Summary Proteomes of interest, such as the human proteome, have such complexity that no single technique is adequate for complete analysis of the constituents. Depending on the goal (e.g., identification of a novel protein vs. measurement of the level of a known protein), the tools required can vary significantly. While existing methods provide valuable information, their limitations drive the development of complementary, innovative methods to achieve greater breadth of coverage, dynamic range, or specificity of analysis. Here, we will discuss affinity-based methods and their applications, focusing on their unique advantages. In addition, we will describe emerging methods with potential value to proteomics as well as the challenges that remain for proteomic studies.
Transcription factors are regulatory proteins that bind to specific sites of chromosomal DNA to enact responses to intracellular and extracellular stimuli. Transcription factor signalling networks are branched and interconnected so that any single transcription factor can activate many different genes and one gene can be activated by a combination of different transcription factors. Thus, trying to characterize a cellular response to a stimulus by measuring the level of only one transcription factor potentially ignores important simultaneous events that contribute to the response. Hence, parallel measurements of transcription factors are necessary to capture the breadth of valuable information about cellular responses that would not be obtained by measuring only a single transcription factor. We have sought to develop a new, scalable, flexible, and sensitive approach to analysis of transcription factor levels that complements existing parallel approaches. Here, we describe proof-of-principle analyses of purified human transcription factors and breast cancer nuclear extracts. Our assay can successfully quantify transcription factors in parallel with ~10-fold better sensitivity than current techniques. Sensitivity of the assay can be further increased by 200-fold through the use of PCR for signal amplification.
PKR (double-stranded RNA-activated protein kinase) is an important component of the innate immunity, antiviral and apoptotic pathways. Recently, our group found that palmitate, a saturated fatty acid, is involved in apoptosis by reducing the autophosphorylation of PKR at the Thr451 residue, however, the molecular mechanism by which palmitate reduces PKR autophosphorylation is not known. Thus, we investigated how palmitate affects the phosphorylation of the PKR protein at the molecular and biophysical levels. Biochemical and computational studies show that palmitate binds to PKR, near the ATP-binding site, thereby inhibiting its autophosphorylation at Thr451 and Thr446. Mutation studies suggests that Lys296 and Asp432 in the ATP binding site on the PKR protein are important for palmitate binding. We further confirmed that palmitate also interacts with other kinases, due to the conserved ATP-binding site. A better understanding of how palmitate interacts with the PKR protein, as well as other kinases, could shed light onto possible mechanisms by which palmitate mediates kinase signaling pathways, that could have implications on the efficacy of current drug therapies that target kinases.
Understanding the functional roles of all the molecules in cells is an ultimate goal of modern biology. An important facet is to understand the functional contributions from intermolecular interactions, both within a class of molecules (e.g. protein–protein) or between classes (e.g. protein-DNA). While the technologies for analyzing protein–protein and protein–DNA interactions are well established, the field of protein–lipid interactions is still relatively nascent. Here, we review the current status of the experimental and computational approaches for detecting and analyzing protein–lipid interactions. Experimental technologies fall into two principal categories, namely solution-based and array-based methods. Computational methods include large–scale data-driven analyses and predictions/dynamic simulations based on prior knowledge of experimentally identified interactions. Advances in the experimental technologies have led to improved computational analyses and vice versa, thereby furthering our understanding of protein–lipid interactions and their importance in biological systems.
BackgroundTranscription factors (TFs) are effectors of cell signaling pathways that regulate gene expression. TF networks are highly interconnected; one signal can lead to changes in many TF levels, and one TF level can be changed by many different signals. TF regulation is central to normal cell function, with altered TF function being implicated in many disease conditions. Thus, measuring TF levels in parallel, and over time, is crucial for understanding the impact of stimuli on regulatory networks and on diseases.ResultsHere, we report the parallel analysis of temporal TF level changes due to multiple stimuli in distinct cell types. We have analyzed short-term dynamic changes in the levels of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB), signal transducer and activator of transcription 3 (Stat3), cAMP response element-binding protein (CREB), glucocorticoid receptor (GR), and TATA binding protein (TBP), in breast and liver cancer cells after tumor necrosis factor-alpha (TNF-α) and palmitic acid (PA) exposure. In response to both stimuli, NF-kB and CREB levels were increased, Stat3 decreased, and TBP was constant. GR levels were unchanged in response to TNF-α stimulation and increased in response to PA treatment.ConclusionsOur results show significant overlap in signaling initiated by TNF-α and by PA, with the exception that the events leading to PA-mediated cytotoxicity likely also include induction of GR signaling. These results further illuminate the dynamics of TF responses to cytokine and fatty acid exposure, while concomitantly demonstrating the utility of parallel TF measurement approaches in the analysis of biological phenomena.Electronic supplementary materialThe online version of this article (doi:10.1186/s12896-016-0293-6) contains supplementary material, which is available to authorized users.
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