dataesr/scientific-paragraphs-categorization huggingface dataset:
Selection of Mass Fragments For malate and glutamate, we took advantage of previously identified fragments for malate_3TMS and glutamate_3TMS derivatives  [29, 31] , summarized in Table  1. These fragments allowed for the calculation of all 13 C-positional enrichments for malate and C1 13 C-enrichment for glutamate (reflecting potentially PEPc contribution). For others, we performed injections of 13 C-reference substances or analyzed GC-MS spectra of the Golm Metabolome Database corresponding to the injection of either 13 C-reference substances or 13 C analytes produced with a controlled 13 C pattern  [37, 38]. The molecular losses and chemical formulas of mass fragments were then determined according to common rules of mass fragmentations for amino acids  [32]  (Table  1 ). Overall, the identity of the carbons contained in each mass fragment was confirmed by mass shifts with 13 C-labeled molecules (Figure  1 ).
When multiple fragments could be used to calculate the same 13 C-positional enrichment, the original fragment used for calculation was denoted by adding its m/z at M0 (see red bars "C1_174", "C1_100" and "C1_186" for glycine_3TMS, for example). The complete dataset in available in Table  S1. Values represent the mean ± SD for four biological replicates. Statistical differences between predicted and measured/calculated mean 13 C enrichments for each isotopologue of each fragment are denoted with an asterisk (*) and were established by considering the 95% confidence intervals of the measured/calculated values. For convenience, fragments containing similar carbons were denoted by adding the m/z at M0 (see blue bars "C2_174", "C2_100" and "C2_186" for glycine_3TMS, for example). When multiple fragments could be used to calculate the same 13 C-positional enrichment, the original fragment used for calculation was denoted by adding its m/z at M0 (see red bars "C1_174", "C1_100" and "C1_186" for glycine_3TMS, for example).
Background. Metabolites produced by the gut microbiota play an important role in the cross-talk with the human host. Many microbial metabolites are biologically active and can pass the gut barrier and make it into the systemic circulation, where they form the gut microbial exposome, i. e. the totality of gut microbial metabolites in body uids or tissues of the host. A major di culty faced when studying the microbial exposome and its role in health and diseases is to differentiate metabolites solely or partially derived from microbial metabolism from those produced by the host or coming from the diet. Objective. To collect data from the scienti c literature and build a database on gut microbial metabolites and on evidence of their microbial origin. Methods: Three types of evidence on the microbial origin of the gut microbial exposome were de ned: 1/ metabolites are produced in vitro by human faecal bacteria; 2/ metabolites show reduced concentrations in humans or experimental animals upon treatment with antibiotics; 3/ metabolites show reduced concentrations in germ-free animals when compared with conventional animals.
Data was manually collected from peer-reviewed publications and inserted in the Exposome-Explorer database. Furthermore, to explore the chemical space of the microbial exposome and predict metabolites uniquely formed by the microbiota, genome-scale metabolic models (GSMMs) of gut bacterial strains and humans were compared. Results: A total of 1854 records on one or more types of evidence on the gut microbial origin of 462 metabolites was collected in Exposome-Explorer. Data on their known precursors and concentrations in human blood, urine and faeces was also collected. About 66% of the predicted gut microbial metabolites (n = 1543) were found to be unique microbial metabolites not found in the human GSMM, neither in the list of 462 metabolites curated in Exposome-Explorer, and can be targets for new experimental studies.
The purpose of the present work was to develop a comprehensive database on gut microbial metabolites with evidence supporting their microbial origin. Data for 462 gut microbial metabolites was extracted from peer-reviewed publications, and has been curated in the Exposome-Explorer database where it can be easily searched  [15]. To further enlarge coverage of gut microbial metabolites, we also compared genome-scale metabolic models (GSMMs) of gut bacterial strains to those of humans to identify metabolites uniquely formed by the microbiota. This new resource should help researchers to identify gut microbial metabolites to be considered in future studies on the gut microbiota, its functionalities and role in health and diseases.
iarc. fr/concentrations). Visualizations for this data are provided in Additional le 1: Fig. S1  and S2. A large fraction of microbial metabolites (64%) shows at least one concentration value in human biospecimens, with blood and urine matrices being most widely documented. Aggregated concentrations in blood, summarized as median, vary from trace amounts to concentrations as high as 4. 9x10 6 nmol/L for glucose (Additional le 1: Fig. S2 ). In vitro experiments, human intervention studies and studies using experimental animals often used speci c substrates biotransformed into microbial metabolites in the gut. Names of 'substrates' were collected in the database. These substrates added to the growth medium of faecal bacteria or administered to the subjects or experimental animals can be pure compounds, foods or food extracts.
In silico predicted microbial metabolites To further explore the chemical space of the microbial exposome, GSMMs of known gut bacteria were used to compare in silico predicted metabolites with microbial metabolites curated in the Exposome-Explorer database. GSMMs are generated by using the genome information of known gut microbes and the enzymatic reactions inferred from the genome sequences. Microbial enzymatic reactions were used to predict the gut microbial exposome. A total of 2325 metabolites were predicted based on both the AGORA  [19]  and MAMBO  [20]  gut microbiome GSMM reconstructions. More than half (251 metabolites) of the Exposome-Explorer metabolites were found in the gut microbiome GSMMs (Fig. 3 ). The extent of overlap between predicted metabolites and metabolites curated in the Exposome-Explorer database varied according to chemical classes, with some classes such as polyketides, terpenoids and avonoids being largely absent in the Exposome-Explorer database.
In contrast, most of the predicted metabolites from the bile acid and amino acid classes are found in the Exposome-Explorer database. Figure 3 The 251 predicted metabolites reported in the Exposome-Explorer database represent less than 11% of all the metabolites found in the gut bacteria GSMMs (Fig. 3 ). Remaining metabolites might be microbial metabolites that are also commonly produced in human cells/tissues and this would explain why they were not recognized as microbial metabolites in our literature search. In order to nd microbial metabolites unique to the microbiome, the in silico predicted microbial metabolites were also compared to human metabolites in the Recon3D human GSMM reconstruction  [21]. About 74% of the predicted gut microbial metabolites (n = 1727) were unique microbial metabolites not found in the human GSMM.
A rst database on the microbial exposome For the rst time a list of manually curated gut microbial metabolites, composed of 462 compounds, is made publicly available in the Exposome-Explorer database. Three types of evidence of the microbial origin of metabolites were collected: (i) in vitro experiments with faecal samples showing that a particular metabolite can be produced by gut bacteria; (ii) in vivo manipulation of the gut microbiota with antibiotics showing a decrease of microbial metabolite concentrations; (iii) comparison of microbial metabolite concentrations in germ-free and conventional animals. The second and third types of evidence are particularly important because many microbial metabolites are also produced in human tissues and the proportion coming from the microbiota is most often unknown.
We systematically searched for concentrations of the 462 microbial metabolites in human biospecimens: 2136 concentration values for 298 microbial metabolites can be found in the Exposome-Explorer database. The chemical space of the gut microbial exposome The chemical space of the gut microbial exposome beyond metabolites unravelled in our literature search was further explored with an in silico approach. We compared GSMMs of humans and gut bacteria to identify metabolites that may be unique to the microbiota and not formed in humans. From a list of 2325 predicted gut microbial metabolites, 1543 were not present in Exposome-Explorer nor in the human GSMM (Additional File 2: Table  S1 ). The number of predicted unique microbial metabolites could even be greater if we would take into account the metabolism of microbial metabolites by the host.
For example, p-cresol, a microbial metabolite formed from tyrosine or hydroxyphenylacetate in the gut, is conjugated to its sulfate ester or glucuronide in the liver. These p-cresol metabolites are the forms detected in urine and plasma in human and animal experiments, but they are absent from the bacteria GSMM models. Furthermore, the list of predicted unique microbial metabolites inferred using the GSMMs is very likely underestimated as GSMMs are well known for containing gaps in the metabolites compared to the real metabolome  [25]. An overlap of 251 compounds was found between predicted microbial metabolites and the microbial metabolites curated in the Exposome-Explorer database. However, many predicted microbial metabolites (n = 1543) are not found in Exposome-Explorer. These include many secondary metabolites such as polyketides, terpenoids, and avonoids, either formed de novo by the bacteria or formed from plant compounds, consumed as part of the diet and biotransformed by the microbiota.
Conclusions This rst database on the gut microbial exposome developed in this work can be used to identify microbial metabolites in metabolomics datasets or to develop speci c assays to quantify them in clinical or epidemiological studies. This new release of the Exposome-Explorer database contains useful information to prioritize metabolites to be annotated or included in an assay, such as level of evidence on their gut microbial origin, biospecimens where they have been measured, analytical methods, concentrations, or reproducibility over time. The database also contains structural information such as InChiKey or SMILES that can be directly used for in silico mass spectrometry fragmentation and metabolite identi cation in metabolomics studies  [31, 32]. This kind of high-quality curated database is also very useful to train natural language processing and machine learning models, for example to automatically nd microbiome-related metabolites in publications  [33].
Data collection Microbial metabolites and experimental evidence on their microbial origin were derived from manual collection of data in the scienti c literature. Three types of evidence were collected. Produced by human faecal bacteria. Metabolites are produced during anaerobic or aerobic fermentation of dietary and non-dietary substrates by faecal samples, isolated individual faecal bacterial species or mixtures of faecal bacteria isolated from faecal samples collected in healthy individuals. Metabolites are measured in the culture medium. Nature of substrates, when added to the culture medium, was recorded, as well as names of bacterial species. Reduced by antibiotic treatment. Antibiotics are used to reduce the number of gut bacteria in humans or experimental animals. They can target speci c types of bacteria or have a large spectrum of action.
Concentrations are compared either before and after antibiotic treatment, or between antibiotic-treated and non-treated groups. These metabolites are measured in blood, urine, or faeces. We included metabolites which were signi cantly reduced upon antibiotic treatment in healthy humans, monkeys, rats and mice. The nature of antibiotics was recorded. If speci c substrates were tested, their names were provided. Reduced in germ-free animals. Germ-free mice and rats are compared with conventional animals. Germ-free animals may be conventionalized by having contact with human faeces, speci c pathogen-free or conventional mice/rat faeces. We included metabolites which were signi cantly reduced in blood, urine, faeces or in the content of intestine-linked organs of germ-free animals. The source of bacteria was recorded in the database.
Database implementation Exposome-Explorer is a web application developed in Ruby on Rails (https://rubyonrails. org/). The data is stored in a MySQL database (https://www. mysql. com/). Chemical structures are hosted on the Wishart lab's MolDB structure server (https://moldb. wishartlab. com/). Chemical information (e. g. IUPAC name, formula, molecular weight) is automatically calculated from the structures. Based on their structure, compounds are automatically classi ed with the ClassyFire webserver (http://classy re. wishartlab. com/) which relies on the ChemOnt chemical taxonomy  [34]. The ChemOnt taxonomy was developed for large public databases such as HMDB to facilitate unambiguous classi cation of chemicals  [35]. The Exposome-Explorer website (http://exposome-explorer. iarc. fr/) is responsive and compatible with different systems and screen sizes, including mobile devices.
Some modi cations have been brought to the user interface in order to seamlessly integrate the new microbial metabolite data with existing data. Two pages displaying the new microbial metabolite data have been incorporated below the 'Biomarker data' menu (http://exposome-explorer. iarc. fr/). The rst page, 'Microbial metabolites', provides the list of 462 metabolites with available information on their microbial origin. For each metabolite, the number of experimental evidence types -from 1 to 3 -is indicated. The number of publications relative to each type of evidence is provided in three additional columns. Additional chemical information (e. g. identi ers from other chemical databases, chemical formula, molecular weight, InChIKey, SMILES) can be displayed in the 'Microbial metabolites' page via the 'Show/Hide columns' button.
A last column in the page indicates if the metabolite has been described in humans. Concentrations in various biospecimens can then be found in the speci c page of the corresponding metabolite, reached through clicking on the name of the metabolite. The second page, 'Associations with microbiota', provides the list of 1854 raw database records of experimental evidence collected on the microbial origin of the metabolites, together with their bibliographic reference. Additional information on organism (bacteria, humans or experimental animals) used in the experiment, biospecimen where the biomarker was identi ed, and substrate is also indicated. The nature of the antibiotic and bacterial source appears in hidden columns displayed with the 'Show/Hide columns' button.
Funding This was supported by the  EU H2020 EXPANSE  grant (grant  N 0 874627 ), the  ANR-DFG   FoodMetCh grant  (project  N 0 ANR-18-CE92-0060-01 ), and the  ANR-DFG   MetClassNet grant  (project  N 0 ANR-19-CE45-0021-02 ).
Microscopy Fluorescence microscopy was performed with a Thunder imaging system (Leica) equipped with a DFC9000 sCMOS camera, a HC PL Fluotar 20x air (N. A. 0. 4) or a HC PL Apo 63x (N. A. 1. 4) oil immersion objective and using the LasX software (Leica). Confocal microscopy was performed with a laser-scanning microscope (Leica DMi8) equipped with ACS APO 40x and 63x oil DIC immersion objectives. Images were acquired using the LasX software (Leica), with a resolution of 1024x1024 pixels and a number of Z-stacks (step size of 0. 3 or 0. 5 μm) encompassing the entire cell soma.
The colocalization analysis was performed with the semi-automated colocalization tool of the IMARIS software (Oxford Instrument). To analyze the cluster size and intensity of synaptic markers, 3 different primary dendrites for each neuron were randomly selected. A ROI was then drawn along the dendrites and synaptic clusters traced with the ImageJ plugin FindFoci (Herbert), using the Max Entropy algorithm and the resulting data was normalized to a ROI length of 10 μm. The computational parameters and post-acquisition modifications were equally applied to analyze pictures belonging to the same experiments and for figure display, which was performed with either Fiji or Imaris.
Western blot Western blots were performed by resolving equal concentrations of protein (determined by Bradford Assay) on 10% acrylamide SDS-PAGE gels, which were then transferred to a nitrocellulose membrane using a Trans-Blot Turbo device (BioRad, USA). To block non-specific binding sites, the membranes were incubated with a 5% BSA solution (diluted in TBS pH 7. 5 + 0. 2% TWEEN) for 2 h and incubated with the primary antibody overnight at 4 • C. Afterwards, blots were washed 3 times with TBS+0. 2% TWEEN, incubated with HRP-conjugated secondary Ab (DAKO) for 1 h, and again washed. Chemiluminescent signal was detected using ECL detection kit (ThermoFischer Scientific, 32 106) and a MicroChemi 4. 2 device (DNR Bio Imaging System). For quantification, Gel-analyzer Software 2010a was used. The values of the proteins of interest were normalized against the loading control β-actin.
1% formic acid in HPLC-grade water prior to MS analysis. The tryptic peptides were resuspended in 250 μL and 400 ng were injected on a nanoelute (Bruker Daltonics, Germany) HPLC (high-performance liquid chromatography) system coupled to a timsTOF Pro (Bruker Daltonics, Germany) mass spectrometer. HPLC separation (Solvent A: 0. 1% formic acid in water, 2% acetonitrile; Solvent B: 0. 1% formic acid in acetonitrile) was carried out at 250 nL/min using a packed emitter column (C18, 25 cm × 75 μm 1. 6 μm) (Ion Optics, Australia) using a 70min gradient elution (2 to 13% solvent B during 42min; 13 to 20% during 23min; 20% to 30% during 5min; 30% to 85% for 5min and finally 80% for 5min to wash the column). Massspectrometric data were acquired using the parallel accumulation serial fragmentation (PASEF) acquisition method in DIA mode.
Since each line has been obtained from an individual and represents n=1, the average of the values obtained from all the neurons analyzed from the independent differentiations (after normalization to the vehicle treatment) was used to obtain a single value for each individual patient to perform statistical analysis (in order to compare the different genotypes or treatments). In this case, the effect of propranolol was evaluated performing a paired t-test. The analysis of proteomic experiments was performed using DIA-NN software (version 1. 8. 1). A search against the rat UniProtKB Swiss-Prot/ TrEMBL Rattus norvegicus database (release 02-2022, 52 910 entries) was performed using library free workflow. For this purpose, "FASTA digest for library free search/library generation" and "Deep learning spectra, RTs and IMs prediction" options were checked for precursor ion generation.
Statistical and bioinformatic analysis, including heatmaps, profile plots and clustering were performed with Perseus software (version 1. 6. 15) freely available at www. perseus-framework. org. R/R Studio (R version 4. 1. 2 (www. R-project. org)  and RStudio version 2021. 09. 1 (www. rstudio. com)) were used for batch effect correction. The pg_report matrix output by DIA-NN was used and intensities were log2 transformed for statistical analysis. For statistical comparison, we set four groups (GFP_propranolol, GFP_vehicle, polyGA_propranolol, poly-GA_vehicle), each containing 3 independent replicates. We then filtered the data to keep only proteins with at least 3 valid values in at least one group. HarmonizR R package (version 0. 0. 0. 9;  Voß et al. , 2022)  was used to correct for batch effect using harmonizR() function with the ComBat algorithm and ComBat_mode was set to 1.
With this approach, we identified 1071 significantly-altered proteins (470 down-and 601 up-regulated; Fig. 1C ) in poly(GA)+ cells when compared to those transduced with the control AAV9-EGFP. By performing enrichment analysis using the Gene Ontology (GO) biological processes database, we found a deep alteration in the ubiquitin-dependent catabolic mechanisms (Fig. 1D ), highlighting that the levels of several proteins involved in these pathways were either significantly reduced or increased in comparison to EGFP + neurons. In fact, protein-protein interaction analysis identified a significant enrichment in proteins clustering into ubiquitination and catabolic processes (Fig. 1E ), confirming a profound impairment in this specific arm of neuronal metabolism in the presence of poly(GA) aggregates.
5B ) as well as the number of apoptotic cleaved caspase 3+ neurons (Fig. 5C ) in the cultures derived from all the 3 patients. Since the proteomic analysis revealed a selective increase of the C9orf72 protein levels in poly(GA) neurons after treatment with propranolol, we asked whether this might hold true also in our human model. First, we looked at the expression of C9orf72 using a RNAseq dataset previously published by our group  (Catanese et al. , 2021; GSE168831)  that included 2 of the ALS C9orf72 lines considered in this study and compared them to a healthy patient and an isogenic control (referred to as Healthy). Confirming the idea that C9orf72 haploinsufficiency contributes to ALS pathogenesis  (Shi et al. , 2018) , we found significantly lower levels of its transcript in the ALS C9orf72 -mutant MNs than in Healthy ones (Fig.
Funding This work was founded from the  Bausteinprogramm of the Medical Faculty of Ulm University  to AC (project L. SBN. 0162 ). TB and AC are supported by the  Deutsche Forschungsgemeinschaft (German Research Foundation ) - SFB1506  " Aging at interfaces " (project  A01 ). AC is also supported by the  Else Kröner-Fresenius Stiftung  (project Nr. 2019_A111 ) and the  Frick Foundation. MS and CS received financial support from the  "Experimental Medicine" graduate program  of the  Medical Faculty of Ulm University.
Introduction The statistical models describing experimental measurements are a key component of LHC data analysis. Consisting of the probability distribution function (PDF) of the measurement together with the observed dataset, they are used to compute the final experimental results -e. g. confidence intervals for model parameters, or significance values for possible excesses over background -often through the use of frequentist profile-likelihood ratio (PLR) methods  [1]. They can also be utilized to make further use of the measurement information, for instance in combinations with other results, or as reinterpretations in the context of alternative signal models. Despite this central role, statistical models are not systematically made available as part of experimental publications. This is partly for technical reasons: first, they are often complex, with up to O (10 4 ) parameters in some cases  [2].
Implementation and storage format A python implementation of the SLLS formalism is provided in the fastprof public package. 2 It describes the full statistical model, including both the PDF of the measurement and the observed data. It includes tools to evaluate and profile the likelihood function and perform maximum-likelihood fits as well as higher-level computations such for hypothesis testing, limit-setting and confidence interval estimation. Other tools are provided to validate the simplified models and perform other operations such as combining or pruning models. The computations make use of the linear algebra routines included in numpy  [15]  and the minimization routines provided by scipy  [16]. The statistical models are stored in a plain-text format using the JSON markup language.