dataesr/scientific-paragraphs-categorization huggingface dataset:
Gene alignment Some of the available MCT8 orthologues and all human MCT family members were aligned to analyze shared and divergent features in amino acid composition. All amino acid sequence alignments were created automatically by means of Clustal Omega  (Sievers and Higgins, 2018)  software applying the Blossum62 amino acid similarity matrix. Alignments were produced with the BioEdit software package. The potential dimensions of transmembrane helices (TMHs) were predicted based on observable helices in the crystal structure of MCT1 (PDB code 6LYY  (Wang et al. , 2021) ), which was ultimately used as a structural template for homology models (see protein structure prediction).
, 1996). MCT8 and MCT8-P321L obtained a great score in all three servers: 83. 53% and 87. 69% 3D-1D score in VERIFY3D (where >80% is optimal), 94. 76% and 94. 1% of the residues in most favored regions (where >90 is optimal) as assessed by PROCHECK and an overall quality factor of 96. 99 and 99. 86 (where >90 is optimal) in ERRAT2, respectively. Two artificial MCT8 mutants (P462L and K207L) were also modeled as a control, to validate the accuracy of the in silico approach in assessing the structural changes of the disease-causing mutation. PyMOL software (PyMOL Molecular Graphics System, V. 2. 4. 0 Schrödinger, LLC) was used for the visualization and analysis of the models. The intra-protein interactions changes between MCT8 and MCT8-P321L models were assessed by generating a residue interaction network, using the RING 2.
0 web server  (Martin et al. , 2011; Piovesan et al. , 2016). CASTp server and PyMOL were used for substrate-binding pocket volume analysis  (Tian et al. , 2018). We then used the structure comparison program FATCAT (Flexible structure AlignmenT by Chaining Aligned fragment pairs allowing Twists)  (Li et al. , 2020; Ye and Godzik, 2003)  to compare the structure of short human MCT8 isoform we had generated with Robetta with the recently published predicted structure of this protein generated by the newly released AlphaFold software  (Tunyasuvunakool et al. , 2021). FATCAT showed that both structures are significatively similar (p-value 4. 48 e-12; 83. 64% identity), and differences are located in the poorly predicted N-terminal region (Supplemental Fig. 2F, G ).
Four limb hanging wire test Muscle strength was evaluated using the hanging wire test  (Bárez-López et al. , 2019; Crawley, 2006). Mice were placed on the top of a wire cage lid quadrant (14 × 14 cm) that was inverted horizontally 50 cm above a surface with bedding to avoid damage associated to the fall. The latency to fall was recorded over a maximum period of 120 s. Mice were tested for 2 consecutive days in 1 trial per day. Data from both days were averaged.
Rotarod test Motor coordination and balance were evaluated with the accelerating-rotarod test as previously described  (Bárez-López et al. , 2014 , 2019). Sixty min before the test, animals were subjected to an initial training period under constant speed. For the accelerated rotarod test, mice were placed on a rotarod (Ugo Basile, Italy) that accelerated from 4 to 44 rpm in 3 min and was maintained at 44 rpm for 2 further min. The latency of the mice to fall off the rod was recorded over the maximum observation period of 5 min. Mice were tested for 5 consecutive days with 3 trials per day with a 20 min intertrial interval. Data from the three trials were then averaged.
Elevated plus maze Elevated-plus maze exploration was evaluated as described  (Bárez-López et al. , 2017a; Lister, 1987). In the present experiment, each mouse was placed in the center of a grey PVC elevated-plus maze (each arm 30 cm long and 5 cm wide, with 30-cm-high walls on closed arms, 50 cm high over the ground. The behavior of the mouse was videoed from above for 10 min. The mouse was then returned to its home cage. The videos were then analyzed by means of the software Ethovision® (Noldus, Wageningen, Netherlands) to study the time spent and the frequency to enter the open and closed arms of the maze.
Open field Open field exploration was evaluated as described  (Bárez-López et al. , 2017a; Richard et al. , 2017). In the present experiment, the open field apparatus was an empty 40 × 40 cm grey PVC arena with 30-cmhigh walls. Each mouse was placed in the center of the apparatus and videoed from above for 10 min. The mouse was then returned to its home cage. All videos were obtained with a Microsoft LifeCam Studio camera (Microsoft Corporation, Redmond, WA, USA), and then analyzed by means of the software Ethovision® (Noldus, Wageningen, Netherlands) to quantify the distance walked by the animals, along with the time spent in both the central area of the field and the area closer to the walls.
Statistics Data were expressed as mean ± SEM. Statistical analyses were performed with GraphPad Software (www. graphpad. com). Assessment of the normality of data was performed by the Shapiro-Wilk test. Means of Mct8 expression levels were compared between WT and P253L groups by Student t-test. Means between all of the experimental groups were compared by the Kruskal-Wallis test for non-parametric data, or oneway ANOVA for parametric data. For motor and behavior tests, repeated measures from all the experimental groups at P90 and P180 were analyzed by two-way ANOVA. Significant differences were represented as *P < 0. 05; **P < 0. 01, and ***P < 0. 001 or indicating the exact p value for values close to 0. 05.
P321L is a conserved residue and its mutation results in structural alterations in the MCT8 protein To elucidate the biological importance of the P321 residue, we explored its evolutionary conservation by performing an alignment with several MCT8 orthologues and the other human MCT family transporters. This alignment showed that this residue is highly conserved, being present in all the available MCT8 orthologues and other MCTs such as MCT10 and MCT5 (Supplemental Fig. 1 ). Given the high evolutionary conservation of the P321 residue, we assessed its structural and functional importance in silico. For this, human and mouse MCT8 proteins were modeled by comparison with the outward-open conformation of MCT1 (RCSB's Protein Data Bank code 6LYY,  (Wang et al. , 2021 )) (Fig. 1A ) and MCT2 (PDB code 7BP3,  (Zhang et al.
Side (left) and top (right) cartoon views of the MCT8 structure were generated with PyMOL software. Proline 321, the residue mutated in two AHDS patients, is shown as red sticks. (B) 3D alignment of MCT8 (light blue) and MCT8-P321L (dark blue) models show a decrease in the distance between TMHs in the mutant. P321L mutation changes the amino acidic interaction network in MCT8. Changes in TMHs position between both models are indicated with red arrows (right panel). MCT8-P321L structure was predicted as described in A. 3D alignment was performed by means of Pymol software. P321L mutation changes the amino acidic interaction network in MCT8 (left panel). While P321 residue only interacts with S194 from TMH1, the P321L mutant also interacts with I197 from TMH1 and W431, L434, and V435 from TMH8.
) (Fig. 6C ). Balance beam test was performed also at both P90 and P180. This test showed no difference in the latency to fall from the beam between genotypes, as no mice fell during the experiment at either P90 or P180 (data not shown). The exploratory and anxiety-like behavior was assessed using the elevated plus maze on all the experimental groups as a decrease in the willingness to explore the open arms of the maze is a classical parameter to determine anxiety. P253L mice exhibited an abnormal behavior when subjected to this test, as compared to WT mice. P253L mice showed at P180 an increase in the time spent in the closed arms of the maze, along with a decrease in the frequency to exit those arms and enter the open arms, as compared to WT mice (Fig. 7A, B ). This difference reveals an anxiety-like behavior in P253L mice.
Acknowledgements We thank  Sabine Richard  for her helpful comments on the manuscript. Also, we thank  Olivier Vincent  and  Ricardo Escalante  for kindly providing the software PyMOL (The PyMOL Molecular Graphics System, Version 2. 4. 0 Schrödinger, LLC) for testing. We would like to thank  José Luis Trejo  for his help with the behavioral analysis, the  Genomics Core Facility  at the IIBM and  Silvia Murillo  for her technical help and Coral.
Single-cell analyses Single-cell analyses were performed on the 10X Genomics platform. For scRNA-seq, promyelocytes were processed with the Chromium Next GEM Single Cell 3 ′ GEM, Library & Gel bead Kit v2 according to the manufacturer's instructions, at a target capture rate of 6000 individual cells per sample. For scATAC-seq, promyelocyte nuclei were purified according 10X Genomics instructions and processed using the Chromium Next GEM Single Cell ATAC Reagent Kits v1. 1 following the manufacturer's protocols at a target capture rate of 10 000 nuclei per sample. scRNA-seq and scATAC-seq were sequenced using a 75-nt single-end and a 150-nt paired-end protocol, respectively. Data were processed using the 10X software Cell Ranger (v4. 0) and the 10X software Cell Ranger ATAC (v1. 2. 0). The mm10 genome was used as reference.
We attempted to purify the ReP cluster after RA treatment based on the expression of specific surface markers identified in our scRNA-seq data set. Taking into account Cd48 and Itgam (Cd11b), we were limited to isolate Prom1-3 and ReP (ProReP) from NeuRA1 and NeuRA2 (NeuRA) cells (Figure  1J  and supplemental Figure  1I ). After RA treatment, whereas the PLZF-RARA fusion was detected neither in the bulk nor in NeuRA populations, ProReP cells did maintain PLZF-RARA expression (Figure  1K  and supplemental Figure  1J ) and kept the potential to develop leukemia in transplanted mice (Figure  1L ). Altogether, these results reveal transcriptional heterogeneity and differentiation states within the PLZF-RARA transformed cells and identify the ReP cluster, which exhibits no differentiation features, high E2F signature, and PLZF-RARA residual expression as the potential driver of RA resistance of PLZF-RARA leukemia.
We showed that reduction of either EZH2 or E2F1 increased the RA response in the absence of PLZF-RARA but had no impact on PLZF-RARA repression activity (Figure  3I ), suggesting that EZH2 per se limits signaling to RA. Collectively, these results reveal a dependence of leukemic cells expressing PLZF-RARA on EZH2, whose activity may itself be modified by the presence of the fusion protein. PLZF-RARA induced H3K27me3 level at specific enhancer genes that marks RA relapse-initiating cells Next, we investigated the effect of PLZF-RARA expression on EZH2 chromatin activity. We compared the epigenetic landscape of PLZF-RARA promyelocytes with the granulocytemonocyte-progenitor (GMP) compartment (public data set GSE124190),  22  which is the closest cell compartment to promyelocytes according to its transcriptional signature (supplemental Figure  4A ).
Consistently, the signature of the switched enhancers was not impacted by RA in our scRNA-seq data (supplemental Figure  5B ). Collectively, these results show that PLZF-RARA modifies EZH2 activity by redirecting the H3K27me3 on a subset of enhancers that are not affected by RA treatment. We also highlighted a discrepancy between EZH2 protein level and its methyltransferase activity during RA-induced differentiation. Figure  2  (continued) in each cluster associated with Ipcef1, Sox6, and Prdx6b genes. (E) Computational scheme to identify key regulon targets in the ReP cluster. The first filtering consists to select TFs with high activity in the ReP cluster by considering the accessibility of their DNA motifs in this cluster. TF motif accessibility scores are calculated with Signac,  17  and TF motif markers are identified for each cluster (supplemental Table  2A ).
Figure2(continued) in each cluster associated with Ipcef1, Sox6, and Prdx6b genes. (E) Computational scheme to identify key regulon targets in the ReP cluster. The first filtering consists to select TFs with high activity in the ReP cluster by considering the accessibility of their DNA motifs in this cluster. TF motif accessibility scores are calculated with Signac, 17  and TF motif markers are identified for each cluster (supplemental Table2A). Selected ReP TFs are cross-referenced with master transcriptional regulators identified from scRNA-seq data using SCENIC 18  (supplemental Table2B). After this filtering, 3 TFs remain. Target genes shared at least by 2 TFs are taken into account for further filtering (second filtering). Target genes considered are: (i) found in all pySCENIC run, (ii) linked with positive regulons, and (iii) filtered based on the sum of normalized importance (>0.
Acknowledgments The authors thank  Pier Pandolfi  for sharing the PLZF-RARA TG mouse model;  Valerie Lallemand-Breitenbach  for facilitating the access to the PLZF-RARA TG bone marrow;  Christophe Lachaud  for helpful discussion and for reading the manuscript; and members of the  Duprez laboratory  for helpful discussions. The authors thank the animal support facility of the  Centre de Recherche en Canc érologie de Marseille , the flow cytometry and cell sorting platform, the microscopy platform, and the CIBI and DISC platforms for computational analyses and support. This work was supported by the  Ligue Nationale contre le Cancer (E. D. ) , the  Association Laurette Fugain  (E. D. ), the  Fondation Recherche M édicale (E. D. ) , the  Fondation A*MIDEX (E. D. ) , the  Fondation de France  (M.
We computed HiFi reads from the raw data (reads with quality above Q20) using PacBio SMRTlink software. We then assembled the raw reads into assemblies using HiCanu and Hifiasm v. 0. 13-r307  (16, 17). To extract and analyze the H-fibroin sequences, we used BLAST  (18)  to search the assembled genomes using the N-terminus and C-terminus sequences from previously published data. We then verified that both BLAST hits were isolated to the same contig in the genome assembly. We then extracted the region encompassed between the BLAST hits along with 20 kbp of flanking sequence on each end. We generated a gene annotation of the sequence using Augustus v. 3. 3. 3 (19). The first species that we sequenced was Hesperophylax magnus. To generate assemblies for both H-fibroin alleles, we mapped HiFi reads back to the gene sequence from the primary assembly.
We then separated reads into two bins by counting the number of mismatches in each read. We then assembled each bin of reads separately using HiCanu v. 1. 9  (17). Both assemblies resulted in full-length fully resolved Hfibroin sequences. We then verified that the allelic sequences matched those in the primary and alternate assemblies generated by Hifiasm  (16). Both assemblers yielded identical sequences. For all other species, we simply extracted the alleles from the primary and alternate assemblies generated by Hifiasm (16) as described above. We then aligned the amino acid sequences of the genes with the MAFFT-linsi algorithm to identify regions of shared alignment  (20). To visualize the differences in repeat structure among species we generated a custom visualization script that splits the gene into repetitive motifs and then generates a bar plot, in which each unique motif is assigned a unique color (Fig.
Characterization tools X-ray diffraction (XRD) patterns were obtained using a Brücker X-ray diffractometer equipped with Cu Kα radiation. The accelerating voltage and current were 40 kV and 100 mA, respectively. The XRD patterns were collected in the 2θ range of 15 -55 • using a continuous scan mode with a step size of 0. 02 • and a scan rate of 4 •. min -. FTIR spectra in diffuse reflectance mode were obtained by acquiring 150 scans at room temperature over a range of 4000-400 cm -1 , by means of a Nicolet AVATAR 370 DTGS spectrometer from Thermo Electron Corporation. TEM analyses (HRTEM), such as TEM imaging and selected area electron diffraction (SAED), were carried out using a FEI Tecnai G2 F20 S-TWIN (S)TEM. The SAED pattern simulation and image processing was performed using MacTempasX2 (based on LiFePO 4 (Pnma) CIF file) and FIJI softwares, respectively.
Acknowledgements A. C. M. was supported by the  EMJMD program  of the  European Union , through a scholarship for the  ERASMUS MUNDUS Master Program  " Materials for Energy Storage and Conversion  (M. E. S. C. ). The  region Hauts-de-France (HdF)  and the  French Research Network on Electrochemical Energy Storage  ( RS2E ) are gratefully acknowledged for financial support through grants accorded respectively to M. R. and W. D.
However the time interval between revisits will be within 11 to 22 days, depending on the location. Our results above tend to show that, for time-lags longer than 10 days, the forecasting system considered in the present study will have lost most of the information in the initial condition regarding SSH structures in the smallest scale range   (10-30 km). This is why nadir altimeter data will remain a key component of the satellite constellation complementing the wide-swath SWOT measurements in space and time.
Code and data availability. The source codes of the MEDWEST60 NEMO-based model and some of the diagnostics tools developed in this study are shared in an open github repository dedicated to MEDWEST60: https://github. com/ocean-next/MEDWEST60. The model outputs are available upon request (∼50 To for the three ensemble simulations). The CDFTOOLS (https://github. com/meom-group/CDFTOOLS) were used for some of the pre-and post-processing of the model outputs. The Power Spectral Density computation in sections 3. 2 and 4. 3 were all performed using A. Ajayi's python module PowerSpec (https://github. com/adeajayi-kunle/powerspec) and the predictability diagnostics based on CRPS score and location score were made with the SESAM (https://github. com/brankart/sesam) and EnsDAM (https: //github. com/brankart/ensdam) softwares.
01) (Supplemental Dataset 1). We first analyzed the impact of heat stress on gene expression deviation in wild-type plants (Supplemental Figure  6 ). As expected, a 37°C treatment has a much stronger effect on gene expression reprogramming as a 27°C treatment: 12189 (5600 upregulated, 6499 downregulated) and 891 (437 upregulated, 454 downregulated) genes whose expression is modified after stress, respectively. A large majority of differentially expressed genes (64% up-, 90% down-regulated) at 27°C treatment was common with 37°C treatments, indicating that both treatments trigger a common gene regulation signature (Supplemental Figure  6C-D ). As expected, gene ontology (GO) analyses associate common responses with 'stimulus, stress and high temperature' responses (Supplemental Figure  6B ).
, 2007). Accordingly, roGFP has been found partially oxidized already at 20°C, in the cad2 mutant which harbors a decreased glutathione level but no affected redox state of glutathione (Figure  3 ). Moreover, partial depletion of the glutathione likely further cause oxidation of the cell upon high temperature, due to a weakness of the antioxidant capacities, which we at least can observe by the accumulation of DAB staining in the root apex after heat stress (Supplemental Figure  2 ). The reason for the decrease of the glutathione pool we observed after high temperature will have to be further determined. This might be partly due to conjugation to other molecules during heat stress through S-glutathionylation. Glutaredoxins, enzymes which catalyze S-glutathionylation, could be involved in the process, and many of them were identified as differentially expressed in our RNAseq analyses (Supplemental Table  1  and Dataset 1 ).
Thermotolerance assays Seeds were first germinated at 20 °C, and after 4 to 7 days, seedlings were either transferred to 27 °C under short day (for hypocotyl length assay) or at 37°C under long day condition (for plant survival assay). Heat treatments were done by transferring plants in pre-heated MLR-352 Plant Growth Chambers (PHCbi, Sanyo). For thermomorphogenesis assays, pictures of each replicate were taken and hypocotyls of at least 20 seedlings were measured using NIH ImageJ software with the NeuronJ plugin. For TMHT experiment, the percentage of viability was calculated by counting the plants that recovered leaf growth 11 days after heat treatment. About 30 to 300 plants were used in each replicate for calculation and mean were plotted ± SD of 3 independent biological replicates. Asterisks indicate a significant difference (P<0.
An equal volume of RNA Lysis buffer was mixed with supernatant, loaded in the gDNA removal column in the 2min (16,000 x g) and the flow through wax mixed with equal volume of 100% ethanol. The mixture was then loaded into the RNA purification column, spin 30s and incubated with DNase 1 for 15 min and washed with RNA priming buffer, RNA Wash buffer two times and RNA was eluted in 70 µl of nuclease-free water and kept at -80 °C. Next, 2 µg of total RNA was purified with Dynabeads Oligo-dT (25) and library quality was determined using Bio-Analyser 2100 (Agilent Genomics). Quantification was done using Q-BIT Qubit Fluorescence Reader (ThermoFisher Scientific) and replicates were then multiplexed with unique barcodes. Library amplification, RNAsequencing and data analysis for each mRNA library, single-end 75 base pair sequences were sequenced using NextSeq 550 instrument (Illumina) at the Bio-environment platform, University of Perpignan Via Domitia (UPVD).
Glutathionedependent reduction of DTNB was followed at 412 nm. The total glutathione level was calculated with the equation of the linear regression obtained from a standard GSH curve. GSSG was determined in the same extracts after derivatization of reduced GSH. Derivatization of 100 ml of plant extract was performed in 0. 5 ml of 0. 5 M K phosphate buffer, pH 7. 6, in the presence of 4 ml of 2-vinylpyridine (Sigma-Aldrich) during 1 h at room temperature. The GSH-conjugated 2vinylpyridine was extracted with 1 vol. of diethylether, GSSG was measured by spectrophotometry as described for total glutathione. Confocal Laser-Scanning Microscopy and Ro-GFP2 imaging Observations were done using the Axio observer Z1 microscope with the LSM 700 scanning module and the ZEN 2010 software (Zeiss). Excitation of roGFP2 was performed at 488 and 405 nm and a bandpass (BP 490-555 nm) emission filter was used to collect roGFP2 signal.
For background subtraction, signal was recovered using a BP 420-480 nm emission filter during excitation at 405 nm. Analysis and quantifications were performed as previously described  (Schwarzländer et al. , 2008) , using the program ImageJ 1. 52i (https://imagej. nih. gov/ij/). ED305). We are grateful to Michèle Laudié and Margot Doberva from the Bio-Environment platform (University of Perpignan Via Domitia) for technical support in library preparation and sequencing.
ACKNOWLEDGMENTS This work was supported by the  Centre National de la Recherche Scientifique , by the  Agence Nationale de la Recherche  (grant nos. ANR-REPHARE 19-CE12-0027  and  ANR-RoxRNase 20-CE12-0025 ). This project was funded by  Labex AGRO  (under I-Site Muse framework) and coordinated by the  Agropolis Fondation  (grant no. Flagship  Project  1802-002 -CalClim ). This study is set within the framework of the  Laboratoires d'Excellence TULIP  ( ANR-10-LABX-41 ). AD is supported by a Ph. D. grant from the  Université de Perpignan Via Domitia (Ecole Doctorale Energie et Environnement.