dataesr/scientific-paragraphs-categorization huggingface dataset:
016 µg/mL) treated wells. After an additional incubation for 44 h, plates were then read on a Magelan Infinite M200 fluorescence plate reader (Tecan, Männedorf, Switzerland) at excitation and emission wavelengths of 530 and 590 nm, respectively. For each sample, growth inhibition percentages were calculated with Microsoft Excel Software; then, from the nonlinear regression fit, concentration-response curves were generated to deduce the median inhibitory concentration (IC 50 ) using the GraphPad Prism 8. 0 software.
The concentration-response curves were plotted using inhibitory percentages versus the logarithm of the drug concentration to determine the concentration of drug that reduces cell viability by 50% (CC 50 ) using GraphPad Prism 8. 0 software. Selectivity indices (SI) [SI = CC 50 /IC 50 ] were determined for each inhibitor based on their antiparasitic activity (IC 50 ) and cell cytotoxicity (CC 50 on Raw cells). Samples with SI greater than 10 were classified as poorly toxic toward Raw cells  [47].
Acknowledgments: The authors are gratefully acknowledge the  University of Maroua , the  Institute for Medical Research and Medicinal Plants Studies (IMPM) , the  Laboratory of Phytobiochemistry and Medicinal Plant Studies, Antimicrobial and Biocontrol Agents Unit  at the  Department of biochemistry of the University of Yaoundé I in Cameroon  for biological tests, the  Aix-Marseille Univ, CNRS ,  Centrale Marseille ,  FSCM, Spectropole, Campus de St Jérôme-Service 511, 13397 Marseille, France  for spectroscopic analysis, the  Yaoundé-Bielefield Graduate School through YaBiNaPA  for their facilities. FFB is gratefully thanking the  Grand Challenges Africa (GCA) program  [ GCA/DD/rnd3/006 ] for supporting the Biological activities. GCA is a program of the  African Academy of Sciences (AAS)  implemented through the  Alliance for Accelerating Excellence in Science in Africa (AESA) platform , an initiative of the AAS and the  African Union Development Agency (AUDA-NEPAD ).
The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs. Designing precision medicine approaches that incorporate microbial metabolism would require strainand molecule-resolved, s ca la ble computational modeling. Here, we extend our previous resource of genome-scale metabolic reconstructions of human gut microorganisms with a greatly expanded version. AGORA2 (assembly of gut organisms through reconstruction and analysis, version 2) accounts for 7,302 strains, includes strain-resolved drug degradation and biotransformation capabilities for 98 drugs, and was extensively curated based on comparative genomics and literature searches. The microbial reconstructions performed very well against three independently assembled experimental datasets with an accuracy of 0.
AGORA2 is predictive against three independent datasets While automated draft reconstructions can be rapidly generated, they still require subsequent curation efforts to be predictive  27. Several (semi) automated reconstruction tools bridge the gap between automated draft and fully manually curated reconstructions including CarveMe  15  , gapseq  18  and MIGRENE  17. To further access the quality of AGORA2 and the DEMETER pipeline, we compared AGORA2's predictive potential and model properties with other resources of microbial genome-scale reconstructions. For this purpose, we retrieved 8,075 reconstructions built through gapseq  18  , 1,333 reconstructions built through MIGRENE, deemed MAGMA  17  , as well as 72 manually curated genome-scale reconstructions deposited in the BiGG database  28.
Here, we present an expansion in scope and coverage of AGORA, called AGORA2, consisting of microbial reconstructions for 7,302 strains, 1,738 species and 25 phyla. AGORA2 summarizes the knowledge and experimental data obtained through manual comparative genomics analyses and literature and textbook review, and demonstrates high accuracy against three independently collected experimental datasets. AGORA2 has been expanded by manually formulated molecule-and strain-resolved drug biotransformation and degradation reactions covering over 5,000 strains, 98 drugs and 15 enzymes, some of which were validated against independent experimental data. The AGORA2 reconstructions are fully compatible with the generic  21  and the organ-resolved, sex-specific, whole-body human metabolic reconstructions  22.
The most crucial aspect of a genome-scale reconstruction is its accuracy in capturing known biochemical or physiological traits of the target organism  9  , that is, its potential to make biologically plausible predictions. Hence, we set out to determine the predictive potential of AGORA2. For an unbiased assessment, we retrieved organism-specific experimental data from three separate sources (Methods). First, we retrieved species-level positive and negative metabolite uptake and secretion data for 455 species (5,319 strains) in AGORA2 from the NJC19 resource  30. Note that a precursor of NJC19, NJS16 (ref. 31 ), containing only positive data, had been used to refine AGORA2. Next, we mapped species-level positive metabolite uptake data, retrieved from Madin et al. 32  , for 185 species (328 strains) in AGORA2 ('Madin' data).
Finally, we retrieved strain-resolved positive and negative metabolite uptake and secretion data for 676 AGORA2 strains as well as positive and negative enzyme activity data for 881 AGORA2 strains from the BacDive database  33. Neither the Madin dataset nor BacDive had been used during the refinement of AGORA2. For metabolite uptake and secretion, the AGORA2 reconstructions captured the known capabilities of the target organisms very well (overall accuracy against NJC19, BacDive and Madin of 0. 82, 0. 81 and 0. 84, respectively; Fig. 3e  and Supplementary Table  4d ). For enzyme activity, a slightly lower accuracy of 0. 72 was achieved (Fig. 3e  and Supplementary Table  4d ). AGORA2 had a lower specificity than the other resources on NJC19. However, the majority of observed false positives in AGORA2 concerned glutamate uptake in Escherichia coli (Supplementary Table  4c ), which was a negative finding in the NJC19 dataset based on a report for a single E.
coli strain. We then compared the predictive potential of AGORA2 with the other four resources where possible. Of the 7,302 reconstructed AGORA2 strains, 7,279 had been reconstructed through CarveMe, 451 overlapped with reconstructions built through gapseq and 60 overlapped with reconstructed strains available at the BiGG database (Supplementary Table  4a ). No strains overlapped with MAGMA as it consists of pan-species reconstructions built from metagenome-assembled genomes  17  , but 216 reconstructions could be mapped at the species level (Supplementary Table  4a ). For the four resources and for each dataset, we then computed the predictive potential for the organisms overlapping with AGORA2 (Fig. 3d -f and Supplementary Table  4b-d ). While MAGMA and AGORA2 achieved significant prediction accuracies for secretion and uptake on the NJC19 and the BacDive datasets, KBase failed to perform better than chance for metabolite uptake and secretion in NJC19, and CarveMe failed to predict significantly secretion in the NJC19 dataset (Fig.
3e  and Supplementary Table  4d ). The gaqseq reconstructions built in the present study for the subset of AGORA2 strains performed comparably to the set of gapseq reconstructions that had been published by the authors  18  (Supplementary Table  4b ). To compare the performance of AGORA2 with KBase, CarveMe, gapseq, BiGG and MAGMA directly, we calculated the accuracy per model separately for uptake and secretion. We then compared the accuracies on models in the overlap of AGORA2 and each resource via a nonparametric sign rank test. AGORA2 was significantly better than all other methods on all three datasets, except for BiGG on the BacDive data, where the overlap in models was too small to achieve       17. a, Fraction of reactions that are stoichiometrically and flux consistent as defined in ref.
29  for each model derived from the five compared resources. Exchange and demand reactions, which are stoichiometrically inconsistent by definition, were excluded. b, Aerobic and anaerobic ATP production on complex medium (mmol per g dry weight per h) by each model derived from the five compared resources. c, Overview of reconstruction properties for the compared resources. d, Overview of number of models and number of predictions tested in validating AGORA2, KBase, BiGG, CarveMe, gapseq and MAGMA against three independent experimental datasets  30, 32, 33. e, Bar plots with 95% confidence intervals of overall accuracies of the five resources in predicting uptake and secretion in the three experimental datasets. Significance of prediction accuracy was determined by mixed effect logistic regressions using the metabolic model as random effect variable to account for the statistical dependence of predictions stemming from the same model.
All 5,438 analyzed strains carried at least one drug-metabolizing enzyme (Supplementary Table  3c ). As these enzymes are also involved in central metabolism, for example, nucleoside metabolism, this high coverage was expected. We then carried out a thorough literature and database review of metabolite structures, formulas and charges for 98 frequently prescribed drugs belonging to ten drug groups and 32 subgroups (Supplementary Table  5b ). We formulated 1,440 drug-related reactions containing 363 metabolites (Supplementary Table  6a ,b) and added, on average, 188 drug-related reactions and 111 metabolites to the reconstructions depending on the genomic evidence. We validated, with an accuracy of 0. 81 (sensitivity: 0. 87, specificity: 0. 74, Fisher's exact test: P = 2. 01 × 10 -23 , mixed effect logistic regression accounting for stochastic dependencies from predictions stemming from the same model: P = 1.
Personalized modeling of drug-metabolizing capacities As human microorganisms do not exist in isolation, we addressed the important question of how the total drug-metabolizing capacities may differ between individual gut microbiomes. A previously developed community modeling framework 10 allows for the scalable, tractable computation of community-wide metabolic capabilities as well as organism-resolved contributions to fecal metabolite levels  37. We used a metagenomic dataset from a Japanese cohort of 365 patients with colorectal cancer (CRC) and 251 healthy controls  38  that had previously allowed us to interrogate the metabolic capabilities of each gut microbiome and validate the fluxes against metabolomic data  37. A total of 97% of the named species could be mapped onto AGORA2 (compared with 72% for AGORA).
nlm. nih. gov/bioproject/PRJNA412637. Finally, we retrieved 761 human microbial genomes from the Human Gastrointestinal Bacteria Culture Collection  55  in FASTQ format from https://www. ebi. ac. uk/ena/data/view/PRJEB23845 and https://www. ebi. ac. uk/ena/data/view/PRJEB10915. Together with AGORA1. 03, which was obtained from the VMH  23  , these combined efforts resulted in 7,302 strains and 1,738 species included in AGORA2.
2. 5 system (cello. life. nctu. edu. tw). (  9 ) For cytoplasmic enzymes, drug transporters were predicted based on genomic context (Supplementary Note 11 and Supplementary Table  3b ). Tools. The PubSEED platform  53, 56  was used to annotate the subsystems. To search for BBHs for previously known proteins, a BLAST algorithm  63  implemented in the PubSEED platform was used. Additionally, the Pub-SEED platform was used for analysis of the genomic context. To analyze the protein domain structure, we searched the Conserved Domains Database (CDD)  64  using the following parameters: an e-value ≤ 0. 01 and a maximum number of hits equal to 500. For the prediction of protein subcellular localization, the CELLO  65  web tool was used. Alignments were performed using MUSCLE v. 3. 8. 31 (ref. 66 ).
Literature and database searches Biochemical and physiological characterization papers were retrieved by entering the names of AGORA2 species into PubMed (https://www. ncbi. nlm. nih. gov/pubmed/). Information on 132 carbon sources, 30 fermentation pathways, 64 growth factors, consumption of 73 metabolites and secretion of 51 metabolites was subsequently manually extracted on the species and/or genus level from 732 peer-reviewed papers and >8,000 pages of microbial reference textbooks  71. Moreover, the traits of each reconstructed strain including taxonomy, morphology, metabolism and genome size were retrieved through database https://doi. org/10. 1038/s41587-022-01628-0 searches. The taxonomic classification of the strains was retrieved from NCBI Taxonomy (https://www. ncbi. nlm. nih. gov/taxonomy/).
Information on morphology, habitat, body site, gram status, oxygen status, metabolism, motility and genome size was manually retrieved from the Integrated Microbial Genomes and Microbiomes 72 database (https://img. jgi. doe. gov/) (Supplementary Table  1 ). All experimental data that were used to refine AGORA2 are available at https://github. com/opencobra/COBRA. papers/tree/master/2021_demeter/input.
As described previously  20  , all refinement and debugging solutions were manually determined for a subset of the reconstructions and subsequently propagated to many reconstructions, as appropriate. All newly included metabolites and reactions were formulated based on literature and/or database  23, 28, 74  searches, while ensuring mass and charge balance through the reconstruction tool rBioNet  75. Reactions identified through comparative genomics (Supplementary Table  3b ,c) were added to up to 5,438 reconstructions. Non-gene-associated reactions, for which the respective gene could not be found through comparative genomics, were removed from the draft reconstructions if doing so did not abolish biomass production. Curation efforts were verified via a test suite  19. Specifically, it systematically tested whether each reconstruction (1) grew anaerobically on complex medium; (2) had correct reconstruction structure, that is, mass and charge balance, and correct syntax for gene-protein-reaction associations; (3) was thermodynamically feasible, for example, produced realistic amounts of ATP; and (4) captured known metabolic traits of the organism according to the collected experimental and comparative genomic data.
Formulation of the drug reactions A literature search for microbial enzymes known to transform, degrade, activate, inactive or indirectly influence commonly prescribed drugs was performed, yielding 15 enzymes in total (Fig. 3a  and Supplementary Table  5 ), which are encoded by 25 genes (Supplementary Table  3b ). To enable comparative genomic analyses, only drug transformations that could be linked to specific protein-encoding genes were considered. As described above, enzyme-encoding genes were analyzed in their genomic context as outlined in ref. 76  using PubSEED subsystems  26, 53. Additional information on the presence of the analyzed genes was retrieved from refs. 39, 77, 78. Literature and database searches were performed for the metabolic fate of commonly prescribed human-targeted drugs.
The structures of 287 drug metabolites and drug degradation products were retrieved from 73 peer-reviewed papers, HMDB  79  , DrugBank  79  and the Transformer database  80. Reactions were formulated based on the collected experimentally determined drug structures, drug downstream product metabolite structures and reaction mechanisms. Both cytosolic and extracellular enzymatic reactions were formulated depending on the identified subcellular protein locations. Since at least six drugs undergoing glucuronidation in the human body have been shown to be substrates for the microbial ß-glucuronidase  81, 82  (Supplementary Table  6 ), it was assumed that all retrieved glucuronidated drug metabolites (118 in total) could serve as substrates. Additionally, ß-glucuronidase reactions were formulated for 33 glucuronidated drug metabolites from a previously reconstructed module of human drug metabolism  83  and three glucuronidated hormones from Recon3D (ref.
Atom-atom mapping The COBRA Toolbox 47 function 'generateChemicalDatabase' was used to generate atom-atom mappings. The process to obtain the atomatom mappings for the AGORA2 reconstructions can be summarized as follows: (1) 1,894 out of 3,533 metabolic structures from the AGORA2 reconstructions were collected from the SMILES and InChIs associated with their metabolites and different chemical databases, such as VMH  23  , KEGG  74  , HMDB  79  , PubChem  84  and ChEBI 85 databases; the metabolic structures were standardized based on the InChI algorithm  86  and can be found in the VMH database  23  ; (2) the standardized metabolites and the reaction stoichiometry in the AGORA2 reconstructions were used to generate 5,583 out of 7,300 MDL RXN files; (3) 5,583 out of 7,300 AGORA2 reactions were atom mapped using the Reaction Decoder Tool algorithm  87  for active transport reactions and a custom algorithm  47  for passive transport reactions and coupled transport reactions.
Simulations All simulations were performed in MATLAB (MathWorks) v. R2020b with IBM CPLEX (IBM) as the linear and quadratic programming solver. Computations were carried out on a tower with a 2. 80-GHz processor and 64-GB RAM with 12 cores dedicated to parallelization. The simulations were carried out using functions implemented in the COBRA Toolbox  47. Flux balance analysis (FBA)  34  was used to simulate metabolic fluxes. All additional scripts for data generation, data analysis and data visualization are available at https://github. com/ThieleLab/CodeBase.
Retrieval of reconstruction resources Manually and semiautomatically curated reconstructions compared with AGORA2 were retrieved as follows: 72 fully manually curated reconstructions were downloaded from the BiGG database  28  (http:// bigg. ucsd. edu/). Reconstructions generated through gapseq  18  (8,075 total) were downloaded from ftp://ftp. rz. uni-kiel. de/pub/medsystbio/ models/EnzymaticDataTestModels. zip and exported in SBML format through the sybilSBML package in R using a custom script. MAGMA  17  reconstructions (1,333 total) were downloaded from https://www. microbiomeatlas. org/data/MSP_GEM_models. zip. To enable comparability with AGORA2, exchange reactions in all retrieved reconstructions were translated to VMH  23  nomenclature through custom MATLAB scripts. Moreover, an ATP demand reaction (VMH reaction ID: DM_atp_c_) was added if not already present and otherwise translated to VMH nomenclature.
Flux and stoichiometrically consistent reactions The subset of flux and stoichiometrically consistent reactions, as defined in ref. 29  , was retrieved through the 'findFluxConsistent-Subset' and 'findStoichConsistentSubset' functions implemented in the COBRA Toolbox  47. The fraction of stoichiometrically and flux consistent reactions, excluding exchange and demand reactions, was subsequently determined for each AGORA2 reconstruction and corresponding KBase draft reconstruction as well as for 5,587 reconstructions generated through CarveMe 15 , 8,075 reconstructions generated through gapseq  18  , 1,333 MAGMA 17 reconstructions and 73 curated reconstructions from the BiGG database  28. Briefly, the subset of stoichiometrically consistent reactions in a reconstruction includes all reactions that are mass and charge conserved, excluding exchange, demand and sink reactions, which are by definition mass and charge imbalanced  29.
Validation against three independent experimental datasets For an independent assessment of predictive potential of genome-scale reconstructions, independent (that is, not used for the reconstruction process) experimental data on metabolite uptake and secretion were retrieved from three sources  30, 32, 33  and mapped onto the VMH  23  nomenclature through custom MATLAB scripts. The experimental data included species-level positive and negative metabolite uptake and secretion data for 457 species (5,341 strains) and 269 metabolites in AGORA2 from the NJC19 resource  30  , and species-level positive metabolite uptake data from ref. 32  for 184 species (328 strains) and 85 metabolites in AGORA2. Moreover, strain-resolved positive and negative metabolite uptake and secretion data for 676 AGORA2 strains and 220 metabolites, and positive and negative enzyme activity data for 881 AGORA2 strains and 31 enzymes, were retrieved from the BacDive database  33.
If the data point was negative and the corresponding model also could not take up or secrete the metabolite or did not produce flux through any reaction(s) mapped to the enzyme, this resulted in a true negative prediction, and otherwise the prediction was a false positive. Prediction accuracies were calculated for the three experimental datasets. For an assessment of the predictive potential of AGORA2 compared with other reconstruction resources, the analysis was repeated for the strains in KBase draft reconstructions; CarveMe reconstructions; and BiGG, gapseq and MAGMA reconstructions that overlapped with the AGORA2 organisms with available data. To this end, the predictive value of all resources was tested via mixed effect logistic regressions with the in silico prediction as predictor and the in vivo behavior (binary) as response variable, while introducing the model as random effect variable accounting for the stochastic dependencies of predictions for different metabolites stemming from the same model.
Moreover, the accuracy per model was calculated for all resources, and then compared with the AGORA2 accuracies via nonparametric sign rank tests. The list of all strains in the compared reconstruction resources that were tested against the three datasets is shown in Supplementary Table  4a. All scripts are available at https://github. com/ ThieleLab/CodeBase. https://doi. org/10. 1038/s41587-022-01628-0.
We corrected for multiple testing using the FDR, adjusting significance values for 13 tests per analyses stream. A test was considered nominal significant with P < 0. 05 and FDR-corrected significant if FDR < 0. 05. For sensitivity analysis, we recomputed the drug-metabolizing capabilities using an average European diet instead of a Japanese diet. Then, we calculated Pearson correlations for each drug metabolite between the secretion potentials under Japanese and an average European diet. All statistical analyses were performed with STATA 17/MP. All scripts are available at https://github. com/ThieleLab/CodeBase.
Sign prediction of fecal metabolite-species associations using AGORA2-based community models We utilized the publicly available metabolome dataset (n = 347) from ref. 38. To test whether AGORA2-based community modeling is capable of predicting the sign of statistical associations between species presence and fecal metabolite concentrations in the CRC sample, we calculated maximal net secretion for 52 metabolites with fecal metabolome data with more than 50% of the samples having concentrations above the limit of detection. Metabolite net secretion was computed using the mgPipe module in the Microbiome Modeling Toolbox  10, 43  while relying on computationally efficient flux variability https://doi. org/10. 1038/s41587-022-01628-0 analysis  92. Then, we calculated for each species present in at least 10% of the microbiomes and at max 90% of the microbiomes the effect of species (binary predictor: species present versus species not present) on each fecal metabolite concentration in multivariable regressions, adjusting for age, sex, BMI and study group.