dataesr/scientific-paragraphs-categorization huggingface dataset:
New trends in drug discovery Digitalization rapidly increases the capacity of medicinal chemists to tackle complex optimisation projects, supporting all aspects of design, synthesis, testing, and evaluation. Digital tools provide searchable aggregated data, can predict some compound properties, and make recommendations to medicinal chemists. Augmented drug design enabled by the combination of human and machine intelligence generate insights that can accelerate drug discovery. The building of integrated platforms driven by artificial intelligence and combining structural, biological, synthetic, and analytical data is progressing, providing a first taste of the tools that might become available to future drug discovery chemists. For instance, synthesis-prediction tools trained on reaction databases are now developing rapidly.
Human brains can ponder the contribution of specific data points dynamically, depending on their relevance and experimental precision. Studying the intuition guiding medicinal chemists might help AI make better choices, but it will not be easy to model the process by which scientists deal with hypothesisbuilding, thought association, and the flexible inclusion of data based on personal experience. Accordingly, one of the most spectacular successes of deep learning is the sequence-based prediction of protein structures. Using 50 years of high-quality protein structure data accumulated in the Protein Data Bank (PDB), the AlphaFold2  [8]  algorithms can predict two third of protein structures, with an accuracy equivalent to experimental data. Even though the method comes with limitations, such as the inability to provide information about disordered domains, a consequence of its exclusive training on folded proteins, it clearly demonstrates the potential of machine learning to address complex issues relevant to drug discovery.
Importance Korarchaeota are an ancient group of archaea, but their biology, physiology and evolution have remained obscure. Analysis of five novel Korarchaeota MAGs, and publicly available reference data provides robust phylogenomic evidence that Korarchaeota are placed at the base of Asgard archaea and TACK, revisiting the phylum's long-assumed position. Gene content reconstruction suggests a versatile thermophilic and autotrophic last Korarchaeota common ancestor. Environmental distribution surveying of public databases places all Korarchaeota in thermophilic environments and indicates that their habitat is limited to hydrothermal vents and hot springs. Our modeling indicates at least two transitions linked to habitat switching between these environments in the evolutionary history of Korarchaeota.
Subsequent phylogenomic analyses based on concatenated sets of marker genes have not managed to unequivocally resolve the position of Korarchaeota. Depending on the phylogenetic method and dataset used, the phylum was placed either at the base of the TACK superphylum, as a sister group of Crenarchaeota inside TACK or as a sister lineage to Asgardarchaeota  (9) (10) (11) (12) (13). However, the varying and frequently low-supported position of Korarchaeota within Archaea may be explained in part because of the use of less sophisticated models of evolution at the time, as well as by the lack of sequence representative of the korarchaeotal diversity. The latter led to the existence of a long branch at the base of Korarchaeota, which is well-known to yield phylogenetic reconstruction artefacts  (14).
Assembly and binning Illumina adapters in the raw reads of each metagenome dataset were removed using Scythe (https://github. com/vsbuffalo/scythe) with default parameters. Low quality regions in the sequences were trimmed with Sickle (https://github. com/najoshi/sickle) with the -q 30 parameter to only keep reads with a PHRED score above 30. (22)  with the following parameters: -min 5000 -max 10000 -t robust. korarchaeotal bins were visually inspected and manually selected using Databionic ESOM Tools  (23). Bins containing SSU genes displaying high similarity to the Ca. K. cryptofilum SSU gene sequence were selected for further processing. A description of the Ca. Korarchaeota archaeon LHC assembly can be found in J. H.
Cleaning of new and reference Korarchaeota bins The five korarchaeotal bins (one from LHC and four from TIV) were screened for non-korarchaeotal contigs using the R package multi-metagenome (https://github. com/MadsAlbertsen/multi-metagenome) (24). Contigs with deviant coverage, GC-or kmer content or with marker genes not phylogenetically clustered to other korarchaeotal genes were manually inspected and removed if deemed not likely to belong to the korarchaeotal clade. Together with the five newly assembled Korarchaeota metagenome-assembled genomes (MAGs), a total of 34 publicly available MAGs classified as Korarchaeota in the NCBI  (8) , SILVA  (25)  and GTDB (3) databases were downloaded from NCBI in October 2020. The completeness and contamination levels of all MAGs were determined using CheckM v1.
Phylogeny of 16S rRNA genes To construct a phylogenetic maximum likelihood (ML) tree of the TACK superphylum, partial 16S rRNA gene sequences were extracted from Korarchaeota MAGs using barrnap v0. 9 (https://github. com/tseemann/barrnap) with the flags --kingdom arc/bac/euk. Additionally, a total of 305 korarchaeotal sequences (Table  S3 ) were retrieved from the SILVA Ref NR database SSU r138. 1  (25) , together with 2367 near full-length reference sequences (>1300 bp) representing the other taxa encompassed in the TACK superphylum. Asgardarchaeaota 16S rRNA gene sequences (>1300 bp; 305 sequences) were retrieved to be used as an outgroup. Several Korarchaeota 16S rRNA gene sequences retrieved from the MAGs were already present in the SILVA database and therefore removed from the dataset to avoid duplicate sequence entries.
The dataset was aligned using MAFFT v7. 310 (--auto)  (31). Subsequently, the alignment was processed using trimAl v1. 4. rev22 to remove all columns with gaps in more than 80% of the sequences (-gt 0. 2)  (32). The resulting alignment was then used to construct a ML tree using IQ-TREE 2. 1. 2  (30) , with settings: "-m GTR+F -bb 1000 -altr 1000 --bcor 0. 98"  (33). The initial phylogenetic tree revealed that several publicly available Korarchaeota MAGs contained contigs harboring non-korarchaeotal 16S rRNA gene copies. These contigs were removed from the MAGs before downstream analyses. Additionally, two Korarchaeota 16S rRNA gene sequences from the SILVA database were shown not to cluster with this phylum. These non-Korarchaeota 16S rRNA gene sequences were removed from the dataset after which the above steps were repeated.
Environmental distribution assessment To detect the presence of Korarchaeota in the environment, the 317 Korarchaeota 16S rRNA gene sequences were subjected to an integrated microbial NGS platform (IMNGS) search using a similarity threshold of 90% and a minimum size of 200 bp  (35). From the results, sequences from datasets with less than 10. 000 reads were deleted. The remaining Sequence Read Archive (SRA) sequence hits returned by the analysis were added to the aforementioned 16S rRNA gene alignment using the -add command in MAFFT. This alignment was then used to construct a ML tree using the GTR+F model and ultrafast bootstrap support values as described above. Only SRA sequences clustering with Korarchaeota were retained from the IMNGS results. The origin of these SRA datasets was retrieved from NCBI and used to create an overview of the relative abundance of Korarchaeota in the environment (Table  S4 ).
Selection of Korarchaeota MAGs for species tree inference To investigate the taxonomic diversity of Korarchaeota, average amino acid identity (AAI) of verified Korarchaeota MAGs was calculated using EzAAI (https://github. com/endixk/ezaai) (Table  S6 ). Additionally, the 32 Korarchaeota MAGs were used as input for a phylogenetic analysis pipeline using GToTree  (36)  with settings "-H Archaea -G 0. 25", together with a previous in-house selection of 119 archaeal proteomes spanning archaeal diversity with an emphasis on Asgard archaeal representatives (Table  S1 ). DPANN archaea were not included because they are only remotely related to TACK archaea and tend to be long-branching taxa in phylogenies, which can artefactually distort the inferred topology. The concatenated alignment obtained by GToTree was subsequently used to construct a ML phylogenetic tree with IQ-TREE v2.
Phylogenetic analyses of concatenated protein alignments for species tree inference Two sets of phylogenetic markers were used to infer the species tree. The first one (RP56) is based on a previously published dataset of 56 RPs  (19). The second one (NM54, for 'new markers') corresponds to 54 proteins extracted from a set of 200 markers previously identified as core archaeal proteins that can be used to robustly infer the tree of archaea  (38, 39). For both RP56 and NM54 marker sets, we gathered orthologs from all korarchaeotal predicted proteomes -predicted using prodigal (40) -as well as the other 119 archaeal proteomes. To retrieve orthologs from the set of selected taxa, we used sequences from the previously published alignments  (19, 38)  as queries for BLASTp against all proteomes. For each marker, the best BLAST hit from each proteome was added to the dataset.
For the first iteration, each dataset was aligned using MAFFTlinsi  (41)  and ambiguously aligned positions were trimmed using BMGE 1. 12 (-m BLOSUM30)  (42). ML individual protein phylogenies were inferred using IQ-TREE v1. 5. 5  (43)  under the LG+C20+F+G4 substitution model with 1000 ultrafast bootstraps and were manually inspected. Individual marker phylogenies were inspected for erroneous inclusion of paralogous or contaminated sequences and to identify putative horizontal gene transfers. After deleting dubious sequences, datasets were realigned and trimmed, and phylogenies re-estimated as described above. Trimmed alignments were then concatenated into two supermatrices (RP56 and NM54 supermatrices). For each dataset, a phylogeny was inferred using IQ-TREE under the LG+C60+F+G4 model.
A model test was performed (-m TESTNEW -mset LG -madd LG+C10,. , LG+C60) for each tree inference  (50). For clusters with only two or three members, only a single topology is possible for an unrooted tree and the unrooted gene trees were created for them. Gene tree-aware ancestral reconstruction ALE v0. 4 (37) was applied to reconcile single gene trees with the species trees based on ribosomal markers (RP56) and the new marker set (NM54). Firstly, conditional clade probabilities were computed from the bootstrap samples (ALEobserve) and 100 reconciliations with the species tree were sampled (ALEml_undated). We thereby adjusted the reconstructed genome copy number with the extinction probability per cluster within ALE (see github. com/maxemil/ALE/commit/136b78e). We applied a threshold of 0.
Annotation of carbohydrate-active enzymes and transporters Presence and distribution of glycosyl hydrolases, glycosyl transferases, polysaccharide lyases, carboxyl esterases, carbohydrate-binding modules and auxiliary activities were identified through the dbCAN2 meta server (52), using the CAZy database (July 2021). Carbohydrate-active enzymes predicted with at least two of the integrated tools were used. Transporters were identified using TransAAP (July 2021)  (53).
Relative evolutionary divergence To determine a robust phylogenomic placement of a potential novel family-level lineage placed at the base of Korarchaeota by F. Vulcano et al. (58) , seven MAGs from lineages Kg_2, Kg_3 and Kg_5, with a completeness estimation above 75% were added to our dataset containing the original 137 MAGs, and MAGs classified as Ca. Njordarchaeales. The single MAG from lineage Kg_4 could not be included due to its low completeness. The expanded dataset was subjected to the same analyses as before to create a NM54 supermatrix alignment. This alignment was χ 2 -trimmed as mentioned before, after which it was used to create a non-parametric bootstrap tree using the PMSF method under the TBE interpretation. The resulting ML tree was used as input for a phylogenomic analysis using the Castor package  (59).
A third MAG (DRAE01. 1) was found to represent a yet undescribed lineage in TACK. Although sometimes classified as a lineage at the base of Korarchaeota, this placement was not supported and resulted from inferior phylogenomic analyses  (3, 58). These findings underline that automated results generated by commonly used binning software, quality control tools and phylogenomic placement pipelines such as CheckM  (26)  and GTDB-tk (61) cannot be blindly trusted, because these tools do not perform phylogenetic validation of marker genes. For example, hybrid MAGs may have high completeness and low contamination values and even have robust phylogenetic placement. Therefore, thorough quality control and manual curation of MAGs is needed prior to their public deposit and classification. Phylogenetic analyses of the MAGs verified to be Korarchaeota, based on the 16S rRNA gene, contigs encoding at least 3 ribosomal proteins encoded on of a conserved ribosomal protein operon, and 76 single-copy archaeal marker proteins all revealed that Korarchaeota formed a monophyletic group in Archaea, however, with an unstable phylogenetic position and internal topology (Figs.
Korarchaeota are placed at the base of the 'ATACK superphylum' The availability of 32 bona fide Korarchaeota MAGs in October 2020 allowed us to robustly place the phylum in the archaeal tree of life. To this end, we subsampled the dataset to reduce genomic redundancy of highly similar taxa in order to reduce computational burden. More Korarchaeota MAGs became publicly available after our initial selection  (8, 58, 62). The vast majority of these comprised taxa already covered by our dataset. Some MAGs, however, represented a novel order at the base of Korarchaeota  (58) , as supported by estimation of relative evolutionary divergence values (Fig. S4 ). These MAGs became public when our project was in its final stage and could thus not be included in the analyses anymore. Further analysis of these MAGs indicated they did not change our findings.
69-1 posterior probability). The only exception was the Bayesian inference based on the RP56-SR4-recoded dataset, which placed Korarchaeota nested within TACK. Regarding the internal topology, we consistently recovered two distinct groups of MAGs originating from hot springs, representing the genera Ca. Korarchaeum and Ca. Methanodesulfokora, respectively. However, these were never found to be monophyletic, since the hydrothermal vent MAG representing the genus Ca. Fumariihydrokora always placed as sister-lineage to Ca. Korarchaeum. The rest of relationships between deep branches within the Korarchaeota varied depending on the analysis. In particular, we observed various placements of the hydrothermal vent genera Ca. Calidiprofundikora. and Ca. Sedimenticalidikora which are characterized by high genomic GC content (Fig.
1 ). Additionally, the PhyloBayes sampling statistics showed sufficient effective sample size, and posterior prediction tests indicated that the model adequately captured the site-specific pattern diversity present in the NM54-χ 2 -trimmed alignment (Table  1 ). Therefore, we opted to use this alignment and the resulting phylogeny as species tree for the ancestral reconstruction analyses (below). We also performed phylogenomic analyses of our dataset (of the untreated and four treated alignments) expanded with Ca. Njordarchaeales (including the so-called Ca. Panguiarchaeales) MAGs. In the NM54 phylogenomic trees, contrary to the RP56 marker set, these lineages are not misplaced outside of the Asgard archaea and next to Korarchaeota but remain in place within the Asgard archaea (Fig. S25 ), thus showing that the NM54 marker set is better suited to resolve deep evolutionary relationships compared to marker sets based on ribosomal proteins.
Korarchaeota have a limited and environmentally confined distribution Interestingly, all bona fide Korarchaeota MAGs originated from either hot springs or hydrothermal vents (Table  S2 ), as was the case for all korarchaeotal 16S rRNA gene sequences from SILVA (Table  S3 ). Because of the limited number of MAGs and 16S rRNA gene sequences, we also explored the phylogenetic diversity of the phylum using ~280000 16S rRNA gene amplicon datasets included in the IMNGS platform  (35). Only 161 datasets were found to contain Korarchaeota sequences (Table  S4 ), as verified with a phylogenetic analysis (Fig. S2 ). Of these, only 5 and 24 had a Korarchaeota relative abundance of more than 1% and 0. 1%, respectively. All but 4 datasets originated from known hydrothermal environments (Fig. S26 ). Together, these results suggest that Korarchaeota exhibit a high level of endemism and low abundance, even in their niche.
To investigate potential explanations for the restricted distribution and limited abundance of Korarchaeota, metadata was gathered for all MAGs, 16S rRNA gene sequences and IMNGS datasets. The majority of amplicon-based 16S rRNA gene sequences were obtained using Korarchaeota-specific and universal primers that were designed at least one decade ago (Table  S5 ). An in silico primer coverage analysis showed that these primers almost exclusively target Ca. Korarchaeaceae with no or relatively few mismatches (Table  S5 ). At the time of designing, only 16S rRNA gene sequences from these clades were available, which explains why these primers fail to target more distant Korarchaeota lineages. Various more recently developed universal primers, however, target the Archaea much better (Table  S5 )  (7).
Accordingly, the IMNGS datasets containing the highest Korarchaeota relative abundances were generated using these universal primers. Altogether, this suggests that the limited availability of Korarchaeota data is the result not only of their true low abundance and restricted distribution, but also of limited sequencing depth and the use of inaccurate primers. We wanted to check if the environmental and geographical split observed on Korarchaeota MAG phylogenies could be expanded to the larger amount of 16S rRNA gene sequence data available. The Korarchaeota topology of the 16S rRNA gene trees (Figs. S1-2 ) revealed that, compared to hot spring lineages, only few sequences grouped with the hydrothermal vent clades -especially the basal oneswhich could be explained by primer bias and undersampling of these latter environments (see above).
Overall, the two 16S rRNA gene trees were congruent with the phylogenomic ones. However, the relationships between hot spring and hydrothermal vent sequences were less well resolved. The hot spring lineages of Ca. Methanodesulfokora and Ca. Korarchaeum were comprised of closely related subclusters originating from Icelandic, Russian, USA and New-Zealand hot springs, and Malaysian, New Zealand, Japanese, Russian, Chinese, Bulgarian and USA locations, respectively (Table  S3 ). Although similar in temperature and pH, these locations have vastly different chemistries. Therefore, despite their lower phylogenetic diversity, hot spring Korarchaeota could still be genetically distinct. To date, however, no MAGs are available from most of these sites. Interestingly, Ca. Methanodesulfokora contained one single hydrothermal vent sequence (accession number AF411237.
1), whereas Ca. Fumariihydrokora, close relative of Ca. Korarchaeum, contained a single hot spring sequence (accession number DQ300332. 1). These sequences originated respectively from a coastal shallow water hydrothermal vent and a geothermal well less than 100m from the beach, thus possibly representing environments with mixed characteristics. Nevertheless, biogeographic clustering suggests the existence of geographic variants. All analyses support a Korarchaeota niche-invasion from marine to terrestrial geothermal habitats and a high degree of endemism. How this transition exactly took place and how genetically similar Korarchaeota from distant locations are remains unclear. Genetically similar korarchaeotal hot spring populations in the USA have been suggested to follow a spatial distribution resulting from a historical connection or an invasion-type event  (69).
LKorCA contains several genes encoding antioxidant enzymes (e. g. , superoxide reductase, rubrerythrin). Previously, J. Pan et al. (62)  suggested these genes to be absent from the Ca. Caldabyssikora (i. e. ,  Kor 7)  and Ca. Thermotainarokora (i. e. , Kor 8). However, our extended Korarchaeota dataset including five newly constructed inhouse MAGs, together with robust sophisticated phylogenomic analyses indicate that this is not the case. A first environmental transition is coupled to the switch to a likely heterotrophic lifestyle and higher virus predation The NM54-χ 2 -trimmed alignment species tree indicated two environmental transitions, both appearing within the Ca. Korarchaeaceae family (Fig. 1 ). The first transition -from hydrothermal vents to hot springs -appeared at the branch before the diversification of the hot spring genera Ca.
taxon that instead belongs to the Ca. Njordarchaeales, a hyperthermophilic Asgard archaeal order (60). A similar problem arises in the work of J. Pan et al. (62) , where several Ca. Njordarchaeales MAGs were considered as Korarchaeota (i. e. , 3300022188_28 and GCA_011042815. 1). Ca. Njordarchaeales have been shown to artefactually branch outside Asgard archaea as a sister lineage to Korarchaeota when inferring phylogenies based on RPs (39). The above examples show the importance of constructing robust phylogenomic trees including a broad diversity of reference data. Failure to do so (e. g. , the work of Y. Liu and M. Li (68)) ultimately leads to incorrect results and conclusions. To overcome limitations and inconsistencies specific to RP datasets, a second supermatrix alignment was constructed based on 54 core archaeal proteins that can be robustly used to infer the tree of Archaea (39).
). Reverse gyrase sequences from the 137 MAGs, if not yet present in UniProt, were added to this dataset. Sequences JTT+C10. C60, WAG, WAG+C10. C60, LG, LG+C10. C60, Q. pfam, were aligned with MAFFT v7. 310 (--auto) after which the alignment was trimmed with trimAl v1. 4. rev22 to remove all columns with gaps in more than 80% of the sequences (-gt 0. 2). The ML phylogenetic tree was then generated using IQ-TREE v2. 0. 3 (-bb 1000 -alrt 1000) with the model LG+C60+F+R8, as chosen by the Bayesian information criterion (BIC) using ModelFinder Plus (-m MFP -mset JTT, Q. pfam+C10…+C60 -mrate G4, G6, G8, R4, R6, R8 -mfreq F) (50).
7; element counts: C ≤ 100, H ≤ 200, O ≤ 80, N ≤ 3, S ≤ 3 and P ≤ 1) applied to exclude rare or impossible formulas. It also enabled the classification of formulas according to their atomic compositions (i. e. , CHO, CHOS, CHON, CHONS, CHOP, and CHONP CHONSP). Mass annotations were obtained by KEGG, HMDB, and LipidMaps databases queries against Vitis vinifera organism (0. 1 ppm tolerance error value) by Masstrix software facility (https://metabolomics. helmholtzmuenchen. de/masstrix3/). The raw formula of the m/z was used for functional categorization of the annotated compounds (lipids, peptides, amino sugars, carbohydrates, nucleotides, phytochemicals (i. e. , secondary metabolites), and NM when not matching) using the multidimensional stoichiometric constraint classification (MSCC) described by Rivas-Ubach et al.
(  2018 ), based on the C/H/O/N/P stoichiometric ratios. Perseus software version 1. 6. 8. 0 (https:// maxquant. net/perseus/) was used to determine significant compounds (ANOVA multivariate statistical analysis (p < 0. 05)) to perform additional t-test comparisons (false discovery rate (FDR) < 0. 05) between two sample groups and to draw heatmaps. The 20 most regulated annotated compounds between the two sample groups were sorted in the Top20 lists. Principal component analyses (PCAs) were performed, and illustrations were built with RStudio software (ade4 and vegan packages). Targeted fungal toxins and plant secondary metabolites were searched in the peak lists based on their theoretical m/z but should be considered putative (Supplementary Table  S1 ).