Measuring precise concentrations of proteins can provide insights into biological processes. Here, we use efficient protein extraction and sample fractionation and state-of-the-art quantitative mass spectrometry techniques to generate a comprehensive, condition-dependent protein abundance map of Escherichia coli. We measure cellular protein concentrations for 55% of predicted E. coli genes (>2300 proteins) under 22 different experimental conditions and identify methylation and N-terminal protein acetylations previously not known to be prevalent in bacteria. We uncover system-wide proteome allocation, expression regulation, and post-translational adaptations. These data provide a valuable resource for the systems biology and broader E. coli research communities.

Fluctuations in intracellular molecule abundance can lead to distinct, coexisting phenotypes in isogenic populations. Although metabolism continuously adapts to unpredictable environmental changes, and although bistability was found in certain substrate-uptake pathways, central carbon metabolism is thought to operate deterministically. Here, we combine experiment and theory to demonstrate that a clonal Escherichia coli population splits into two stochastically generated phenotypic subpopulations after glucose-gluconeogenic substrate shifts. Most cells refrain from growth, entering a dormant persister state that manifests as a lag phase in the population growth curve. The subpopulation-generating mechanism resides at the metabolic core, overarches the metabolic and transcriptional networks, and only allows the growth of cells initially achieving sufficiently high gluconeogenic flux. Thus, central metabolism does not ensure the gluconeogenic growth of individual cells, but uses a population-level adaptation resulting in responsive diversification upon nutrient changes.

Systems biology modeling typically requires quantitative experimental data such as intracellular concentrations or copy numbers per cell. In order to convert population-averaging omics measurement data to intracellular concentrations or cellular copy numbers, the total cell volume and number of cells in a sample need to be known. Unfortunately, even for the often studied model bacterium Escherichia coli this information is hardly available and furthermore, certain measures (e.g. cell volume) are also dependent on the growth condition. In this work, we have determined these basic data for E. coli cells when grown in 22 different conditions so that respective data conversions can be done correctly. First, we determine growth-rate dependent cell volumes. Second, we show that in a 1 ml E. coli sample at an optical density (600 nm) of 1 the total cell volume is around 3.6 µl for all conditions tested. Third, we demonstrate that the cell number in a sample can be determined on the basis of the sample's optical density and the cells' growth rate. The data presented will allow for conversion of E. coli measurement data normalized to optical density into volumetric cellular concentrations and copy numbers per cell - two important parameters for systems biology model development.

Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.

Regulation of metabolic operation in response to extracellular cues is crucial for cells' survival. Next to the canonical nutrient sensors, which measure the concentration of nutrients, recently intracellular "metabolic flux" was proposed as a novel impetus for metabolic regulation. According to this concept, cells would have molecular systems ("flux sensors") in place that regulate metabolism as a function of the actually occurring metabolic fluxes. Although this resembles an appealing concept, we have not had any experimental evidence for the existence of flux sensors and also we have not known how these flux sensors would work in detail. Here, we show experimental evidence that supports the hypothesis that Escherichia coli is indeed able to measure its glycolytic flux and uses this signal for metabolic regulation. Combining experiment and theory, we show how this flux-sensing function could emerge from an aggregate of several molecular mechanisms: First, the system of reactions of lower glycolysis and the feedforward activation of fructose-1,6-bisphosphate on pyruvate kinase translate flux information into the concentration of the metabolite fructose-1,6-bisphosphate. The interaction of this "flux-signaling metabolite" with the transcription factor Cra then leads to flux-dependent regulation. By responding to glycolytic flux, rather than to the concentration of individual carbon sources, the cell may minimize sensing and regulatory expenses. R egulation of metabolic operation is crucial for cells' survival. The canonical view is that this regulation occurs in response to extracellular cues, where, for instance, nutrient-specific transmembrane or intracellular receptors sense the presence of a nutrient and transfer respective commands to the regulatory machinery (1-5).Recently, however, a novel impetus for metabolic regulation was proposed: cells could regulate their metabolism as a function of the actually occurring intracellular metabolic fluxes (6, 7). According to this concept, changes in extracellular nutrient abundances would first-in a rather passive manner-result in changes in intracellular metabolic fluxes. In a second instance, the metabolic fluxes would be sensed by molecular systems ("flux sensors"), which in turn would transmit the sensed "flux signal" to the regulatory machinery that consequently would adjust metabolic operation (6). On the basis of a detailed mathematical model of Escherichia coli's central metabolism and its regulation, Kotte et al. suggested that this organism would have such flux sensors in place that establish a correlation between a metabolic flux and the concentration of certain so-called flux-signaling metabolites, which in turn affect the activity of transcription factors and thus would allow for transcriptional regulation in a flux-dependent manner.Using intracellular flux to regulate metabolism is an appealing concept as it would omit the need for nutrient-specific sensors for many different nutrients, and would allow the integration of multiple nutrient inputs directl...

Mutations in myotubularin-related protein-2 (MTMR2) or MTMR13/set-binding factor-2 (SBF2) genes are responsible for the severe autosomal recessive hereditary neuropathies, Charcot-Marie-Tooth disease (CMT) types 4B1 and 4B2, both characterized by reduced nerve conduction velocities, focally folded myelin sheaths and demyelination. MTMRs form a large family of conserved dual-specific phosphatases with enzymatically active and inactive members. We show that homodimeric active Mtmr2 interacts with homodimeric inactive Sbf2 in a tetrameric complex. This association dramatically increases the enzymatic activity of the complexed Mtmr2 towards phosphatidylinositol 3-phosphate and phosphatidylinositol 3,5-bisphosphate. Mtmr2 and Sbf2 are considerably, but not completely, co-localized in the cellular cytoplasm. On membranes of large vesicles formed under hypo-osmotic conditions, Sbf2 favorably competes with Mtmr2 for binding sites. Our data are consistent with a model suggesting that, at a given cellular location, Mtmr2 phosphatase activity is highly regulated, being high in the Mtmr2/Sbf2 complex, moderate if Mtmr2 is not associated with Sbf2 or functionally blocked by competition through Sbf2 for membrane-binding sites.

Reduced-intensity/reduced-toxicity conditioning and allogeneic T-cell replete hematopoietic stem cell transplantation are curative in patients with hemophagocytic lymphohistiocytosis (HLH). Unstable donor chimerism (DC) and relapses are clinical challenges . We examined the effect of a reduced-intensity conditioning regimen based on targeted busulfan to enhance myeloid DC in HLH. The European Society for Bone and Marrow Transplantation–approved reduced-intensity conditioning protocol comprised targeted submyeloablative IV busulfan, IV fludarabine, and serotherapy comprising IV alemtuzumab (0.5-0.8 mg/kg) for unrelated-donor and IV rabbit anti–T-cell globulin for related-donor transplants. We assessed toxicity, engraftment, graft-versus-host disease (GHVD), DC in blood cell subtypes, and overall survival/event-free survival. Twenty-five patients from 7 centers were treated (median age, 0.68 year). The median total dose and cumulative area under the curve of busulfan was 13.1 mg/kg (6.4-26.4) and 63.1 mg/L × h (48-77), respectively. Bone marrow, peripheral blood stem cell, or cord blood transplants from HLA-matched related (n = 7) or unrelated (n = 18) donors were administered. Donor cells engrafted in all patients (median: neutrophils d+20/platelets d+28). At last follow-up (median, 36 months; range, 8-111 months), the median DC of CD15+ neutrophils, CD3+ T cells, and CD16+56+ natural killer cells was 99.5% (10-100), 97% (30-100), and 97.5% (30-100), respectively. Eight patients (32%) developed sinusoidal obstruction syndrome, resolving after defibrotide treatment. The 3-year overall survival and event-free survival rates were both 100%. None of the patients developed acute grade III to IV GHVD. Limited chronic GVHD was encountered in 4%. This regimen achieves excellent results with stable DC in patients with HLH.

Two unrelated patients with GATA2-haploinsufficiency developed a hemophagocytic lymphohistiocytosis (HLH)-like disease during a varicella zoster virus (VZV) infection. High copy numbers of VZV were detected in the blood, and the patients were successfully treated with acyclovir and intravenous immunoglobulins. After treatment with corticosteroids for the HLH, both patients made a full recovery. Although the mechanisms leading to this disease constellation have yet to be characterized, we hypothesize that impairment of the immunoregulatory role of NK cells in GATA2-haploinsufficiency may have accentuated the patients' susceptibility to HLH. Expansion of a double negative T-lymphocytic population identified with CyTOF could be a further factor contributing to HLH in these patients. This is the first report of VZV-triggered HLH-like disease in a primary immunodeficiency and the third report of HLH in GATA2-haploinsufficiency. Since HLH was part of the presentation in one of our patients, GATA2-haploinsufficiency represents a potential differential diagnosis in patients presenting with the clinical features of HLH—especially in cases of persisting cytopenia after recovery from HLH.