dataesr/scientific-paragraphs-categorization huggingface dataset:
| Simulation systems In this study, we were interested in various systems surrounding IL-3 and its receptor IL-3Rα. Therefore, we evaluated the human IL-3Rα/ IL-3 complex as well as the mutated complex human IL-3Rα/IL-3 K135W. We also considered apo state of these different complexes. For MD simulations, initial coordinates of these proteins, human IL-3 (PDB ID: 1JLI,  24  human IL-3Rα/IL-3 wild-type complex (PDB ID: 5UV8  9  ), human IL-3Rα/IL-3 K135W complex (PDB ID: 5UWC 9 ) were retrieved from the Protein Data Bank (PDB)  25  (www. rcsb. org-Table S1 ). Some refinements were realized for the study based on Uniprot reference sequences (Table  S1 ). First, protein sequences in all crystal structures were renumbered to fit the numbering of the reference sequence. It is important to notice that a different sequence enumeration may be found in literature, since the mutated position 135 might be 116.
Engineered mutations found in crystal structure (Table  S1 ) were reverted, thanks to SCWRL tool  26  in PyMOL. 27 An exception was made for the K135W mutation present in the chain I of the human IL-3 Rα/IL-3 K135W complex (PDB ID: 5UWC) as this mutation enhanced receptor binding affinity. 10, 11 In order to complete the structure of non-solved residues and fill the gaps in structures, unresolved residues were rebuilt with Modeler 9. 17. 28.
| MD simulations Classical MD simulations were carried out on the different systems using GROMACS software version 2019. 1  31  with the CHARMM36m force field. 32 Initially, the energy of solvated structures was minimized during 10 000 cycles of steepest descend algorithm that was dismissed when the overall force was lower than 1000 kJ mol À1 nm À1. The equilibration stage was performed in different steps. Firstly, the minimized system was heated to 300 K and then equilibrated with a timestep of 2 fs during 100 ps under NVT conditions (constant volume and temperature). Positional restraints of 400 kJ mol À1 nm À1 on backbone and 40 kJ mol À1 nm À1 on side chain atoms were applied. In the following step, the same equilibration step was applied but under NPT conditions (constant pressure and temperature).
| Analysis of MD trajectories For all systems, trajectories were centered in the periodic box and concatenated to reach a total of 3 μs of simulation (30 000 frames), using standard GROMACS tools. Root mean square deviation (RMSD) and root mean square fluctuation (RMSF) were computed along 30 000 frames using python scripts with MDAnalysis library version 1. 0  33  RMSDs on Cα atoms of the overall protein or specific protein domains were computed. Fluctuations of Cα atoms were also evaluated through RMSF. Protein Blocks (PBs) were also used for analysis purpose. PBs are structural elements for the description of local structures of proteins. 34, 35 There are 16 independent PBs (from a to p), each assigned to a structure type like central α-helix, terminal α-helix, or coil. The PBxplore tool  36  were used to compute a density map of PBs along protein sequences and the equivalent number of PBs (N eq ) which is a local measurement of deformability or local flexibility.
I G U R E 3 NTD and D2 contacts on IL-3Rα/IL-3. (A) IL-3Rα structure oriented towards NTD and D2 domains shows the different contact patches named i (loop A-B of NTD), ii (loop E-F of NTD), i' (from β-strand C to β-strand D of D2), ii' (β-strand F of D2), iii' (β-strand G of D2). (B) Contact maps of the total interaction existence time between NTD and D2 domains. Contact patches are also indicated. Secondary structure labels are underlined. D2 and D3, domains 2 and 3; NTD, Nterminal domain. F I G U R E 4 NTD motions along MD simulations. (A) Global NTD motions of IL-3Rα/IL-3 complex taken from PDB ID: 5UV8 and illustrated by 150 snapshots from 3 μs MD simulations corresponding to the total of the 3 independent runs. NTD allowed the receptor opening and shutting on the left side and described a perpendicular movement on the right side.
(B) Cluster analysis of trajectories based on NTD and D2 contacts over the simulation. Each cluster was related to a conformational state (open, closed, or alternative-open) and the cluster population was mentioned in term of percentage. Center or representative structures of the different clusters were also shown. Disulfide bond residues and contact patches are represented with spheres. Cα distances are mentioned in Å. (C) Distance evolution of Cα contact patches of IL-3Rα/IL-3 WT (PDB ID: 5UV8) complex over 3 μs simulation trajectory. (D) Distance evolution of Cα contact patches of IL-3Rα/IL-3 K135W (PDB ID: 5UWC) complex over 3 μs simulation trajectory. The dotted lines mark the separation for the three independent runs.
DATA AVAILABILITY STATEMENT The three starting structures from our calculations are all available on the RCSB database: www. rcsb. org, with PDB ID 1JLI, 5UV8 and 5UWC. All software used for this study are publicly available: Modeler (https://salilab. org/modeller/), PyMOL (https://pymol. org/2/), CHARMM-GUI (https://www. charmm-gui. org/), H++ (http:// biophysics. cs. vt. edu/H++), Gromacs (https://www. gromacs. org/), CONAN (https://github. com/HITS-MBM/conan), MDAnalysis (https://www. mdanalysis. org/) and gmx_MMPBSA (https://valdestresanco-ms. github. io/gmx_MMPBSA/). The following files are available free of charge below. RMSF, RMSD, PBs, PCA and clusters analyses, per-residue decomposition of energy binding, sequence, structure, and interaction information (PDF).
F I G U R E 3 NTD and D2 contacts on IL-3Rα/IL-3. (A) IL-3Rα structure oriented towards NTD and D2 domains shows the different contact patches named i (loop A-B of NTD), ii (loop E-F of NTD), i' (from β-strand C to β-strand D of D2), ii' (β-strand F of D2), iii' (β-strand G of D2). (B) Contact maps of the total interaction existence time between NTD and D2 domains. Contact patches are also indicated. Secondary structure labels are underlined. D2 and D3, domains 2 and 3; NTD, Nterminal domain. F I G U R E 4 NTD motions along MD simulations. (A) Global NTD motions of IL-3Rα/IL-3 complex taken from PDB ID: 5UV8 and illustrated by 150 snapshots from 3 μs MD simulations corresponding to the total of the 3 independent runs. NTD allowed the receptor opening and shutting on the left side and described a perpendicular movement on the right side.
Fungi have shaped the biosphere since the development of life on Earth. Despite fungi being present in all environments, most of the available fungal research has focused on soils. As a result, the role and composition of fungal communities in aquatic (marine and freshwater) environments remain largely unexplored. The use of different primers to characterise fungal communities has additionally complicated intercomparisons among studies. Consequently, we lack a basic global assessment of fungal diversity across major ecosystems. Here, we took advantage of a recently published 18S rRNA dataset comprising samples from major ecosystems (terrestrial, freshwater, and marine) to attempt a global assessment of fungal diversity and community composition. We found the highest fungal diversities for terrestrial > freshwater > marine environments, and pronounced gradients of fungal diversity along temperature, salinity, and latitude in all ecosystems.
Materials and Methods Data retrieval. Data were retrieved from the metaPR2 web browser accessed in February 2022 (https://shiny. metapr2. org/metapr2/, accessed on 4 March 2022)  [18]. Users need to 'enter' the website portal in order to access the database. The following samples were collected: V4 gene regions, DNA and RNA, all available ecosystems, all available substrates, and all available size fractions and depth levels for the Opisthokonta, including the Kingdom of Fungi. The data were downloaded as an RDS element that was directly imported into R using the command 'readRDS("metapr2_phyloseq_ALLFungi_2022-03-01. rds", refhook = NULL)'. All following analyses were performed in the R version 4. 1. 3 (2022-03-10)-"One Push-Up". Statistical Analysis. The RDS format was a phyloseq object which is an R package  [19]  to import, store, analyse, and graphically display complex phylogenetic sequencing data that has already been clustered into Amplicon sequencing variants (ASVs) or more appropriately denoised, and it is most useful when there is also associated sample data, phylogeny, and/or taxonomic assignment of each taxon.
phyloseq leverages and builds upon many of the tools available in R for ecology and phylogenetic analysis (vegan13, ade414, ape15), while also using advanced/flexible graphic systems (ggplot216)  [20]  to easily produce publication-quality graphics of complex phylogenetic data. The phyloseq package uses a specialised system of S4 data classes to store all related phylogenetic sequencing data as a single, self-consistent, self-describing experiment-level object, making it easier to share data and reproduce analyses. All multivariate statistical analyses were performed in the vegan package in R  [21]. Diveristy plots were plotted using the plot_bar() function in phyloseq. The metadata (all environmental parameters) to the dataset were included in the downloaded RDS file. DESeq2. Statistical significance of differences in ASV relative abundances between treatments was determined using DESeq2 (version 1.
10. 1) [22]. The workflow described as part of the phyloseq addition with DeSeq2 was used (https://joey711. github. io/phyloseqextensions/DESeq2. html, accessed on 4 March 2022) and modified following the methodology used by Pelikan et al. [23]. Only ASVs that had ≥5 reads and that were present in ≥5 of the samples per ecosystem were kept. The ASV table from phyloseq was used as raw count data for DESeq2; however, all 0 values had to be transformed to 1. Results were extracted with the command: results(cooksCutoff = FALSE, contrast = c("ecosystem","terrestrial","oceanic")) and were considered statistically significant if the false-discovery-rate (FDR)-adjusted p value was below 0. 05. Data visualisation and availability. All Figures were produced in R using ggplot  [20]  and exported as svg formats, and merged in the open-source program Inkscape (https: //inkscape.
Global Comparison of Fungal Diversity by Ecosystem The fungi dataset comprised 2332 samples compiled from 41 independent terrestrial, coastal, and oceanic sampling campaigns (Figure  1A ). This dataset is extensive, although there is a high difference in sampling effort that is to be expected due to the heterogeneity of the datasets. The coastal dataset was the largest, with 1130 samples, followed by the oceanic and terrestrial (n = 924 and n = 800, respectively), and the smaller freshwater datasets (freshwater rivers, n = 154; and freshwater lakes, n = 246). The number of species per observation was clearly highest for terrestrial ecosystems, with a mean value of 57 fungal taxa by sample (Figure  1B ). This was also represented by the highest Shannon diversity, which was significantly different (p < 0.
We retrieved environmental parameters from the published database and focused our analysis on those common to all ecosystems. To understand the influence of environmental factors on fungal diversity, we tested salinity, temperature, and latitude as environmental indicators for the different ecosystems (Figure  1D-F ). We focused on the specific V4 region of the 18S rRNA and calculated alpha diversity, inverse Simpson, and the Chao estimator. These further confirmed the observed diversity observations (Supplementary Figure  S1 ). Maximum alpha diversities were found in terrestrial systems, increasing from 8. 2 to 19. 7 • C and then decreasing towards 30 • C (Figure  1D , Supplementary Figure  S1A ). In contrast, the highest diversities in rivers were found between 1. 3 and 7 • C. Marine fungi communities increased in diversity with increasing salinity up to 37, while the highest diversities for rivers were found at 0 salinity (Figure  1E , Supplementary Figure  S1B ).
Fungal Communities by Ecosystem We further identified the main contributors to fungal diversity across ecosystems. In all ecosystems, the dominating fungal phyla were Ascomycota and Basidiomycota, followed by Chytridiomycota, with the highest contribution of Cryptomycota found in freshwater rivers (16. 7%) (Figure  2A , Supplementary Figures  S2  and S3 ). In terrestrial communities, Mucoromycota (16. 6%) and Glomeromycota (4. 8%) also contributed substantially. There was also a relevant contribution of fungi that are not classified (termed 'Unkn') in all ecosystems, particularly in the freshwater river (1. 4%), lake (3. 2%), coastal (0. 9%), and oceanic ecosystems (5. 8%). Although it is difficult to compare our relative abundances to previous work due to the lack of comparative analyses across multiple ecosystems, the main subphyla and class contributions obtained are in consensus to specific soil  [8] , marine  [7, 13] , or freshwater datasets  [33].
ASV-Specific Differences by Ecosystem The marine communities differentiated from the terrestrial ones by several specific ASVs, mostly Rhodotorula mucilaginosa (Figure  3A, B ) and an undefined Chytridiomycota. The highest difference between oceanic datasets to coastal datasets was observed for Emericellopsis alkalina and Rhodosporidium diobovatum (Figure  3C ). Rhodotorula species are predominant basidiomycete yeasts in the marine environment  [14]. Rhodotorula mucilaginosa has been isolated from a sediment core at 3600m depth and cultivated at atmospheric pressure. It is a fungal species of interest due to its ability to produce valuable natural products, such as lipids and carotenoids, with potential applications as surfactants, food additives, and pharmaceuticals  [41]. Chytridiomycota have been defined as one of the dominant groups of parasites in aquatic ecosystems.
2. 5 Ocean Freshwater R. The subphyla from coastal datasets with the highest differences (represented by greatest log 2 fold changes) to terrestrial communities were Pucciniomycotina, Cryptomycotina, and Agaricomycotina (Figure  2B ). For ocean datasets the subphyla Ustilaginomycotina, Pucciniomycotina, and not classified fungi had highest log2 fold changes to terrestrial communities (Figure  2C ). All of these have been previously found in coastal and oceanic environments  [35]. Members of the Ustilaginomycotina were described as marine smut fungi and have been isolated from anoxic zones of the Arabian Sea  [36, 37]. Pezizomycotina have shown to be dominant fungal symbionts of sponges together with Agaricomycotina in marine environments  [38, 39]. The coastal communities differentiated from the oceanic ones mostly by the subphyla Cryptomycotina and Agaricomycotina compared to Pucciniomycotina and Ustilaginomycotina (Figure  2D ).
ASV-Specific Differences by Ecosystem The marine communities differentiated from the terrestrial ones by several specific ASVs, mostly Rhodotorula mucilaginosa (Figure  3A, B ) and an undefined Chytridiomycota. The highest difference between oceanic datasets to coastal datasets was observed for Emericellopsis alkalina and Rhodosporidium diobovatum (Figure  3C ). Rhodotorula species are predominant basidiomycete yeasts in the marine environment  [14]. Rhodotorula mucilaginosa has been isolated from a sediment core at 3600 m depth and cultivated at atmospheric pressure. It is a fungal species of interest due to its ability to produce valuable natural products, such as lipids and carotenoids, with potential applications as surfactants, food additives, and pharmaceuticals  [41]. Chytridiomycota have been defined as one of the dominant groups of parasites in aquatic ecosystems.
The free-living zoosporic stage of Chytridiomycota actively searches for and infects host cells, extracting nutrients and developing into mature sporangia that release new zoospores  [42]. These zoospores are a good food source for zooplankton in terms of size and shape and led to the development of the "mycoloop" concept  [28]. When large inedible phytoplankton species are infected by chytridiomycota, nutrients within host cells are transferred to zooplankton via the zoospores of parasitic chytridiomycota. The "mycoloop" may play an important role in shaping aquatic ecosystems by altering sinking fluxes or determining system stability  [43]. The identification of zoospores remains challenging, and the presence of these taxa in relation to the terrestrial datasets highlights their importance in marine ecosystems.
The highest difference between oceanic datasets to coastal datasets was observed for Emericellopsis alkalina and Rhodosporidium diobovatum (Figure  3C ). Emericellopsis alkalina has foremost been described in alkali-tolerant soils with a growth optimum at pH above 9. The molecular adaptations to these alkaline conditions are of high interest for biotechnology, and the similarity of other Emericellopsis to the marine taxa has raised the hypothesis of its marine origin  [44]. Its adaptation to alkaline environments could potentially explain its presence in the oceanic dataset that experiences changes in pH with depth. Rhodosporidium diobovatum was slated from marine and estuarine waters as well as deep-sea sediments and is defined as a relationship between the yeast form genus Rhodotorula and heterobasidiomycetous fungi  [45, 46].
Additionally, the degree of submergence of the substrates of freshwater ascomycetes, for example, can vary with fluctuations in water level. Whether these taxa are truly freshwater species and not of terrestrial origin is difficult to assess  [49]. This is also reflected in the number of shared ASVs between freshwater ecosystems to terrestrial ecosystems (Figure  3F ). Taken together, our results provide the exploration of fungal diversity on the order and ASV level across major ecosystems. We found that fungal diversities decrease from terrestrial > freshwater > marine environments. We also revealed the most distinct taxa by ecosystem, and concluding that the communities are shaped by environmental parameters resulting in ASV-specific communities. We acknowledge the difficulties associated with potential database heterogeneity; nonetheless, this study provides a base for ecosystem intercomparisons of fungal diversity.
Source data are provided as a Source data file. (Supplementary Fig. 5b-g ). Non-significant changes in the same directions were also observed for mice fed diet A, indicating that the dietary lipids in diets A and C provoked similar shifts in both microbial composition and fermentation processes. To investigate how dietary fatty acids affect the association between the gut microbiota and host features, we performed multivariate DIABLO modeling. We found that samples from diets A and C separated from other samples when integrating bacterial zOTUs with either host metabolic phenotype (Fig. 4a ) or hepatic gene expression (Fig. 4b ), indicating that these diets were the most important contributors of the observed coordinated changes in host and microbiota features. Notably, we found that abundance of zOTUs belonging to the Roseburia genus was negatively correlated to expression of hepatic genes associated with cholesterol synthesis (23-fold enrichment (p = 0.
5c  and Supplementary Data 1). rCCA was performed to also study covariation between fatty acids in cecum and serum and hepatic gene expression. Here the relative number of correlations was 10-fold higher for serum fatty acids than for cecum fatty acids (Supplementary Fig. 6d ). The fatty acids covarying with hepatic genes clustered into two groups, one mainly including n-3 PUFA and the other n-6 PUFA and MUFA (Supplementary Fig. 6e ). Both groups correlated with genes enriched in processes related to lipid metabolism (Supplementary Table  4 ). Samples from mice fed diets E and G, and to a lesser extent diets A and C, were the main drivers of the concomitant variation between serum fatty acids and hepatic gene expression (Supplementary Fig. 6f ). Taken together, we find that increased cecum levels of long-chain SFA are associated with a compositional and possible functional shift in the gut microbiota, while fatty acids in serum correlate with hepatic gene expression.
The FFQ had nine possible responses, ranging from "never or <1 per month" to "6 or more per day. " Average daily nutrient intakes were obtained by multiplying the consumption frequency of each food item by the nutrient content in the specified portion/serving specified on the FFQ. The nutrient values were obtained from the food composition tables of the US Department of Agriculture (http://www. nal. usda. gov/fnic/foodcomp), which includes data on practically all food items available in Spain and on a wide variety of ethnic foods, as well as other published sources for Spanish foods and portion sizes. To investigate how consumption of dietary fats with different fatty acid compositions affects gut microbiota composition in humans, we divided subjects (n = 117, n = 52 lean, n = 65 obese) into tertiles (low, medium, and high consumers) based on their consumption of saturated fatty acids (SFA, n = 39 per tertile), monounsaturated fatty acids (MUFA, n = 39 per tertile) or polyunsaturated fatty acids (PUFA, n = 39 per tertile).
Analysis of metagenomics data from human feces samples Zero counts in the shotgun metagenomics data were imputed using a geometric Bayesian-multiplicative replacement with the zCompositions (v. 1. 4. 0-1) R package  48  and the "cmultRepl" function. To consider the compositional structure of the microbiome, counts were then transformed using a centered log-ratio (clr) transformation as implemented in the "compositions" R package  49. Bacterial species associated with the dietary fatty acids were then identified using robust linear regression models as implemented in the Limma (v. 3. 30. 13) R package  50  , adjusting for age, BMI, sex and fiber consumption. Taxa were previously filtered so that only those with more than 10 reads in at least two samples were selected. The p values were adjusted for multiple comparisons using a Sequential Goodness of Fit 51 as implemented in the SGoF (v2.
Analysis of metabolic and inflammatory parameters in mice Fat and lean mass were determined by magnetic resonance imaging. Glucose tolerance tests were performed by injecting glucose (2 g/kg body weight) intraperitoneally after a 5 h fast. Tail blood samples were collected at -30, 0, 15, 30, 60, 90, and 120 min and blood glucose levels were determined using a glucose meter (Accu Check Aviva, Roche). The glucose tolerance test for the diet experiment (data presented in Supplementary Fig. 2k, l ) was performed over 4 days (20 mice per day) with the cages from each dietary group tested on different days. In the glucose tolerance tests for the microbiota transfer experiment (data presented in Supplementary Fig. 7f, g ) measurements were made on every other cage with MF microbiota recipients and every other cage diet A recipient.
Insulin levels were measured with a Crystal Chem kit (Downers Grove, IL, USA) according to the manufacturers' protocols. Paraffin-embedded epididymal adipose tissue and liver sections (7 μm) were stained with hematoxylin-eosin and quantified by densitometric analysis using Biopix iQ software (v. 2. 1. 3; Biopix, Sweden). Hepatic steatosis and lobular inflammation score were semiquantitatively determined by blinded histological evaluation  53. Serum levels of cytokines in systemic circulation were determined using a V-PLEX Proinflammatory Panel 1 Mouse Kit (K15048D, Mesoscale) according to manufacturer's protocols.
Microarray analysis of mouse liver samples Total RNA was extracted with beta-mercaptoethanol using RNeasy kit (QIAGEN, Hilden, Germany). Gene expression profiles were obtained at the GeT-TRiX facility (GénoToul, Génopole Toulouse Midi-Pyrénées, France) using Sureprint G3 Mouse GE v2 microarrays (8 × 60 K; design; 074,809; Agilent Technologies, Santa Clara, CA, USA) following the manufacturer instructions. Microarray data were processed using Bioconductor (v. 3. 0)  54. Raw data (median signal intensity) were filtered, log2 transformed and normalized using quantile method  55. Microarray data and experimental details are available in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE222060.