dataesr/scientific-paragraphs-categorization huggingface dataset:
Detection in Fungi No characterized or crystal structure of HPOs has been described in fungi so far. However, this class of haloperoxidases seems to be present in fungal genomes as, in UniProt database, 57 sequences related to non-heme chloroperoxidases which share features with α/β hydrolases like HPOs were computationally detected. Two sequences were found in a Mucoromycete, Rhizophagus irregularis and the 55 others from various Ascomycota. A Blastp conducted on MycoCosm with the HPO sequence of Pseudomonas fluorescens (UniProt KB-O31158 (PRXC_PSEFL)) also revealed 306 hits with an acceptable E-value (<1. 0 × 10 -50 ) and a percentage of identity between 66% and 49% suggesting the probable presence of HPOs in fungal genomes. So far, even in bacteria, where they were first described, no halogenated metabolite has been related to these enzymes.
Here, a highly conserved lysine residue is found. It is not clear if the halide reacts with this lysine residue to form a haloamine residue or if it is just stabilized in a pocket next to this residue  [116]. In the fungal reign, only nine FDHs have been crystallized. All of them are the flavindependent halogenases MalA (one is the wild type, and the eight others are mutants from MalA) from Malbranchea aurantiaca  [30]. This FDH (PDB: 5WGR) (Figure  9 ) allows the dichlorination of a fungal indole alkaloid, premalbrancheamide to form malbrancheamide which is a calmodulin antagonist  [117].
Here, a highly conserved lysine residue is found. It is not clear if the halide reacts with this lysine residue to form a haloamine residue or if it is just stabilized in a pocket next to this residue  [116]. In the fungal reign, only nine FDHs have been crystallized. All of them are the flavindependent halogenases MalA (one is the wild type, and the eight others are mutants from MalA) from Malbranchea aurantiaca  [30]. This FDH (PDB: 5WGR) (Figure  9 ) allows the dichlorination of a fungal indole alkaloid, premalbrancheamide to form malbrancheamide which is a calmodulin antagonist  [117]. 5WGR-crystal structure of wild-type MalA', premalbrancheamide complex: separation between substrate binding pocket (green), FAD binding pocket (red), and lysine 108 (blue)  [30]. The analysis of the protein structure shows that the active site (substrate binding pocket) and the FAD binding pocket are clearly separated  [30].
Because we found more hits than the number of genomes available (2008), it can be supposed that some species may possess multiple copies of radH-like proteins in their genome. According to databases (UniProt or MycoCosm), the most detected FDHs in fungi are the radH-like flavin-dependent halogenases. It is present in a lot of Aspergillus species (Figure  S3 ), which can be explained by the fact that this genus has been extensively studied in relation with the production of the chlorinated mycotoxin ochratoxin A (9) (OTA), leading to its important presence in fungal sequence databases such as MycoCosm. OTA is a food mycotoxin which is highly controlled because of its important toxicity for humankind  [33]. For this reason, many studies have targeted the ochratoxin A BGC. A radH-like flavindependent halogenase  [32, 70]  named AcOTAhal has been described in 2016, which catalyses the chlorination of ochratoxin B (8) (OTB) (the non-halogenated form of OTA) to form OTA (Figure  13 )  [33].
The chlorination occurs in ortho of the hydroxyl group in the three cases ((R)-monocillin II and 4-or 6-hydroxyisoquinoline)  [71]. A homologous enzyme was found in the radicicol-producing fungus Chaetomium cheversii and named radH (Table  1 ). [32, 70]. A Blastp analysis performed on MycoCosm using radH sequence and an E-value threshold of 1. 0 × 10 -50 revealed 2300 hits with a percentage of identity from 37% to 79%, suggesting a frequent presence of radH homologues in fungal genomes. Because we found more hits than the number of genomes available (2008), it can be supposed that some species may possess multiple copies of radH-like proteins in their genome. According to databases (UniProt or MycoCosm), the most detected FDHs in fungi are the radH-like flavin-dependent halogenases. It is present in a lot of Aspergillus species (Figure  S3 ), which can be explained by the fact that this genus has been extensively studied in relation with the production of the chlorinated mycotoxin ochratoxin A (9) (OTA), leading to its important presence in fungal sequence databases such as MycoCosm.
The modified enzymes were then successfully used in chemical synthesis to generate small halogenated  Another specific class of halogenases exists in nature, namely the non-heme irondependent halogenases (NHFe halogenases) such as WelO5 discovered in 2014 in the cyanobacteria Hapalosiphon welwitschii  [136]. Up to now, no three-dimensional nor characterized structure of a NHFe fungal halogenase has been published in protein databanks. However, some putative sequences in fungal genomes could correspond to this class of halogenating enzymes, which mechanism of action and reactivity could be of interest to chemists. Indeed, NHFe halogenases allow halogen (mainly chlorine) incorporation by using radical chemistry on unactivated aliphatic chains  [88]  and closely mirrors that of NHFe hydroxylases.
Detection in Fungi In UniProt database, 24 putative sequences corresponding to NHFe halogenases were found in fungi, but none has been analysed and characterized in the literature. All these sequences have been computationally detected. A Blastp analysis on MycoCosm using the WelO5 protein from Hapalosiphon welwitschii  [138] , with an E-value threshold of 1. 0 × 10 -50 revealed no hit in fungal genomes. In the same way, no more result was obtained when using the syrB2 NHFe halogenase from Pseudomonas syringae  [139].
Prospect This review of the literature and the databases shows that this enzyme class needs to be more studied in fungi to better assess their occurrence and functions. In other kingdoms like bacteria or algae, NHFe halogenases are able to halogenate substrates bound to a carrier protein. They can also halogenate free small molecules  [136]. The presence of this enzyme class in fungal genomes is questionable because no hit was obtained using bacterial NHFe halogenases as query sequences in Blastp analysis. Of course, not all fungal genomes were investigated and there can still exist such enzymes in fungi, especially regarding the 24 putative sequences found on UniProt database, which is more complete compared to the recent database MycoCosm. However, compared to other classes of halogenating enzymes reviewed so far, NHFe halogenases seem to be-if really present in fungi-very rare or very different from bacterial NHFe halogenases.
Methodology Further investigation of these 865 halogenated fungal metabolites was performed using the open source DataWarrior software to investigate their chemical similarities and differences  [146]. A molecular network was constructed using the default "FragFp" descriptor, allowing to filter the molecular structures and link them together according to their similarities (Figure  24 ). The DataWarrior's default descriptor FragFp is a substructure fragment-based binary fingerprint. It relies on a dictionary of 512 predefined structure fragments. This descriptor can be used to calculate similarities between molecules. The FragFp similarity between two molecules is the number of fragments that both molecules have in common divided by the number of fragments being found in any of the two molecules.
Typically, chemists will consider molecules to be structurally close if their similarity value is about 0. 90 or above. The constructed network which included all fungal halogenated metabolites available in the NP Atlas database allowed us to explore the chemical diversity found in fungal halogenated metabolites. Moreover, for each compound, information about 1) the type of halogens present (Cl, Br, I, F), 2) the halogenating enzyme described and 3) the type of halogen bond was added to the list (using the automated chemical classification package "ClassyFire" on R Cran  [147] ). These three filters allowed to highlight meaningful information on the network and different hypotheses could be raised about halogenation enzyme classes being involved for the biosynthesis of many compounds. Some clusters group together structures, including metabolites for which the halogenation enzyme is described (Figure  24 ).
Further studies on fungal HPOs are then needed to answer these questions. molecules have in common divided by the number of fragments being found in any of the two molecules. Typically, chemists will consider molecules to be structurally close if their similarity value is about 0. 90 or above. The constructed network which included all fungal halogenated metabolites available in the NP Atlas database allowed us to explore the chemical diversity found in fungal halogenated metabolites. Moreover, for each compound, information about 1) the type of halogens present (Cl, Br, I, F), 2) the halogenating enzyme described and 3) the type of halogen bond was added to the list (using the automated chemical classification package "ClassyFire" on R Cran  [147] ). These three filters allowed to highlight meaningful information on the network and different hypotheses could be raised about halogenation enzyme classes being involved for the biosynthesis of many compounds.
Classification of the structures according to their halogen bonds was performed using the classyfireR package on R Cran  [147]  and the Chebi descriptors. Black and numerated circles (1 to 10) correspond to studied and detailed clusters in the present review. Figure  24. Similarity molecular network of the 865 halogenated fungal metabolites described in the Natural Product Atlas in 2021. This plot was created with DataWarrior software, where structures were clustered based on their similarity using the "FragFp" descriptor (set to 80% for similarity analysis). Each structure is represented by a marker whose shape is defined by the halogens present: chlorine ( ), bromine ( ), iodine ( cules. The FragFp similarity between two molecules is the number of fragments that both molecules have in common divided by the number of fragments being found in any of the two molecules.
Typically, chemists will consider molecules to be structurally close if their similarity value is about 0. 90 or above. The constructed network which included all fungal halogenated metabolites available in the NP Atlas database allowed us to explore the chemical diversity found in fungal halogenated metabolites. Moreover, for each compound, information about 1) the type of halogens present (Cl, Br, I, F), 2) the halogenating enzyme described and 3) the type of halogen bond was added to the list (using the automated chemical classification package "ClassyFire" on R Cran  [147] ). These three filters allowed to highlight meaningful information on the network and different hypotheses could be raised about halogenation enzyme classes being involved for the biosynthesis of many compounds.
Classification of the structures according to their halogen bonds was performed using the classyfireR package on R Cran  [147]  and the Chebi descriptors. Black and numerated circles (1 to 10) correspond to studied and detailed clusters in the present review. Some clusters group together structures, including metabolites for which the halogenation enzyme is described (Figure  24 ). In many cases, they correspond to biosynthetic ), chlorine and bromine ( ), chlorine and iodine ( ), and bromine and iodine ( ). Structures for which a halogenating enzyme was described as being involved in their biosynthesis appeared as colored: in green for hybrid enzyme, in blue for flavin-dependent halogenases (FDH), and in red for haloperoxidases (HPO). Finally, the type of halogen bond for each molecule is represented by a halo surrounding the marker, which is blue for haloarenes, turquoise for haloalkenes, red for haloalkanes, and green for haloalkynes.
This hypothesis is reinforced by the genomic analysis previously carried out on My-coCosm  [23]  in the search for fungal strains that possess genes in their genome that are homologous to the RadH protein (like Rdc2). It has been shown that there is a strong presence of "RadH like proteins" in the fungal genomes available in the database. More specifically, if we look at the four molecules mentioned above for which the halogenation enzyme is not known, for one of their producers, Humicola sp. , one related genome could be found in MycoCosm (Thermomyces lanuginosus SSBP, previously Humicola lanuginosa). In this available genome, we could find a putative protein that matched with a flavindependent halogenase of the "radH like protein" type with a homology percentage of 60%. This protein could hypothetically be involved in the biosynthetic pathway of monorden E  (46).
Funding: This research was funded by grants from the  Ministère de l'Enseignement Supérieur et de la Recherche (Paris, France)  and  IBSAM (Institut Brestois Santé Agro Matière). has been supported by the  French National Research Agency (ANR)  through the  JCJC funding instrument  for the " HALO-CAT " project with the reference number  ANR-21-CE44-0003-01.
| Strain data generation This analytical expression allows one to simulate the slitting method using the equations developed by Cheng et al  [2]  and thus obtain synthetic strains. The strains are obtained at the back edge of the plate, during an incremental slit cutting of 35 equal spaced cuts of 0. 44 mm depth. Experimental procedures successfully carried out show that it is possible to obtain incremental slits depth as small as 0. 05 mm. [16] n order to simulate real measuring conditions, 11 sets of random noise are added to the synthetic strains. A comparison of the noiseless and noise random added strain data is presented in Figure  3  for one example. The description of the mean standard deviation of each set is presented in Table  2. These levels of noise are considerably high compared with those obtained experimentally with good practice.
Data were obtained and analyzed by the EZChrom Elite software at 235 and 275 nm for caffeine and atenolol, respectively. The caffeine and atenolol concentrations were calculated from standard curves established from known serial dilutions of each compound. The molecular absorption was defined as the percentage of basal molecules/total molecules introduced. The transepithelial resistance (TEER) was measured regularly during the time course of the experiment (total duration = 3 h) with a volt/ohmmeter (World Precision Instruments, Hessen, Germany). Three independent biological replicates were performed.
RNA Extractions Eukaryotic RNAs were extracted with the RNeasy Plus Mini Kit (Qiagen, Germany). Total bacterial RNAs were extracted using the TRIzol ® method (Invitrogen, Waltham, MA, USA), as already described  [43] , with an additional purification step with a MinElute Cleanup Kit (Qiagen, Hilden, Deutschland). The nucleic acid purity was checked and RNA was quantified using the NanoDrop ND-1000 (Thermo Fisher Scientific, Waltham, MA, USA). To remove any contamination by genomic DNA, a DNAse treatment was performed  [43]. 2. 9. Quantitative Reverse Transcription (RT-qPCR) Analysis of ETEC Virulence Genes cDNA amplification was achieved using a CFX96 apparatus (Bio-Rad, Hercules, CA, USA), and q-PCR was performed using the primers listed in Table  2. qPCR data were analyzed using the comparative E -∆∆Ct method and were normalized with the reference genes tufA and ihfB.
The amplification efficiency of each primer pair was controlled from the slope of the standard curves (E = 10 (-1/slope) -1), based on a serial dilution of a pool of three ETEC cDNA samples. Differences in the relative expression levels of each virulence gene were calculated as follows: ∆∆Ct = (Ct target gene -Ct reference gene ) in the tested condition - (Ct target gene -Ct reference gene ) in the reference condition , and data were derived from E -∆∆Ct. Reverse transcriptions were first performed with the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, Waltham, MA, USA). Then, quantitative PCR was carried out using PowerUp™ SYBR™ Green Master Mix (Thermo Fisher Scientific, Waltham, MA, USA) and a TaqMan 7900 Fast instrument (Thermo Fisher Scientific, Waltham, MA, USA) with the primers listed in Table  2.
The expression of host genes related to mucin synthesis, tight junction proteins, and inflammation were investigated. The data were analyzed with SDS 2. 3 software (Thermo Fisher Scientific, Waltham, MA, USA) using the comparative 2 -∆∆Ct method and were normalized with the reference genes GAPDH, HPRT, and PPIA. The amplification efficiency of each primer pair was controlled from the slope of the standard curves (E = 10 (-1/slope) -1) based on a serial dilution of a pool of six RNA samples from the experiments.
ETEC Quantification by qPCR qPCR was performed using a StepOnePlus real-time PCR system (Applied Biosystems, Waltham, MA, USA). The reactions were conducted in a total volume of 20 µL, consisting of 10 µL of 2× iTaq universal SYBR Green supermix (Bio-Rad Laboratories, Hercules, CA, USA), 2 µL of DNA template, 0. 8 µL (10 µM stock) of each primer, and 6. 4 µL of nuclease-free water. The primers used for ETEC quantification are listed in Table  2. The data were analyzed using the comparative E -∆∆Ct method. The amplification efficiency of the primer pairs was determined by the generation of a standard curve based on the serial dilution of five ETEC-infected samples. Differences in the number of copies of the eltB gene were calculated as follows: ∆∆Ct = (Ct target gene -Ct reference gene ) sample of interest -(Ct target gene -Ct reference gene ) reference sample , and data were derived from E -∆∆Ct.
The samples were diluted and stained with SYBR ® Green I (100× concentrate in 0. 22 µm filtered dimethyl sulfoxide, Invitrogen) and incubated for 20 min at 37 • C. The samples were analyzed immediately after incubation with an Attune NxT BRXX flow cytometer (Thermo Fisher Scientific, Waltham, MA, USA). The flow cytometer was operated with Attune™ Focusing Fluid as the sheath fluid. The threshold was set on the primary emission channel of blue lasers (488 nm). The Attune Cytometric Software was used to draw the gates, and the percentage of active E. coli in the total bacteria population was expressed as the number of cells showing the E. coli probe fluorescence out of the number of cells fluorescently labeled with the Eubacteria probes and SYBR green fluorescence.
16S Metabarcoding Analysis of Gut Microbial Communities Next-generation 16S rRNA gene amplicon sequencing of the V3-V4 region was performed by LGC Genomics (Berlin, Germany) on an Illumina MiSeq platform (Illumina, San Diego, CA, USA), as previously described  [61] , except that the luminal and mucosal samples had undergone 30 and 33 amplification cycles, respectively. All data analysis was performed in R (4. 1. 2). The DADA2 R package was used to process the amplicon sequence data according to the pipeline tutorial  [65]. In a first quality control step, the primer sequences were removed, and reads were truncated at a quality score cut-off (truncQ = 2). Besides trimming, additional filtering was performed to eliminate reads containing any ambiguous base calls or reads with a high number of expected errors (maxEE = 2.
4-fold induction, p < 0. 05), and this effect was conserved after ETEC inoculation (1. 3-fold compared to the non-treated control, p < 0. 05, Figure  6F ). MTX cells were infected with the ETEC strain H10407 (10 7 CFU•mL -1 , MOI 100) after a 3 h pretreatment with the lentil extract (brown dots, 'lentils') or specific yeast cell walls (yellow dots, 'yeast'). Non-infected and non-treated conditions (purple dots, 'non-treated') were used as control experiments. The expression of mucus-related genes (MUC1, MUC2, MUC5AC, MUC5B, MUC12, MUC17, and TFF3) (A-G) and KLF4 (Kruppel-like factor 4), which is involved in goblet cell differentiation (H), were analyzed by RT-qPCR. The results are expressed as fold changes compared to the non-infected and non-treated control condition. The data represent the replicates of three independent experiments with their means.
(C,D) Percentages of ASV1 reads detected by 16S RNA gene amplicon sequencing in luminal and mucosal bacteria. ASV1 is the ASV with the highest read abundance in all samples and its reads were assigned to the Escherichia/Shigella genus and to Escherichia albertii/boydii/coli/dysenteriae/fergusonii/flexneri/marmotae/sonnei species. (E) Proportion of active E. coli in the total bacterial populations as detected by RNA fluorescent in situ hybridization. Bars represent the means of data (n = 6). Results that are not significantly different from each Figure  8. Impact of dietary-fiber-containing products on ETEC survival in in vitro batch colonic conditions. Penicillin bottles containing nutritive medium enriched in dietary-fiber-containing products were inoculated with feces from six healthy donors and then challenged with pre-digested ETEC strain H10407 at 10 8 CFU•mL -1.
(C,D) Percentages of ASV1 reads detected by 16S RNA gene amplicon sequencing in luminal and mucosal bacteria. ASV1 is the ASV with the highest read abundance in all samples and its reads were assigned to the Escherichia/Shigella genus and to Escherichia albertii/boydii/coli/dysenteriae/fergusonii/flexneri/marmotae/sonnei species. (E) Proportion of active E. coli in the total bacterial populations as detected by RNA fluorescent in situ hybridization. Bars represent the means of data (n = 6). Results that are not significantly different from each.
Nutrients 2022 , 40 Figure 6. 2022406 Figure 6. Modulation of mucus-related gene expression by fiber-containing products. Caco-2/HT29-MTX cells were infected with the ETEC strain H10407 (10 7 CFU•mL -1 , MOI 100) after a 3 h pre-treatment with the lentil extract (brown dots, 'lentils') or specific yeast cell walls (yellow dots, 'yeast'). Non-infected and non-treated conditions (purple dots, 'non-treated') were used as control experiments. The expression of mucus-related genes (MUC1, MUC2, MUC5AC, MUC5B, MUC12, MUC17, and TFF3) (A-G) and KLF4 (Kruppel-like factor 4), which is involved in goblet cell differentiation (H), were analyzed by RT-qPCR. The results are expressed as fold changes compared to the noninfected and non-treated control condition. The data represent the replicates of three independent.
tinctorius could be due to real sex differences only emerging in longer-term space use patterns, or the limited statistical power to detect a slight sex difference with a much smaller sample size in the tracking dataset. D. tinctorius males also moved the longest distances on tadpole transport days. During tadpole transport, males often climbed vertically to reach water-filled tree holes high above the ground, which they are known to use for tadpole deposition  (Fouilloux et al. , 2021). Our space-use data do not capture these vertical movements and thus might considerably underestimate D. tinctorius male mobility and associated sex differences in vertical space use qualitatively and quantitatively. We could not identify what drove wide-ranging horizontal movements in D. tinctorius females, but seeking better foraging sites has been suggested among the movement drivers in D.
The La Florida study area (enclosure site hereafter) consisted of a free-ranging population of O. sylvatica introduced and kept in two forest enclosures of ~0. 25 ha each inside their natural habitat. The natural breeding pools in the enclosures were supplemented by a high density of small artificial plastic pools and suitable plants. The Reserva Canandé study site (natural site hereafter) consisted of a natural O. sylvatica population, relying only on natural pools (water-filled plants). For summarized study site and different dataset information see Supplementary file 1 (Table  1i  and j ).