dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments: We are grateful to  La Dauphinoise , which kindly provided soybean seeds. We thank  Frank Jamois , the director of research analytical platforms in the  Agro Innovation International Roullier , and  Adrian Schwarzenberg  for his contribution during the earlier stages of the metabolomic analysis. We thank  Catherine Rey ,  Séverine Croze  and  Joel Lachuer  from  ProfileXpert , a core facility of the University Lyon 1 and belonging to the  Gis-Ibisa  and the  France Genomics network  for RNA sequencing. We thank  Aline Planchon ,  Marc Ropitaux ,  Gaelle Durambur  and  Sophie Bernard  for their help during soybean root and RET harvesting. We are also grateful to  Benjamin Lefranc  from  Plateforme de Recherche en Imagerie Cellulaire de Normandie (PRIMACEN) URN , who also provided us the PEP-13 peptide elicitor.
Data preprocessing and treatment were performed using the Workflow4Metabolomics platform and the metabolite annotation was performed using our in-house database by matching accurate masses, retention times, and MS/MS spectra to those of pure standards. We successfully identified up to 229 metabolites at a high confidence level in human meconium, belonging to diverse chemical classes and from different origins. A progressive evolution of the metabolic profile was statistically evidenced, with sugars, amino acids, and some bacteria-derived metabolites being among the most impacted identified compounds. Our implemented analytical workflow allows a unique and comprehensive description of the meconium metabolome, which is related to factors, such as maternal diet and environment.
The largest mapping of early feces metabolome was very recently provided by Petersen et al. Within this study, a non-targeted LC-HRMS analysis of 100 meconium samples was performed by Metabolon, Inc (Morrisville, NC, USA), which reported the detection of 714 compounds belonging to different metabolic pathways, including predominately complex lipid species (e. g. , lysophospholipids, sphingomyelins), fatty acids, amino acids, xenobiotics, vitamins, and cofactors  [18]. In the present study, we aimed to provide a comprehensive description of the meconium metabolome, and provide a list of metabolites identified at a high confidence level. We thus implemented an MS-based metabolomics workflow involving two untargeted distinct and complementary LC-HRMS platforms combined with annotation based on an in-house chemical database comprising more than 1200 pure authentic standards analyzed under identical conditions  [19] [20] [21]  and confirmed by MS 2 analysis.
A given metabolite is commonly detected under ESI conditions as a multiplicity of molecular species (i. e. , monoisotopic peak, adducts, dimers, and/or fragments), resulting in an overestimation of the real number of unique metabolites in the meconium samples  [25]. Overall, 224 metabolic features from the C18-ESI + conditions and 149 from the HILIC-ESI -conditions matched with accurate RT and m/z values of a pure standard included in our chemical reference database. Additional MS/MS (MS 2 ) analyses were performed to confirm these putative annotations, and we finally identified 197 and 89 metabolites for the HILIC-ESI -and C18-ESI + conditions, respectively. Only 57 metabolites were identified by both methods, further demonstrating the complementarity of the 2 LC-HRMS platforms. When excluding the overlapping metabolites, 229 unique metabolites were finally identified in meconium with at least 2 orthogonal parameters (retention time, m/z or/and MS/MS spectra) as proposed by the Metabolomics Standards Initiative (MSI)  [26].
A Map of the Human Meconium Metabolome The chemical families of the 229 identified metabolites were assigned using the human metabolome database (HMDB)  [27]. The meconium showed a rich metabolic composition covering several molecular families, as summarized in Figure  2a. We observed a major representation of amino acids, peptides, and analogues (32%); carbohydrates and carbohydrate conjugates (15%); and nucleosides, nucleotides, and analogues (13%), with these 3 families covering 60% of the whole meconium metabolome. Within the remaining 40%, we detected mainly organic acids and derivatives (11%) and fatty acids conjugates (7%). The high representation of amino acid, peptide, and analogue families is consistent with previous studies  [15, 16] , and this may also be linked to the high representation of amino acids and derivatives in our chemical library.
metabolite is commonly detected under ESI conditions as a multiplicity of molecular species (i. e. , monoisotopic peak, adducts, dimers, and/or fragments), resulting in an overestimation of the real number of unique metabolites in the meconium samples  [25]. Overall 224 metabolic features from the C18-ESI + conditions and 149 from the HILIC-ESI -conditions matched with accurate RT and m/z values of a pure standard included in our chemical reference database. Additional MS/MS (MS²) analyses were performed to confirm these putative annotations, and we finally identified 197 and 89 metabolites for the HILIC-ESI -and C18-ESI + conditions, respectively. Only 57 metabolites were identified by both methods, further demonstrating the complementarity of the 2 LC-HRMS platforms. When excluding the overlapping metabolites, 229 unique metabolites were finally identified in meconium with at least 2 orthogonal parameters (retention time, m/z or/and MS/MS spectra) as proposed by the Metabolomics Standards Initiative (MSI)  [26].
A Map of the Human Meconium Metabolome The chemical families of the 229 identified metabolites were assigned using the human metabolome database (HMDB)  [27]. The meconium showed a rich metabolic composition covering several molecular families, as summarized in Figure  2A. We observed a major representation of amino acids, peptides, and analogues (32%); carbohydrates and carbohydrate conjugates (15%); and nucleosides, nucleotides, and analogues (13%), with these 3 families covering 60% of the whole meconium metabolome. Within the remaining 40%, we detected mainly organic acids and derivatives (11%) and fatty acids conjugates (7%). The high representation of amino acid, peptide, and analogue families is consistent with previous studies  [15, 16] , and this may also be linked to the high representation of amino acids and derivatives in our chemical library.
Unsupervised PCA highlighted similar structuration of the whole dataset in both analytical conditions (Figure  3 ). A time-dependent distribution of samples was evidenced, with a non-linear and progressive change in the overall metabolome composition of the meconium (Figure  3 ). The proximity of samples with close sampling times, and, to a lesser extent, excreted by the same newborn were highlighted using non-supervised hierarchical clustering (not shown). We observed a group of features with a low intensity in the early collection times that increased over time while another group of features showed an opposite trend.
Canonical and Regression Analyses Confirmed a Major Impact of Time on the Meconium Metabolome To more deeply characterize the inter and intra metabolic variability within the newborns, canonical analyses were performed between the C18-ESI + and HILIC-ESI -whole datasets, on the one hand, and the dummy matrix built from repeatedly collected newborns combined with the true time scale at which feces were sampled, on the other hand. Both canonical analyses showed that the first principal components (PC1) represented 38. 4% and 38. 8% of the total variance and revealed nearly continuous variation in the metabolome for both datasets with rather limited intra-newborn variance (Figure  4 ). Interestingly, PC2s, which represent 17. 2% and 24. 0% of the total variance, respectively, dis- Unsupervised PCA highlighted similar structuration of the whole dataset in both analytical conditions (Figure  3 ).
Canonical and Regression Analyses Confirmed a Major Impact of Time on the Meconium Metabolome To more deeply characterize the inter and intra metabolic variability within the newborns, canonical analyses were performed between the C18-ESI + and HILIC-ESI -whole datasets, on the one hand, and the dummy matrix built from repeatedly collected newborns combined with the true time scale at which feces were sampled, on the other hand. Both canonical analyses showed that the first principal components (PC1) represented 38. 4% and 38. 8% of the total variance and revealed nearly continuous variation in the metabolome for both datasets with rather limited intra-newborn variance (Figure  4 ). Interestingly, PC2s, which represent 17. 2% and 24. 0% of the total variance, respectively, displayed a particular behavior for 4 newborns (NB10 for the C18-ESI + dataset, and NB08, NB09, and NB11 newborns for the HILIC-ESI -dataset).
The statistical models used here, which considered the meconium sampling time highlighted a significant correlation between PC1 scores and time for both analytical con ditions (regressions not shown). Accordingly, sparse PLS (sPLS) regressions were signifi cantly established between the 500 most informative features selected from either the C18 ESI + or HILIC-ESI -datasets, on the one side, and the time variable, on the other side. Among the 500 features retained for sPLS regression analysis from either the C18 ESI + or HILIC-ESI -dataset, the 20 variables that correlated most with the collection time were sorted according to the absolute value of the correlation with the Comp  [1]  variable in the sPLS regression. In parallel, thanks to the sPLS regression model used, VIP values were also calculated (Table  S3 ).
Whatever the dataset considered, the absolute values o the correlation of the first 20 features explaining the regressions were above 0. 862 when their VIP values were higher than 6. 50 for the C18-ESI + dataset and 2. 50 for the HILIC-ESI dataset. Unfortunately, among these 20 features, none was assigned to metabolites presen in our in-house database. Among the 500 features selected, only 2 annotated metabolites were found for the C18-ESI + dataset (glycyl-leucine, xylulose), whereas 9 were found for the HILIC-ESI -dataset (ribose phosphate, N-acetylglycine, etc. ) (Table  S3 ); however, al were sorted in the less correlated feature sets with a rank above 112. These global regressions analyzed by sPLS were then reinforced by a mixed mode analysis between the Comp  [1]  scores and the time variable considering the newborn fac tor as a random factor.
For both datasets, every newborn-specific linear regression dis played a positive slope (Figure  5A, C ), except for NB05 in the HILIC-ESI -dataset (Figure  5C ). Moreover, the quality of the statistical models was estimated by a quantile-quantile diagram (or QQ plot) of the estimated residues (Figure  5B, D ). All samples, except those collected for NB09, were predicted very well by such modeling. The statistical models used here, which considered the meconium sampling time, highlighted a significant correlation between PC1 scores and time for both analytical conditions (regressions not shown). Accordingly, sparse PLS (sPLS) regressions were significantly established between the 500 most informative features selected from either the C18-ESI + or HILIC-ESI -datasets, on the one side, and the time variable, on the other side.
Among the 500 features retained for sPLS regression analysis from either the C18-ESI + or HILIC-ESI -dataset, the 20 variables that correlated most with the collection time were sorted according to the absolute value of the correlation with the Comp  [1]  variable in the sPLS regression. In parallel, thanks to the sPLS regression model used, VIP values were also calculated (Table  S3 ). Whatever the dataset considered, the absolute values of the correlation of the first 20 features explaining the regressions were above 0. 862 when their VIP values were higher than 6. 50 for the C18-ESI + dataset and 2. 50 for the HILIC-ESI - dataset. Unfortunately, among these 20 features, none was assigned to metabolites present in our in-house database. Among the 500 features selected, only 2 annotated metabolites were found for the C18-ESI + dataset (glycyl-leucine, xylulose), whereas 9 were found for the HILIC-ESI -dataset (ribose phosphate, N-acetylglycine, etc.
) (Table  S3 ); however, all were sorted in the less correlated feature sets with a rank above 112. These global regressions analyzed by sPLS were then reinforced by a mixed model analysis between the Comp  [1]  scores and the time variable considering the newborn factor as a random factor. For both datasets, every newborn-specific linear regression displayed a positive slope (Figure  5A, C ), except for NB05 in the HILIC-ESI -dataset (Figure  5C ). Moreover, the quality of the statistical models was estimated by a quantile-quantile diagram (or QQ plot) of the estimated residues (Figure  5B, D ). All samples, except those collected for NB09, were predicted very well by such modeling.
Modeling of the Whole Datasets and Metabolic Changes Associated with Timedependent Variation Global PLS regression thus illustrated the association between the metabolomic da tasets and collection time. Better modeling of the link between these two variables, i. e Comp  [1]  and time, was examined considering a log-transformation of the time variabl and curvilinear modeling based on polynomials expressed in log(time) with degrees 4 and 3 for the C18-ESI + and HILIC-ESI -datasets, respectively (Figure  6  and Table  S4 ). For both datasets, the model prediction was superimposed on the loess modeling of the data wel (Figure  6 ). In addition, the statistical parameters summarizing this curvilinear modeling were conveniently optimized, as shown by the high significance of the coefficients as signed to polynomials equal to or above degree 2 (Table  S4 ).
These 2 curves were adjusted with strongly significant alpha risks of 5. 2 × 10 -15 and 2. 2 × 10 -16 , respectively. Interestingly if we analyze the parallel variations of these 2 curves by discarding the simultaneous var iation of log(time) and keeping the higher degree of polynomials, that is, 4 and 3, respec tively, we can roughly estimate the allometric variation between C18-ESI + Comp  [1]  and HILIC-ESI -Comp  [1]  with an exponentiation coefficient equal to 0. 75. When we more pre cisely considered the log-transformation of C18-ESI + and HILIC-ESI -data predicted using.
Modeling of the Whole Datasets and Metabolic Changes Associated with Time-Dependent Variation Global PLS regression thus illustrated the association between the metabolomic datasets and collection time. Better modeling of the link between these two variables, i. e. , Comp  [1]  and time, was examined considering a log-transformation of the time variable and curvilinear modeling based on polynomials expressed in log(time) with degrees 4 and 3 for the C18-ESI + and HILIC-ESI -datasets, respectively (Figure  6  and Table  S4 ). For both datasets, the model prediction was superimposed on the loess modeling of the data well (Figure  6 ). In addition, the statistical parameters summarizing this curvilinear modeling were conveniently optimized, as shown by the high significance of the coefficients assigned to polynomials equal to or above degree 2 (Table  S4 ).
These 2 curves were adjusted with strongly significant alpha risks of 5. 2 × 10 -15 and 2. 2 × 10 -16 , respectively. Interestingly, if we analyze the parallel variations of these 2 curves by discarding the simultaneous variation of log(time) and keeping the higher degree of polynomials, that is, 4 and 3, respectively, we can roughly estimate the allometric variation between C18-ESI + Comp  [1]  and HILIC-ESI -Comp  [1]  with an exponentiation coefficient equal to 0. 75. When we more precisely considered the log-transformation of C18-ESI + and HILIC-ESI -data predicted using polynomials, as indicated in Figure  6 , we obtained the following allometric equation: Predicted-C18 ESI + = (Predicted-HILIC-ESI -) 0. 7285 -0. 1034  (1)  with adjusted R 2 = 0. 9668, F 1,29 = 874, and p-value < 2 × 10 -16 (Supplementary Figure  S1 ), that is, with an exponentiation coefficient of 0.
Identification of Metabolites Showing Time-Dependent Variations: Complementarity of Nonsupervised and Supervised Analyses Based on all these results, and to interpret more deeply and directly the observed differences and/or similarities between the samples, we finally performed non-supervised and supervised analyses solely using the 229 annotated metabolites and considering the 33 samples independently of their origin (i. e. , not taking into account the fact that stool samples were repeatedly for some newborns). We first performed PCA, which globally reproduced the same structuring observed for the whole dataset (Supplementary Figure  S2A  vs. Figure  3 ). This suggests that the set of annotated metabolites is quite representative of the whole datasets. Non-supervised hierarchical clustering of the 33 samples based on the 229 annotated metabolites identified 2 clusters of samples (clusters 1 and 2) (Figure  S2B ).
The first cluster of samples is composed of 23 meconium samples, of which 20 were excreted before 24 h, while the second cluster is composed of 10 samples, 8 of which were excreted after 24 h. This spontaneous classification suggests a 24-h cutoff regarding the global metabolic composition in our cohort of meconium samples. The intensities of some metabolites increased (mainly enriched in the cluster >24 h) while others decreased (mainly enriched in the cluster <24 h) over time (see below). As shown by nonsupervised analysis of the whole dataset, evolution of the meconium composition is thus mainly driven by the time post-partum, independently of the newborn. The distinction between samples excreted in the first 24 h from others was already detectable in the PCA score plot (Figure  S2A ). According to these results, we finally performed univariate and multivariate supervised analyses on the 229 annotated features, with samples classified into 2 groups, i.
Identification of Metabolites Showing Time-Dependent Variations: Complementarity of Non-Supervised and Supervised Analyses Based on all these results, and to interpret more deeply and directly the observed differences and/or similarities between the samples, we finally performed non-supervised and supervised analyses solely using the 229 annotated metabolites and considering the 33 samples independently of their origin (i. e. , not taking into account the fact that stool samples were repeatedly collected for some newborns). We first performed PCA, which globally reproduced the same structuring observed for the whole dataset (Supplementary Figure  S2A  vs. Figure  3 ). This suggests that the set of annotated metabolites is quite representative of the whole datasets. Non-supervised hierarchical clustering of the 33 samples based on the 229 annotated metabolites identified 2 clusters of samples (clusters 1 and 2) (Figure  S2B ).
The first cluster of samples is composed of 23 meconium samples, of which 20 were excreted before 24 h, while the second cluster is composed of 10 samples, 8 of which were excreted after 24 h. This spontaneous classification suggests a 24-h cutoff regarding the global metabolic composition in our cohort of meconium samples. The intensities of some metabolites increased (mainly enriched in the cluster > 24 h) while others decreased (mainly enriched in the cluster < 24 h) over time (see below). As shown by non-supervised analysis of the whole dataset, evolution of the meconium composition is thus mainly driven by the time post-partum, independently of the newborn. The distinction between samples excreted in the first 24 h from others was already detectable in the PCA score plot (Figure  S2A ).
On the highest MSI level 1, 216 metabolites were identified thanks to our in-house database and using pure authentic standards with an accurate mass, retention time, and MS/MS matching. An additional set of 13 metabolites were annotated with the same procedure but at level 2 since some isomers could not be distinguished. Overall, 229 metabolites were annotated in meconium, which compares favorably with the 222 unique metabolites counted by Aristizabal-Henao et al. using 3 distinct MS-based platforms (2 LC-HRMS and 1 GC-MS)  [30]. Using the same analytical approach and database, we identified more than 400 unique metabolites in adult stools  [31] , highlighting the comparatively lower metabolite richness of meconium. Of note, only 74 metabolites from our dataset were also described by Petersen et al.
within their list of 714 detected metabolites and complex lipids (level of confidence not provided)  [18]. Non-supervised analysis and modeling of the whole datasets evidenced that time post-partum drastically affected the meconium composition, which evolves rapidly independently of the newborn. Within 79 h post-partum, quantitative and qualitative changes in the metabolome core found in the HILIC-ESI -dataset were more pronounced than in the C18-ESI + dataset, although these changes appeared highly dynamically coordinated. Interestingly, most of the metabolites highly impacted by time were not annotated, i. e. , not present in our in-house database. This probably reflects that they mostly correspond to metabolites derived from microbiota metabolism. Non-supervised analysis, i. e. , PCA and hierarchical clustering, of the 229 annotated metabolites further suggested a 24-h cutoff for the metabolic composition of our samples.
Metabolic Profiling Metabolic profiling experiments were performed by LC-HRMS following optimized protocols routinely used in our laboratory  [20, 21]. LC-HRMS was performed on an Ultimate 3000 chromatographic system coupled to a Q-Exactive mass spectrometer (both from Thermo Fisher Scientific, Courtaboeuf, France) fitted with an electrospray (ESI) source operating in the positive (ESI + ) and negative (ESI -) ionization modes. LC was performed using two types of columns to obtain a more comprehensive description of the metabolic landscape: C18 (Hypersil GOLD C18 column, 1. 9 µm, 2. 1 × 150 mm, Thermo Fisher Scientific; ESI + ) and ZIC-pHILIC (Hydrophilic Interaction Liquid Chromatography; Sequant ZICpHILIC column, 5 µm, 2. 1 × 150 mm, Merck, Darmstadt, Germany; ESI -). Diluted QC samples were analyzed in triplicates at the beginning of the sequence while non-diluted QC samples were introduced every 5 biological samples for data normalization/standardization purposes.
Raw data (. raw files) were manually inspected using the Qual-browser module of Xcalibur (version 4. 1, Thermo Fisher Scientific) and then converted to. mzXML format using MSconvert (ProteoWizard). Peak extraction, peak picking, alignment, and integration were performed using the Workflow4Metabolomics (W4M) platform  [32]. Data were filtered based on three criteria: (i) ratio of chromatographic peak areas obtained for+ biological to blank samples > 3, (ii) coefficient of variation (CV) of metabolites in the QC samples < 30%, and (iii) correlation between QC dilution factors and areas of chromatographic peaks > 70%. The output files were used for metabolite annotation and further statistical analyses. Metabolite annotation was performed thanks to an in-house chemical database by matching accurate measured masses and chromatographic retention times to those of more than 1200 pure authentic standards analyzed under identical conditions  [19] [20] [21].
Retention time (RT) tolerances accepted were ±15 and ±90 s for the C18 and ZIC-pHILIC columns, respectively. The mass to charge (m/z) tolerance was 10 ppm for both the positive and negative ionization modes. Each annotated peak was manually checked on the Qual-browser module of Xcalibur by considering the peak shape, isotope pattern, and presence of the considered peak in at least 6 successive MS scans. To limit the presence of irrelevant peaks, an intensity cut-off of 10,000 and 30,000 was applied for the C18-ESI + and HILIC-ESI -conditions, respectively. MS/MS analysis was then conducted on the relevant signals to confirm their annotation. In total, 4 normalized collision energies (NCEs) were used to obtain the optimal MS/MS spectra  (10, 20, 40, and 80%). Of note, some isomeric metabolites could not be resolved using the LC-HRMS approach.