BUSCADOR RECOLECTA

(A) Ribbon representation of the two 3Dpol molecules present in the asymmetric unit of the P212121 crystals (yellow and green ribbons for the 3Dpol I and 3Dpol II molecules, respectively). The 3B1 molecule bound to the bottom of the palm of the two 3Dpol molecules is shown as sticks in atom-type colour (carbons white). The ordered triphosphate moieties of the UTP molecules bound at the nucleotide-binding site of the polymerases are also shown in atom-type sticks (phosphates in orange). (B) Omit map around the 3B1 molecule, displayed at a contour of 1,0 σ (light blue mesh). 3B1 is shown in sticks as in A with the amino acids explicitly labelled. (C) Details of the intermolecular contacts stablished between 3Dpol I and the 3B1 N-terminus (Interface I). (D) Intermolecular interactions between 3Dpol II and the 3B1 C-terminus (Interface II). The interacting residues are shown in sticks representation, coloured as in A. Hydrogen bonds and salt bridges are shown as dashed lines in black., Peer reviewed

† Rwork = ∑hkl ||Fobs(hkl)|—|Fcalc(hkl)|| / ∑hkl |Fobs(hkl)|, where Fobs and Fcalc are the structure factors, deduced from measured intensities and calculated from the model, respectively. ‡ Rfree = as for Rwork but for 5% of the total reflections chosen at random and omitted from refinement., Peer reviewed

SUPPLEMENTARY FIGURE 1. Entrainment of surrounding water into a Lagrangian patch modifies its internal tracers distribution. The amount of water that enters in the patch for unit of time corresponds to the area increase rate. In a given time step ∆t, the portion of the ocean surface ∆pat is added to the patch region pat. Consequently, means, variances and covariance change depending on the different proprieties of the intruded water., SUPPLEMENTARY FIGURE 2. Predictions of the SOIREE bloom behave when we arbitrarily increase or decrease strain and diffusion. Solid lines correspond to modeled variables when we reproduce the observed bloom dilution matching the evolution of the patch length and width (as in Fig. 5 of main text), with Spearman correlation of 0.86 (p-value=0.001). Dashed lines represent model predictions when we decrease dilution using γ = 0.06 and κ = 0.05, with Spearman correlation of 0.84 (p-value=0.002). Dotted lines represent instead model predictions when we increase dilution using γ = 0.18 and κ = 0.2, with Spearman correlation of 0.76 (p-value=0.009). We stress that the statistical robustness of the above analysis is however modest due to the relatively low number of data points., SUPPLEMENTARY FIGURE 3. Sensitivity analysis to the physical parameters of the model. We show the LBA surface of the heterogeneous ensemble for different choices of specific parameters (all the others are equal to the ones used in Fig. 6 of main text). In panel a) we use an initial patch size of S = 7.5 km while in panel b) we use S = 12.5. In panel c) we use an integration time of τ = 15 days while in panel d) we use τ = 45., SUPPLEMENTARY FIGURE 4. Sensitivity analysis to the biological parameters of the model. We show the LBA surface of the heterogeneous ensemble for different choices of specific parameters (all the others are equal to the ones used in Fig. 6 of main text). In panel a) we use a half-saturation constant of k = 1.5 μmol/m3 with a surrounding nutrient concentration of 0.08 μmol/m3 while in panel b) we use k = 2.5 with a surrounding nutrient concentration of 0.128. In panel c) we use a maximum growth-rate of ν = 0.94 day−1 with a surrounding nutrient concentration of 0.138 μmol/m3 while in panel d) we use ν = 1.56 with a surrounding nutrient concentration of 0.081 μmol/m3., SUPPLEMENTARY FIGURE 5. Sensitivity analysis to the recycling of resource. We show the model the LBA surface of the heterogeneous ensemble when we implement a complete remineralization dynamics, that means setting α = 1. All other parameters are equal to the ones used in Fig. 6 of main text., SUPPLEMENTARY FIGURE 6. LBA surface of the heterogeneous ensemble of Fig. 6 of main text., SUPPLEMENTARY FIGURE 7. LBA versus dilution scatter plot ensembles of heterogeneous (red) and well-mixed (blue) patches as in Fig. 7 of main text but setting the surrounding tracer concentrations to zero, that means: 〈sr 〉 = 〈sb〉 = 0. Each dot corresponds to a single simulation., SUPPLEMENTARY FIGURE 8. LBA versus dilution scatter plot ensembles of heterogeneous (red) and well-mixed (blue) patches as in Fig. 7 of main text but using a quadratic mortality term in Eq. (10). Each dot corresponds to a single simulation., Peer reviewed

(A) Table showing the different contact surfaces calculated with PISA [41]. (B) Two different of the views of the packing contacts (related by a 90° rotation). The unit cell represented as a reference. The long 3Dpol-3B1 fibers, formed along the ab diagonal, are highlighted in green and yellow as in Fig 2, and the contacting neighbours in grey. (C) Close up views showing the main interacting regions., Peer reviewed

Polyploidy is an extended phenomenon in biology. However, its physiological significance and whether it defines specific cell behaviors is not well understood. Here we study its connection to macroautophagy/autophagy, using the larval respiratory system of Drosophila as a model. This system comprises cells with the same function yet with notably different ploidy status, namely diploid progenitors and their polyploid larval counterparts, the latter destined to die during metamorphosis. We identified an association between polyploidy and autophagy and found that higher endoreplication status correlates with elevated autophagy. Finally, we report that tissue histolysis in the trachea during Drosophila metamorphosis is mediated by autophagy, which triggers the apoptosis of polyploid cells., The work was supported by the Ministerio de Ciencia y Tecnologia from the Spanish Government and by the Generalitat de Catalunya., Peer reviewed

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).--, Lung cancer (LC) is the leading cause of cancer deaths, and chronic obstructive pulmonary disease (COPD) can increase LC risk. Metallomics may provide insights into both of these tobacco-related diseases and their shared etiology. We conducted an observational study of 191 human serum samples, including those of healthy controls, LC patients, COPD patients, and patients with both COPD and LC. We found 18 elements (V, Al, As, Mn, Co, Cu, Zn, Cd, Se, W, Mo, Sb, Pb, Tl, Cr, Mg, Ni, and U) in these samples. In addition, we evaluated the elemental profiles of COPD cases of varying severity. The ratios and associations between the elements were also studied as possible signatures of the diseases. COPD severity and LC have a significant impact on the elemental composition of human serum. The severity of COPD was found to reduce the serum concentrations of As, Cd, and Tl and increased the serum concentrations of Mn and Sb compared with healthy control samples, while LC was found to increase Al, As, Mn, and Pb concentrations. This study provides new insights into the effects of LC and COPD on the human serum elemental profile that will pave the way for the potential use of elements as biomarkers for diagnosis and prognosis. It also sheds light on the potential link between the two diseases, i.e., the evolution of COPD to LC., This work has been supported by the project “Heteroatom-tagged proteomics and metabolomics to study lung cancer. Influence of gut microbiota” (Ref.: PY20_00366) (Project of Excellence, Regional Ministry of Economy, Knowledge, Business and University, Andalusia, Spain). The authors are also grateful for grants 651/2018 and 115/2020 from the Spanish Society of Pneumology and Surgery (SEPAR) and grant 08/2018 from the Association of Pneumology and Thoracic Surgery (Neumosur), which were used to facilitate recruitment at the hospitals and biobank registration. The authors also thank Instituto de Salud Carlos III (AES16/01783) and wish to express their gratitude for the unrestricted funding from the Menarini Group and AstraZeneca., Peer reviewed

The second messenger cyclic di-GMP (c-di-GMP) controls the transition between motility and sessility in many bacterial species by a variety of mechanisms, including the production of multiple exopolysaccharides. Pseudomonas syringae pv. tomato (Pto) DC3000 is a plant pathogenic bacteria able to synthesize acetylated cellulose under high c-di-GMP levels thanks to the expression of the wssABCDEFGHI operon. Increased cellulose production enhances air-liquid biofilm formation and generates a wrinkled colony phenotype on solid media. We previously showed that under low levels of c-di-GMP, the regulators FleQ and AmrZ bound to adjacent sequences at the wss promoter inhibiting its expression, but only FleQ responded to the presence of c-di-GMP by activating cellulose production. In the present work, we advance in the knowledge of this complex regulation in Pto DC3000 by shedding light over the role of FleN in this process. The distinctive features of this system are that FleN and FleQ are both required for repression and activation of the wss operon under low and high c-di-GMP levels, respectively. We have also identified three putative FleQ binding sites at the wss promoter and show that FleQ/FleN-ATP binds at those sites under low c-di-GMP levels, inducing a distortion of DNA, impairing RNA polymerase binding, and repressing wss transcription. However, binding of c-di-GMP induces a conformational change in the FleQ/FleN-ATP complex, which relieves the DNA distortion, allows promoter access to the RNA polymerase, and leads to activation of wss transcription. On the other hand, AmrZ is always bound at the wss promoter limiting its expression independently of FleQ, FleN and c-di-GMP levels., Peer reviewed

The second messenger cyclic di-GMP (c-di-GMP) controls the transition between motility and sessility in many bacterial species by a variety of mechanisms, including the production of multiple exopolysaccharides. Pseudomonas syringae pv. tomato (Pto) DC3000 is a plant pathogenic bacteria able to synthesize acetylated cellulose under high c-di-GMP levels thanks to the expression of the wssABCDEFGHI operon. Increased cellulose production enhances air-liquid biofilm formation and generates a wrinkled colony phenotype on solid media. We previously showed that under low levels of c-di-GMP, the regulators FleQ and AmrZ bound to adjacent sequences at the wss promoter inhibiting its expression, but only FleQ responded to the presence of c-di-GMP by activating cellulose production. In the present work, we advance in the knowledge of this complex regulation in Pto DC3000 by shedding light over the role of FleN in this process. The distinctive features of this system are that FleN and FleQ are both required for repression and activation of the wss operon under low and high c-di-GMP levels, respectively. We have also identified three putative FleQ binding sites at the wss promoter and show that FleQ/FleN-ATP binds at those sites under low c-di-GMP levels, inducing a distortion of DNA, impairing RNA polymerase binding, and repressing wss transcription. However, binding of c-di-GMP induces a conformational change in the FleQ/FleN-ATP complex, which relieves the DNA distortion, allows promoter access to the RNA polymerase, and leads to activation of wss transcription. On the other hand, AmrZ is always bound at the wss promoter limiting its expression independently of FleQ, FleN and c-di-GMP levels., Peer reviewed

pbio.3002315.t002 - Lineage tracing identifies heterogeneous hepatoblast contribution to cell lineages and postembryonic organ growth dynamics, Peer reviewed

pbio.3002315.t001 - Lineage tracing identifies heterogeneous hepatoblast contribution to cell lineages and postembryonic organ growth dynamics, Peer reviewed