Time-course mRNA and protein levels during batch growth of budding yeast

Fermenting glucose in the presence of enough oxygen to support respiration, known as aerobic glycolysis, is believed to maximize growth rate. We observed increasing aerobic glycolysis during exponential growth, suggesting additional physiological roles for aerobic glycolysis. We investigated such roles in yeast batch cultures by quantifying O2 consumption, CO2 production, amino acids, mRNAs, proteins, posttranslational modifications, and stress sensitivity in the course of nine doublings at constant rate. During this course, the cells support a constant biomass-production rate with decreasing rates of respiration and ATP production but also decrease their stress resistance. As the respiration rate decreases, so do the levels of enzymes catalyzing rate-determining reactions of the tricarboxylic-acid cycle (providing NADH for respiration) and of mitochondrial folate-mediated NADPH production (required for oxidative defense). The findings demonstrate that exponential growth can represent not a single metabolic/physiological state but a continuum of changing states and that aerobic glycolysis can reduce the energy demands associated with respiratory metabolism and stress survival

Bayesian framework for global RT alignment and matching spectra to peptides.

(a) DART-ID defines the global reference RT as a latent variable, Eq 1. (b) The observed RTs are modeled as a function of the reference RT, which allows incorporating experiment specific weights and the uncertainty in measured RTs and peptide identification as shown in Eq 3. Then the global alignment model simultaneously infers the reference RT and aligns all experiments by solving Eq 4. (c) A conceptual diagram for updating the confidence in a peptide-spectrum-match (PSM). The probability to observe each PSM is estimated from the conditional likelihoods for observing the RT if the PSM is assigned correctly (blue density) or incorrectly (red density). For PSM 1, P(δ = 1 | RT) P(δ = 0 | RT), and thus the confidence decreases. Conversely, for PSM 2, P(δ = 1 | RT) > P(δ = 0 | RT), and thus the confidence increases. (d) The Bayes’ formula used to formalize the model from panel c and to update the error probability of PSMs.</p

Data-Driven Optimization of DIA Mass Spectrometry by DO-MS

Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever-increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data-independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of MS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows one to optimize and evaluate results from both label-free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant to single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication of quality figures that can be easily shared. The source code is available at github.com/SlavovLab/DO-MS

Application of DART-ID on bulk LC-MS/MS runs.

Residual RTs after DART-ID alignment for (a) label-free dataset [57] and TMT-labelled dataset [58]. (b) DART-ID doubles the PSMs at 0.01% FDR and increase them by about 40% at 1% FDR. Each circle corresponds to the number of PSMs in an LC-MS/MS run. (c) Number of PSMs per run at 1% FDR, after applying DART-ID versus before its application. The x-coordinate represents the Spectra PSMs and and y-coordinate represents the DART-ID PSMs at 1% FDR.</p

Calmodulin Transduces Ca²⁺ Oscillations into Differential Regulation of Its Target Proteins

Diverse physiological processes are regulated differentially by Ca²⁺ oscillations through the common regulatory hub calmodulin. The capacity of calmodulin to combine specificity with promiscuity remains to be resolved. Here we propose a mechanism based on the molecular properties of calmodulin, its two domains with separate Ca²⁺ binding affinities, and target exchange rates that depend on both target identity and Ca²⁺ occupancy. The binding dynamics among Ca²⁺, Mg²⁺, calmodulin, and its targets were modeled with mass-action differential equations based on experimentally determined protein concentrations and rate constants. The model predicts that the activation of calcineurin and nitric oxide synthase depends nonmonotonically on Ca²⁺-oscillation frequency. Preferential activation reaches a maximum at a target-specific frequency. Differential activation arises from the accumulation of inactive calmodulin-target intermediate complexes between Ca²⁺ transients. Their accumulation provides the system with hysteresis and favors activation of some targets at the expense of others. The generality of this result was tested by simulating 60 000 networks with two, four, or eight targets with concentrations and rate constants from experimentally determined ranges. Most networks exhibit differential activation that increases in magnitude with the number of targets. Moreover, differential activation increases with decreasing calmodulin concentration due to competition among targets. The results rationalize calmodulin signaling in terms of the network topology and the molecular properties of calmodulin

DART-ID decreases missing datapoints across runs.

(a) Map of quantified proteins across 209 SCoPE-MS runs, before and after applying DART-ID. A red mark denotes a protein quantified in an run at 1% FDR. Only peptides seen in >50% of experiments are included. (b) Decrease in missing data across all runs after applying DART-ID, for SCoPE-MS and the two bulk sets from Fig 4 at 1% FDR. All corresponding Spectra and DART-ID distributions differ significantly; the probability that they are sampled from the same distribution ≪ 1 * 10−10.</p

Design of 100 × M and 1 × M SCoPE-MS sets.

Design of 100 × M and 1 × M SCoPE-MS sets.</p

DART-ID identifies more differentially abundant proteins.

The difference in protein abundance between T-cells and monocytes was visualized in the space of fold-change and its significance, i.e., volcano plots. The volcano plot using only proteins quantified from Spectra PSMs (a) identifies fewer proteins than the volcano plot using proteins from Spectra + DART-ID PSMs (b). Fold changes are averaged normalized RI intensities of T-cells (Jurkat cell line) / monocytes (U-937 cell line). q-values are computed from two-tailed t-test p-values and corrected for multiple hypotheses testing. (c) Number of differentially abundant proteins as a function of the significance FDR from panels a and b.</p

Incorporating RTs increases confident peptide identifications.

(a) A 2D density distribution of error probabilities derived from spectra alone (Spectral PEP), compared to that after incorporating RT evidence (DART-ID PEP). (b) Map of all peptides observed across all experiments. Black marks indicate peptides with Spectral FDR c) Increase in confident PSMs (top), and in the fraction of all PSMs (bottom) across the confidence range of the x-axis. The curves correspond to PEPs estimated from spectra alone, from spectra and RTs using percolator and from spectra and RTs using DART-ID. DART-ID identifications are split into DART-ID1 and DART-ID2 depending on whether the peptides have confident spectral PSMs as marked in panel (b). (d) Distributions of number of unique peptides identified per experiment. (e) The fraction of decoys, i.e. the number of decoy hits divided by the total number of PSMs, as a function of the FDR estimated from spectra alone or from DART-ID. The Spectral FDR is estimated from separate MaxQuant searches, with the FDR applied on the peptide level.</p

Comparison of inferred reference RTs to empirical RTs.

(a) Scatter plots of observed RTs versus inferred RTs. The comparisons include 33,383 PSMs with PEP 30], BioLCCC [31], and ELUDE [34]. The right column displays comparisons for alignment methods—precision iRT [52], MaxQuant match-between-runs [7, 8], and DART-ID. (b) Distributions of residual RTs: ΔRT = Observed RT − Reference RT. Note the different scales of the x-axes between the prediction and alignment methods. (c) Mean and median of the absolute values of ΔRT from panel (b).</p