Warburg effect and translocation-induced genomic instability: two yeast models for cancer cells

Yeast has been established as an efficient model system to study biological principles underpinning human health. In this review we focus on yeast models covering two aspects of cancer formation and progression (i) the activity of pyruvate kinase (PK), which recapitulates metabolic features of cancer cells, including the Warburg effect, and (ii) chromosome bridge-induced translocation (BIT) mimiking genome instability in cancer. Saccharomyces cerevisiae is an excellent model to study cancer cell metabolism, as exponentially growing yeast cells exhibit many metabolic similarities with rapidly proliferating cancer cells. The metabolic reconfiguration includes an increase in glucose uptake and fermentation, at the expense of respiration and oxidative phosphorylation (the Warburg effect), and involves a broad reconfiguration of nucleotide and amino acid metabolism. Both in yeast and humans, the regulation of this process seems to have a central player, PK, which is up-regulated in cancer, and to occur mostly on a post-transcriptional and post-translational basis. Furthermore, BIT allows to generate selectable translocation-derived recombinants ("translocants"), between any two desired chromosomal locations, in wild-type yeast strains transformed with a linear DNA cassette carrying a selectable marker flanked by two DNA sequences homologous to different chromosomes. Using the BIT system, targeted non-reciprocal translocations in mitosis are easily inducible. An extensive collection of different yeast translocants exhibiting genome instability and aberrant phenotypes similar to cancer cells has been produced and subjected to analysis. In this review, we hence provide an overview upon two yeast cancer models, and extrapolate general principles for mimicking human disease mechanisms in yeast

Exact exchange-correlation potential of a ionic Hubbard model with a free surface

We use Lanczos exact diagonalization to compute the exact exchange-correlation (xc) potential of a Hubbard chain with large binding energy ("the bulk") followed by a chain with zero binding energy ("the vacuum"). Several results of density functional theory in the continuum (sometimes controversial) are verified in the lattice. In particular we show explicitly that the fundamental gap is given by the gap in the Kohn-Sham spectrum plus a contribution due to the jump of the xc-potential when a particle is added. The presence of a staggered potential and a nearest-neighbor interaction V allows to simulate a ionic solid. We show that in the ionic regime in the small hopping amplitude limit the xc-contribution to the gap equals V, while in the Mott regime it is determined by the Hubbard U interaction. In addition we show that correlations generates a new potential barrier at the surface

Morphometrical analysis of transbronchial cryobiopsies

The recent introduction of bronchoscopically recovered cryobiopsy of lung tissue has opened up new possibilities in the diagnosis of neoplastic and non-neoplastic lung diseases in various aspects. Most notably the morphological diagnosis of peripheral lung biopsies promises to achieve a better yield with a high quality of specimens. To better understand this phenomenon, its diagnostic options and perspectives, this study morphometrically compares 15 cryobiopsies and 18 transbronchial forceps biopsies of peripheral lung tissue a priori without considering clinical hit ratio or integration of results in the clinical diagnostic processing. Cryotechnically harvested specimens were significantly larger (mean: 17.1 ± 10.7 mm2 versus 3.8 ± 4.0 mm2) and contained alveolar tissue more often. If present, the alveolar part in cryobiopsies exceeded the one of forceps biopsies. The alveolar tissue of crybiopsy specimens did not show any artefacts. Based on these results cryotechnique seems to open up new perspectives in bronchoscopic diagnosis of lung disease

Global Health Governance and the Commercial Sector: A Documentary Analysis of Tobacco Company Strategies to Influence the WHO Framework Convention on Tobacco Control

Heide Weishaar and colleagues did an analysis of internal tobacco industry documents together with other data and describe the industry's strategic response to the proposed World Health Organization Framework Convention on Tobacco Control

Four simple recommendations to encourage best practices in research software [version 1; referees: awaiting peer review]

Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations. Keyword

Spin dynamics from time-dependent density functional perturbation theory

We present a new method to model spin-wave excitations in magnetic solids, based on the Liouville-Lanczos approach to time-dependent density functional perturbation theory. This method avoids computationally expensive sums over empty states and naturally deals with the coupling between spin and charge fluctuations, without ever explicitly computing charge-density susceptibilities. Spin-wave excitations are obtained with one Lanczos chain per magnon wave-number and polarization, avoiding the solution of the linear-response problem for every individual value of frequency, as other state-of-the-art approaches do. Our method is validated by computing magnon dispersions in bulk Fe and Ni, resulting in agreement with previous theoretical studies in both cases, and with experiment in the case of Fe. The disagreement in the case of Ni is also comparable with that of previous computations

Theoretical study of the electronic spectra of small molecules that incorporate analogues of the copper-cysteine bond

The copper-sulphur bond which binds cysteinate to the metal centre is a key factor in the spectroscopy of blue copper proteins. We present theoretical calculations describing the electronically excited states of small molecules, including CuSH, CuSCH_3, (CH_3)_2SCuSH, (imidazole)-CuSH and (imidazole)_2-CuSH, derived from the active site of blue copper proteins that contain the copper-sulphur bond in order to identify small molecular systems that have electronic structure that is analogous to the active site of the proteins. Both neutral and cationic forms are studied, since these represent the reduced and oxidised forms of the protein, respectively. For CuSH and CuSH^+, excitation energies from time-dependent density functional theory with the B97-1 exchange-correlation functional agree well with the available experimental data and multireference configuration interaction calculations. For the positive ions, the singly occupied molecular orbital is formed from an antibonding combination of a 3d orbital on copper and a 3pπ orbital on sulphur, which is analogous to the protein. This leads several of the molecules to have qualitatively similar electronic spectra to the proteins. For the neutral molecules, changes in the nature of the low lying virtual orbitals leads the predicted electronic spectra to vary substantially between the different molecules. In particular, addition of a ligand bonded directly to copper results in the low-lying excited states observed in CuSH and CuSCH_33 to be absent or shifted to higher energies

A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

A proteomic survival predictor for COVID-19 patients in intensive care

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care