Search

The majority of microbes on earth, whether they live in the ocean, the soil or in animals, are not growing, but instead struggling to survive starvation1–6. Some genes and environmental conditions affecting starvation survival have been identified7–13, but despite almost a century of study14–16, we do not know which processes lead to irreversible loss of viability, which maintenance processes counteract them and how lifespan is determined from the balance of these opposing processes. Here, we used time-lapse microscopy to capture and characterize the cell death process of E. coli during carbon starvation for the first time. We found that a lack of nutrients results in the collapse of ion homeostasis, triggering a positive-feedback cascade of osmotic swelling and membrane permeabilization that ultimately results in lysis. Based on these findings, we hypothesized that ion transport is the major energetic requirement for starving cells and the primary determinant of the timing of lysis. We therefore developed a mathematical model that integrates ion homeostasis and ‘cannibalistic’ nutrient recycling from perished cells16,17 to predict lifespan changes under diverse conditions, such as changes of cell size, medium composition, and prior growth conditions. Guided by model predictions, we found that cell death during starvation could be dramatically slowed by replacing inorganic ions from the medium with a non-permeating osmoprotectant, removing the cost of ion homeostasis and preventing lysis. Our quantitative and predictive model explains how survival kinetics are determined in starvation and elucidates the mechanistic underpinnings of starvation survival.

Mass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes of protein expression in a wide range of biological and biomedical applications. Protein expression changes need to be reliably derived from many measured peptide intensities and their corresponding peptide fold changes. These peptide fold changes vary considerably for a given protein. Numerous instrumental setups aim to reduce this variability, whereas current computational methods only implicitly account for this problem. We introduce a new method, MS-EmpiRe, which explicitly accounts for the noise underlying peptide fold changes. We derive data set-specific, intensity-dependent empirical error fold change distributions, which are used for individual weighing of peptide fold changes to detect differentially expressed proteins (DEPs). In a recently published proteome-wide benchmarking data set, MS-EmpiRe doubles the number of correctly identified DEPs at an estimated FDR cutoff compared with state-of-the-art tools. We additionally confirm the superior performance of MS-EmpiRe on simulated data. MS-EmpiRe requires only peptide intensities mapped to proteins and, thus, can be applied to any common quantitative proteomics setup. We apply our method to diverse MS data sets and observe consistent increases in sensitivity with more than 1000 additional significant proteins in deep data sets, including a clinical study over multiple patients. At the same time, we observe that even the proteins classified as most insignificant by other methods but significant by MS-EmpiRe show very clear regulation on the peptide intensity level. MS-EmpiRe provides rapid processing (< 2 min for 6 LC-MS/MS runs (3 h gradients)) and is publicly available under github.com/zimmerlab/MS-EmpiRe with a manual including examples.

1AbstractMass spectrometry based proteomics is the method of choice for quantifying genome-wide differential changes of proteins in a wide range of biological and biomedical applications. Protein changes need to be reliably derived from a large number of measured peptide intensities and their corresponding fold changes. These fold changes vary considerably for a given protein.Numerous instrumental setups aim to reduce this variability, while current computational methods only implicitly account for this problem. We introduce a new method, MS-EmpiRe (github.com/zimmerlab/MS-EmpiRe), which explicitly accounts for the noise underlying peptide fold changes. We derive dataset-specific, intensity-dependent empirical error distributions, which are used for individual weighing of peptide fold changes to detect differentially abundant proteins. The method requires only peptide intensities mapped to proteins and, thus, can be applied to any common quantitative proteomics setup. In a recently published proteome-wide benchmarking dataset, MS-EmpiRe doubles the number of correctly identified changing proteins at a correctly estimated FDR cutoff in comparison to state-of-the-art tools. We confirm the superior performance of MS-EmpiRe on simulated data. MS-EmpiRe provides rapid processing (< 2min) and is an easy to use, general-purpose tool.

Spectral libraries play a central role in the analysis of data independent acquisition (DIA) proteomics experiments. DIA experiments require spectral libraries, as most current methods cannot apply traditional peptide identification via database searching on DIA data. A central assumption in current spectral library tools is that a single characteristic intensity pattern (CIP) suffices to describe the fragmentation of an unmodified peptide in a particular parent ion charge state (peptide charge pair). However, we find that this is often not the case. We analyze a heterogeneous dataset of 440.000 MaxQuant - preprocessed peptide spectra from a QToF mass spectrometer, stemming from over 100 different LC-MS/MS runs. The dataset corresponds to 10.580 peptide charge pairs, which have each been measured and identified at least 20 times. We demonstrate that the same charged and unmodified peptide can fragment in multiple reproducible ways, even within the same LC-MS/MS run. We integrate multiple reference CIPs (MCIPs) in our model library and observe a >99% coverage of replicate fragmentation spectra for 95% of peptide charge pairs (using up to four CIPs). Using a single CIP (as in current spectral library approaches), we find >99% coverage for only 50% of the peptide charge pairs. Our approach achieves substantially greater sensitivity in comparison to the popular SpectraST library generation tool. Using randomized decoy spectra, we demonstrate that identification accuracy of the MCIP approach is improved by up to 12% compared to a single CIP approach. We test the MCIP approach on a SWATH data set and observe a ~30% increase in peptide recognition. We conclude that including MCIPs in spectral library approaches would yield increased sensitivity without compromising the false discovery rate.