In silico evolution of diauxic growth

The glucose effect is a well known phenomenon whereby cells, when presented with two different nutrients, show a diauxic growth pattern, i.e. an episode of exponential growth followed by a lag phase of reduced growth followed by a second phase of exponential growth. Diauxic growth is usually thought of as a an adaptation to maximise biomass production in an environment offering two or more carbon sources. While diauxic growth has been studied widely both experimentally and theoretically, the hypothesis that diauxic growth is a strategy to increase overall growth has remained an unconfirmed conjecture. Here, we present a minimal mathematical model of a bacterial nutrient uptake system and metabolism. We subject this model to artificial evolution to test under which conditions diauxic growth evolves. As a result, we find that, indeed, sequential uptake of nutrients emerges if there is competition for nutrients and the metabolism/uptake system is capacity limited. However, we also find that diauxic growth is a secondary effect of this system and that the speed-up of nutrient uptake is a much larger effect. Notably, this speed-up of nutrient uptake coincides with an overall reduction of efficiency. Our two main conclusions are: (i) Cells competing for the same nutrients evolve rapid but inefficient growth dynamics. (ii) In the deterministic models we use here no substantial lag-phase evolves. This suggests that the lag-phase is a consequence of stochastic gene expression

Chemotaxis: a feedback-based computational model robustly predicts multiple aspects of real cell behaviour

The mechanism of eukaryotic chemotaxis remains unclear despite intensive study. The most frequently described mechanism acts through attractants causing actin polymerization, in turn leading to pseudopod formation and cell movement. We recently proposed an alternative mechanism, supported by several lines of data, in which pseudopods are made by a self-generated cycle. If chemoattractants are present, they modulate the cycle rather than directly causing actin polymerization. The aim of this work is to test the explanatory and predictive powers of such pseudopod-based models to predict the complex behaviour of cells in chemotaxis. We have now tested the effectiveness of this mechanism using a computational model of cell movement and chemotaxis based on pseudopod autocatalysis. The model reproduces a surprisingly wide range of existing data about cell movement and chemotaxis. It simulates cell polarization and persistence without stimuli and selection of accurate pseudopods when chemoattractant gradients are present. It predicts both bias of pseudopod position in low chemoattractant gradients and-unexpectedly-lateral pseudopod initiation in high gradients. To test the predictive ability of the model, we looked for untested and novel predictions. One prediction from the model is that the angle between successive pseudopods at the front of the cell will increase in proportion to the difference between the cell's direction and the direction of the gradient. We measured the angles between pseudopods in chemotaxing Dictyostelium cells under different conditions and found the results agreed with the model extremely well. Our model and data together suggest that in rapidly moving cells like Dictyostelium and neutrophils an intrinsic pseudopod cycle lies at the heart of cell motility. This implies that the mechanism behind chemotaxis relies on modification of intrinsic pseudopod behaviour, more than generation of new pseudopods or actin polymerization by chemoattractant

Impact of post-transplantation maintenance therapy on health-related quality of life in patients with multiple myeloma: data from the Connect® MM Registry

Maintenance therapy after autologous stem cell transplantation (ASCT) is recommended for use in multiple myeloma (MM); however, more data are needed on its impact on health-related quality of life (HRQoL). Presented here is an analysis of HRQoL in a Connect MM registry cohort of patients who received ASCT ± maintenance therapy. The Connect MM Registry is one of the earliest and largest, active, observational, prospective US registry of patients with symptomatic newly diagnosed MM. Patients completed the Functional Assessment of Cancer Therapy-MM (FACT-MM) version 4, EuroQol-5D (EQ-5D) questionnaire, and Brief Pain Inventory (BPI) at study entry and quarterly thereafter until death or study discontinuation. Patients in three groups were analyzed: any maintenance therapy (n = 244), lenalidomide-only maintenance therapy (n = 169), and no maintenance therapy (n = 137); any maintenance and lenalidomide-only maintenance groups were not mutually exclusive. There were no significant differences in change from pre-ASCT baseline between any maintenance (P = 0.60) and lenalidomide-only maintenance (P = 0.72) versus no maintenance for the FACT-MM total score. There were also no significant differences in change from pre-ASCT baseline between any maintenance and lenalidomide-only maintenance versus no maintenance for EQ-5D overall index, BPI, FACT-MM Trial Outcomes Index, and myeloma subscale scores. In all three groups, FACT-MM, EQ-5D Index, and BPI scores improved after ASCT; FACT-MM and BPI scores deteriorated at disease progression. These data suggest that post-ASCT any maintenance or lenalidomide-only maintenance does not negatively impact patients' HRQoL. Additional research is needed to verify these findings

Lead-related quantum emitters in diamond

We report on quantum emission from Pb-related color centers in diamond following ion implantation and high-temperature vacuum annealing. First-principles calculations predict a negatively charged Pb-vacancy (PbV) center in a split-vacancy configuration, with a zero-phonon transition around 2.4 eV. Cryogenic photoluminescence measurements performed on emitters in nanofabricated pillars reveal several transitions, including a prominent doublet near 520 nm. The splitting of this doublet, 5.7 THz, exceeds that reported for other group-IV centers. These observations are consistent with the PbV center, which is expected to have a combination of narrow optical transitions and stable spin states, making it a promising system for quantum network nodes.U.S. Army Research Laboratory. Center for Distributed Quantum InformationNational Science Foundation (U.S.). Graduate Research Fellowship ProgramNational Science Foundation (U.S.) (Grant DMR-1231319)United States. National Aeronautics and Space Administration (Space Technology Research Fellowship)MIT-Harvard Center for Ultracold Atoms MIT International Science and Technology Initiativ

Recognition of early mortality in multiple myeloma by a prediction matrix

Early mortality (EM; death ≤ 6 months from diagnosis) has been reported in several newly diagnosed multiple myeloma (NDMM) trials. Before the era of novel agents, the incidence was 10%-14%. Causes of death included infections/pneumonia, renal failure, refractory disease, and cardiac events. Staging systems, such as the revised International Staging System (r-ISS), and prognostic factors including cytogenetics, lactate dehydrogenase levels, and myeloma-specific factors, are useful to assess overall prognosis; however, they cannot predict EM. We evaluated patients treated with novel agents in the Connect MM® Registry and identified risk factors of the EM cohort. Eligible patients were enrolled in the registry within 60 days of diagnosis. Univariate and multivariate analyses were conducted to evaluate associations between baseline characteristics and EM. Prediction matrices for EM were constructed from a logistic model. Between September 2009 and December 2011, 1493 patients were enrolled in the registry and had adequate follow-up. Of these patients, 102 (6.8%) had EM and 1391 (93.2%) survived for > 180 days. Baseline factors significantly associated with increased EM risk included age > 75 years, higher Eastern Cooperative Oncology Group performance status, lower EQ-5D mobility score, higher ISS stage, lower platelet count, and prior hypertension. Renal insufficiency trended toward increased EM risk. These risk factors were incorporated into a prediction matrix for EM. The EM prediction matrix uses differential weighting of risk factors to calculate EM risk in patients with NDMM. Identifying patients at risk for EM may provide new opportunities to implement patient-specific treatment strategies to improve outcomes