Advances in phosphopeptide enrichment methods enable the identification of thousands of phosphopeptides from complex samples. Current offline enrichment approaches using TiO 2 , Ti, and Fe immobilized metal ion affinity chromatography (IMAC) material in batch or microtip format are widely used, but they suffer from irreproducibility and compromised selectivity. To address these shortcomings, we revisited the merits of performing phosphopeptide enrichment in an HPLC column format. We found that Fe-IMAC columns enabled the selective, comprehensive, and reproducible enrichment of phosphopeptides out of complex lysates. Column enrichment did not suffer from bead-to-sample ratio issues and scaled linearly from 100 g to 5 mg of digest. Direct measurements on an Orbitrap Velos mass spectrometer identified >7500 unique phosphopeptides with 90% selectivity and good quantitative reproducibility (median cv of 15%). The number of unique phosphopeptides could be increased to more than 14,000 when the IMAC eluate was subjected to a subsequent hydrophilic strong anion exchange separation. Fe-IMAC columns outperformed Ti-IMAC and TiO 2 in batch or tip mode in terms of phosphopeptide identification and intensity. Permutation enrichments of flow-throughs showed that all materials largely bound the same phosphopeptide species, independent of physicochemical characteristics. However, binding capacity and elution efficiency did profoundly differ among the enrichment materials and formats. As a result, the often quoted orthogonality of the materials has to be called into question. Our results strongly suggest that insufficient capacity, inefficient elution, and the stochastic nature of data-dependent acquisition in mass spectrometry are the causes of the experimentally observed complementarity. The Fe-IMAC enrichment workflow using an HPLC format developed here enables rapid and comprehensive phosphoproteome analysis that can be applied to a wide range of biological systems. Molecular & Cellular

Gene expression noise arises from stochastic variation in the synthesis and degradation of mRNA and protein molecules and creates differences in protein numbers across populations of genetically identical cells. Such variability can lead to imprecision and reduced performance of both native and synthetic networks. In principle, gene expression noise can be controlled through the rates of transcription, translation and degradation, such that different combinations of those rates lead to the same protein concentrations but at different noise levels. Here, we present a "noise tuner" which allows orthogonal control over the transcription and the mRNA degradation rates by two different inducer molecules. Combining experiments with theoretical analysis, we show that in this system the noise is largely determined by the transcription rate whereas mean expression can be independently adjusted by mRNA stability. This noise tuner enables twofold changes in gene expression noise over a fivefold range of mean protein levels. We demonstrated the efficacy of the noise tuner in a complex regulatory network by varying gene expression noise in the mating pathway of Saccharomyces cerevisiae, which allowed us to control the output noise and the mutual information transduced through the pathway. The noise tuner thus represents an effective tool of gene expression noise control, both to interrogate noise sensitivity of natural networks and enhance performance of synthetic circuits.

Gene expression noise arises from stochastic variation in the synthesis and degradation of mRNA and protein molecules and creates differences in protein numbers across populations of genetically identical cells. Such variability can lead to imprecision and reduced performance of both native and synthetic networks. In principle, gene expression noise can be controlled through the rates of transcription, translation and degradation, such that different combinations of those rates lead to the same protein concentrations but at different noise levels. Here, we present a “noise tuner” which allows orthogonal control over the transcription and the mRNA degradation rates by two different inducer molecules. Combining experiments with theoretical analysis, we show that in this system the noise is largely determined by the transcription rate, whereas the mean expression is determined by both the transcription rate and mRNA stability and can thus be decoupled from the noise. This noise tuner enables 2-fold changes in gene expression noise over a 5-fold range of mean protein levels. We demonstrated the efficacy of the noise tuner in a complex regulatory network by varying gene expression noise in the mating pathway of Saccharomyces cerevisiae, which allowed us to control the output noise and the mutual information transduced through the pathway. The noise tuner thus represents an effective tool of gene expression noise control, both to interrogate noise sensitivity of natural networks and enhance performance of synthetic circuits.