dataesr/scientific-paragraphs-categorization huggingface dataset:
We expect that other groups will attempt to independently test the replicability of their own ESM in order to establish the robustness of our findings obtained with one particular model. In any case, such a practice should become a common standard to gain more confidence in the future use of ESMs. Code and data availability. The code for testing the reproducibility of EC-Earth is available at: https://doi. org/10. 5281/zenodo. 3474777 (Le Sager et al. , 2019 ) and also at: https://github. com/plesager/ ece3-postproc. git (Le Sager, 2020). Two sample datasets can be used to test the methodology at: https://doi. org/10. 23728/b2share. 1931aca743f74dcb859de6f37dfad281  (Acosta et al. , 2019). Note that the entire code of EC-Earth is not available due to restrictions in the distribution of IFS.
Acknowledgements. We thank  Carlos Fernandez Sanchez  and three anonymous reviewers for constructive input on this paper. We thank  Editor Juan Antonio Añel  for useful comments and handling our paper. We are grateful to  Paolo Davini ,  Uwe Fladrich ,  Philippe Le Sager ,  Klaus Wyser ,  Ralf Döscher ,  Etienne Tourigny ,  Virginie Guemas ,  Kim Serradell ,  Oriol Mula-Valls ,  Miguel Castrillo ,  Oriol Tinto-Prims ,  Omar Bellprat , and  Javier Garcia-Serrano  for helpful discussions. We acknowledge the  ECMWF  and  MareNostrum3  for providing computational resources to carry out the experiments. FM is an F. R. S. -FNRS research fellow. Financial support. This research has been supported by the  EU Commission -H2020  (grant nos. 727862: APPLICATE, 312979: IS-ENES2, 675191:  ESiWACE , and  641727: PRIMAVERA ) and the  Spanish Ministerio de Economía y Competitividad (MINECO)  (grant no.
Heating rate is 50 • C/min. For illustration purposes, a typical sintering cycle is displayed on Figure  3. The microstructural characterization of the samples was performed using a scanning electron microscope SUPRA 40VP (ZEISS, Iena, Germany), equipped with a field-emission gun and an Electron BackScatter Diffraction (EBSD) acquisition system. Chemical analysis was performed using Wavelength Dispersion Spectroscopy (WDS) or Energy Dispersive Spectroscopy (EDS) techniques on an electron probe microanalyser SX100 (CAMECA, Gennevilliers, France). The WDS analyses were recorded at a voltage of 15 kV and an electric current of 40 nA. For EBSD acquisition 400 µm 2 or 800 µm 2 zones have been analyzed most of the time, with a respective step size of 0. 25 µm or 0. 5 µm. Both β and α phase data have been used to index patterns using OimDC NORDIF software (version VS2005, EDAX, Weiterstadt, Germany).
OIM Analysis software (version 5. 3, EDAX, Weiterstadt, Germany) was used to analyze EBSD data. X-ray diffraction (XRD) measurements were acquired using a diffractometer Equinox 1000 (INEL, Artenay, France) with a copper (λ = 0. 1540 nm) or cobalt (λ = 0. 1789 nm) monochromatic radiation. Data from the cobalt radiation diffractometer are converted to data which would have been collected using a copper experimental setup in order to ensure coherency of the results. Phase identification was based on Match software database. Density measurements were performed using Archimedes' principle in o-xylene liquid. Mechanical properties were investigated via simple shear tests, using a home-made shear setup mounted on an MTS M20 testing machine (MTS Systems Corporation, Eden Prairie, MN, USA). The load capacity of the shearing device is 100 kN.
Microstructural Features of the As-Sintered Samples The fabrication process-which includes both BM and SPS-has resulted in dense samples with a relative density of 100%. For each processed sample, the density is measured via Archimede's method and compared with the theoretical density of the material. The theoretical density of single β-phase Ti-2448 has been calculated using XRD data of an as-sintered single-phase IP sample. The cell parameter of the single phase β-BCC structure, which has been computed via Rietveld refinement of the XRD results, is 3. 300 Å and the corresponding calculated theoretical density of a full β Ti-2448 structure is 5. 44 g/cm 3. This value is confirmed in literature by Li et al. , who have found a close value of 5. 46 g/cm 3  [32]. Illustrations of a typical heterogeneous microstructure, which is obtained at the end of the processing route, are given in Figure  4.
Gene disruption has been drastically facilitated by recent genome editing tools. While the efficiency of gene disruption in cell culture has improved, clone isolation remains routinely performed to obtain fully mutated cells, potentially leading to artifacts due to clonal variances in cellular phenotypes. Here we report GENF, a highly efficient strategy to disrupt genes without isolating clones, which can be multiplexed. We obtained reliable lipidomics datasets from mutant cells generated with GENF, which was impossible when using clones. Through this, we found that an enzyme involved in congenital generalized lipodystrophy regulates glycerophospholipids with specific acyl-chains. We also demonstrate the possibility to dissect complex lipid co-regulatory networks and provide some common mechanisms explaining the adaptations to altered lipid metabolism.
The efficiency of gene disruption by CRISPR-Cas9 has improved through advances in sgRNA design algorithms and delivery strategies of the components  [2] [3] [4]. However, complete gene disruption in the whole population of cells remains difficult, and therefore many researchers isolate cellular clones after delivery of CRISPR-Cas9 component to obtain mutated cells. While it is known that different clones of cell lines can have phenotypic variabilities  [5, 6] , this potential pitfall of CRISPR-Cas9 technology has been discussed in the literature only rarely, and cellular phenotypes are often investigated through the analysis of a few representative clones. This can be very problematic in most fields, especially in -omics studies where a large number of variables is analyzed. Due to the presence of random variabilities introduced during clone isolation, it can be difficult to discriminate whether changes in -omics datasets are due to the genetic perturbation or due to clonal differences.
In addition, clonal differences add noise to datasets, making quantitative changes between various mutants difficult to assess, and limiting the use of CRISPR-Cas9 in quantitative systems biology. Lipidomics became a major -omics approach in biomedical research  [7, 8]. It can identify hundreds of different lipid species, including the structurally diverse membrane lipids. Lipids affect the physicochemical properties of membranes as well as the activity of proteins in contact with them  [9]. However, the molecular links between membrane lipid composition and biological processes are still poorly understood. It is therefore critical to accumulate more knowledge about mechanisms regulating membrane lipid composition. The metabolism of membrane lipids is complex due to the interconnection of metabolites from different lipid classes  [9, 10].
It has been shown that the change in one lipid can affect the levels of other ones, including those that are metabolically distant, generating a network of co-regulated lipids  [11]. This co-regulation leads to membrane lipid compositions that modulate immune responses upon various stimuli  [11]. In addition, we recently found that sphingolipids (SLs) and ether phosphatidylcholine (ether PC) are co-regulated, which is critical to maintain the integrity of the secretory pathway  [12]. While these studies establish the importance of lipid co-regulation, the mechanisms linking the quantitative change of one lipid to that of another one remains poorly understood. A combination of genetic disruption of lipid metabolic enzymes and lipidomics should be extremely useful to solve the problem. However, one requisite for such approaches would be that lipid co-regulation is correctly detected in the dataset, which is difficult when using clonal cells as discussed above.
Extremely high gene disruption was achieved with this protocol in multiple targets, including in multiplex experiments (Figures  1J  and 1K ). One of the few targets we could not mutate efficiently with GENF was HSPA9, which we found to be an essential gene according to DepMap database (Figure  S3A ). We note that another essential gene, TMED2, could be mutated with GENF, for which we found that indels were strongly enriched for in-frame mutations (Figures  S3A  and S3B ). The other two targets with low mutation rates were ECI2 and HBB (Figure  1J ). Transient transfection experiments revealed that the sgRNAs for these targets were extremely poorly active (Figure  S3C ). For the other targets, nearly perfect mutations were achieved without pre-testing of sgRNA activity. Therefore, GENF enables highly efficient gene disruption also in HeLa MZ cells, unless the sgRNA against a target is very poorly active, or when the target is essential.
This revealed that Agpat2 is responsible for the production of PC 34:1 (34 carbons and 1 double bond in acyl-chains). Using these datasets, we performed multivariate analyses to compare the performance of both approaches (clone isolation versus GENF) to detect lipid changes induced by gene disruption. Principal component analysis (PCA) could not discriminate control and Agpat2 mutant clones with the first dimension, while mutants generated with GENF were clearly discriminated (Figures  3H  and 3I ). When splitting the datasets into two groups with K-means clustering, data from one control clone was clustered together with the two Agpat2 mutant clones (Figure  3H ). On the other hand, datasets obtained from GENF-generated control and Agpat2 mutant cells were perfectly split (Figure  3I ). Finally, when we assessed data similarity using hierarchical clustering, clones could not be correctly separated based on Agpat2 genotypes, while cell lines generated by GENF were clearly distinguished (Figures  3J  and 3K ).
Manipulation of sphingolipid acyl-chains with GENF Using GENF, we generated a series of mutant cells lacking individual ceramide synthases (CERS1-CERS6), which are enzymes incorporating the N-acyl-chains of sphingolipids (SLs)  [28]  (Figure  1J ). Published RNA-seq data of HeLa MZ cells  [29]  revealed that only CERS2 and CERS5 are expressed in the parental cell line (Figure  S6A ). Therefore, mutants of the four non-expressed CERSs should not have any phenotypes unless GENF generated artifacts. We performed a lipidomic analysis of the mutant cell lines to investigate changes in SLs, while glycerophospholipids and cholesterol were also analyzed (see supplemental note for discussion about the methodologies and their limitations). CERS2 mutants had drastic decreases in C24 ceramide (C24 Cer) and compensatory increases in C16 ceramide (Figures  5A-5C ).
The reverse reaction of ceramidases has also been reported to generate ceramides  [31]. Despite the drastic reductions in SLs seen in ∆/∆CERS cells (Figure  5K ), the cells could be maintained in culture similarly to control cells with similar passage frequencies, showing the strong abilities of cell lines to survive with minimal amounts of some SLs. We further analyzed the whole lipidomics datasets of single CERS mutants. Consistently with the lack of their expression in parental cells, CERS1, CERS3, CERS4, and CERS6 mutants had no significant changes in all lipid analyzed (Figures  S6F-S6I ). It is unlikely that clonal mutants, with their high variances, could lead to similar unchanged datasets comprising >230 lipids. CERS5 mutants had only two significantly changed lipids, both being C16 SLs (Figure  5L ).
We also observed decreases in minor saturated PS species (PS 32:0 and PS 34:0, Figures  7G  and 7H ). In addition, although many PE species were increased in CERS2 mutants, PE 36:1 was reduced (Figure  6J ). Thus, it is likely that C24 SLs, mainly localized in the outer leaflet of the plasma membrane, generate a local environment that affect the physical properties of the opposing leaflet, which recruits PS 36:1 and other monounsaturated or saturated lipids, probably through local membrane physical properties. C16SLs, in contrast, are unable to affect the opposing leaflet similarly (PS 36:1 is reduced in CERS2 mutants despite the increase in C16 SLs). Our results provide experimental evidence for a recent concept of "hand-shaking" interactions between C24 SLs and PS 36:1, which was proposed based on correlations in lipidomics datasets and molecular dynamics simulations  [37, 38].
While such approaches could be a good alternative to GENF, some studies revealed that the transduced cell populations are not always fully mutated  [42]. This incompleteness could be problematic especially when multiplex gene disruption is performed to revert phenotypes, as has been done in our analysis of the co-regulation of ether lipids and SLs. In addition, GENF is a virus-free approach, enabling its use in most laboratories. Through a combination of highly efficient gene disruption enabled by GENF and lipidomics analyses, we could obtain reliable datasets to propose novel concepts in lipid biology. Based on the continuity from our previous research  [22, 23] , we mainly focused on the roles of membrane lipid acyl-chains, and revealed a potential role of reduced linoleic acid-containing glycerophospholipids in the genetic disease congenital generalized lipodystrophy type 1.
Then sums of each lipid class were calculated after removing low abundance lipids. For this filtering, the averages of the five lowest values for each lipid class were used to estimate the noise levels, and only signals at least two-fold higher than this noise were considered. Due to the low numbers of sphingolipids and sterols analyzed, this filtering was performed only for glycerophospholipids. The sums of all major lipids were calculated from phosphatidylcholine, ether phosphatidylcholine, phosphatidylethanolamine, ether phosphatidylethanolamine, phosphatidylinositol, phosphatidylserine, cardiolipin, ceramide, hexosylceramide, and sphingomyelin. Lysophospholipids and sterols were not used to calculate the sums. The sums were used to normalize data variances coming from the small differences in sample amounts used for lipid extraction, and thus lipid data are illustrated as "% of total".
Informatics and Data analysis Gene essentiality across multiple cell lines was analyzed using the depmap portal website, where data of genome-wide CRISPR screenings in 789 cell lines is available  [44]. Raw data were downloaded from the website and values were extracted with R software. Gene expression profiles of Agpat genes across multiple mouse tissues were obtained from BioGPS  [45]. LPAAT activity in multiple tissues was analyzed previously  [23]. The correlation between datasets was calculated from the data of lung, heart, liver, spleen, kidney, stomach, small intestine, colon, testis, adipose tissue, and skeletal muscle, which are the ones overlapping between the two. Statistical analyses were performed using Prism 8 (GraphPad). Multivariate analyses (principal component analysis, K-means clustering, and hierarchical clustering) were performed using R.
(A) Essentiality of selected targets. Gene effect scores were obtained from Depmap database  [44]. The scores illustrate the degree of sgRNA depletion/enrichment from cell populations during genome-wide CRISPR-Cas9 screenings in multiple cancer cell lines. A shift of the traces to the left (as seen for HSPA9 and TMED2) illustrate that the sgRNA targeting the gene is depleted upon culture, showing that the target is critical for cell survival in multiple cell lines. Note that HPRT1, which was used for co-targeting throughout this study, is not essential. (B) Indel patterns in TMED2 mutants analyzed by TIDE. Note that this target could be highly mutated with GENF with barely detectable wild type alleles (white bar), but with a high enrichment of in-frame mutations (red bars). (C) TIDE analysis of cells transiently transfected with pX330-based plasmids encoding the sgRNAs targeting the indicated genes.
(I) Mutation rates in L/P/P mutants generated in the first step illustrated in (H). (J) Mutation rates of genes targeted in the second step illustrated in (H). (K and L) So1P levels in mutants generated in the first (K) and second (L) steps. p values: one-way ANOVA followed by Dunnett' s multiple comparison test to compare with controls C1 (K) or C1/C2 (L). We know empirically that the content of polyunsaturated glycerophospholipids vary largely between datasets obtained in different days, which is likely due to differences in various factors including the conditions of the serum, which is the sole source of polyunsaturated fatty acids in standard culture conditions. The differences in control cells between (D) and (E) are likely to be caused by this variability, since the datasets were obtained years apart.
(F-K) Comparison of lipid analysis datasets obtained from clones and polyclonal cells generated with GENF. (F and G) Levels of the most abundant phosphatidylcholine (PC) species, PC 34:1, in different control and Agpat2 mutant clones (F) or in different polyclonal cell lines generated with GENF (G). p values: one-way ANOVA followed by Tukey's multiple comparisons test comparing all pairs. (H and I) Principal component analysis of PC acyl-chain composition datasets obtained from clonal cell lines (H) or polyclonal cell lines generated with GENF (I). Encircled are the results of Kmeans clustering to split datasets into two groups. (J and K) Hierarchical clustering to assess the similarity of the same datasets from clonal cell lines (J) or polyclonal cell lines generated with GENF (K). See also Figure S4.
ACKNOWLEDGEMENTS We are grateful to all members of the  Riezman laboratory and Shimizu laboratory  for valuable comments and continuous support. We especially thank  Hideo Shindou  for providing support,  Noemi Jiménez-Rojo  for constructing some of the pX330-based plasmids and sharing preliminary observations,  Isabelle Riezman  for all technical assistance, and  Keisuke Yanagida  for critical reading of the manuscript. T. H. was supported by the  Japan Society for the Promotion of Science (JSPS) Postdoctoral Fellowships for Research Abroad , the  French Government (National Research Agency, ANR)  through the  "Investments for the Future" programs LABEX SIGNALIFE   ANR-11-LABX-0028  and  IDEX  UCAJedi  ANR-15-IDEX-01 , and the  ATIP-Avenir program  ( CNRS/Inserm ). T. H. -Y. was supported by  MEXT/JSPS KAKENHI  grant  16K21651.
Harayama et al. , 2020) 10 20 30 D PC level (% of total) Figure S5 (200 p < 0. 01 p < 0. 0001 250 300 350 m/z 0 50 100 Relative Abundance 279. 25 255. 25 281. 22 253. 35 16:1 16:0 18:2 18:1 A E 400 450 200 0 50 100 Relative Abundance B Control Agpat2 mutant sgRNA1 Agpat2 mutant sgRNA2 F 250 300 350 m/z 18:2 279. 21 255. 20 281. 25 253. 19 16:1 16:0 18:1 400 fatty acid composition (% of total) 10 20 30 p < 0. 0001 450 C Signal ratio (18:2/18:1) 0 4 1 2 3 G o n t r o l C A g p a t 2 s g 1 A g p a t 2 s g 2 p < 0. 0001 Control Agpat2 mutant sgRNA1 Agpat2 mutant sgRNA2 0 0 30:0 32:0 32:1 34:1 34:2 36:1 36:2 36:3 36:4 38:3 38:4 38:5 38:6 40:4 40:5 40:6 40:7 C16:0 C16:1 n-7 C18:0 C18:1 n-9 C18:2 n-6 C20:4 n-6 C22:6 n-3 C22:4 n-6 C22:5 n-3 0 2 4 6 8 Dataset H LPAAT activity (nmol/min/mg protein) 16:0-CoA 18:1-CoA 18:2-CoA 20:4-CoA Control 1421024_at 1421025_at 1428821_at 1433817_at 1433818_at 1433819_s_at 1450504_a_at 1428336_at 1436640_x_at 1434287_at 1453257_at 1458669_at Agpat2 sg1 Agpat2 sg2 0 5 10 Selectivity for 18:2-CoA (%) p < 0.
Study design The study is part of PITUIGENE, a French multicentric retrospective study (ClinicalTrials. gov Identifier: NCT01903967) based on 212 frozen surgical samples of PitNETs. Data are registered according the French data protection agency CNIL. Written informed consent was given by all patients, and the procedure was in accordance with the ethical standards and approved by a local ethics committee (committee for the protection of persons CPP SUD-EST IV LYON). Recruited patients presenting a lactotroph (PRL), somatotroph (GH), corticotroph (ACTH) and gonadotroph (FSH/LH) immunoreactive PitNET, were selected from 10 different centers in France. All patients were operated, via trans-sphenoidal route, between 1988 and 2010. Patients were selected based on a clinical follow-up of at least 5-years combined with the availability of matching frozen tumor samples.
CGH data Tumor DNA was extracted from ~ 15 mg frozen tissue using MasterpureTM Complete DNA and RNA Purification Kit (Epicentre ® Biotechnologies, Madison, WI, USA). aCGH was performed using SurePrint G3 Human genome CGH + SNP Microarray, 4x180K (Agilent Technologies, Santa Clara, CA, USA). After enzymatic digestion by RsaI and AluI enzymes, 1. 5 µg of tumor DNA and 0. 6 µg of sex-matched human reference DNA (Agilent) were labelled by random priming with Cyanine 5 and Cyanine 3, respectively. Hybridization was performed at 65 °C for 24 h and the arrays scanned on Agilent DNA Microarray Scanner. Fluorescence was quantified with Feature Extraction 11. 5. 11 software whose output is the L2R (log2 (tumor DNA fluorescence)/(reference DNA fluorescence)) of each probe.
Statistical analysis CGH data preparation For each patient, raw CGH data were normalized and subsequently centralized as reported previously  [13]. The centralization step based on FISH analysis was applied on patient profiles with at least one alteration longer than 5 Mb. Circular binary segmentation was applied on the centralized and normalized Log2Ratios (L2R) (with significance level for the test to accept changepoints = 10 -6 )  [18]. X and Y chromosomes were excluded from the analysis. All identified CNV were manually reviewed by an experienced cytogenetician (EA). Loss of heterozygosity (LOH) calling was performed using Cytogenomics 3. 0. 3. 3 software (Agilent) for tumors with derivative log ratio spread (DLRS) less than 0. 3.
Descriptive analysis Genome instability was determined using the number of altered (deleted + gained) probes compared to the total number of probes. The association between clinical data and quantity of altered probes was tested using Wilcoxon rank tests. Non-supervised hierarchical clustering was done using Jaccard distance and Ward criterion, by tumor type and in the whole cohort. The associations between clinical data and clusters, and between clinical data and USP8 or GNAS mutations, were tested using Fisher and Kruskal-Wallis tests.
Transcriptomic analysis mRNA transcripts showing at least 2-fold variation were considered as differentially expressed after statistical analysis using Student's t-test with a p-value ≤ 0. 05. Gene Set enrichment analysis (GSEA) was performed to search for implicated biological pathways, following the recommended protocol from the Broad Institute Gene Set Enrichment Analysis website (https ://www. gsea-msigd b. org/gsea). GSEA software v7. 0. 0 and the Molecular Signatures Database v7. 0. were used for running GSEA. A ranked-list metric was generated by calculating the signal-to-noise ratio. The number of permutations was set to 1000. Nominal p-values < 0. 05 and adjusted q-values (FDR) < 0. 25 were considered as significant. All analyses were performed with R software version 3. 5. 2. P-values and adjusted p-values less than 0.
Acknowledgements We thank  Samia DAIKH , clinical trial assistant, for her work in the initiation and monitoring of this study. Co-investigators of Pituigene study:  Guillaume Assie ,  Camille Boulagnon-Rombi ,  Olivier Chabre ,  Brigitte Delemer ,  Emmanuel Gay ,  Peter Kamenický ,  Claude-Alain Maurage ,  Antoine Tabarin ,  Chiara Villa.