Characterization of Protein-Protein Interactions Using Protein Microarrays

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

Proteins That Interact with the Mucin-Type Glycoprotein Msb2p Include a Regulator of the Actin Cytoskeleton

Transmembrane mucin-type glycoproteins can regulate signal transduction pathways. In yeast, signaling mucins regulate mitogen-activated protein kinase (MAPK) pathways that induce cell differentiation to filamentous growth (fMAPK pathway) and the response to osmotic stress (HOG pathway). To explore regulatory aspects of signaling mucin function, protein microarrays were used to identify proteins that interact with the cytoplasmic domain of the mucin-like glycoprotein Msb2p. Eighteen proteins were identified that comprised functional categories of metabolism, actin filament capping and depolymerization, aerobic and anaerobic growth, chromatin organization and bud growth, sporulation, ribosome biogenesis, protein modification by iron-sulfur clusters, RNA catabolism, and DNA replication and DNA repair. A subunit of actin capping protein, Cap2p, interacted with the cytoplasmic domain of Msb2p. Cells lacking Cap2p showed altered localization of Msb2p and increased levels of shedding of Msb2p's N-terminal glycosylated domain. Consistent with its role in regulating the actin cytoskeleton, Cap2p was required for enhanced cell polarization during filamentous growth. Our study identifies proteins that connect a signaling mucin to diverse cellular processes and may provide insight into new aspects of mucin function.

Protein Microarrays: Flexible Tools for Scientific Innovation

Protein microarrays have emerged as a powerful tool for the scientific community, and their greatest advantage lies in the fact that thousands of reactions can be performed in a parallel and unbiased manner. The first high-density protein microarray, dubbed the "yeast proteome array," consisted of approximately 5800 full-length yeast proteins and was initially used to identify protein-lipid interactions. Further assays were subsequently developed to allow measurement of protein-DNA, protein-RNA, and protein-protein interactions, as well as four well-known posttranslational modifications: phosphorylation, acetylation, ubiquitylation, and SUMOylation. In this introduction, we describe the advent of high-density protein microarrays, as well as current methods for assessing a wide variety of protein interactions and posttranslational modifications.