Toward functional analysis of protein interactome using “in vitrovirus”:In silicoanalyses of Fos/Jun interactors

Next-generation technologies for multiomics approaches including interactome sequencing

The development of high-speed analytical techniques such as next-generation sequencing and microarrays allows high-throughput analysis of biological information at a low cost. These techniques contribute to medical and bioscience advancements and provide new avenues for scientific research. Here, we outline a variety of new innovative techniques and discuss their use in omics research (e.g., genomics, transcriptomics, metabolomics, proteomics, and interactomics). We also discuss the possible applications of these methods, including an interactome sequencing technology that we developed, in future medical and life science research.

A comprehensive resource of interacting protein regions for refining human transcription factor networks

Large-scale data sets of protein-protein interactions (PPIs) are a valuable resource for mapping and analysis of the topological and dynamic features of interactome networks. The currently available large-scale PPI data sets only contain information on interaction partners. The data presented in this study also include the sequences involved in the interactions (i.e., the interacting regions, IRs) suggested to correspond to functional and structural domains. Here we present the first large-scale IR data set obtained using mRNA display for 50 human transcription factors (TFs), including 12 transcription-related proteins. The core data set (966 IRs; 943 PPIs) displays a verification rate of 70%. Analysis of the IR data set revealed the existence of IRs that interact with multiple partners. Furthermore, these IRs were preferentially associated with intrinsic disorder. This finding supports the hypothesis that intrinsically disordered regions play a major role in the dynamics and diversity of TF networks through their ability to structurally adapt to and bind with multiple partners. Accordingly, this domain-based interaction resource represents an important step in refining protein interactions and networks at the domain level and in associating network analysis with biological structure and function.

An efficient and economic high-throughput cell screening model targeting the glucocorticoid receptor

AIM

To discover compounds or proteins that can efficiently bind the glucocorticoid receptor (GR) and trigger the transcription of target genes, resulting in clinical improvement of diseases such as rheumatoid arthritis, asthma, inflammatory bowel disease, a high-throughput drug screening cell model using green fluorescent protein 4 (GFP4) as a marker expressed in response to GR activation has been established and evaluated.

METHODS

Eight repeats of the glucocorticoid response element (GRE) were cloned into the Peak12SxSynGFP4 vector, and the resulting recombinant plasmid Peak12GRE8 x SxSynGFP4 was stably transfected into the 293E cells. The stable and sensitive cell line 293E/GRE8 x /GFP4 was selected by dexamethasone (DEX) using fluorescent microscopy and fluorescence-activated cell sorting. DEX induction and phorbol myristate acetate (PMA) inhibition of the green fluorescence intensity of the cell line were tested.

RESULTS

The expression of GFP4 in the cell line was under the control of GRE, up-regulated by DEX treatment and down-regulated by phorbol myristate acetate (PMA). The up-regulation of the GFP4 expression was DEX concentration-dependent, with an EC(50) at approximately 5 x 10(- 8) M. The down-regulation of the GFP4 expression was phorbol myristate acetate (PMA) concentration-dependent, with an IC(50) at approximately 3 x 10(- 6) gl - 1. The expression of GFP4 was effectively activated when cells were treated with triamcinolone acetonide.

CONCLUSION

This drug screening cell line can be used for GR-targeted high-throughput drug screening for the treatment of inflammatory diseases.