dataesr/scientific-paragraphs-categorization huggingface dataset:
cerasifera (N=66) (or 'cherry plum', alternatively called 'myrobolan' in Europe, P. divaricata Ledeb. in Caucasia and P. sogdiana in Central Asia) and other species from the Prunophora subgenus: P. mexicana (N=1), P. munsoniana (N=1, also called Prunus rivularis), P. maritima (N=1), P. americana (N=1) and P. subcordata (N=1). P. salicina (Japanese plum) samples (N=10) were composed of five cultivated accessions which included one plumcot, a hybrid between P. salicina and P. armeniaca (called 'Rutland' in the ARS-USDA database, P0489) and five wild P. salicina accessions, sampled in China (Table  S1b ). Prunus cerasifera accessions used in this study originated from Europe (N=13), from Caucasia and Russia (N=29) and from Central Asia (more precisely from Kazakhstan and Kyrgyzstan, N=24) (see Table  S1b  for details).
Analyses of population subdivision and genetic relationships To assess the probability of observing unrelated individuals with the detected similar genotypes given the population allelic frequencies, we used GENODIVE and the corrected Nei's diversity estimate with a threshold of 50  (Meirmans and Van Tienderen 2004). We later retained only one individual of each pair detected as clonemates or siblings for further analyses. We identified population subdivision with the STRUCTURE software v. 2. 3. 3 (Pritchard et al. 2000) , without the use of a priori grouping information and assuming that individuals had mixed ancestry with correlated allele frequencies among populations. The clustering method implemented in STRUCTURE is based on Monte Carlo Markov Chain (MCMC) simulations and is used to infer the proportion of ancestry of genotypes in K distinct clusters.
We simulated K values ranging from 2 to 10 for the P. brigantina population and three additional datasets (Table  1  and S1c ), obtained with the same genetic markers on Armeniaca and Prunus species originating from Central and Eastern Asia  (Liu et al. 2019)  (Supplemental information). For each K, we ran 10,000 generations of 'burn-in' and 100,000 MCMC. Simulations were repeated 10 times for each K value; the resulting matrices of estimated cluster membership coefficients (Q) were permuted with CLUMPP  (Jakobsson and Rosenberg 2007). STRUCTURE barplots were displayed with DISTRUCT 1. 1  (Rosenberg 2004 ). The strongest level of the genetic subdivision was determined using ΔK  (Evanno et al. 2005) , as implemented in the online post-processing software Structure Harvester (http://taylor0.
biology. ucla. edu/structureHarvester/) (Earl and vonHoldt 2012). Principal components analyses (PCA) were performed to investigate the genetic structure of P. brigantina using the scatterplot3d R package  (Ligges and Mächler 2003)  or among the five Prunophora species, using the DARwin software package v 6. 0. 017 (Perrier and Jacquemoud-Collet 2006). Further genetic differentiation and relationships were also estimated using a weighted neighbour-joining tree as implemented in the DARwin software package v 6. 0. 017 (Perrier and Jacquemoud-Collet 2006). We performed a three-step population subdivision analysis, the first one with only P. brigantina samples, and the second one adding previously obtained datasets of Armeniaca species, including P. armeniaca, P. sibirica, P. mandshurica and P.
mume wild and cultivated samples  (Liu et al. 2019 ) (dataset 2 in Table  1 ). In the third step of the analysis, we added samples of the Prunus (P. cerasifera and P. salicina) and Prunocerasus (P. mexicana, P. munsoniana, P. maritima, P. americana and P. subcordata) sections (dataset 3 in Table  1 ). The same procedure to investigate population subdivision and structure analysis was used with the Armeniaca, Prunus and Prunocerasus diploid samples. In parallel, we also performed a population subdivision analysis along the native distribution of P. cerasifera (Supplemental information 'Prunus cerasifera diversity and population structure analysis' and dataset 4 in Table  1 ). In P. cerasifera, a Neighbour-Joining tree based on Nei's standard genetic distance was built with a bootstrap of 30,000 in PopTreeW  (Takezaki et al.
The maximization (M) strategy  (Schoen and Brown 1993)  implemented in the COREFINDER software was used to generate a core P. brigantina tree collection maximizing the number of alleles based on our dataset. The maximization strategy consisted in detecting the smallest sample that captured 100% of the genetic diversity present within the entire germplasm collection. We further used the Mann-Whitney U test to check the genetic diversity difference between the core collection and the entire P. brigantina sample.
Genetic relationships between P. brigantina and other Prunophora species To obtain a better understanding of the genetic relationships between P. brigantina and other Armeniaca species, we combined the current P. brigantina data with a former Armeniaca dataset built with the same 24 microsatellite markers  (Liu et al. 2019 ) (Tables  1  and S1c ). We performed a Bayesian clustering analysis on the full Armeniaca dataset, including wild and cultivated P. armeniaca, P. sibirica, P. mandshurica and P. mume (Table  1 ). We obtained a similar structure as the one described in  (Liu et al. 2019 ) for the previous dataset, and P. brigantina differentiated in a distinct cluster, from K=3 and above (yellow colour in Figure  S4 ). No gene flow with other species of the section Armeniaca was detected (i.
We found genetically differentiated clusters of cherry plums, with contrasted geographical distributions from Central Asia to Europe (detailed results are presented in the supplemental information 'Prunus cerasifera diversity and population structure analysis). Caucasia appears to be a diversification center of wild cherry plums, with two distinct genetic clusters that may result from geographical isolation. This dataset was later merged with representatives of the Prunus, Armeniaca and Prunocerasus sections, to infer the origin of P. brigantina and its genetic relationships with species of the Prunophora subgenus (Tables  1, S1b  and S1c ). In the following step, we focused on species that shared, partly, habitats with P. brigantina, i. e. P. cerasifera and cultivated P. armeniaca, together with other Armeniaca (P.
mume), Prunus (P. salicina) and Prunocerasus species. For this, we used genotyping data based on 23 microsatellite markers (see the supplemental information 'Prunus cerasifera diversity and population structure analysis'). The delta K peaked at K=3, indicating that this was the strongest level of population subdivision (Figure  S1B ). However, further relevant clustering was observed at higher K values (Figure  S5 ). From K=7 and above, all taxonomic species separated in specific clusters: green for P. brigantina, pink for P. armeniaca, blue for P. cerasifera, grey for P. mume, orange for P. salicina and black for Prunocerasus (Figure  S5 ). Again, we could not find any admixture footprints between P. brigantina and other Prunus species, while there may be some footprints of introgression from P.
Construction of a P. brigantina core collection We used the COREFINDER program to identify the smallest core collection that would be sufficient to capture the whole diversity detected based on our 24 microsatellite markers. Based on the maximizing strategy implemented in COREFINDER, we propose the use of a core set of 36 individuals (~49% of the whole P. brigantina sample) that captures 100% of the detected diversity (Figure  6 , Table  S3 ). Pairwise comparisons using Mann-Whitney U tests showed no significant differences in diversity indexes (I, HO, and HE) between the P. brigantina entire Alpine sample (N=71) and the core collection (N=36) (Tables  S2, S4  and S5 ). This indicates that our core collection can be used as an ex-situ germplasm repository.
brigantina population for ex situ conservation would be very useful in the perspective of Alpine ecosystem restauration and future breeding programs. Core collections are representative subsets of germplasm collections that are developed to improve the efficiency of germplasm evaluation while increasing the probability of finding genes of interest  (Simon and Hannan 1995). Therefore, our current core collection will serve in the future for P. brigantina conservation as well as for stone fruit breeding programs benefiting from P. brigantina resilience characteristics, especially in a context of Mediterranean climate changes. However, the most efficient strategy for biodiversity conservation remains the preservation of the natural habitat of endangered species. Thanks to an extensive dataset of Prunophora species, we also questioned here the genetic relationships of P.
maritima (N=1), P. americana (N=1) and P. subcordata (N=1). The blue stars (*), at the bottom of the bar plots, correspond to Japanese plums (P. salicina) admixed with P. cerasifera. Table  S5. Mann-Whitney U tests (two-tailed) between the whole Prunus brigantina dataset and its core collection. The sampling for this analysis included P. cerasifera (N=66) in blue, P. armeniaca (N=87) in pink, P. brigantina (N=73) in green, the Chinese apricot tree P. mume (N=9) in grey and Japanese plum, P. salicina (N=10) in orange. Black dots correspond to Prunocerasus species (P. mexicana, P. munsoniana and P. maritima). Colours refer to the genetic clusters inferred from the STRUCTURE analysis, according to the barplots at K=8 in Figure  S5. S3.
Acknowledgements S. L. is a recipient of a  Chinese Scholarship Council PhD grant. Molecular analysis was performed at the  GenoToul Get-PlaGe (INRAE Center of Toulouse)  and  GENTYANE (INRAE Center of Clermont-Ferrand ) platforms. The authors wish to acknowledge all the people who helped in collecting the samples, in particular, collaborators from  Le Plantivore at Château Ville-vieille, Histoire de Confiture at Plampinet, Nevache ,  C. Gatineau  from  Cervières  and the managers of the Queyras and Mercantour national parks. We thank the curators of the  French Genetic Resources Centre  ( Marine Delmas ) and of the  US ARS-USDA repository (John Preece ); we acknowledge the care of the plants at the  UMR BFP (INRAE) by Jean-Philippe Eyquard and Pascal Briard.
Cyto-Nuclear Coadaptation Fine-Tunes the Response of Central Metabolism to N Starvation MapMan software v6. 3  [36]  was used to gain insight into the biological processes affected by N starvation and Cyt × Nuc interaction. Figure  4  illustrates the DAMs and DEGs involved in the central metabolism (see complete lists in Tables  S1  and S3 ). Under N starvation, most of the individual amino acids decreased whereas the sugars, such as glucose, fructose, raffinose and galactose, were accumulated (Figure  4A ). We also noticed that pyruvate amount was reduced while most of organic acids were increased under N starvation. The changes in individual amino acid amounts were coherent with our measurement of total free amino acids in shoot (Figure  1 ), and are typical of N starvation  [3]. Changes in mRNA amounts of genes coding for enzymes involved in the central metabolism (Figure  4B ) were in accordance with the metabolite amount variations in sugars, amino acids and organic acids pathways (Figure  4A ).
Transcriptomic Data Total RNA extracted from the ground samples following TRI-reagent protocols (Sigma-Aldrich, St Louis, MO, USA) and treated twice with RNase-free DNase I (Thermo Fisher Scientific, Les Ulis, France) was finally purified using NucleoSpin RNA kits (Macherey-Nagel, Hoerdt, France). RNA samples were checked for quality on a bioanalyser (Agilent, Les Ulis, France), quantified with a Quant-iT RiboGreen RNA Assay Kit (Thermo Fisher Scientific, Les Ulis, France). Microarray analyses were carried out on the INRA transcriptomic platform at the IPS2 (Orsay, France) using CATMAv6. 1 (Roche-NimbleGen technology, Madison, WI, USA) arrays. High density CATMAv6. 1 microarray slides contained, per chamber, 270,000 primers representing all Arabidopsis genes, i. e. , 30,834 probes referring to the TAIRv8 annotation (including 476 probes of mitochondrial and chloroplast genes) (www.
arabidopsis. org/), plus 1289 probes corresponding to EUGENE software predictions, 5352 probes corresponding to repeated elements, 658 probes for miRNAs, 342 probes for other non-coding RNAs (rRNAs, tRNAs, snRNAs, soRNAs) and finally 36 control probes. Each long primer is triplicate in each chamber for robust analysis. For each comparison, one technical replicate with fluorochrome reversal was performed for each biological replicate (i. e. , four hybridizations per comparison). cRNAs were labeled with Cy3-dUTP or Cy5-dUTP (Perkin-Elmer-NEN Life ScienceProducts, Courtaboeuf, France). The samples were normalized as described in  [108]  and then a normalized intensity per gene for each sample was calculated using the method described in  [109]. Microarray data from this study were deposited into the CATdb database  [110, 111]  (tools.
ips2. upsud. fr/CATdb/, Project: BioAdapt2011_Cytopheno) and into the Gene Expression Omnibus (GEO) repository  [112]  at the National Center for Biotechnology Information (NCBI) (accession number GSE144597) according to the "Minimum Information About a Microarray Experiment" standards  [113]. The RT-qPCR analysis of organellar transcripts was carried out as described in  [114, 115] , except that the qPCR was performed on a CFX384 real-time System (BioRad, Marnes-la-Coquette, France) with the SybrGreen Premix Ex Taq kit (Tli RNaseH plus, Takara, Japan).
LC-MS/MS analyses were run in random order on a Nano-LC U3000 RSLC system (Thermo-Fisher) coupled to a LTQ Orbitrap Velos mass spectrometer (Thermo-Fisher). We injected 6 µL of samples to be separated on a 50-cm C18 column (Acclaim Pepmap C18, 75 µm internal diameter, Thermo-Fisher). A linear gradient of 112 min increasing from 4% to 70% of B (90% acetonitrile, 0. 08% formic acid) was applied with a flow rate of 300 nL/min. The MS scan resolution was set at 60,000 between 400 and 2000 m/z. The MS/MS fragmentation was performed by CID on the 8 top precursors of the MS1 spectrum. MS raw data were converted into mzXML files thanks to MS Convert (ProteoWizard) to be analyzed with the tools developed and provided by the PAPPSO platform (INRAE, Gif-sur-Yvette, France). The protein identifications were done thanks to the X!Tandem software by first querying the MS/MS data against the TAIR10_pep_20101214 fasta database and a contaminant one for trypsin and keratins.
Proteins were then filtered on a basis of at least two corresponding peptides with an E-value smaller than 0. 001 and a protein log(E-value) smaller than -4. Relative peptide quantification by peak-area integration on XICs (eXtracted Ion Chromatogram) was performed using MassChroQ software. Relative protein abundance was then obtained thanks to an R script using the package MassChroqR for filtering, normalization, imputation of missing data and calculation of proteins abundance from peptides data. This calculation was done by summing the intensities of at least two specific (non-redundant) and reproducible peptides by quantified protein. Finally, this pipeline for quantitative proteomic provided us with a table of 665 proteins quantified on the 24 samples of the project, ready for statistical analysis.
Metabolomic Data Extraction and gas chromatography-mass spectrometry (GC-MS) analyses were performed as described by Tcherkez et al. [117]. The relative levels of metabolites were determined in an untargeted manner. Leaf samples (100 mg of fresh powder) were ground in liquid nitrogen using a Mixer Mill MM400 (Retsch, Germany) for 2 min at a frequency of 30 Hz and then in 2 mL of 80% methanol in which ribitol (100 µmol L -1 ) was added as an internal standard. After centrifugation, aliquots of each extract (0. 2 mL) were spin-dried under vacuum. The extracts were dissolved with methoxyamine (in pyridine) and N-methyl-N (trimethyl-silyl) trifluoroacetamide (MSTFA). The derivatization mixture was then incubated for 2 h at room temperature. Before loading into the GC autosampler, a mix of a series of eight alkanes (chain lengths: C10-C36) was included.
Analyses were performed by injecting 1 µL in splitless mode at 230 • C (injector temperature). Gas chromatography coupled to time-of-flight mass spectrometry was performed on a LECO Pegasus III with an Agilent (Massy, France) 6890N GC system and an Agilent 7683 automatic liquid sampler. The column was an RTX-5 w/integra-Guard (30 m × 0. 25 mm internal diameter + 10 m integrated guard column; Restek, Evry, France). The chromatographic separation was performed in helium as a gas-carrier at 1 mL min -1 in the constant flow mode and using a temperature ramp ranging from 80 to 330 • C between 2 and 18 min, followed by 6 min at 330 • C. Electron ionization at 70 eV was used and the MS acquisition rate was 20 spectra s -1 over the m/z range 80-500 as described by Weckwerth et al. [118]. Peak identity was established by comparison of the fragmentation pattern with MS available databases  [119] , using a match cut-off criterion of 700/1000 and by retention time using the alkane series as retention standards.
The integration of peaks was performed using the Chroma TOF software, version 2. 22 (Leco, Garges-lès-Gonesse, France). Because automated peak integration was occasionally erroneous, integration was verified manually for each compound in all analyses. As a quality control filter, samples were checked for the presence of a strong ribitol peak with a peak area of at least 35,000 and a deviation from the median internal standard peak area (for that GC/MS batch sequence) of less than 15% of the median value. Metabolite contents are expressed in arbitrary units (semi-quantitative determination). Peak areas determined using the LECO Pegasus software have been normalized to fresh weight and ribitol area (internal standard).
(iii) The cytoplasm effect = mean(genotypes with Jea cytoplasm in both nitrogen conditions)mean(genotypes with Ct-1 cytoplasm in both nitrogen conditions). (iv) The cytoplasm × nucleus interaction effect = mean(both cytolines in both nitrogen conditions)mean(both parental lines in both nitrogen conditions). p-values of the seven contrasts were adjusted altogether by the Benjamini-Hochberg method to control FDR. For a given contrast, an accumulation was declared different if its adjusted p-value was lower than 0. 05. Although the p-value adjustment was calculated on the seven contrasts, we focused the interpretation on the effects (i), (iv) and (vii) (see Results and Discussion). The scripts and datasets used for this analyses are accessible at  [34] , together with the results of the analyses.
Enrichments in Biological Processes of DEGs The assignment of protein functions was based on the TAIR GO categories from the aspect "biological process". Enrichment analysis of GO categories was done in R (version 3. 6, http://www. rproject. org) using the "elim" method from the topGO package  [122]  that is part of the Bioconductor project (version 3. 10, http://www. bioconductor. org/). TopGO is a software package, which provides tools for testing GO terms by identifying and removing local dependencies between GO terms  [123]. The elim algorithm iteratively removes the genes mapped to significant terms from higher-level GO terms. Fisher's exact test was used for assessing the GO term significance. Over-representation of biological processes was calculated for 1) the DEGs up and down regulated under N starvation as compared to all transcripts identified in our dataset and predicted in TAIR10; 2) the DEG responsive to both N starvation and Cyt × Nuc intersection with common or opposite senses as compared to all up and down DEG by N starvation.
Acknowledgments: We are very grateful to  Pepe Gimenez-Gomez  and  Delphine Charif  for their help in bioinformatic analyses of Ct-1/Jea polymorphisms, and to  Gilles Curien  for his help to keep the chloroKB server [124] available at any time. We thank  Céline Masclaux-Daubresse  and  Etienne Meyer  for fruitful discussions. This work has benefited from the support of IJPB's Plant Observatory technological platforms.
Location 1 DEGs in N Nutrition 2 DEGs in Cytolines vs. Parents 2 DEGs in Third Order Interaction Cyt × Nuc × N 2 DEGs in Both N Nutrition and Cyt × Nuc 3 Cytosol 2237 * 1234 96 769 Endoplasmic Reticulum 303 176 21 103 Extracellular 989 449 34 249 Golgi 281 218 * 32 * 137 # Mitochondrion 853 622 * 55 370 # Nucleus 3010 * 2230 * 154 1405 # Peroxisome 138 78 12 52 Plasma Membrane 1483 * 920 * 89 610 # Plastid 1294 * 598 75 * 370 Vacuole 238 * 105 8 68 Total 12255 7153 604 4437 1 according to SUBA4con. 2 * indicate enrichments compared to the dataset of analysed genes (p < 0. 001). 3 # indicate enrichments compared to the dataset of DEGs in N starvation (p < 0. 001).
The crystal structure of tFprA anox obtained at 1. 83 Å resolution is in an active reduced state comparable to that observed in mFprA structure (PDB code: 2OHI). tFprA and mFprA have a sequence identity of 70%. The tFprA oxo-Kr structure at 1. 80 Å resolution is similar to the active oxidized state of mFprA (PDB code: 2OHH) (RMSD: 0. 88 Å) and tFprA anox superposes well with mFprA in the active reduced state (PDB code: 2OHI) (RMSD: of 0. 77 Å). The unusual non-prolyl cis-peptide bond between Leu150 and His151 is observed in both structure tFprA anox and tFprA oxo-Kr. One main difference between the reduced and oxidized states is the loop 24 to 28 pointing from a b-hairpin in the b-lactamase-like domain which is unique in FDPs and covers the active site. 20 This loop is observed in the closed position in both tFprA structures as observed in the active reduced state of mFprA (Fig.
S4 , ESI †). 7 In the oxidised state (tFprA oxo-Kr ), the loop 24-28 shows an average B-factor higher than in the reduced states (tFprA anox ) suggesting a higher loop flexibility in the oxidized form although this feature is not observed in mFprA structures (Table  S2 , ESI †). These inconsistencies could be explained by differences of iron occupancy in mFprA structures. A close analysis of the variation of the loop 24-28 unravelled a flip of Y28 close to L203 in tFprA anox structure (Fig. S4 , ESI †) which probably acts as a gate to control solvent access or sealing. In mFprA and tFprA structures, the di-iron centre is coordinated by four histidines, one aspartate, one glutamate and one bridging aspartate residues, which are also perfectly conserved in other FDPs (Fig. S1 , ESI †). In mFprA (PDB codes: 2OHH and 2OHI) and tFprA structures (tFprA anox and tFprA oxo-Kr ) the distance between the two irons is between 3.
3 to 3. 5 Å, which corresponds to van-der-Waals contacts. Iron atom occupancies are close to 100% in tFprA anox and slightly lower in tFprA oxo-Kr (between 0. 7 to 0. 9). In the case of mFprA structures (PDB codes: 2OHH and 2OHI), one iron of the catalytic centre is present at low occupancy (0. 4) that provokes a high increase of flexibility for the switch loop 149-154 coordinating the Fe (Fig. S5 , ESI †). tFprA oxo-Kr structure, exposed to oxygen during the purification and crystallization, exhibits the classic oxobridging ligand, which is a chemical proof of the oxidized state because such bridging ligand was also observed in one active site of the mFprA tetramer in the active oxidized state (PDB 2OHH, chain E). This reaction intermediate Fe(III)-O-Fe(III) is produced after the first reduction step.
20 To characterize experimentally this possible gas channel, we produced Kr-derivatives of oxidized tFprA by flash cooling crystals under gas pressure and collected datasets below and above the Kr K-edge (SI Method and Table  S1 , ESI †). Anomalous signal coming unambiguously from Kr atoms have been detected in an anomalous Fourier map at similar positions in each of the eight monomers constituting the asymmetric unit (Fig. S8, ESI † ). Up to six Kr were localized in a hydrophobic channel (Fig. 1b  and 2b, c  Kr 4,5 and Kr 6 highlight two possible entrances for the O 2 (Fig. 1b , 2c and Fig. S8, ESI † ). Interestingly, a modulation in the Kr atoms occupancies along the channel is observed. Two main Kr atoms Kr 2 and Kr 3 are trapped in a high hydrophobic/ rigid part of the channel formed by three phenylalanine's side chains (F158, F173, F215) (Fig.