System-wide analysis of the transcriptional network of human myelomonocytic leukemia cells predicts attractor structure and phorbol-ester-induced differentiation and dedifferentiation transitions.

We present a system-wide transcriptional network structure that controls cell types in the context of expression pattern transitions that correspond to cell type transitions. Co-expression based analyses uncovered a system-wide, ladder-like transcription factor cluster structure composed of nearly 1,600 transcription factors in a human transcriptional network. Computer simulations based on a transcriptional regulatory model deduced from the system-wide, ladder-like transcription factor cluster structure reproduced expression pattern transitions when human THP-1 myelomonocytic leukaemia cells cease proliferation and differentiate under phorbol myristate acetate stimulation. The behaviour of MYC, a reprogramming Yamanaka factor that was suggested to be essential for induced pluripotent stem cells during dedifferentiation, could be interpreted based on the transcriptional regulation predicted by the system-wide, ladder-like transcription factor cluster structure. This study introduces a novel system-wide structure to transcriptional networks that provides new insights into network topology.

Soybean proteome database: a data resource for plant differential omics.

The Soybean Proteome Database aims to be a data repository for functional analyses of soybean responses to flooding injury, recognized as a major constraint for establishment and production of this plant. The current release contains 21 reference maps of soybean (Glycine max cv. Enrei) proteins electrophoresed on two-dimensional polyacrylamide gels of which the samples were collected from several organs, tissues and organelles. These reference maps include 7311 detected proteins and 532 identified proteins, or proteins for which a sequence or peptide peak has been determined. The database is searchable by protein properties such as accession number, description and isoelectric point and molecular weight range. The Soybean Proteome Database also integrates multiple "omes". An omics table reveals relationships among 106 mRNAs, 51 proteins and 89 metabolites that vary over time under flooding stress. The tabulated metabolites are anchored to a metabolome network. A unified temporal-profile tag attached to the mRNAs, proteins and metabolites facilitates retrieval of the data based on the temporal expression profiles. A graphical user interface based on dynamic HTML facilitates viewing the metabolome network as well as the profiles of multiple omes in a uniform manner. The entire database is available at http://proteome.dc.affrc.go.jp/Soybean/.

Expression and function of proteins during development of the basal region in rice seedlings.

A differential display of proteins with a two-dimensional polyacrylamide gel electrophoresis approach was used to analyze protein expression changes during development of the basal region in rice seedlings (Oryza sativa L. cv. Nipponbare). The proteins were detected as 700 Coomassie Brilliant Blue-stained spots with pI values from around 3.5 to 9.0. A proteome reference map was established for the basal region of two-week-old seedlings. The basal region proteome map was used to analyze quantitative variations in the tissue during development from 2-, 4-, 6-, 8-, and 10-week-old seedlings. During development, 31 proteins were up-regulated, and 30 proteins were down-regulated compared with the 2-week-old basal region proteome map. The main functions of these proteins were primary metabolism and protein synthesis or maintenance. Calreticulin precursor, enolase, and voltage-dependent anion channel were identified among the up- and down-regulated proteins. The twin spots of calreticulin precursor and enolase with different pI values are possibly due to post-translational modifications such as phosphorylation. In addition, seven proteins showed developmental stage-specific expression. All of the developmentally regulated proteins of the basal region were clustered by the S-system, a differential equation that fit to time course of cluster and analyzed for cluster relationships. Proteins with unknown functions were tentatively assigned to functional groups based on cluster relationships. Basal region development proteome data will be valuable for resolving questions in functional genomics. In addition, cluster analysis of the basal region proteome during development will be useful for the assessment of functional proteins.