BUSCADOR RECOLECTA

15 pages. -- This file includes: Supp. Fig. 1-13. -- Supp. Table 1. List of protein constructs used in this work: vector, expression system, detailed protein encoded, and reference study., Peer reviewed

Supplementary Data 1: ‘folded’ user guide. -- Supplementary Data 2: effect of image size and orientation. -- Supplementary Data 3: allometric scaling. -- Supplementary Data 4: additional tests. -- Supplementary table captions. -- Table S1. Sheet 1 ‘Rhinocerotidae’: dataset with the Rhinocerotidae dental sample used in the present study. Included references are detailed in this section. Sheet 2 ‘Additional samples’: ancillary information of the dental sample used in Supplementary Data 4. Included references are detailed in this section. -- Table S2. Selected parameters generated by our workflow on the Rhinocerotidae dataset included in Table S1 – Sheet 1. -- Table S3. Results of ordinary and phylogenetic regression tests. See Supplementary Data 2: allometric scaling (included in the Supplementary text). -- Supplementary figures S1–S8., Peer reviewed

18 pages. -- Supplementary Figures and Tables. -- Figure S1. Quality parameters for the MD simulations of RFK in complex with the products of its reaction. -- Figure S2. Impact of the starting conformation of RFK in complex with the products of its reaction on the MD trajectories of selected features. -- Figure S3. The conformational space of aPNPOx in monomer (left panels) and homodimer (right panels) states. -- Figure S4. Docking of hRFKop (1P4M) and hRFKcl (1Q9S) crystal structures to PNPOx crystallographic model. -- igure S5. Quality parameters for the MD simulations of hRFK:aPNPOx interaction models. -- Figure S6. MD simulations of hRFKop:aPNPOx putative interaction models. -- Figure S7. MD simulations of hRFKcl:aPNPOx putative interaction models. -- Figure S8. The aPNPOx interaction surface. -- Figure S9. Detail of the molecular coupling of hRFKop to aPNPOx in a representative model (R3). -- Figure S10. Detail of the molecular coupling of hRFKcl to aPNPOx in a representative model (R3). -- Figure S11. RFK and PNPOx protein:protein interactions networks retrieved from the BioGrid server (https://thebiogrid.org/). -- Table S1. Human FMN-dependent flavoproteins., Peer reviewed

The following Supporting Information is available for this article: Table S1. Description of the target shrub species at the Aranjuez (Helianthemum squamatum) and Sorbas (Helianthemum squamatum, Helianthemum syriacum and Gypsophila struthium) field sites. Table S2. Results of the linear mixed model analyses of leaf gas exchange parameters measured in Helianthemum squamatum shrubs in Aranjuez and Helianthemum squamatum, Helianthemum syriacum and Gypsophila struthium shrubs in Sorbas in Spring in three consecutive growing seasons (2015-2017). Table S3. Mean (± SE) values for leaf nutrient concentrations (N, P and K in mg g-1), leaf mass area (LMA, mg cm-2), leaf area (LA, cm2), leaf thickness (LT, mm) and leaf isotopic C composition (13C) in Aranjuez (Helianthemum squamatum) and Sorbas (Helianthemum squamatum, Helianthemum syriacum and Gypsophila struthium) and across species and sites (Grand mean) in spring for three consecutive growing seasons (2015-2017). Table S4. Results of the linear mixed model analyses of for leaf nutrient concentrations (N, P and K), and contents (N, P and K content), leaf mass area (LMA), leaf thickness (LT) and leaf isotopic C composition (13C) measured in Helianthemum squamatum shrubs in Aranjuez and Helianthemum squamatum, Helianthemum syriacum and Gypsophila struthium shrubs in Sorbas in Spring in three consecutive growing seasons (2015-2017). Figure S1. Experimental plot of the combined warming and rainfall reduction treatment (W+RR). Figure S2 Overview of the experimental plots in the Aranjuez and Sorbas experimental stations and layout of the experimental plots. Figure S3. A priori conceptual structural equation model (SEM) depicting pathways by which climate change (W: warming and RR: rainfall reduction) influences plant photosynthetic nutrient use efficiency (PNutUE) through effects on the plant’s stomatal conductance (gs), photosynthetic rates (A), leaf morphology (leaf thickness: LT and leaf area: LA) and three leaf nutrients involved in photosynthesis and stomatal conductance regulation (Leaf Nut: N, P and K). Figure S4. Leaf gas exchange (Photosynthetic rate, stomatal conductance: gs, transpiration, ci/ca ratio and intrinsic and instantaneous water use efficiency) in Helianthemum squamatum in Aranjuez (H. sq A) and Helianthemum squamatum (H. sq S), Helianthemum syriacum (H. syr S) and Gypsophila struthium (G. st S) in Sorbas measured in spring. Figure S5 Relationships between net photosynthetic rates in a mass basis (Amass) and a) leaf N, b) leaf P and c) leaf K concentrations and net photosynthetic rates in an area basis (A) and d) leaf N, e) leaf P and f) leaf K concentrations. Figure S6. Relationships between the integrated water use efficiency (foliar 13C) and the photosynthetic nutrient use efficiency of a) leaf N (PNUE), b) leaf P (PPUE) and c) leaf K (PKUE). Figure S7. Standardized total (upper panel), direct (middle panel) and indirect (lower panel) effects derived from the structural equation modeling, including the effects of rainfall reduction (RR), warming (W), leaf nutrients (N, P or K, respectively), stomatal conductance (gs) and photosynthetic rates (A) on the photosynthetic use of leaf N (PNUE, black bars), leaf P (PPUE, light grey bars) and leaf K (PKUE, dark grey bars)., Peer reviewed

9 pages. -- Figure S1. Expression of EXD2 and EXD1 during spermatogenesis. -- Figure S2. Co-localization of EXD2 and EXD1 with DDX4. -- Figure S3. Characterization of the genomic deletion in Exd2∆ mice. -- Figure S4. RNA-sequencing analysis of Exd2∆ testes., Peer reviewed

24 pages. -- Figures S1-S9. -- Table S1. Single particle data processing and modeling of the yeast NPC and sub-assemblies, related to Figures 1-6. -- Animated gif of sequential XY cross sections of the composite multiscale 3D structure of the isolated yeast NPC depicted in with the same color coding as in Figure 1 with FG-connectors shown as rods using the color key from Figure 6, related to Figures 1, 3, 5 and 6. -- Animated gif of a complete rotation series in 10° increments about the x axis for the composite multiscale 3D structure of the isolated yeast NPC with color coding from Figure 1 with FG-connectors shown as rods (color key from Figure 6), related to Figures 1 and 6., Peer reviewed

Under a Creative Commons license BY NC ND 4.0., Figure S1: Calibration mix chromatogram obtained for ASTM D2887 method. Figure S2: Calibration curve obtained for ASTM D2887 method. Figure S3: Chromatograms obtained for the a) TPO, the first distillation of TPO b) light fraction (LF), c) heavy fraction (HF) and the second distillation of the light fraction of TPO d) LF and e) HF. Table S1: Percentage of relative area obtained with the quantification ion (m/z) for the TPO by GC-MS according to the NIST2020 library (BTEX=benzene, toluene, ethylbenzene, xylenes; CC= cyclic compounds, AAA= alkanes, alkenes, alkynes, SB= Substituted benzenes, no BTEX, HC= heterocyclic compounds, I= indenes, PAH= polycyclic aromatic hydrocarbons, O= others). Table S2: Percentage of relative area obtained with the quantification ion (m/z) for the first distillation of the TPO, LF-1, by GC-MS according to the NIST2020 library. Table S3: Percentage of relative area obtained with the quantification ion (m/z) for the first distillation of the TPO, HF-1, by GC-MS according to the NIST2020 library. Table S4: Percentage of relative area obtained with the quantification ion (m/z) for the second distillation of the TPO, LF-2, by GC-MS according to the NIST2020 library. Table S5: Percentage of relative area obtained with the quantification ion (m/z) for the second distillation of the TPO, HF-2, by GC-MS according to the NIST2020 library., This work is part of the BLACKCYCLE project (For the circular economy of tyre domain: recycling end of life tyres into secondary raw materials or tyres and other product applications) which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 869625. The authors would also like to thank the Regional Government of Aragon (DGA) for the support provided under the research groups support programme and CSIC for the interdisciplinary thematic platform SUSPLAST., Peer reviewed

10 pages. -- Figure S1. Negative stain electron microscopy of purified ITV. -- Figure S2. c44H10 Fab targets an epitope largely conserved across major MHC Class II allele groups. -- Figure S3. Intramuscular ITV-TpD immunization elicits neutralizing antibody responses superior relative to subcutaneous administration. -- Figure S4. ITV-TpD is thermostable when stored at -20°C, 4°C or 40°C for up to 3 weeks. -- Figure S5. Bi-antigenic ITV-TpD elicits broad sarbecovirus-neutralizing antibody responses. -- Supplementary Table 1: Data collection and refinement statistics for the c44H10 Fab-MHC Class II (HLA-DRA*01:01, HLA-DRB1*04:01) complex (related to Figure 2). -- Supplementary Table 2: Table of contacts between the HLA-DR α chain and c44H10 Fab (Related to Figure 2). -- Supplementary Table 3: Table of contacts between the HLA-DR β chain and c44H10 Fab, Peer reviewed

37 pages. -- Further details on experimental methods: Synthesis of particle-based pH-sensors. -- Fabrication of pH-sensing hybrid nanofibers. -- Characterization of pH-sensing hybrid nanofibers. -- Calibration of pH-sensing hybrid nanofibers. -- Cell proliferation assay. -- Cell co-cultures on pH-sensing hybrid nanofibers. -- Extracellular lactate quantification. -- Statistical significance. -- Sensing of pH in the cell cultures over time. -- Further details on image analysis: [A] Particle (cell or sensor) detection. -- [B] Intensity Evaluation. -- [C] Tracking cells and probes across frames. -- Further details on computational methods: Gaussian approximation. -- Enforcing positivity constraints of the reconstructed concentration profile. -- Priors and Lagrange multipliers: Tikhonov regularizer for the scale !1. -- Time continuity across frames !2. -- Matching the bulk trend !3. -- The Monte Carlo Markov chain. -- Errors and confidence interval. -- Ruling out flow contributions from advection. -- Flux distributions conditioned on the cell type. -- pH maps. -- Supporting files description. - Data. -- Codes. -- Supplementary Figures., The homeostatic control of their environment is an essential task of living cells. It has been hypothesized that, when microenvironmental pH inhomogeneities are induced by high cellular metabolic activity, diffusing protons act as signaling molecules, driving the establishment of exchange networks sustained by the cell-to-cell shuttling of overflow products such as lactate. Despite their fundamental role, the extent and dynamics of such networks is largely unknown due to the lack of methods in single-cell flux analysis. In this study, we provide direct experimental characterization of such exchange networks. We devise a method to quantify single-cell fermentation fluxes over time by integrating high-resolution pH microenvironment sensing via ratiometric nanofibers with constraint-based inverse modeling. We apply our method to cell cultures with mixed populations of cancer cells and fibroblasts. We find that the proton trafficking underlying bulk acidification is strongly heterogeneous, with maximal single-cell fluxes exceeding typical values by up to 3 orders of magnitude. In addition, a crossover in time from a networked phase sustained by densely connected “hubs” (corresponding to cells with high activity) to a sparse phase dominated by isolated dipolar motifs (i.e., by pairwise cell-to-cell exchanges) is uncovered, which parallels the time course of bulk acidification. Our method addresses issues ranging from the homeostatic function of proton exchange to the metabolic coupling of cells with different energetic demands, allowing for real-time noninvasive single-cell metabolic flux analysis., Peer reviewed

31 pages. -- Figure S1. Barrier to interconversion from cis- to trans-p-aminoBA. -- Figure S2. Barrier to interconversion from cis- to trans-p-hydroxyBA-II. -- Figure S3. Barrier to interconversion from cis- to trans-p-hydroxyBA-I. -- Figure S4. Barrier to interconversion from cis- to trans-p-chloroBA. -- Figure S5. trans-conformations of p-substituted benzoic acids. -- Figure S6. Experimental and simulated spectra of p-aminoBA. -- Figure S7. Barrier to inversion motion in p-aminoBA. -- Figure S8. Barrier to internal rotation in p-nitroBA. -- Figure S9. Experimental and simulated spectra of p-nitroBA. -- Figure S10. Experimental and simulated spectra of p-chloroBA. -- Figure S11. Barrier to conformer interconversion in p-hydroxyBA through para-OH rotation. -- Figure S12. Barrier to conformer interconversion in p-hydroxyBA through COOH rotation. -- Figure S13. Difference in electrostatic potential, Vp, vs. dihedral angle τ. -- Table S1. Spectroscopic parameters of substituted benzoic acids. -- Table S2. Assigned rotational transitions of p-aminoBA. -- Table S3. Assigned rotational transitions of p-nitroBA. -- Table S4. Assigned rotational transitions of p-35 26 chloroBA. -- Table S5. Assigned rotational transitions of p37 27 chloroBA. -- Table S6. Assigned rotational transitions of p-hydroxyBA-I. -- Table S7. Assigned rotational transitions of p-hydroxyBA-II. -- Table S8. Difference in electrostatic potential, Vp, as function of dihedral angle τ. -- Table S9. Mayer bond order index of p-substituted trans-benzoic acids., Peer reviewed