Resultados de investigación

1 table. -- Supplementary Table 1. Primary and secondary antibodies used in this study., There exists considerable interest to unveil preclinical period and prodromal stages of Alzheimer's disease (AD). The mild cognitive impairment (MCI) is characterized by significant memory and/or other cognitive domains impairments, and is often considered the prodromal phase of AD. The cerebrospinal fluid (CSF) levels of β-amyloid (βA), total tau (t-tau), and phosphorylated tau (p-tau) have been used as biomarkers of AD albeit their significance as indicators during early stages of AD remains far from accurate. The new biomarkers are being intensively sought as to allow identification of pathological processes underlying early stages of AD. Fifty-three participants (75.4 ± 8.3 years) were classified in three groups as cognitively normal healthy controls (HC), MCI, and subjective memory complaints (SMC). The subjects were subjected to a battery of neurocognitive tests and underwent lumbar puncture for CSF extraction. The CSF levels of estrogen-receptor (ER)-signalosome proteins, βA, t-tau and p-tau, were submitted to univariate, bivariate, and multivariate statistical analyses. We have found that the components of the ER-signalosome, namely, caveolin-1, flotilin-1, and estrogen receptor alpha (ERα), insulin growth factor-1 receptor β (IGF1Rβ), prion protein (PrP), and plasmalemmal voltage dependent anion channel 1 (VDAC) could be detected in the CSF from all subjects of the HC, MCI, and SMC groups. The six proteins appeared elevated in MCI and slightly increased in SMC subjects compared to HC, suggesting that signalosome proteins undergo very early modifications in nerve cells. Using a multivariate approach, we have found that the combination of ERα, IGF-1Rβ, and VDAC are the main determinants of group segregation with resolution enough to predict the MCI stage. The analyses of bivariate relationships indicated that collinearity of ER-signalosome proteins vary depending on the stage, with some pairs displaying opposed relationships between HC and MCI groups, and the SMC stage showing either no relationships or behaviors similar to either HC or MCI stages. The multinomial logistic regression models of changes in ER-signalosome proteins provide reliable predictive criteria, particularly for the MCI. Notably, most of the statistical analyses revealed no significant relationships or interactions with classical AD biomarkers at either disease stage. Finally, the multivariate functions were highly correlated with outcomes from neurocognitive tests for episodic memory. These results demonstrate that alterations in ER-signalosome might provide useful diagnostic information on preclinical stages of AD, independently from classical biomarkers., Peer reviewed

It contains technical information provided for transparency and reproducibility of the results presented in the manuscript., Peer reviewed

13 pages. -- Table S1. Division of data for 289 pesticides against A. bahia for mortality at 96 h (mean lethal concentration, (LC50). -- Fig. S1. Linear Backward Selection Regression results for variable selection. -- Fig. S2. Correlation heat maps of selected descriptors. -- Fig. S3. Density and frequency of pLC50 (R package). -- Fig. S4. Density line (A), histogram (B) and scatter plot (C) of the molecular descriptors. (R package). -- Fig. S5. Observed values versus predicted values for the training set. -- Fig. S6. Observed values vs. predicted values for prediction set. -- Table S2. Symbols, definitions and sign value of molecular descriptors in the QSTR-MLR model. -- Table S3. RPTree. Training Sets., Peer reviewed

Fig. S1. Cln311A accumulates in the nucleus during G1 and reaches a maximum around Start Fig. S2. Cln3 boosts nuclear import of Cdc28-GFP during cell cycle entry Fig. S3. Mad3 regulates Cln3 levels in G1 but does not modulate Whi5 levels at Start Fig. S4. Mad3 protein levels oscillate during the cell cycle as a function of APC activity Fig. S5. Mad3 tilts the sizer behavior of G1 control Table S1. Yeast strains. Data S1., Peer reviewed

Archivo Excel, This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 861088., Peer reviewed

Table S1: Descriptive statistics of included samples. Abbreviations: PANSS (Positive and Negative Symptoms Scale), SAPS (Scale for the Assessment of Positive Symptoms), SANS (Scale for the Assessment of Negative Symptoms). Table S2: Mean and standard deviation BMI for each group, and overall, at each data collection site. Inter-site differences in outcomes based on BMI or other factors were controlled in all models. Table S3: Image acquisition parameters and software versions used at each site Table S4: Number of participants removed from analysis in each region based on poor data quality or unreliable segmentation (cortical thickness) Table S5: Number of participants removed from analysis in each region based on poor data quality or unreliable segmentation (cortical surface area) Table S6: Number of participants removed from analysis in each region based on poor data quality or unreliable segmentation (cortical surface area) Table S7 Interactions between diagnosis effect and BMI in predicting cortical thickness for each region, using FDR-adjusted p-values Table S8 Interactions between diagnosis effect and BMI in predicting cortical surface area for each region, using FDR-adjusted p-values Table S9 Interactions between diagnosis effect and BMI in predicting subcortical volume for each region, using FDR-adjusted p-values Table S10 Significance of the nonlinearity of BMI effects (BMI quartile by within-quartile BMI interaction) in predicting cortical thickness. FDR-adjusted p-values are shown. Table S11 Significance of the nonlinearity of BMI effects (BMI quartile by within-quartile BMI interaction) in predicting cortical surface area. FDR-adjusted p-values are shown. Table S12 Significance of the nonlinearity of BMI effects (BMI quartile by within-quartile BMI interaction) in predicting cortical thickness. FDR-adjusted p-values are shown. Table S13 Among participants with schizophrenia taking atypical antipsychotics at the time of scanning, the effects of BMI on cortical thickness when modeled alone, and the partial effects of BMI and antipsychotic medication dose on cortical thickness (chlorpromazine equivalent, in mg). Effect sizes (part r) are shown with FDR-adjusted p-values. Table S14 Among participants with schizophrenia taking atypical antipsychotics at the time of scanning, the effects of BMI on cortical surface area when modeled alone, and the partial effects of BMI and antipsychotic medication dose on cortical surface area (chlorpromazine equivalent, in mg). Effect sizes (part r) are shown with FDR-adjusted p-values. Table S15 Among participants with schizophrenia taking atypical antipsychotics at the time of scanning, the effects of BMI on subcortical volume when modeled alone, and the partial effects of BMI and antipsychotic medication dose on subcortical volume (chlorpromazine equivalent, in mg). Effect sizes (part r) are shown with FDR-adjusted p-values. Significance is shown with asterisks (*, α=0.05). Figure S1: BMI distribution of all participants Figure S2 The interaction between BMI categories and within-category relative BMI in predicting rostral anterior cingulate gyrus thickness, demonstrating equivalent slopes at all BMI ranges, for an approximately linear relationship. Supplemental Methods: Description of fixed and random effects in each model., Peer reviewed

Supplementary Fig. 1. Adjustment of the pH of the solutions used in Figs. 2 and 3. Supplementary Fig. 2. Pulsed-field gelelectrophoretic analyses of genomic DNA after PA treatment of the permeabilized nuclei. Supplementary Fig 3. Spermidine binding to DNA demonstrated by EBr displacement in agarose embedded nuclear halos. Supplementary Fig 4. Comparison of PA binding of different topological forms of plasmid DNA. Supplementary Figure 5. CellMiner analyses based on the NCI60 cell line panel, using CellMinerCDB. Supplementary Fig 6. CellMiner analyses based on the NCI60 cell line panel, using CellMinerCDB. Supplementary Figure 7. CellMiner analyses based on the NCI60 panel, using CellMinerCDB. Suppl. Figure 8. CellMiner analyses based on the NCI60 panel, using CellMinerCDB. Supplementary Figure 9. CellMiner analyses based on the NCI60 panel, using CellMinerCDB., Peer reviewed

32 pages. -- Supporting Text: A. Reactions mapping; B. Mathematical details of the Expectation Propagation algorithm; 1. Modeling the posterior probabilities of the fluxes given experimental evidence; 2. Pre-process of the fluxes; 3. Determining a and d, fixed c and y; 4. Determining c and , given a and d; 5. Implementation details; C. Projections of the coefficients along individual flux directions; D. Dimensionality reduction; 1. Reconstruction of the coefficients; E. Accessing the most informative fluxes; 1. Compression of the inferred coefficients: mathematical details; F. Fitting averages using HR-based Boltzmann machine learning. -- Supplementary Figures: G. Fitting quality; H. Correlation matrices; I. Probability densities of the non-measured fluxes; J. Notation used for the biomass production rate, Peer reviewed

Supplemental tables (24). Supplemental note 1, 2, 3. Supplemental Figure 1., Peer reviewed

21 pages. -- Table S1-S15 Observed rotational transition frequencies of the FA conformers. -- Table S16-S19 Structural parameters if II from different analyse. -- Table S20 NBO analysis of the detected cis-COOH conformers of FA and THFA. -- Point S1 The equations used for De and D0. -- Table S21-S25 Observed rotational transition frequencies of the FA dimer. -- Figure S1 Geometries of the I-III and II-III binary conformers., Peer reviewed