Dataset Information

Interpreting the MicroRNA-15/107 family: interaction identification by combining network based and experiment supported approach.

ABSTRACT:

Background

The highly conservative miR-15/107 family (also named as miR-15/107 gene group) including ten miRNA members is currently recognized strongly implicated in multiple human disorders. Some studies focus on the entire family rather than individual miRNA for a bigger picture, while there is also certain signature dysregulation for some of the individual miRNA implicated even in the same disorder.

Methods

Faced with the exponential growth of experimental evidence, our study tries to analyze their function and target interactions using various bioinformatics tools.

Results

Firstly, the evolutionary conservative "AGCAGC" sequence and possible clustered transcriptional pattern were described. Secondly, both the experimentally validated and bioinformatically predicted miRNA-target gene relationship of the entire family was analyzed to understand the mechanism of underlying collective effects for target regulation from the miR-15/107 family. Moreover, pathway analysis among miR-15/107 family was performed and displayed in detail, while its impact on cell proliferation is experimentally validated. Eventually, the dysregulation of miR-15/107 in diseases was discussed.

Conclusions

In summary, our study proposes that the collective functions and implication of miR-15/107 family in various human diseases are achieved relying on the massive overlapping target genes. While the minor differences within target gene interaction among family members could also explain the signature behavior for some of the individual miRNA in aspects such as its disease-specific dysregulation and various participation in pathways.

SUBMITTER: Wang S

PROVIDER: S-EPMC6544937 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundA wealth of protein interaction data has become available in recent years, creating an urgent need for powerful analysis techniques. In this context, the problem of finding biologically meaningful correspondences between different protein-protein interaction networks (PPIN) is of particular interest. The PPIN of a species can be compared with that of other species through the process of PPIN alignment. Such an alignment can provide insight into basic problems like species evolution and network component function determination, as well as translational problems such as target identification and elucidation of mechanisms of disease spread. Furthermore, multiple PPINs can be aligned simultaneously, expanding the analytical implications of the result. While there are several pairwise network alignment algorithms, few methods are capable of multiple network alignment.ResultsWe propose SMAL, a MNA algorithm based on the philosophy of scaffold-based alignment. SMAL is capable of converting results from any global pairwise alignment algorithms into a MNA in linear time. Using this method, we have built multiple network alignments based on combining pairwise alignments from a number of publicly available (pairwise) network aligners. We tested SMAL using PPINs of eight species derived from the IntAct repository and employed a number of measures to evaluate performance. Additionally, as part of our experimental investigations, we compared the effectiveness of SMAL while aligning up to eight input PPINs, and examined the effect of scaffold network choice on the alignments.ConclusionsA key advantage of SMAL lies in its ability to create MNAs through the use of pairwise network aligners for which native MNA implementations do not exist. Experiments indicate that the performance of SMAL was comparable to that of the native MNA implementation of established methods such as IsoRankN and SMETANA. However, in terms of computational time, SMAL was significantly faster. SMAL was also able to retain many important characteristics of the native pairwise alignments, such as the number of aligned nodes and edges, as well as the functional and homologene similarity of aligned nodes. The speed, flexibility and the ability to retain prior correspondences as new networks are aligned, makes SMAL a compelling choice for alignment of multiple large networks.