dataesr/scientific-paragraphs-categorization huggingface dataset:
Pairwise comparisons (panel E) were performed on the average relative abundances at the phylum, family (only ≥ 0. 5%) and species (only ≥ 0. 5%) levels for all six datasets (biop_cirr, biop_ctrl, feces_cirr, feces_ctrl, periph_cirr, portal_cirr) and reported as pie charts. All families or species whose mean relative abundance is < 0. 5% collectively fall within the 'Other' group. Lefse analysis (panel F) was performed on all bacterial species, reporting the most discriminant ones (LDA score > 3. 5) in decreasing order for each dataset. P values: *≤0. 05, **≤0. 01, ***≤0. 001. here, Gemmiger formicilis is the most important keystone species  21. The cirrhotic FMN had different properties from the controls: i) a lower number of edges; ii) a lower percentage of synergistic interactions (blue edges); iii) a lower synergistic/competitive ratio; and iv) lower density and modularity (Table  1 ).
Notably, threonine had the highest importance within the cirrhotic network (was a keystone metabolite), having the highest betweenness centrality (Table  1 , Fig. 2D ). Threonine was positively linked (blue edges) with Escherichia fergusonii (Otu4) and PICRUSt analysis (panel B) was employed to predict metagenomes from the 16S data and to infer differences in mean proportions (expressed as %) among cirrhotic patients (red) and controls (green), for the first 20 Kegg Orthologues (KOrths) ordered by decreasing effect size (η 2 ). The specific mean contributions of bacterial phyla and genera to the 10 KOrths overrepresented and to the 8 KOrths underrepresented in cirrhotic patients were calculated with PICRUSt (see supplementary Fig. S4 ). Co-occurrence network analysis was performed on faecal 16S and NMR merged datasets for both controls (panel C) and cirrhotic patients (panel D).
Interestingly, most of these species are strong producers of iron chelators (siderophores)  [22] [23] [24]  , and PICRUSt analysis showed that both the peripheral blood and the portal blood of cirrhotic patients were significantly enriched in bacterial KO genes linked to active iron transport (Fig. 3D ). After normalization for gene copies and number of samples, PICRUSt showed great contributions of the Pseudomonas, Sphingomonas, Acinetobacter and Delftia genera in the peripheral and portal blood to iron-related KOrths (K02014, K07165, K03832) (Fig. 3E ), while only Pseudomonas contributed greatly to the same KOrths in cirrhotic biopsies or faeces. Hence, even if intra-and intercompetitive interactions (red edges) dominated within Proteobacteria, the higher diversity in KOrth contributions seen within the peripheral blood could be an advantage enabling iron-demanding species to thrive in this harsh environment  22, 25.
5%. Network properties as in Fig. 2. The crosscorrelation heatmap (panel C) depicts interrelationships and was built using Pearson coefficients, with a white star indicating a significant correlation (P < 0. 05 after FDR correction). PICRUSt analysis (panel D) shows differences, among the four cirrhotic patient datasets, in the sequence numbers (expressed as % of the total) relative to three KOrths involved in bacterial iron transport: the peripheral and portal blood are significantly enriched in these three genes (Kruskal-Wallis test, Benjamini-Hochberg FDR q-value). Within boxes, stars represent the mean, while horizontal bars represent the median. Species percentage contributions to the three iron-related KOrths (panel E) were computed with PICRUSt and are reported as 'normalized KO mean relative abundance (%)' on the y-axis: normalization was performed according to the total number of sequences and number of samples for each cohort (biop_cirr, feces_cirr, periph_cirr, portal_cirr).
Interestingly, n-butyrate was protective against HE; thus, providing LC subjects with a strong butyrate producer such as Faecalibacterium prausnitzii (a keystone species, Table  1 ) would probably ameliorate their conditions, although this hypothesis deserves further evidence. Despite some potential improvements in patient number and NGS approach (shotgun), this study represents one of the first attempts at integrating metagenomic and metabolomic datasets to obtain a clinical meaning. We aimed to improve our knowledge of the gut-liver axis and to advance our ability to correct or prevent liver-related pathologies. Intervention by prebiotics/probiotics/synbiotics, diet or faecal microbiota transplant (FMT)  59  , along with cautious iron supplementation, could support the development of new customized treatments for LC patients with systemic inflammation and/or HE.
One hundred microliters of supernatant was used to evaluate the cytokine levels via enzyme immunoassays carried out with commercial kits (Human ELISA Ready-SET-Go!, cat# 88-7066-22 for IL-6, cat# 88-7346-22 for TNF-α, cat# 88-7261-22 for IL1-β, eBioscience, San Diego, CA, United States), and assays were performed in triplicate following the manufacturer's instructions. Plates (96-well ELISA plate, Corning Costar 9018, included in the kit) were read at 450 nm (subtracting the 570 nm readings as a baseline), and the intensity measurements were analysed with online software (http://www. elisaanalysis. com/) to retrieve values expressed in pg/mL (a four-parameter logistics curve was used for standard curve interpolation). A matrix of ELISA data was generated for subsequent multivariate statistical analysis.
The Mann-Whitney U test was used to assess significant comparisons (P ≤ 0. 05). S3 ) were clustered into operational taxonomical units (OTUs), after the elimination of singletons and doubletons, by de novo OTU picking at 97% pairwise identity using standardized parameters and the SILVA rDNA Database v. 1. 19  61 or alignment. In all, 1990 OTUs were identified. Given the high heterogeneity of the six datasets (biop_cirr, biop_ctrl, feces_cirr, feces_ctrl, periph_blood, portal_blood) in terms of OTUs and filtered quality read numbers, all samples were normalized to the number of reads present in the least rich sample (3101 reads for a portal blood sample). Sample coverage was computed with Mothur and found to be equal to 99% on average for all samples (mean ±SDM, 99. 1% ± 0. 5%), indicating that the normalization procedure was suitable for subsequent analyses.
The analysis of molecular variance  62   (metric = Bray-Curtis, method = complete linkage) were also generated for intra-and interdataset (biop_cirr, biop_ctrl, feces_cirr, feces_ctrl, periph_blood, portal_blood, NMR metabolomics) cluster generation and the discovery of positive/negative correlation coefficients. The Mann-Whitney U and Kruskal-Wallis tests were employed to assess significance for pairwise or multiple comparisons, respectively, considering a P value less than or equal to 0. 05 to be significant. Cross-correlation Pearson matrices for network analysis (metric = Bray-Curtis, method = complete linkage) were generated with in-house scripts (Python v. 2. 7. 11) and visualized with Gephi v. 0. 9. 1  64  , considering OTUs having a mean relative abundance ≥ 0. 5% and Pearson correlation coefficients -0.
7 > r > 0. 7, as previously reported  12. A network analysis was performed on each dataset (biop_cirr, biop_ctrl, feces_cirr, feces_ctrl, periph_cirr, portal_cirr, NMR metabolomics, and merged 16S/NMR) using co-occurrences and visual representation as proposed by current guidelines  [65] [66] [67] [68] [69]. The degree value, measuring the in/out number of edges linked to a node, and the betweenness centrality, measuring how often a node appears on the shortest paths between pairs of nodes in a network, were computed with Gephi v. 0. 9. 1. Intranetwork communities (even for functional metagenomics communities, or FMCs) were retrieved using the Blondel community detection algorithm  70  by means of randomized composition and edge weights, with a resolution equal to 1  71. Clustering validation (K-means++  72  , Birch  73  , affinity propagation  74  ) and performance measures (Silhouette score  75  , Calinski-Harabasz score  76  ) were employed to confirm intranetwork clustering into communities.
To find correlations among bacterial species, genes (KOrth, effect size ≥ 0. 47), faecal metabolites and the presence/absence of hepatic encephalopathy (HE), we used logistic regression (-∞ < β < ∞), randomized lasso (0 < β < 1), elastic net (-∞ < β < ∞), and SGD classifier (-∞ < β < ∞) within the Python SciKit learn module  77, 78  on mean-centred and unit-variance dataframes. was then utilized, employing the two-sided Welch's t-test and η 2 (effect size), to detect specific KOrths with discriminant power (P ≤ 0. 05) and functional relationships to the NMR data on the cirrhotic/control faecal samples. The mean relative KOrth abundances, normalized by sample number, were computed with in-house Python scripts, and those lower than a definite threshold (2*10 -6 % for phylum, 5*10 -6 % for genus) were excluded from the KOrth contribution graphical analysis.
The experiment was carried out by using a 90° pulse of 11. 75 μs and 32 K data points. PICRUSt spectra were transformed with 0. 5 Hz line broadening and zero filling, size 65 K, manually phased, calibrated on the methyl group signals of TSP (δ = 0. 00 ppm), and baseline corrected using the TOPSPIN v1. 3 software. The spectra were prepared for statistical analysis by dividing the entire spectrum into small regions (0. 02 ppm width), called "buckets". Regions containing only background noise, water resonance, and the extreme regions of spectra were excluded from the buckets. The total integral (as the sum of all 418 buckets) for each spectrum was normalized to 1000. Moreover, 2D NMR experiments, namely, 1 H-1 H total correlation spectroscopy (TOCSY), and 1 H-  13  C heteronuclear single quantum coherence (HSQC) were performed using the same experimental conditions previously reported  82.
The mixing time for 1 H-1 H TOCSY was 80 ms. The HSQC experiments were performed using a coupling constant 1JC-H of 150 Hz. A diffusion ordered spectroscopy (DOSY) experiment was performed using a bipolar LED sequence with a sine-shaped gradient of different intensities. The gradient strength was incremented in 32 steps from 2 to 95% of the maximum gradient strength (55 G/cm). The following experimental settings were used: diffusion time (Δ), 100 ms; gradient duration (δ/2), 1. 1 ms, longitudinal eddy current delay, 25 ms, and gradient pulse recovery time, 100 µs. After Fourier transformation and baseline correction, the diffusion dimension was processed by means of the Bruker TOPSPIN software (version 1. 3). NMR spectra were bioinformatically analysed by in-house scripts written with Python v.
S4). Co-occurrence network analysis was performed on faecal 16S and NMR merged datasets for both controls (panel C) and cirrhotic patients (panel D). The Pearson coefficient (r), ranging from positive (blue) to negative (red) values, is reported (edges with -0. 7 > r > 0. 7), based on correlation heatmaps (see supplementary Fig. S5). The edge thickness is proportional to the number of co-occurrences found between two nodes (species or metabolites) linked by the edge itself. Bacterial species having a mean relative abundance ≥0. 5% were reported with their OTU number (squared brackets) and represented as circles, while metabolites were represented as squares within networks. Node size is proportional to the number of edges departing from the node, indicating its degree of interaction. Node name size is proportional to the betweenness centrality, meaning the bridging/key importance of that node within the network.
80Biom file was generated with Mothur v. 1. 38. 1 using the Greengenes database (v. 13_5_99) and used with PICRUSt 1. 0. 0 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved80with default parameters, in order to predict the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologues (KOrths) from 16S V3-V4 amplicon data. Specific KOrths related to significant NMR metabolites were bioinformatically assigned to each sample by means of the KEGG online website (http://www. genome. jp/kegg/ko. html) and Integrated Database Retrieval System (http://www. genome. jp/dbget/), taking into account the Orthology and Reactions databases for a refined search. STAMP.
Acknowledgements This study was funded by the following: (i) Under 40  Researcher Grant ,  Istituto Pasteur Fondazione Cenci Bolognetti , to  Dr. Valerio Iebba  -protocol # 15/2014-#14/2015-#60/2016 ; and (ii)  Sapienza University Annual Grant  ( 2012 ), to  Prof. Manuela Merli  -protocol # C26A12W222.
63MOVA, which represents the difference in datasets' centroids), homogeneity of molecular variance (HOMOVA, representing the difference in datasets' standard deviations), parsimony test, LEfSe63, and random forest (RF) error rate were computed with Mothur v. 1. 38. 1. OTU species assignment and multivariate statistical analyses. Bioinformatic and statistical analyses on recognized OTUs were performed with Python v. 2. 7. 11. The most representative and abundant read within each OTU (as shown in the previous step with Mothur v. 1. 38. 1) was subjected to a nucleotide Blast using the National Center for Biotechnology Information (NCBI) Blast software (ncbi-blast-2. 3. 0) and the latest NCBI 16S Microbial Database (ftp://ftp. ncbi. nlm. nih. gov/blast/db/). The retrieved species (first 500 OTUs) had the following Blast parameters values (mean ±SDM) (see supplementary file): E-value (1.
Only a small proportion of replication origins (5-30%) are conserved in position between S. cerevisiae, L. waltii, L. kluyveri or K. lactis  3, 23, 28. Such a level of reprogramming contrasts with the global conservation observed between Saccharomyces genomes and precludes any chance to identify the selective forces responsible for the conservation, gain and loss of replication origins over evolutionary time. To overcome these limitations, we focus on the continuous evolutionary range covered by the genus Lachancea, from closely related to more diverged species  31. We first characterize the genome replication dynamics and origin usage at the population level in ten Lachancea species. This unique dataset allows us to infer all events of origin gains and losses since these species diverged from their last common ancestor.
05, Supplementary Fig. 13 ). To further characterize the mechanisms by which origins are lost, we compared our set of 30 origin losses in the L. waltii branch to an independent dataset previously generated in this species  3. The authors identified 194 ARS, of which 156 were shown to function as chromosomal replication origins during Sphase  3. We found that 21 of our losses did not overlap with any ARS and 4 corresponded to an ARS but were devoid of chromosomal activity. The five remaining cases of origin losses in our study matched chromosomally active origins in the previous dataset (17%, by comparison 79% of the 156 active origins were also detected in our experiments). Therefore, a minority of our loss events would correspond to low efficiency origins that escaped detection because the proximity of a highly active origin.
The remaining 83% correspond to true origin losses that could occur through the loss of ARS activity. In order to experimentally confirm this result, we performed an ARS assay for 16 loci of the L. thermotolerans genome corresponding to Fig. 4  Physical and functional properties of lost, gained, and conserved replication origins. The original dataset comprises 1073 conserved, 207 gained, and 187 lost origins; however, after filtering for dubious cases the number of total gain and loss is both 151 events. In all panels, *** and * represent P < 10 -03 and P < 5×10 -02 , respectively, using a chi-square two-sample test. a Distribution of the distance separating Conserved (C), Gain (G), and Lost (L) origins from their nearest origins. The pie charts show the expected, i. e. the theoretical percentages if the nearest origins were randomly sampled in the population of origins and the observed proportions of C, G, and L nearest origins for the three categories.
Subsequently, ratios were re-scaled between 1 and 2 and for each window, the time where the scaled ratio equaled 1. 5 was defined as the Trep. Finally, mean replication times were obtained by smoothing the data with a loess regression (see ref. 34  for details). For the MFA experiments, reads were counted in all 500 bp non-overlapping windows, along the genome. Windows where the number of sequences was defined as an outlier (> or <1. 5 times interquartile spaces) were filtered out. The MFA ratio is calculated by dividing, for each window, the number of sequences from the Expo sample by the number of sequences from the Stat sample as described in ref. 35. The MFA profile is obtained by smoothing the data with a loess regression. All timing data are available in the Supplementary Data 1. Identification of replication origins.
For each origin, we determined in how many simulation runs it was actively or passively replicated. The fraction of simulation runs where the origin was actively replicated is our estimation of the origin efficiency. This procedure is identical to the one defined by Nieduszynski and coworkers  13. Comparison of temporal programs of genome replication. The identification of orthologous genes and the construction of synteny blocks were computed with the SynChro algorithm  51  for all pairwise combinations between the ten Lachancea species, the four Saccharomyces and the C. albicans species. Genome annotations were downloaded from GRYC (http://gryc. inra. fr), saccharomycessensustricto (http://www. saccharomycessensustricto. org), and CGD (http://www. candidagenome. org). We used our mean replication timing profiles for the Lachancea species and the previously published replication data for the Saccharomyces species and C.
In particular, the first two constraints guarantee that the average number of origins is globally conserved, even if it may slightly fluctuate between samples drawn randomly from the null model. Thereafter, for each pairwise comparison, origin positions were randomized 100 times in the first genome and projected on the second genome, and reciprocally. Randomized origin families were then constructed with the same procedure as for the real origin families. We used the randomized families to define the optimal number of syntenic genes allowed between two conserved origins. The threshold of Delta = 2 syntenic genes was determined after testing all values between 0 and 6 intervening genes and looking for the value that maximized the differences between real origin families and null model. We found that Delta = 2 was the value that jointly (i) maximized the number of conserved origins in the real versus the random dataset, (ii) maximized the number of families comprising ten origins (one per species) in real vs random dataset comparisons and (iii) minimized the number of families with more than ten members (Supplementary Fig.
7d ). Finally, we also found that Delta = 2 limited the proportion of families with more than one origin per species (1. 9% for Delta = 2 vs 8% for Delta = 3). To check that our methodology indeed captured a true evolutionary signal corresponding to the orthology relationship between replication origins, we compared the distributions of the number of origins per family between the real and the random dataset and found that they were clearly different from what is expected from a null model (Supplementary Fig. 8a ). Moreover, we constructed two phylogenetic trees based on the composition of the real origin families in the ten Lachancea species. First, we performed hierarchical clustering implemented in MEV (http://www. tm4. org. /#/welcome) based on the phylostratigraphic patterns of origin families.
Second, we built a distance matrix representing the proportion of conserved origins between any two pairs of species and generated an NJ tree, using Phylip version 3. 695  52. The two resulting tree topologies are very similar to the topology of the reference species tree based on the concatenation of 3598 orthologous protein sequences  31  , with only few bipartitions being different (Supplementary Fig. 8b ). Inference of replication origin history. Based on the composition of the origin families, the evolutionary history of origin conservation, gain and loss was inferred using the Gloome online program  53. As input, we used simplified phyletic patterns limited to the presence (1) or absence (0) of an origin family within Lachancea species. We used an evolutionary model where the probability of gain and loss is equal across all sites.
We tolerated more than one possible creation event per family. The parsimony cost of the gains was set to 2. Other parameters were set to default values. To compare the physical and functional properties between conserved, lost, and gained replication origins, we only focused on the three most closely related pairs of species (L. fantastica/L. meyersii, L. thermotolerans/L. waltii, and L. fermentati/L. cidri). This resulted in an initial dataset of 1280 cases (886 conserved origins, 207, gains and 187 losses). We then filtered from this dataset the dubious cases of losses and gains that correspond to origins that were detected by only one of the two methods (MFA or mean replication time). This resulted in a final dataset composed of 1148 cases (846 conserved origins, 151 gains, and 151 losses).
Plasmids were transformed into L. thermotolerans cells using the LiAc/PEG method  55. Cells were plated on YPD with G418 (200 µg/mL) and growth for 4 days at 30 °C and then the number of colonies and their size were measured. ACS detection. We selected 123 chromosomally active origins in L. waltii that corresponded to known ARS that were previously published  3. We then selected in the genomes of all other Lachancea species, the replication origins that were conserved in families with these 123 origins. We used the known ACS position in L. waltii to identify the corresponding gene/intergene strictly conserved in synteny in the other species. Based on these datasets, we use MEME (zoops, size 9-40, evalue threshold 10) to search for motives, both in L. waltii, as a control, and in the other Lachancea species.
Acknowledgements This work was supported by the  Agence Nationale de la Recherche  ( GB-3G ,  ANR-10-BLAN-1606  and  Phenovar ,  ANR-16-CE12-0019 ). We thank our colleagues  Gianni Liti ,  Bertrand Llorente ,  Carolin Müller ,  Conrad Nieduszynski , and  Samuel O'Donnell  for fruitful discussions and constructive suggestions.
Changes in the microbiome reflect tumor stage We performed whole-exome sequencing on a set of 88 samples, comprised of 44 pairs of tumor (adenocarcinomas) and normal colon tissue sample from the same patient, with previously characterized tissue-associated microbiomes  [2]. The mutations in each of the tumors' protein-coding regions were identified relative to the patient-matched normal sample and annotated as either synonymous, non-synonymous, or loss-of-function (LoF) mutations (S1 and S2 Tables, S1 and S2 Figs). The ranges of mutations per tumor in our data set were between 19-6678 (total), 10-3646 (missense) and 0-208 (LoF). The mutations were collapsed by gene as well as by pathways using both Kyoto Encyclopedia of Genes and Genomes (KEGG) and pathway interaction database (PID) annotations  [31] [32] [33] [34]  (see Materials and methods).
We performed quality control of the data and stringent filtering at every step, with the goal of reducing false positives in mutation calling and statistical prediction (e. g. , requiring 30x coverage at a site in both the tumor and matched normal sample to call a mutation; see Materials and methods). While these requirements are likely to increase the frequency of false negatives (true mutations that simply do not meet our criteria), this rigorous strategy is appropriate as a means of increasing the biological relevance of our findings. Of note, when comparing the common LoF mutations found in our dataset to those found in colorectal tumors sampled as part of The Cancer Genome Atlas (TCGA) project, we find several commonalities, including a high frequency of LoF mutations in APC, as well as numerous missense mutations in KRAS, NRAS, and TP53, as expected (S1 Table)  [35].