dataesr/scientific-paragraphs-categorization huggingface dataset:
PKA Pathway Is Activated Following SA and Is Required for The Expression of IEGs As the main pathway involved in the regulation of IEGs transcription is the PKA/CREB pathway, we questioned whether AMPK could cross-talk with this signaling pathway. We first assessed the activation of the PKA/CREB pathway following SA. To this end, we used an antiphospho-PKA substrate antibody that detects proteins containing a phosphorylated Ser/Thr residue within the consensus sequence for PKA, thus giving an indirect readout of PKA activation status. Bic/4-AP stimulation led to a rapid and sustained activation of PKA, as observed using the antiphospho-PKA substrate antibody as well as to the phosphorylation of CREB at Ser 133 , a direct target of PKA (Figure  3a-c ). Altogether, these data demonstrated that the PKA pathway was rapidly activated following SA and led to the activation of CREB.
PKA Pathway Is Activated Following SA and Is Required for The Expression of IEGs As the main pathway involved in the regulation of IEGs transcription is the PKA/CREB pathway, we questioned whether AMPK could cross-talk with this signaling pathway. We first assessed the activation of the PKA/CREB pathway following SA. To this end, we used an anti-phospho-PKA substrate antibody that detects proteins containing a phosphorylated Ser/Thr residue within the consensus sequence for PKA, thus giving an indirect readout of PKA activation status. Bic/4-AP stimulation led to a rapid and sustained activation of PKA, as observed using the anti-phospho-PKA substrate antibody as well as to the phosphorylation of CREB at Ser 133 , a direct target of PKA (Figure  3a-c ). Altogether, these data demonstrated that the PKA pathway was rapidly activated following SA and led to the activation of CREB.
Funding: This research was funded by the  French Fondation pour la cooperation Scientifique-Plan Alzheimer   2008-2012  ( Senior Innovative Grant   2013 ) to VV, by the  Fondation Vaincre Alzheimer  (n • FR-16071p to VV), and in part through the  Labex DISTALZ (Development of Innovative Strategies for a Transdisciplinary Approach  to Alzheimer's disease). FS holds a doctoral scholarship from  Lille 2 University.
Each probe pair covered between one and three CpG dinucleotides in the regulatory regions of the respective candidate gene. The methylation score for each CpG site was calculated from the fluorescence intensity values of the methylated (=FIm) and unmethylated (=FIu) oligonucleotides. To stabilize the variance, the score was transformed using the generalized log transformation (gLOG): methylation score = gLOG(FIm/FIu)  17. For statistical analysis, methylation scores for each amplificate were determined by averaging measurements from all probe pairs belonging to one amplificate using the median. Multiple amplificates from the same candidate gene entered data analysis independently. According to Maier et al. (shown in Fig. 1 ) 3 CpGs with the most relevant clinical impact were selected from the PITX2 promoter 2 gene (PITX2P2; 4q25) region on the respective custom array, which were represented by two of the four probe pairs  11.
Reference sequence (Ref Seq) ID: NT_016354. 18 (4q25. Detected CpG in Ref Seq: 3 CpG in 36106573-36106600  11. After bisulfite exposure to distinguish between methylated and unmethylated PITX2, the percent methylation ration (PMR) of three CpG motifs of the PITX2 gene promoter 2 is quantified by qMSP and calculated by the Rotor-Gene AssayManager® software with Gamma Plug-in plus a kit-specific PITX2 Assay Profile for automated analysis and quality control including all validity criteria. Validity criteria for run controls and sample testing include cycle threshold (Ct), amplification curve anomalies, PMR range for the controls (PITX2 RGQ PCR reference 50, PITX2 RGQ PCR reference low, PITX2 RGQ PCR negative control (NC), PITX2 RGQ PCR NTC (NTC)) and delta-PMR threshold for sample duplicates as described in detail in the handbook  18.
The PMR (percent methylation ratio) is calculated applying the following formula: PMR = 100/(1 + 2exp(Ct-FAM(methylated) -Ct HEX(unmethylated) )]  15. The PITX2 qMSP assay was optimized for the usage as an in vitro diagnostic assay following the CLSI guidelines (https://clsi. org/; see also  18  ). PCR analysis is performed on Rotor-Gene Q MDx 5plex HRM instrument (Qiagen, Hilden, Germany). Patients and tumor samples. For the analytical and technical evaluation of the therascreen PITX2 RGQ PCR assay, Formalin-Fixed Paraffin-Embedded (FFPE) tumor tissue samples from invasive breast cancers (n = 131; thereof 61 ER+ and 68 ER-) were collected at the Institute of Pathology, Technische Universität München. From these cases incomplete clinical and follow-up data were available. Therefore, the FFPE samples were only used to optimize the workflow, perform the analytical and technical evaluation including determination of DNA extraction yields, pass rates (run pass rate and sample pass rate), and tumor heterogeneity.
From 121 of the 384 patients (Fig. 1 ), both fresh-frozen tumor tissue specimens and microarray data of PITX2 DNA methylation were available, but not all samples met the inclusion criteria of the clinical study population Also, the available clinical data of this group of 121 patients were partly incomplete and, therefore, could not be used for outcome analysis. The 121 samples, which were collected at the Erasmus Medical Center in Rotterdam were used to analyze the correlation between mathematically converted microarray gLOG (FIm/FIu) data to PMR values [PMR calc = 2exp(gLOG (FIm/FIu))] and their respective PMR values experimentally (PMR exp ) determined with the therascreen PITX2 RGQ PCR assay according to the formula: PMR exp = 100/[1 + 2exp(Ct FAM(meth- ylated) -Ct HEX(unmethylated) )] from fresh frozen tumor tissue samples.
Analysis was performed for DFS follow-up time of 120 months. The PITX2 cut-off value for disease-free survival (DFS) was established with the "maximum-selected log-rank statistics" using the maxstat. test function as implemented by the program library "maxstat" of the program "R" (R Development Core Team 2012)  21. The PITX2 cut-off value is the Percentage Methylation Ratio (PMR) of PITX2 which separates good survivors and poor survivors (responder vs. non-responder) by using the log-rank statistics algorithm (R Software version: R version 3. 4. 1, 2017; the R Foundation for Statistical Computing).
Results Assay performance. The verification data and the analytical validity of the therascreen PITX2 RGQ PCR DNA-methylation assay including limit of blank (PMR 0 for the methylated probe; PMR 98 for the unmethylated probe), limit of detection (PMR 4 for the lower limit; PMR 92 for the upper limit), repeatability and reproducibility was tested with three biological samples (PMRs 7, 16, and 77) in a single-site precision study (variability for each sample = 20. 39%, 21. 76%, 4. 23%, respectively; average of 15. 46%) and multi-site precision study (variability for each sample = 16. 93%, 28. 72%, 4. 61%, respectively; average of 16. 75%) according to CLSI/NCCLS/EP5-A3 guideline  22  , including intra-run variability (for each sample = 12. 29%, 19. 99%, 3. 90%, respectively; average of 12. 06%) and inter-run variability (for each sample = 13.
2b ). PITX2 DNA-methylation also identifies patients in the group of breast cancer patients who received in addition to anthracycline-based chemotherapy endocrine therapy (n = 96) who have a significant longer DFS and those patients who have a significantly shorter DFS (HR 2. 285, p = 0. 024) (Fig. 2c ). In order to confirm these results with a PCR-based analysis method we determined the PITX2 DNA-methylation applying the analytically validated therascreen PITX2 assay in 121 tumor cell nuclei pellets from fresh frozen (FF) tissue samples available from the same study (Fig. 1 ). However, as the clinical data for these 121 samples were partly incomplete, we established a transformation curve with the PMR values derived from microarray (PMR calc ) and qPCR (PMR exp ; assessed with the therascreen PITX2 RGQ PCR Kit) from these 121 samples.
Acknowledgements We thank  Dr. Peter Schmieder  for help with the solution NMR measurements and  Dr. Veniamin Chevelkov  for helpful discussions. This work was supported by the  Leibniz-Forschungsinstitut fu ¨r Molekulare Pharmakologie (FMP)  and the  European Research Council  ( ERC Starting Grant  to A. L. ; Grant agreement no. :  337490 ).
Renal diseases associated with porphyrias will be addressed in describing the clinical and experimental basis of porphyria cutanea tarda (PCT) associated with end-stage renal disease (ESRD) and kidney diseases associated with acute intermittent porphyria (AIP). The available data on the management of kidney transplantation in individuals with acute porphyria will be examined. Finally, in addition to kidney diseases, acute porphyrias also affect water homeostasis by inducing inappropriate antidiuretic hormone secretion.
Data for each patient was processed using a previously described bioinformatics pipeline, and a patient-specific amyloid proteome profile was created. For each patient, a pathologist reviewed the microdissection images to confirm that the amyloid deposits were included in the analyzed tissue. Next, the proteome profile was scrutinized for the presence of universal amyloid tissue biomarkers (apolipoprotein E, serum amyloid P component, and APOAIV). 18 inally, the proteome was searched for potential amyloid precursor proteins. In all 5 cases, previously described canonical amyloid precursor proteins were not present in their corresponding proteomes. However, abundant spectra corresponding to ApoCII protein were detected. An advanced bioinformatics pipeline was used to search for known or unknown mutations in proteomes of all patients.
Sanger Sequencing A DNA sample from patient 1 was extracted from peripheral blood using standard procedures. Direct sequencing of the 3 coding exons and flanking intronic sequences of APOC2 gene was conducted on an ABI 3730 DNA automated sequencer (Applied Biosystems, Frankfurt, Germany) and sequences were analyzed using Sequencher software (Gene Codes). Primer sequences for exon 1 are GGACACCGAGCTCACACAGA (forward) and GGGCTGGGAAGATGCTTGGA (reverse); for exon 2 are CCCAGGCCCTTCTTACCTCT (forward) and GGCCAGACCCCATTTCTCCA (reverse); and for exon 3 are CCCCTCCTCCCTCTAACCATCT (forward) and GGGGAGCTCAGTCTGAACCT (reverse). Conditions for polymerase chain reaction amplification are available upon request. DNA numbering of mutation was based on the APOC2 cDNA sequence (GenBank accession number NM_000483.
Molecular Dynamic Simulation of ApoCII Variants Generalized Born implicit solvent molecular dynamics simulations were carried out using NAMD and the CHARMM22 with CMAP force field. 19, 20 The protein data bank structure 1SOH was used for initial conformation. Initial mutant conformations were generated using PyMOL (The PyMOL Molecular Graphics System, Version 1. 5. 0. 3: Schrödinger, LLC). 21 We used an interaction cutoff of 15 Å with strength tapering (or switching) beginning at 12 Å, a simulation time step of 1 fs, and conformations recorded every 2 ps. Each initial conformation was used to generate 5 replicates, and each was energy minimized for 20,000 steps, heating to 300 K over 300 ps via Langevin thermostat. A further 50 ns of simulation trajectory was generated and the final 40 ns analyzed.
All trajectories were first aligned to the initial WT conformation using Ca atoms. Root-mean-SD and principal component analysis were calculated using Ca atoms. We computed an alignmentfree median residueÀresidue distance matrix within each trajectory. 22À24  The distance difference matrix quantifies the median pairwise proximity of residues to each other. Simulations were analyzed using custom scripts, the Bio3D R package and Visual Molecular Dynamics (VMD). 24, 25 Differences between simulations were compared using t tests after subsampling to 100 data points from each condition. The subsampling was repeated 100 times and the median P value reported.
Probability legend: over 95% 80% to 94% 50% to 79% 20% to 49% 0% to 19% Bio View: identified proteins (56/57) including 0 decoys 1 # Visible? Starred? Apolipoprotein C-II Accession Number APOC2_HU. Molecular Wieght 11kDa Protein Grouping Ambiguity Case#1 Case#2 Case#3 Case#4 Case#5 2 Apolipoprotein E APOE_HUM. 36 kDa 3 Apolipoprotein A-IV APOA4_HU. 45 kDa 4 Serum amyloid P-component SAMP_HUM. 25 kDa 5 Complement C3 CO3_HUMAN 187 kDa 6 Vitronectin VTNC_HUM. 54 kDa 7 Actin,cytoplasmic 1 ACTB_HUM. 42 kDa 8 Complement factor H-related pro. FHR5_HUM. 64 kDa 9 Clusterin CLUS_HUM. 52 kDa 10 Complement factor H-related pro. FHR1_HUM. 38 kDa.
Statistical analysis Approximately 150 patients were to be randomly assigned and the final analysis/evaluation of overall survival was to be performed after approximately 135 events (deaths) had been recorded or 18 months after the last patient was enroled, whichever was sooner. The pre-specified Bayesian-augmented design consisted of borrowing data from trials in which patients with similar characteristics received the control treatment to optimise treatment comparison of interest and minimise the number of patients enroled in the concurrent control group. Using patient-level control data from two previous randomised gemcitabine trials  12, 13  and assuming a hazard ratio [HR] of 0. 7 for survival, the study had 90% power to identify if the probability of HR < 1 was >0. 85. The type 1 error rate was 0.
16. Phase 2 efficacy analyses were conducted on the intention-totreat population, unless otherwise specified. This population included all patients randomly assigned to study treatment. The primary method of analysis for comparing overall survival between the treatment groups used a Bayesian exponentiallikelihood model with a hierarchical random-effects distribution on treatment effects. Additional details in the Supplementary Materials. Sensitivity analyses included an additional Bayesian analysis with minimal borrowing of historical data for the control. In addition, to assess whether unexpected bias was introduced in this trial design, frequentist analyses of overall survival and progression-free survival using only the data from this study were conducted. Efficacy analyses of overall survival and progressionfree survival used Kaplan-Meier estimates and the log-rank test stratified by randomisation factors of Eastern Cooperative Oncology Group (ECOG) status, previous gemcitabine treatment, and disease stage (as assessed by investigator and confirmed by central re-read) (Supplementary Table  S1 ).
Cox proportional hazards models also adjusted for these randomisation factors, and stepwise analysis adjusted for multivariate factors including ECOG, previous gemcitabine treatment, baseline liver metastasis, sex, post-discontinuation systemic therapy and CA19-9. Overall response rates (complete response + partial response), clinical benefit rates (complete response + partial response + stable disease) and the exact 95% confidence interval (CI) were estimated for each group. The comparison between groups was done using the Fisher's exact test. All Bayesian analyses were carried out using the statistical software FACTS v2. 4 (Berry Consultants LLC, Austin, TX) and all other analyses were carried out using SAS/Version 9. 4. Details on pharmacokinetics and exploratory analyses on circulating biomarkers are available in the Supplementary Materials.
The positive efficacy signals for galunisertib from this present study and role in modulating TGF-β pathway have prompted interest in future validation of the efficacy of galunisertib plus gemcitabine as well as ongoing clinical trials of galunisertib with immunotherapeutic agents (anti-PD-L1 mAb durvalumab, NCT02734160). The intentions of these trials are to generate evidence for development of TGF-β pathway inhibitors in combination with more modern therapeutic approaches. Pancreatic cancer is characterised by a highly immunosuppressive tumour microenvironment that aids rather than controls cancer development and progression,  15  and TGF-β is commonly viewed as the most powerful immunosuppressive cytokine. 16 In a recently integrated genomic analysis of pancreatic cancer, programmes enriched with a macrophage or T-cell co-inhibition specific signature co-segregated with poor survival.
ACKNOWLEDGEMENTS The team thanks patients and families for their willingness to participate in this study. Further, we thank all site staff and investigators at the institutions, the trial personnel at  Eli Lilly and Company , and  Covance. Durisala Desaiah, PhD , of  Eli Lilly and Company and Larry Macke, ELS ,  Meghan Greenwood, PhD ,  Casie Polanco  and  Antonia Baldo  of  Syneos Health  provided medical writing and editing support. Work in the unit of the corresponding author was supported by the Investigator Grant number  19111  through the  Associazione Italiana per la Ricerca sul Cancro (AIRC).
Our results indicate that mild drought induces phenotypic plasticity in each of the five accessions, but does not lead to any significant change in terms of heritable effects. In addition, we obtained methylome data at single cytosine resolution for Col-0, which again indicate that mild drought induces intragenerational DNA methylation changes only, which are restricted to CHH sites and affect TE sequences predominantly. Taken together, our findings add to the growing body of evidence indicating that plants do not commonly generate transgenerational effects in response to changes in the environment.
Only uniquely mapping reads were retained. The methylKit package v0. 9. 4  (Akalin et al. , 2012)  was used to calculate differential methylation in individual positions (DMPs) or in 100 bp non-overlapping windows (DMRs). Significance of calculated differences was determined using Fisher's exact test and Benjamin-Hochberg (BH) adjustment of p-values (FDR < 0. 05) and methylation difference cutoffs of 40% for CG, 20% for CHG and 20% for CHH. Differentially methylated windows within 100bp of each other were merged to form larger DMRs. Cytosine positions covered by more than 100 reads were not considered. For DMP analysis only cytosines covered by a minimum of 6 (CG and CHG) and 10 (CHH) reads in all libraries were considered. Bisulfite conversion rates were estimated by the number of methylated cytosine calls in the chloroplast genome.
, 2013). Reads aligning to multiple locations were removed using samtools' view with parameter -q 5  (Li et al. , 2009). After this filter, between 95. 5 and 96. 8 percent of the obtained reads were aligned to the reference genome. The number of reads per transcript was counted using the Bioconductor packages Rsamtools and ShortRead  (Morgan et al. , 2009). Differential expression between samples in control and drought conditions was calculated with the DEseq2 package in R  (Love et al. , 2014). Genes with q-values lower than 0. 05 and log2FC above 0. 5 were considered as differentially expressed. TE differential expression was analyzed using TETOOLs  (Lerat et al. , 2016). RNA-seq and MethylC-seq sequencing data have been deposited in the ENA short read archive under project number PRJEB27682.
To determine if genes with changes in DNA methylation near or within them are drought stress responsive, we performed RNA-seq on leaves isolated from Col-0 plants directly exposed to mild drought or control treatments and grown for 3 weeks on the Phenoscope. Consistent with the results of previous studies of the transcriptional response to mild drought stress  (Cubillos et al. , 2014; Clauw et al. , 2015) , differential analysis of the two RNA-seq datasets identified significant changes in steady state mRNA levels for 468 genes (FDR < 0. 05, >0. 5 log2 fold change, 205 and 263 genes with lower and higher expression under mild drought, respectively; Supporting Information Table  S8 ), but not for any of the annotated TE sequences. Gene Ontology analysis revealed enrichment for several stress-related categories, including response to water deprivation, for which the highest significance level was observed.
Acknowledgments We thank members of the  Colot lab  for discussions. This work was supported by funding from the  Agence Nationale de la Recherche  (project  MEMOSTRESS , grant n⁰  ANR-12-ADAP-0020-01  (to V. C, O. L. and T. J. M. V. D. ) and the  European Commission   Framework Programme 7 ,  ERC Starting Grant  ' DECODE' / ERC-2009-StG-243359  (to O. L). The IJPB benefits from the support of the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). ABS was supported by a postdoctoral fellowship from the  Brazilian National Council for Scientific and Technological Development (CNPq -Brazil ). LQ was recipient of postdoctoral fellowships from the  ANR-10-LABX-54   MEMOLIFE  and  ANR-11-IDEX-0001-02 PSL Research University.
Panel IV shows RT-PCR of cytoplasmic cDNA using primers that amplify spliced β-actin mRNA. Owing to the large number of samples, gels have been spliced together as indicated by vertical white spaces in different panels to create part C for clarity. D) Cytoplasmic expression and relative packaging efficiency (RPE) of transfer vector RNAs, and hygromycin-resistance (Hyg r ) colony forming unit per ml (CFU/ml) for mutant transfer vector RNAs relative to the wild type DA024 construct. CFU/ml expressed for each mutant was normalized to the luciferase expression observed in the transfected cultures. The histograms represent data from multiple independent experiments (±SD). The RPE for each mutant was determined by dividing the packaged mutant viral RNA values by the luciferase-normalized cytoplasmic expression of the respective mutant (to take into account the varying transfection efficiencies).
CFU/ml expressed for each mutant was normalized to the luciferase expression observed in the transfected cultures. The histograms represent data from multiple independent experiments (±SD). The RPE for each mutant was determined by dividing the packaged mutant viral RNA values by the luciferase-normalized cytoplasmic expression of the respective mutant (to take into account the varying transfection efficiencies). Finally, the results of RNA packaging of all the mutants were presented relative to the wild type (WT, DA024) construct. destabilize stem I of SL4, while the corresponding paired mutant harbored an additional compensatory mutation expected to recreate the stem, but using heterologous sequences. Thus, in SL4-15, six of the 9-nucleotides of stem I (nucleotides 298-303) were substituted by flipping the sequence.
Structure-function analysis To determine the effect of the introduced mutations on the secondary structure of the MMTV packaging signal RNA, the relevant sequences of the mutant vector RNAs starting from R to 120 nts within gag were folded using the MFold program that uses energy minimization to predict the energetically most stable structures  [34, 35]. Comparison of the mutant RNA structures SL4-01 to SL4-08 predicted by MFold with that of the wild type DA024 RNA revealed that in most cases, the structure of SL4 with its bifurcated stem loop was disrupted except in SL4-06 (Supplemental Fig. 3 ). In these cases, the ssPurines were base-paired with other sequences, while the pal II stem loop was largely intact except in the case of SL4-07 where both ssPurines and pal II sequences were basepaired with other RNA regions (Supplemental Figure  3 ).