New analysis pipeline for high-throughput domain-peptide affinity experiments improves SH2 interaction data.

Protein domain interactions with short linear peptides, such as those of the Src homology 2 (SH2) domain with phosphotyrosine-containing peptide motifs (pTyr), are ubiquitous and important to many biochemical processes of the cell. The desire to map and quantify these interactions has resulted in the development of high-throughput (HTP) quantitative measurement techniques, such as microarray or fluorescence polarization assays. For example, in the last 15 years, experiments have progressed from measuring single interactions to covering 500,000 of the 5.5 million possible SH2-pTyr interactions in the human proteome. However, high variability in affinity measurements and disagreements about positive interactions between published data sets led us here to reevaluate the analysis methods and raw data of published SH2-pTyr HTP experiments. We identified several opportunities for improving the identification of positive and negative interactions and the accuracy of affinity measurements. We implemented model-fitting techniques that are more statistically appropriate for the nonlinear SH2-pTyr interaction data. We also developed a method to account for protein concentration errors due to impurities and degradation or protein inactivity and aggregation. Our revised analysis increases the reported affinity accuracy, reduces the false-negative rate, and increases the amount of useful data by adding reliable true-negative results. We demonstrate improvement in classification of binding versus nonbinding when using machine-learning techniques, suggesting improved coherence in the reanalyzed data sets. We present revised SH2-pTyr affinity results and propose a new analysis pipeline for future HTP measurements of domain-peptide interactions.

Different Epidermal Growth Factor Receptor (EGFR) Agonists Produce Unique Signatures for the Recruitment of Downstream Signaling Proteins.

The EGF receptor can bind seven different agonist ligands. Although each agonist appears to stimulate the same suite of downstream signaling proteins, different agonists are capable of inducing distinct responses in the same cell. To determine the basis for these differences, we used luciferase fragment complementation imaging to monitor the recruitment of Cbl, CrkL, Gab1, Grb2, PI3K, p52 Shc, p66 Shc, and Shp2 to the EGF receptor when stimulated by the seven EGF receptor ligands. Recruitment of all eight proteins was rapid, dose-dependent, and inhibited by erlotinib and lapatinib, although to differing extents. Comparison of the time course of recruitment of the eight proteins in response to a fixed concentration of each growth factor revealed differences among the growth factors that could contribute to their differing biological effects. Principal component analysis of the resulting data set confirmed that the recruitment of these proteins differed between agonists and also between different doses of the same agonist. Ensemble clustering of the overall response to the different growth factors suggests that these EGF receptor ligands fall into two major groups as follows: (i) EGF, amphiregulin, and EPR; and (ii) betacellulin, TGFα, and epigen. Heparin-binding EGF is distantly related to both clusters. Our data identify differences in network utilization by different EGF receptor agonists and highlight the need to characterize network interactions under conditions other than high dose EGF.

Function of the C. elegans T-box factor TBX-2 depends on SUMOylation.

T-box transcription factors are critical developmental regulators in all multi-cellular organisms, and altered T-box factor activity is associated with a variety of human congenital diseases and cancers. Despite the biological significance of T-box factors, their mechanism of action is not well understood. Here we examine whether SUMOylation affects the function of the C. elegans Tbx2 sub-family T-box factor TBX-2. We have previously shown that TBX-2 interacts with the E2 SUMO-conjugating enzyme UBC-9, and that loss of TBX-2 or UBC-9 produces identical defects in ABa-derived pharyngeal muscle development. We now show that TBX-2 is SUMOylated in mammalian cell assays, and that both UBC-9 interaction and SUMOylation depends on two SUMO consensus sites located in the T-box DNA binding domain and near the TBX-2 C-terminus, respectively. In co-transfection assays, a TBX-2:GAL4 fusion protein represses expression of a 5xGal4:tk:luciferase construct. However, this activity does not require SUMOylation, indicating SUMO is not generally required for TBX-2 repressor activity. In C. elegans, reducing SUMOylation enhances the phenotype of a temperature-sensitive tbx-2 mutant and results in ectopic expression of a gene normally repressed by TBX-2, demonstrating that SUMOylation is important for TBX-2 function in vivo. Finally, we show mammalian orthologs of TBX-2, Tbx2, and Tbx3, can also be SUMOylated, suggesting SUMOylation may be a conserved mechanism controlling T-box factor activity.

Avoiding common pitfalls when clustering biological data.

Clustering is an unsupervised learning method, which groups data points based on similarity, and is used to reveal the underlying structure of data. This computational approach is essential to understanding and visualizing the complex data that are acquired in high-throughput multidimensional biological experiments. Clustering enables researchers to make biological inferences for further experiments. Although a powerful technique, inappropriate application can lead biological researchers to waste resources and time in experimental follow-up. We review common pitfalls identified from the published molecular biology literature and present methods to avoid them. Commonly encountered pitfalls relate to the high-dimensional nature of biological data from high-throughput experiments, the failure to consider more than one clustering method for a given problem, and the difficulty in determining whether clustering has produced meaningful results. We present concrete examples of problems and solutions (clustering results) in the form of toy problems and real biological data for these issues. We also discuss ensemble clustering as an easy-to-implement method that enables the exploration of multiple clustering solutions and improves robustness of clustering solutions. Increased awareness of common clustering pitfalls will help researchers avoid overinterpreting or misinterpreting the results and missing valuable insights when clustering biological data.

Transcription factor binding sites are highly enriched within microRNA precursor sequences.

BACKGROUND: Transcription factors are thought to regulate the transcription of microRNA genes in a manner similar to that of protein-coding genes; that is, by binding to conventional transcription factor binding site DNA sequences located in or near promoter regions that lie upstream of the microRNA genes. However, in the course of analyzing the genomics of human microRNA genes, we noticed that annotated transcription factor binding sites commonly lie within 70- to 110-nt long microRNA small hairpin precursor sequences. RESULTS: We report that about 45% of all human small hairpin microRNA (pre-miR) sequences contain at least one predicted transcription factor binding site motif that is conserved across human, mouse and rat, and this rises to over 75% if one excludes primate-specific pre-miRs. The association is robust and has extremely strong statistical significance; it affects both intergenic and intronic pre-miRs and both isolated and clustered microRNA genes. We also confirmed and extended this finding using a separate analysis that examined all human pre-miR sequences regardless of conservation across species. CONCLUSIONS: The transcription factor binding sites localized within small hairpin microRNA precursor sequences may possibly regulate their transcription. Transcription factors may also possibly bind directly to nascent primary microRNA gene transcripts or small hairpin microRNA precursors and regulate their processing.