associations (built in the factors) that can then be manually investigated further to elucidate the exact nature of the association. Results have shown that the designed approach is more helpful than traditional approaches (that analyze distinctly the two tables of mRNA and miRNA data, or use hierarchical clustering, correlation or tools specific for differential analyses [2
]) in identifying non-trivial biological properties [1
]. In fact, in contrast to traditional approaches, we were able to discover the relevance of two miRNA clusters
(miR-17-92 and miR-106-363), which appear to be important for the diagnosis of glioblastoma versus gliosarcomas. A cluster
is a group of co-localized miRNAs, in this particular case one maps onto Chromosome X (miR-17-92) and one maps onto Chromosome 13 (miR-106-363).
Briefly, these polycistronic miRNA genes are involved in cell proliferation, apoptosis suppression, tumor angiogenesis [10
] and T cell leukemia [11
]. Although lying on different areas of the genome, the two clusters are closely related because each miRNA on one cluster has at least one homologue in the other cluster except for miR-17-3p and miR-363 that do not share homology with the other miRNAs. Finally, we have observed that the list of predicted targets (using the Targetscan software, [12
]) is identical for all miRNAs.