dataesr/scientific-paragraphs-categorization huggingface dataset:
LC-MS analysis Metabolites were separated on a nanoAcquity UPLC (Waters) equipped with a BEH Amide capillary column (150 mm x130mm, 1. 7 mm particle size, Waters), applying a gradient of 0. 5 mM ammonium acetate in water (A) and 0. 5 mM ammonium acetate in acetonitrile (B) from 10% A to 50% A over 10 min. The injection volume was 1 mL. The flow rate was adjusted over the gradient from 3 to 2 ml/min. The UPLC was coupled to Synapt G2Si mass spectrometer (Waters) by a nanoESI source. MS1 (molecular ion) and MS2 (fragment) data was acquired using negative polarization and MS E over a mass range of 50 to 1200 m/z at MS1 and MS2 resolution of > 20'000. Targeted Data analysis Target metabolites (NAD + and NADH) were quantified by the area under the peak (AUP) of the MS1 extracted ion chromatogram (EIC) of the respective [M-H] -ion (m/z of 662.
101 for NAD + ; m/z of 664. 117 for NADH) extracted from raw data by using MassLynx v4. 2 software (Waters). For all targets reference samples were used to locate and verify the correct peaks on the EIC of the samples by retention time and MS2 fragment information. AUP values reported (Figure  S6C ) are unit-less and are proportional to the concentration of the targets in the samples (relative quantification). AUP values can be used to calculate fold changes. Untargeted Metabolomics Data analysis Metabolomics datasets were evaluated in an untargeted fashion with Progenesis QI software (Nonlinear Dynamics), which aligns the ion intensity maps based on a reference dataset, followed by a peak picking on an aggregated ion intensity map. Detected ions were identified based on accurate mass, detected adduct patterns and isotope patterns by comparing with entries in the Human Metabolom Data Base (HMDB).
The structural zinc ions are shown as yellow spheres. (C and D) A zoomed-in view of the catalytic pocket of human NUDT12 showing the bound m 7 GTP (two orientations are seen) and two Cd 2+ ions. The bound m 7 GTP remains uncleaved. (E) Zoomed-in view of mouse NUDT12 with bound AMP and three catalytic Mg 2+ ions. (F) NAD bound in the E. coli NudC (PDB: 5IW5). (G) Decapping assays with FL human NUDT12 or a core version lacking the ANK repeats (103-462 aa). Indicated point mutations were introduced into the core version of the protein. Mutations (red) abolishing activity on both m 7 G-capped and NAD-RNAs or mutations (blue) that render the enzyme active only for NAD-RNAs are indicated. See also Figures S2A-S2C.
ACKNOWLEDGMENTS We thank  Prof. Ueli Schibler  for discussions and advice,  Johanna Mattay  for critical reading of the manuscript and kindly providing the DCP2 enzyme, and  Nicolas Roggli  for scientific illustration. We thank the following  University of Geneva  core facilities: iGE3  Genomics Platform  for deep sequencing, the  Transgenic Mouse Facility  for mouse creation, the  Bioimaging Centre at the Faculty of Sciences , and the  Electron Microscopy Platform (PFMU). We thank the  EMBL Genomics core facility  for deep sequencing; the  Functional Genomics Center, Zurich  for proteomics analyses;  Aline Le Roy of the Biophysical Platforme at IBS, Grenoble , France; and beamline staff at  Diamond Light Source , Didcot, UK;  EMBL-Hamburg, Germany, Synchrotron SOLEIL, Gifsur-Yvette, France , especially  M.
no. M8823 Cat. no. AM1354 Rabbit anti-hNUDT12 Pierce Detergent Compatible Bradford Assay Kit This study Thermo Fisher N/A 23246 Rabbit anti-hBLMH Dual-Luciferase Reporter Assay System kit This study Promega N/A E1910 Mouse anti-FLAG SuperScript III Reverse Transcriptase kit STAR+METHODS Rabbit anti-HA LightCycler 480 Sybr Green I Master Sigma Thermo Fisher Abcam Roche Cat. no. F3165 18080085 Cat. no. ab9110 04707516001 Detailed methods are provided in the online version of this paper Mouse anti-MYC Cell Signaling Technology The TruSeq Stranded Total RNA kit Illumina Cat. no. 2276s RS-122-2301 and include the following: Rabbit polyclonal Anti-GFP Deposited Data Thermo Fisher Scientific A-6455 Protein-A-10nm Gold conjugate Deep sequencing datasets Cytodiagnostics This study SKU:AC-10-05 GEO: GSE138019 Pierce Anti-HA Agarose beads All raw gel data will be deposited at Mendeley Data.
Thermo Fisher This study Cat. no. cat. no. #26181 https://doi. org/10. 17632/ykzx9h339r. 1 EZview Red Anti-c-Myc Affinity Gel Structure: NUDT12-core in complex with m 7 GTP and Cd 2+ This study Sigma Cat. no. E6654 PDB: 6SCX Anti-rabbit IgG HRP-linked Diffraction images: NUDT12-core in complex with m 7 GTP GE Healthcare This study Cat. no. NA934 https://doi. org/10. 15785/SBGRID/716 Anti-mouse IgG HRP-linked and Cd 2+ GE Healthcare Cat. no. NA931 Goat anti-Rabbit IgG (H+L) Highly Cross-Adsorbed Diffraction images: NUDT12-core Invitrogen This study Cat. no. A11034 https://doi. org/10. 15785/SBGRID/719 Secondary Antibody, Alexa Fluor 488 Experimental Models: Cell Lines Goat anti-Rabbit IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor 594 Sf9 insect cells for protein production Grenoble, France Invitrogen Eukaryotic Expression Facility, EMBL Cat.
By combining these data with a mathematical framework, we also extracted the local probe density (Supplementary Note 1). To understand the limits of detection in low density regions, we analyzed MT-FLIM photon statistics (Supplementary Note 2, Supplementary Fig. 10 ). Note that low photon count pixels, such as within depletion zones, produce less reliable lifetime values; these pixels were excluded according to lifetime and photon count cutoffs described in Supplementary Note 2 (Supplementary Fig. 11 ). Images represent the accumulation of signal over 1 min, so there is some spatiotemporal averaging. Nevertheless, these images provide the first quantitative maps of integrin forces on an SLB and within podosomes. Moreover, our conclusions are generalizable: MT-FLIM imaging of Src-transformed mouse embryonic fibroblasts indicated that this spatial distribution of forces is also common in invadosomes and invadosome belts (Supplementary Fig.
Closed and opened MT-FLIM probes were hybridized as described above and were combined in known ratios in solution immediately prior to incubation with SLBs. For 19 pN probe calibration, a scrambled hairpin was used in opened samples to lower the ΔG of hybridizaton  24. SLB fluorescence intensity was measured using epifluorescence, and the background subtracted data were fit using linear regression to determine QE. Nineteen piconewton and 4. 7 pN surfaces did not display statistically significant differences; thus, intensity data were combined for this calculation. For MTFM probes used in MFM and PCB experiments, QE was determined directly from background subtracted opened and closed images using the equation: Quenching Efficiency QE ð Þ ¼ 100% I Open;BS À I Closed;BS I Open;BS : ð1Þ To determine the average fluorescence lifetime of MT-FLIM probes, surfaces containing 0-100% open probes were imaged using the same conditions as in cell experiments.
For SLBs with linear probes lacking a stem-loop structure, the percent open was assumed to be zero. Image processing. Image processing was performed in MATLAB 2018a (Math-Works) using semiautomated custom scripts unless otherwise stated. Nikon ND2 files were directly transferred into MATLAB using the Bioformats Toolbox. Linescans were generated in MATLAB or ImageJ, and kymographs were generated in FIJI using the MultiKymograph plugin. The diffusion coefficient, D, was calculated according to the following equation 40 D ¼ w 2 =4t 1=2 ð3Þ in which w is the radius of the bleached region and t 1=2 is the time of 50% recovery determined by exponential curve fitting. MT-FLIM analysis methods are described in Supplementary Notes 1 and 2 and Supplementary Fig. 11. PC biotin time-lapse experiments were processed as shown in Supplementary Fig.
22. For LifeAct analysis, podosomes were identified by intensity-based thresholding of RGD depletion, and podosomes multiples were separated with a 2-pixel line. Clear single podosomes were selected by hand. For each cell, the data were normalized to the corresponding measurement from the podosome containing the brightest LifeAct signal. For time-lapse image analysis, podosome tracking was performed by hand, starting with the final frame and selecting the center of each podosome or corresponding cluster until its emergence or the time-lapse start. To track clusters, images were thresholded based on size and lack of local depletion zone in MATLAB and then tracked using TrackMate in FIJI. Cluster tracking did not allow merging or splitting events. For TGT analysis, because some 12 pN podosomes were challenging to identify computationally, podosomes were counted and identified manually.
To determine percent depletion, the centroid of each podosome was selected and dilated by 2 pixels. Fluorescence intensity was then quantified within the dilated region. MFM imaging was analyzed in MATLAB using published  49  code that was modified to threshold podosome rings. The XY orientation of the force was given by the phase of the sinusoidal fluorescence variation, while the amplitude of the sinusoidal variation gave information about the Z orientation. To determine the anisotropy from emission-resolved polarization images, large numerical aperture correction factors were applied in MATLAB  81. Cell culture. NIH 3T3 fibroblasts and mouse embryonic fibroblasts stably transformed with SrcY527F (MYF) were cultured in Dulbecco's Modification of Eagle's Medium (B003K32, Corning) with 10% fetal bovine serum (USDAFBS, MidSci), 2.
Immunostained cells were imaged using total internal reflection fluorescence microscopy (TIRFM). Drug treatment. Cells spread and formed podosomes on 4. 7 pN MT-FLIM probes for ~1 h prior to imaging and addition of drug dissolved in DMSO (final DMSO concentration ~0. 1%). Cells were either treated with 500 nM jasplakinolide (J7473, Thermo Fisher) for 8 min, with 50 μM Y27632 dihyrdochloride (Y0503, Millipore-Sigma) for 20 min, or with 50 μM (-)-blebbistatin (B0560, Millipore-Sigma) for 30 min. Blebbistatin was first heated to 45 °C in hanks balanced salts to improve solubility  82. Microscopy. Epifluorescence and TIRFM were performed on a Nikon Eclipse Ti microscopes using the Nikon Elements 4. 40. 00 or 4. 13. 05 software and a 1. 49 NA CFI Apo 100x objective. To maintain focus during time-lapse experiments, a Nikon perfect focus system was used.
TIRFM images were collected with 80 mW 488, 561, and 647 nm lasers using a Chroma quad cube (ET-405/488/561/640 nm Laser Quad Band) or Chroma quad band C148022 and C-TIRF Cube C125986. RICM images were collected with Nikon cube 97270, and epifluorescence images were collected with Chroma cubes 49004 and C121664. For emission-resolved polarization imaging, the emission fluorescence was split into parallel and perpendicular channels using an Andor TuCam system with a wire grid polarizer (Moxtek, Andor: TR-EMFS-F03). Fluorescence was projected two Andor iXon Ultra 897 electron-multiplying charge-coupled devices. The polarization bias of the microscope (G-factor) was computed by taking the ratio of the parallel and perpendicular fluorescence emission of fluorescein in solution. To correct for the polarization scrambling effect of the objective, large numerical aperture corrections were applied to the raw fluorescence data as previously described  81.
To validate the ability of the microscope to measure systematic spatial variation in anisotropy produced by highly ordered fluorophores, the fluorescence anisotropy of 5 µm silica beads coated with DOPC SLBs doped with DiI was measured (Supplementary Fig. 16 )  49. Fluorescence lifetime and confocal measurements were performed on a Nikon Ti Eclipse Inverted confocal microscope with a Plan Apo Lambda 60×/1. 40 Oil objective and Nikon Elements 4. 40. 00. Focus was maintained during timelaspse imaging via a Nikon perfect focus system. The confocal microscope was equipped with a C2 Laser launch with 405 and 561 nm diode lasers, Nikon Elements software, and a Picoquant Laser Scanning Microscope Time Correlated Single Photon Counting (TCSPC) Upgrade with SymPhoTime 64 2. 1. 3813. TCSPC settings are summarized in Supplementary Table  3.
Then, time-lapse imaging was performed using the Nikon Elements Photostimulation Module. In phase 1, a 561 nm image was collected (-12 s). In phase 2, the ROI was photostimulated for five frames using a 405 nm laser (15%). In phase 3, the cell's response was tracked every 30 s by imaging in the 561 nm channel (0-180 s). MFM  49  experiments were performed on a custom-built system (Supplementary Fig. 17a ) consisting of a Nikon-T2 microscope with a 60 × 1. 49 NA objective (Nikon), a cleanup polarizer, half-wave plate, and a focusing lens (ThorLabs), an Obis 561 nm LS 150 mW laser (Coherent), and an ORCA-Flash 4. 0 v3 CMOS camera (Hamamatsu) The half-wave plate was rotated by a motorized mount (PRM1Z8; ThorLabs) driven by the Kinesis software. The half-wave plate was set in motion at 25°(rotating the excitation polarization by 50°).
Once the half-wave place reached maximal rotational velocity, 73 images were taken at 50 ms exposure time via Nikon Fast acquisition in Nikon Elements v5. 1. Each 50 ms exposure image contained the average fluorescence as the laser polarization rotates through 2. 5°of arc. Podosome model. To determine the net integrin tensile force exerted per podosome, F Pod , we modeled a podosome with an outer radius, R, of 1 μm and an inner depletion radius, r, of 0. 3 μm. Using the published value for the DOPC footprint within a membrane  83  , we calculated the approximate number of biotinylated lipids per square micron, l. Based on our MT-FLIM data and % depletion data, we assumed that probes have a relative oligonucleotide density of 0. 5-2 probes per biotinylated lipid and that integrin receptors exert F Int up to 50 pN.
Note that this data point represents the minimum force applied by the podosome on an SLB, because F Int is likely greater than F 1/2 and could also be a distribution. For modeling of a hypothetical podosome with isometric tension for emission-resolved polarization measurements, we modeled a 1 μm structure exerting inward tensile forces with a tilt angle of 20°(Supplementary Fig. 17c ). Statistics and reproducibility. Statistics were performed in MATLAB 2018a or in GraphPad Prism 7. For biological experiments, each experiment is defined as one flask or well of cells. For surface characterization, each experiment is defined as one SLB. Biological experiments were repeated at least three times, except for emissionresolved polarization imaging and immunostaining of β1 integrin with MT-FLIM probes and actin, which were repeated twice.
Acknowledgements This work was supported by the  NIH  ( R01-GM131099  and  R01-GM124472 ) and the  NSF   CAREER Award  ( 1350829 ). This material is based upon work supported by the  National Science Foundation Graduate Research Fellowship Program  under Grant No. DGE-1444932  (R. G. and J. M. B. ) and by  NCI-1F99CA234959-01  (J. M. B. ). This study was supported in part by the  Emory Comprehensive Glycomics Core , which is subsidized by the  Emory University School of Medicine  and is one of the  Emory Integrated Core Facilities. Additional support was provided by the  National Center for Advancing Translational Sciences  of the  National Institutes of Health  under Award number  UL1TR000454. This work was supported by the  National Center for Advancing Translational Sciences  of the  National Institute of Health  under Award number  UL1TR002378.
Subjects and colony management We collected data for 486 different grey mouse lemurs (M. murinus, figure  1 ) aged from 1 to 10 years and present in the captive colony of Brunoy (Muséum National d'Histoire Naturelle), originated from eight maternal lines. Individuals are housed in large cages in monosexual groups of three or four individuals. Ambient air temperature is maintained at 25°C and humidity is stable around 30%. All individuals are fed ad libitum, weighed monthly and maintained under artificial light conditions mimicking natural seasons.
Thus, each infant has a known mother and three to four potential fathers, which were assigned thanks to microsatellite analysis (see  [38] [39] [40]  and electronic supplementary material for further details). In addition, half-sibs were present in our dataset since several females underwent several reproductive seasons and mated with different males, and because multipaternity is possible in this species  [41]. The pedigree was stored in a three-column Excel file with the following information required for the statistical analysis in AsRELM-R: individual identity, mother identity, father identity. Squares with unknown paternities were left empty, as well as maternities and paternities of 'founder individuals', that correspond to the more ancient common parent of tested individuals (see electronic supplementary material, files).
Morphology We recorded the length of the lower arm (ulna/radius) and head width with a pair of digital callipers (±0. 01 mm; Mitutoyo, Kanagawa, Japan), as reported in previous studies  [32]. We extracted body weight at the time of each test from the laboratory colony database. We extracted body weight at birth, body weight at three months, litter size and mother identity from the colony database. We calculated growth rate as the weight gain in grams over the first three months of life, which is the period during which most of the growth occurs in this species  [43]. Complete data were not available for all individuals, which explains why the sample size for each phenotypic trait varies and is different from the total number of individuals present in the study (N = 486). Indeed, we could not collect all the phenotypic data at the same time, in particular, to avoid additional stress during the behavioural tests.
Acknowledgements. We thank  Éric Gueton-Estrade ,  Sandrine Gondor-Bazin  and  Lauriane Dezaire  for their help with the care of the animals. We thank  Justine Mezier  and  Grégoire Boulinguez-Ambroise  for their contribution to the data collection. We thank  Mark Krasnow  for his technical and financial support. We thank  Valérie Ducret  for kindly accepting to revise the manuscript.
DNA methylation microarray processing and normalization Blood DNA methylation was assessed for each study using the Illumina HumanMethylation450 (450k) microarray, which measures CpG methylation across >485,000 probes covering 99% of RefSeq gene promoters  [35] , following the manufacturer's protocols. Standardised quality control procedures and quantile normalization were performed using the minfi Bioconductor package in R  [36]. Please refer to Supplementary Materials and Methods for more details.
Epigenome-wide association analysis EWAS with volumes of the thalamus, hippocampus and NAcc were performed for each site separately with standardised procedures (see Supplementary Materials and Methods for details), where control variables included sex, age, age  [2] , intracranial volume, methylation composition (the first four components), blood cell-type composition (the first two components), sample batches (when applicable), recruitment centres (when applicable) and disease status (when applicable). For studies with data collected across several centres, dummy-coded covariates were also included in the model. Results from each cohort were then meta-analysed by combining correlations (fisher's r-to-Z transformed) across all 11 cohorts with a fixed effect model, weighted by the corresponding inversed variance  [37].
Effects of differential methylation on gene expression We measured the impact of DNA methylation on expression of neighbouring genes (cis-effects) in 631 IMAGEN subjects for which DNA methylation and mRNA expression data were available (see Supplementary Materials and Methods). Methylation at most loci affected gene expression, with the effects of DMRs being larger than that of individual CpGs (i. e. cg26927218). Several isoforms are expressed from BAIAP2, and isoform-specific effects were observed for cg26927218; methylation at this locus correlated with increased expression of the short isoform for BAIAP2 (β = 0. 016, P = 5 × 10 -3 ; Fig. 2a ). There were no significant effects of cg17858098 on ECH1 mRNA levels (β = -0. 008, P = 0. 201). Given the correlations between the selected three DMRs noted above, we controlled for methylation at the other two DMRs when testing for effects of a given DMR on gene expression.
14, df = 2, P one tailed = 0. 018 for DMR2; r = -0. 748, t = -2. 52, df = 5, P one tailed = 0. 027 for DMR3). There was also moderate prediction by the degree of covariation of DNA methylation in prefrontal cortex, across all three DMRs (r = 0. 417, t = 1. 84, df = 16, P one tailed = 0. 042), and with DMR2 (r = 0. 966, t = 5. 26, df = 2, P one tailed = 0. 017). These results strongly suggest that associations between hippocampal volume and blood DNA methylation levels at the selected DMRs are largely mediated by their DNA methylation levels in the brain (see Supplementary Materials and Methods for more details). Another comparison between methylation in blood and other brain regions-Brodmann area (BA)7 (parietal cortex); BA10 (anterior prefrontal cortex) and BA20 (ventral temporal cortex)-using a smaller dataset of 16 BECon post-mortem samples  [57]  revealed similar patterns (see Supplementary Materials and Methods; Supplementary Fig.
Genetic contributions to differential DNA methylation associated with hippocampal volume Given that genetic factors may underlie the correlations between DNA methylation in different tissues, we searched for methylation QTLs in two datasets. A search in the ARIES mQTL database  [58]  identified several SNPs associated with methylation at the DMR1 and DMR3 loci (see Supplementary Materials and Methods; Supplementary Table  5A ). The strongest mQTLs, rs131758 and rs4441859, affected methylation such that the A-allele at these SNPs associated with increased methylation at DMR3 and DMR1, respectively. These effects were replicated in two other datasets  [59, 60]  (see Supplementary Materials and Methods; Supplementary Table  5B ). Remarkably, eQTL analyses indicated that these alleles correlated with expressions of CMYA5 and CPT1B, albeit differently.
While the effects of the rs4441859_A allele were tissue-specific, the rs131758_A allele increased CPT1B expression in all tissues, including the brain (Supplementary Table  5C  and Supplementary Fig. 3 ). Furthermore, we considered whether there was a significant overlap between DNA methylation differences identified in this study and SNPs associated with hippocampal volume. To test this, we used the recent genome-wide association studies of hippocampal volume conducted by ENIGMA  [18]  (excluding the IMAGEN data; GWAS association thresholds P < 5 × 10 -6 and P < 5 × 10 -7 ) as a dataset for significant hippocampal SNP regions, adapting MAGENTA  [61]  (see Supplementary Materials and Methods), the gene setsbased enrichment analysis tool for GWAS data to the analysis of methylation data. SNPs were merged into genomic regions that were then examined for overlap with DNA methylation identified in hippocampal EWAS performed in the IMAGEN sample.
In conclusion, we have identified DNA methylation at several loci that correlate with hippocampus volume, which suggest for the first time possible biological pathways by which modifiable and metabolic factors might contribute to the pathology of neurodegenerative diseases. A clear limitation is the small number of cohorts for which both MRI and DNA methylation data are available, we nonetheless provide a rigorous roadmap that should encourage larger and more extensive future studies. We also acknowledge several other limitations, such as the shortage of datasets enabling direct comparison of blood and brain DNA methylation patterns. In particular, the lack of datasets including the hippocampus as a brain region prevented us from establishing a direct link between DNA methylation in blood and in the hippocampus.