BUSCADOR RECOLECTA

59 pages. -- Contents: Point S1. Gaussian keywords used in the calculations. -- Figure S1. Experimental spectra of PFPG+g+/G-g- and its isotopologues species. -- Table S1-S7. Transition frequencies of PFPG+g+/G-g- and its isotopologues and PFPTg+/Tg-. -- Figure S2. Experimental spectra of the PFPTg+/Tg- monomer. -- Figure S3. Conformational interconversion barrier of the PFP conformers. -- Table S8-S10. Kraitchamn and STRFIT results of PFPG+g+/G-g. -- Table S11-S13. The semi-experimental equilibrium structural parameters of PFPG+g+/G-g-. -- Table S14. Spectroscopic properties of the predicted binary PFP conformers. -- Table S15-S19. Rotational transition frequencies of the five binary PFP conformers. -- Figure S4. QTAIM and NCI analyses of the five PFP and PrOH conformers. -- Figure S5. QTAIM and NCI analyses of the five binary PFP conformers. -- Completion of reference 24., Peer reviewed

29 pages. -- PDF file includes: Strong-field coherence breaking (SFCB) details; Internal rotation methyl barrier of 4-methyl guaiacol (MG). -- Figure S1: Experimental and simulated variation of the internal rotational barrier of methyl group. -- Figure S2: Summary of bond changes of lignin relative to the average bond length rC-C. -- Figure S3: Optimized structure of guaiacol at B3LYP-D3BJ/def2tzvp level of theory. -- Table S1: Experimental and calculated constants derived from the broadband rotational spectrum of guaiacol. -- Table S2: List of spectroscopic parameters used to fit guaiacol isotopomers. -- Table S3: Experimental and calculated constants derived from the broadband rotational spectrum of syringol. -- Table S4: Molecular parameters of 4-methyl guaiacol. -- Table S5: Molecular parameters for Z- and E-4-vinyl guaiacol. -- Table S6: Line list of transitions fitted for guaiacol (G). -- Table S8: Line list of transitions fitted for 13C(2) guaiacol. -- Table S9: Line list of transitions fitted for 13C(3) guaiacol. -- Table S10: Line list of transitions fitted for 13C(4) guaiacol. -- Table S11: Line list of transitions fitted for 13C(5) guaiacol. -- Table S12: Line list of transitions fitted for 13C(6) guaiacol. -- Table S13: Line list of transitions fitted for 13C(7) guaiacol. -- Table S14: Line list of transitions fitted for syringol (S). -- Table S15: Line list of transitions fitted for methyl guaiacol (MG). -- Table S16: Line list of transitions fitted for vinyl guaiacol (Z-VG). -- Table S17: Line list of transitions fitted for vinyl guaiacol (E-VG)., Peer reviewed

Peer reviewed

S1 Table: Top table of the differential expressed proteins between AF and no AF. Results from the discovery study., Results from the discovery study. Proteins selected to be verified in the whole Phase 1 are highlighted in grey. Proteins in bold but not highlighted were already tested in the whole phase 1 as part of a previous published work., Peer reviewed

13 pages. -- PDF file includes: 1. Supplementary Methods: 1.1. LDL Acetylation; 1.1. Quantitative and Qualitative Analysis of Foam Cell Formation; 1.2. Immunofluorescent Imaging of Co-culture Inserts; 1.4. Western Blot Analysis. -- Supplementary Figure 1. miRNA transfer capacity by DPPC:CE:LPC rHDL to foam cells cultured in monolayer. -- Supplementary Figure 2. Combination of TO901317 and antagomiR-33a-rHDL caused a synergistic upregulation of ABCA1 and ABCG1 protein levels in foam cells. -- Supplementary Figure 3. Intracellular Cholesterol Levels in Foam Cells After TO901317 Treatment, AntagomiR-33a-rHDL Delivery or Combined Treatment of TO901317 and AntagomiR-33a-rHDL in 1D Grown Foam Cells or 2D Atheroma Model Foam Cells. -- Supplementary Figure 4. Cholesterol efflux promoted in foam cells by sequential rHDL administration in a triple cell 2D atheroma model. -- Supplementary Figure 5. Agarose gel electrophoresis of acetylated LDL (LDLac). -- Supplementary Figure 6. Quantitative and Qualitative Analysis of Foam Cell Formation. -- Supplementary Figure 7. Development of triple-compartment cell culture 2D atheroma model. -- Supplementary Figure 8. Timeline showing cholesterol efflux experimental design., Peer reviewed

Peer reviewed

Peer reviewed

48 samples, 6 replicates, 4 conditions, 2 timepoints. Control (0.1% DMSO), 100 nM rotenone, 500 nM antimycin A, 1 µM Oligomycin A. Cells treated for 1 or 5 days., Mitochondrial dysfunction induces a strong adaptive retrograde signaling response, however many of the down-stream effectors remain to be discovered. Here, we studied the shared transcriptional responses to three different mitochondrial respiratory chain inhibitors in human primary skin fibroblasts using QuantSeq 3’RNA-sequencing. We found that mevalonate pathway genes were concurrently downregulated irrespective of the respiratory chain complex affected. Targeted metabolomics demonstrated that impaired mitochondrial respiration at any of the three affected complexes also had functional consequences on the mevalonate pathway, reducing cholesterol precursor metabolites. A deeper study of complex I inhibition showed a reduced activity of ER-bound sterol sensing enzymes through impaired processing of the transcription factor SREBP2 and accelerated degradation of the ER cholesterol sensors SQLE and HMGCR. These adaptations of mevalonate pathway activity neither affected total intracellular cholesterol levels nor the cellular free (non-esterified) cholesterol pool. Measurement of intracellular cholesterol using the fluorescent cholesterol binding dye filipin revealed that complex I inhibition elevated cholesterol on intracellular compartments. Our study shows that mitochondrial respiratory chain dysfunction elevates intracellular free cholesterol levels and therefore attenuates the expression of mevalonate pathway enzymes, which lowers endogenous cholesterol biosynthesis, disrupting the metabolic output of the mevalonate pathway. Intracellular disturbances in cholesterol homeostasis may alter systemic cholesterol management in diseases associated with declining mitochondrial function., Peer reviewed

In this study, independent biological replicates of HCT116 wt (n =3) and p21 (CDKN1A) ko cells (n = 3) were screened for gene expression of cancer progression related genes (770 genes including 30 housekeeping genes). Tumor cells were cultured in a 100 mm cell culture dish in RPMI medium supplemented with 1% penicillin/streptomycin and 10% FBS until 80% confluency and harvested via scraping off the plate. RNA was isolated from snap-frozen cell pellets. Gene expression analysis was performed using the human nCounter® PanCancer Progression Panel (NanoString Technologies). Total RNA was isolated from frozen cell pellets by QIAzol-chloroform extraction followed by RNeasy Mini Kit (Qiagen, Hilden, Germany) preparation. Isolated RNA (100 ng) was processed through the NanoString nCounter Prep Station. Subsequently, samples were processed according to the manufacturer’s instructions and signals of reporter probes were counted and tabulated using the nCounter Digital Analyzer (NanoString Technologies). Finally, housekeeping gene (geometric mean of 30 genes) normalization for quantitating gene expression levels, positive control normalization for background noise correction and data analysis was performed using nSolver™ Analysis Software 3.0 (NanoString Technologies, Hamburg, Germany) and standard settings., Gene expression profiles were obtained via Nanostring nCounter Expression Assay (PanCancer Progression Panel, Nanostring Technologies, Hamburg, Germany). We aimed to obtain a gene expression signature associated to the kockout of p21 (i.e. Cyclin Dependent Kinase Inhibitor 1A, CDKN1A) in colorectal carcinoma samples and its association with of epithelial-mesenchymal transition (EMT). We analysed and compared three independent cultures of HCT116 p21 wt cells and three HCT116 p21-/- ko cells., Peer reviewed

Glucocorticoids (GCs) are metabolic hormones that regulate physiological and behavioural responses to environmental change and mediate homeostasis maintenance in vertebrates. Despite the assumption that GCs covary with energy metabolism, we yet lack a mechanistic understanding of how environmental factors such as temperature modulate GC variation through their effect on organismal energy balance. In particular, the mechanisms linking temperature-dependent metabolic rate and GCs at broad spatial scales and across species remain poorly investigated., Here we used biophysical models to calculate thermoregulatory costs (i.e., the amount of heat required to keep body temperature in homeostasis) of free-living birds as a function of environmental conditions, body size, shape, and insulating layer of feathers. We then investigated the link between ambient temperature, cost of thermoregulation and baseline plasma GC concentrations in a comparative study including GC data from 94 bird species from HormoneBase., We found a significant, positive association between thermoregulatory costs and baseline GC concentrations. Interestingly, models including thermoregulatory costs better explained GC variation when compared to those including ambient temperature as a predictor variable. This result suggests that body size and shape fundamentally modulate energy requirements for thermoregulation and thereby GC concentrations in the wild., By providing a mechanistic description of the link between ambient temperature and thermoregulatory metabolism, biophysical models provide a tool to predict the impact of environmental conditions on energy metabolism. Our work demonstrates that differences in thermoregulation modulate variation in GC concentrations across a broad climatic gradient., Peer reviewed