dataesr/scientific-paragraphs-categorization huggingface dataset:
03 Å)), with an exposure time of 1000 ms and a gap time of 500 ms and measured by a two-dimensional CCD detector localized at a distance of 2131 mm (respectively 3500 mm) from the sample. The measurements were performed at different concentrations and different temperatures thanks to a thermostated capillary-holder device. Standard correction procedures were applied for X-ray beam transmission, signal subtraction of the 1. 5 mm capillary filled with the solvent and detector efficiency. The softwares Foxtrot ® and SASview ® were used to achieve such data reduction. The data was fitted with the SASview ® software (http://www. sasview. org/, accessed in 15 May 2021). According to the morphologies observed by cryo-TEM the data were fitted with the form factor of a lognormal distribution of either spheres, cylinders or vesicles.
The complete list of antibodies can be found in Supplementary Table  1. Acquisition was performed on a LSR Fortessa (BD Biosciences) or an Aurora spectral cytometer (Cytek). Data were analyzed with FlowJO software V10. 7. 2 (Tree Star, www. flowjo. com). Histology. Colons were fixed in 4% formaldehyde solution (Sigma) for 1 day and stored at 4 °C in EtOH 70%. They were then embedded in paraffin, sectioned and stained with hematoxylin/eosin (Sigma-Aldrich). Intestinal inflammation was scored in a blinded fashion using a scoring system based on the following criteria: colon length, inflammatory cell infiltrate (severity and extent), crypt hyperplasia, and presence of neutrophils within the crypts, presence of crypt abscesses, erosion, granulation tissues and villous blunting. ELISA. ELISA were performed using "ELISA MAX Deluxe Set Mouse IL-2", and "ELISA MAX Deluxe Set Mouse GM-CSF" (Biolegend) following manufacturer's instructions.
Statistics. Statistics were performed using GraphPad Prism Software v9 (https:// www. graph pad. com/ scien tific-softw are/ prism/). Unless mentioned otherwise, two-tailed Mann-Whitney t test was used to calculate statistical significance. P values < 0. 05 were considered significant. ns: non-significant; *p < 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001.
Acknowledgements We are grateful to all members of the team for their technical help and constructive discussions; to  Christophe Caux  and his team for technical support; to the staff of the core facilities at  CRCL  for their expertise; and to  Brigitte Manship  for critical reading of the manuscript. This work was supported by the  CNRS/INSERM ATIP-Avenir young investigator program , the  Labex DEVweCAN  and the  Association pour la Recherche sur la Sclérose en Plaques (ARSEP Foundation ). AV is supported by a postdoctoral fellowship from the  Association pour la Recherche sur le Cancer (ARC).
We show that subtle changes in sample preparation protocols may influence interpretation of biological findings. Two-step database search strategies led to significant underestimation of false positive protein identifications. Unipept software provided the highest sensitivity and specificity in taxonomic annotation of the identified peptides of unknown origin. Comparison of matching metaproteome and metagenome data revealed a positive correlation between protein and gene abundances. Notably, nearly all functional categories of detected protein groups were differentially abundant in the metaproteome compared to what would be expected from the metagenome, highlighting the need to perform metaproteomics when studying complex microbiome samples.
Although bacterial MS-based proteomic approaches are well established, metaproteomic sample preparation is hindered by many challenges, such as physical structure of the sample, the presence of contaminating proteins and the coexistence of hundreds of microorganisms. Many studies have reported increased protein identification due to laboratory optimisation for the analysis of metaproteome samples  [19] [20] [21] [22] [23]. In humans, different sample preparation methodologies have been shown to result in significant changes in the taxonomic composition and functional activities represented  [19, 24, 25]. Beyond sample preparation, the bioinformatic processing of metaproteomic data remains challenging, due to the choice of representative protein sequence database, elevated false discovery rate for peptide identification and the redundancy in protein functional annotation.
Some of these challenges have already been addressed by published software packages, such as MetaProteomeAnalyzer  [26]  and MetaLab  [27] , which are all-in-one metaproteomic analytical workflows, or UniPept  [28] , which allows peptide-based taxonomic representation. In addition, the choice of protein sequence database has been shown to play a major role in protein identification from metaproteome samples, with notably matching metagenome-derived protein sequence databases displaying the best identification rate performance  [29] [30] [31] [32]. Previous studies have also investigated ways to determine taxonomic representation from metaproteome samples, which has been shown to differ between metagenome (bacterial presence) and metaproteome (bacterial activity)  [32, 33]. Here, we present a state-of-the-art MS-based workflow for the optimal metaproteome characterisation of murine faecal samples.
We focused on a number of aspects that remain under-investigated in murine stool samples:  (1)  the impact of sample preparation methods, namely low speed centrifugation (LSC) and no LSC (nLSC), on protein identification and taxonomic representation;  (2)  the high false positive rates in searches involving very large databases; (3) the differences in taxonomic annotation of MS-identified peptides based on different software; and (4) the lack of assessment of the functional enrichment provided by the metaproteome compared to its matching metagenome potential.
Low-speed centrifugation increases peptide identification rates Our initial experiment involved the establishment of an optimal sample preparation workflow applied to the mouse faecal metaproteome. In this context, we assessed two sample preparation steps that are commonly employed in metaproteomic studies: 1) the usage of LSC  [19, 24, 34]  versus nLSC  [24, 35] ; and 2) in-solution digestion  [19, 22]  versus filter-aided sample preparation (FASP)  [20, 21]  (Figure  S1A , Table  S1 ). The resulting LC-MS/MS data were processed using the MaxQuant software  [36]. The number of peptide spectral match (PSM) identified per MS raw file in the LSC group was significantly higher with 26 % more identifications (Figure  1A ). This was also observed at the peptide and protein group level, but to a lower extent for the latter.
44) (Figure  S1E  and F ). Besides, LFQ correlation among the samples prepared via LSC was superior to samples prepared with nLSC (Figure  S1G ). Our findings indicate that while the two procedures have a poor identification overlap, the main differences may still result from biological variations. Using LFQ intensities, we then performed a t-test to identify which protein groups have different abundances between the two procedures. Out of 2,589 quantified protein groups, 365 and 267 showed a significant increase and decrease in abundance between LSC and nLSC samples, respectively (FDR ≤ 0. 01 and absolute fold-change ≥ 2. 5) (Figure  1E , Table  S1 ). We gained functional insights into these differences by performing an over-representation analysis of KEGG pathways using the clusterProfiler R package  [39].
Two-step database search strategy shows a dramatic increase in false positive rate After measurement via liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) and acquisition of LC-MS/MS raw data, the MS/MS spectra are searched against a protein sequence database. One aspect of database search is the controversial use of a two-step search strategy  [26, [40] [41] [42] , as opposed to the single-step search traditionally used in proteomics  [30, 43]. For two-step search approach, LC-MS/MS measurements are initially processed against a large protein sequence database with no FDR control (FDR ≤ 1%). Subsequently, the original database is filtered to retain only protein sequences that were identified during the first search. During the second database search, the measurements are processed against the reduced database with FDR control (e.
g. FDR ≤ 0. 01)  [40]. To assess these search strategies, we searched a single HeLa cell LC-MS/MS file using MaxQuant software against a Homo sapiens protein sequence database supplemented with different number of bacterial protein sequences (Figure  S2A ). The HeLa measurement is used here as a proxy for a complex microbiome measurement, with the exception that the sample composition is known and from a single organism. We initially established a benchmarked standard by processing the HeLa measurement only against an H. sapiens database, which resulted in approximately 5,000 human (eukaryota) protein groups identified for the single-step search at FDR ≤ 0. 01 (Figure  2A , Table  S2 ). Notably, the same database used in a two-step search identified less than 1 % additional protein groups in comparison to a single-step search, despite nearly twice as much processing time.
We then processed our HeLa measurement against the H. sapiens database supplemented with 1×, 2×, 5×, 10× and 20× bacterial protein sequences, resulting in increasingly large databases (Figure  S2A , Table  S2 ). For the single-step database search against the 1:20 database, we observed a 10 % decline in the number of human protein groups identified, while 132 bacterial protein groups were identified (false positives). On the contrary, the 1:20 two-step database search resulted only in a 1 % decrease compared to the benchmarked standard. This processing also revealed a large number of bacterial protein groups identification (980 protein groups). Furthermore, the two-step search led to large number of MS/MS spectra to be assigned to different sequences (or newly assigned) in comparison to the benchmarked standard (Figure  S2B , Table  S2 ); this phenomenon was much less pronounced when performing the single-step search.
We then calculated the factual FDR for each processing approach using either the reverse hits or the reverse hits plus the bacterial hits (which in our case are false positives). For both the single-step and the two-step search, we obtained an FDR of 2. 6 % when using only the reverse hits for FDR calculation (Figure  2B ). However, when using the reverse hits plus the bacterial hits, we calculated a factual FDR of 8 % and 34 % for the single-and two-step search with 1:20 database, respectively. This represents a dramatic increase in the rate of false positive identification when using two-step search, despite controlling for 1 % FDR. Notably, these false positive hits would remain unnoticed in a microbiome sample of unknown composition, thus highlighting the inherent problem associated with the two-step database search.
Optimisations of the two-step database search cancels out its higher sensitivity To further assess database search strategies used by the metaproteomic community  [26, 30, [40] [41] [42] [43] [44] , we retrieved a metaproteome dataset of known taxonomic composition that was published by Kleiner and colleagues  [32]. This dataset consisted of 32 organisms of uneven abundances, including bacteria  (25) , archaea (1), eukaryotes (1) and viruses  (5). We processed eight LC-MS/MS measurements against a database containing the proteomes of these 32 organisms, which we supplemented with 0. 5×, 1×, 2×, 5×, 10× and 20× bacterial protein sequences, resulting in increasingly large databases. We then compared the results obtained from singlestep search strategy against: (1) "two-step protein" search to keep identified proteins  [40] ;  (2)  "two-step taxa" search to keep identified taxa  [30] ; and (3) "two-step two sections" search to keep identified proteins after sectioned search  [44].
While all search strategies resulted in similar accuracies, the "two-step protein" search maintained a high sensitivity even when using large databases (i. e. 20×) (Figure  2C  and S2C ). However, upon investigation of the factual FDR (reverse hits plus the false bacterial hits), the "two-step protein" search resulted in twice as many false positive identifications compared to the single-step search (Figure  2D , Table  S2 ). Similar results were also observed when focusing on the precision (Figure  S2D ). Our investigations revealed that the "two-step taxa" search behaved nearly identically to the singlestep search, whereas the "two-step two sections" search displayed performance in-between the first-step and "two-step protein" searches. Because, all assessed search strategies underestimated the real FDR, we attempted to identify any particularity of the false positive protein groups identification and thus focused on processings against the largest database (20×).
We show that the median number of unique peptides (i. e. peptides that are uniquely assigned to a protein group) are 1 and 2 for the false and true positive hits, respectively (Figure  S2E ). We then compared results obtained using a post-processing filtering step requiring a minimum of 1 or 2 unique peptides per protein groups. Our results show that requiring a minimum of 2 unique peptides would efficiently control the FDR (≤ 1%) at the expense of a significant drop in protein identification (Figure  2E  and S2F, Table  S2 ). This investigation of different database search strategies applied to metaproteome samples further highlighted the limitations (i. e. factual FDR) of two-step searches, even following optimisation (i. e. sectioned search) or filtering. Unipept software provides the most accurate and precise taxonomic annotation Another important aspect of metaproteomic studies is the determination of taxonomic activity (protein biomass), which has been reported to differ from taxonomic representation derived from metagenomic studies  [32, 45].
While it is straightforward to compute taxonomic activity from the abundance of peptides (or proteins) of known taxonomic origin, there has not been an exhaustive assessment of software that can taxonomically annotate MS-identified peptides. Here, we assessed three software packages-i. e. Kraken2  [46, 47] , Diamond  [21, 48, 49]  and Unipept  [28, 30, 50] -that are commonly used by the metaproteomic and metagenomic communities. The taxonomic annotation performance of these software was evaluated on the dataset from Kleiner and colleagues  [32]. The Kraken2 software provided consistently higher percentage of peptides that could be taxonomically annotated (c. a. 18% peptides annotated to species level), followed by Unipept (5%) and Diamond (1%) (Figure  3A ). However, Kraken2 also identified a very large number of taxa that were not present in the artificial samples from Kleiner and colleagues (Table  S3 ) and thus would be false positive hits.
Unsurprisingly, these false positive hits were characterised by low PSM counts in comparison to true positives (Figure  S3A ). This led us to assess these software packages in terms of accuracy, precision, sensitivity, specificity and Fmeasure for taxonomic identification using a range of PSM count thresholds (Figure  3B  and S3B-E ). In this context, the Unipept software significantly outperformed Kraken2 and Diamond, especially with regard to the F-measure and precision. Notably, the implementation of a minimum PSM count threshold (i. e. between 1 and 5) resulted in accuracy, precision and specificity improvements for all software, but at the cost of a reduced sensitivity. Thus, without a PSM count threshold, we correlated the taxonomic abundance derived from each software annotation against the known input protein from Kleiner and colleagues' artificial samples (Figure  3C ).
Overall, the Unipept software provided the highest correlation (Spearman ρ = 0. 83), as well as at most taxonomic levels (including species). Interestingly, the dynamic range of taxon detection by MS spanned two orders of magnitude, with Salmonella enterica being approximately 230 times more abundant than Nitrosomonas europaeae (Figure  3D , Table  S3 ). Unipept was also the only software allowing identification of Nitrosomonas ureae, Paraburkholderia xenovorans and Nitrosospira multiformis. Importantly, none of the software could identify the five viral organisms present in the samples, the reason being technical since no peptide coming from those viral proteins was detected by MS. Finally, we assessed the impact of different database search strategies on taxonomic abundance derived by the Unipept software (Figure  S3F ).
Unipept software To check whether our results are also applicable to the microbial composition of faecal samples, we prepared samples using the LSC method from faeces collected in a cohort of 38 mice. The resulting LC-MS/MS data were processed using a single-step search strategy against a matching metagenome protein database (with no knowledge of taxonomic composition). We initially annotated the MS-identified peptides using Kraken2, Diamond and Unipept, which revealed an overlap of 232 taxon (1. 9%) between all three software. Such low overlap was largely driven by the suspected large number of false positive hits identified by Kraken2 (10,203 uniquely identified taxon), as seen in the previous section. We then performed pairwise correlation between every samples combination within each software using taxonomic abundance (Figure  S3G ).
While Diamond displayed a higher correlation (median spearman ρ = 0. 71), this is likely driven by the small number of identified taxa, most of which at the taxonomic levels closer to the root (e. g. superkingdom, phylum) and is thus a poor performance estimate. To determine which taxa are likely true or false positive hits, we made use of the taxonomic composition foreknowledge (at the species level only) from the mouse microbiome catalogue  [51]. With this approach, the Kraken2 software showed the best sensitivity (median = 0. 17) compared to Unipept (0. 13) and Diamond (0. 03) (Figure  S3H , Table  S3 ). Based on precision and F-measure, Kraken2 performance collapsed, whereas Unipept software had median precision and F-measure superior to 0. 1 (Figure  3F  and S3I ). Using taxonomic foreknowledge, our findings suggest that the Unipept software provides superior predictive power for taxonomic annotation of faecal samples.
Metaproteome to metagenome correlation highlights an over-representation in the core microbiome functions Multi-omic studies are now increasingly common in context of microbiome investigation to provide interconnected information, such as microbial presence and activity, genetic potential, gene expression and functional activity  [52] [53] [54] [55]. Due to the availability of matching metagenomic and metaproteomic data for our cohort of 38 mice, we assessed the correlation between gene and protein abundances. To deal with the intrinsic differences between the two datasets, the gene entries were grouped in a similar fashion as the protein groups (i. e. based on peptide identification) and the maximum expression was calculated per gene group. Here, we show that a majority of gene-protein pairs (91 %) have a positive correlation, with a median of 0.
Our results confirm the central role of carbon fixation and general metabolism, which are associated with bacterial energy production, in the murine faecal microbiome under the analysed conditions. The metaproteome is enriched in functionally active pathways compared to the matching potential encoded in the metagenome The metagenome corresponds to the microbiome's genetic potential, whereas the metaproteome represents its truly expressed functional activities. Thereby, we compared the functional abundance derived from the metagenomic versus metaproteomic datasets within our cohort of 38 mice. To allow comparison, the KEGG level 2 categories were quantified and normalised separately for each omic datasets (Figure  S4C , Table  S4 ). Out of 55 KEGG categories, we found 15 and 37 to be significantly increased and decreased in abundance at the metaproteome level in comparison to the metagenome (FDR ≤ 0.
GSEA revealed an enrichment of a number of overlapping KEGG pathways, with 19 and 6 pathways positively and negatively enriched, respectively (Figure  4D , Table  S4 ). Interestingly, we found the ribosome pathway enriched in protein with increased abundance (between metaproteome and metagenome datasets), therefore highlighting the functional activation of this pathway (Figure  4E  and S4E ). Conversely, homologous recombination, DNA replication and mismatch repair were enriched in protein with decreased abundance, suggesting no or low activation of these pathways. Overall, our findings highlight the critical importance of metaproteomics to characterise microbiome samples particularly when it comes to their functional activity.
Discussion Here, we investigate some key aspects of metaproteomic workflow applied to murine faecal samples in order to enhance protein identification, taxonomic and functional coverage. We focused on the assessment of (1) different sample preparation methods, (2) strategies to control for false positive rates during database search, (3) taxonomic annotation software for accurate MS-derived taxonomic representation and (4) the importance of metaproteomics to determine functionally enriched pathways. Our results led to an overview of the strengths and weaknesses of each assessed methods (Table  1 ) in the context of murine faecal metaproteomics. To the best of our knowledge this is one of the largest and most extensive comparisons undertaken to date, comprising over 40 different biological samples and over 50 LC-MS/MS runs.
Overall, we reached identification rates that are similar to bacterial shotgun proteomics (ca. 20-40 %). In comparison to previous murine faecal metaproteomic studies, we identified more non-redundant peptides per samples (approximately 20,000 non-redundant peptides on a 60 min gradient)  [57, 58]. Several parameters may have influenced such performance, among which are the use of a faster and more sensitive Orbitrap instrument (i. e. Q Exactive HF)  [59, 60]  and a more representative protein sequence database (i. e. mouse metagenome catalogue or mouse matching metagenome)  [51]. Importantly, the impact of mass spectrometer speed and sensitivity should not be overlooked in a typical metaproteomic measurements. Indeed, the type and model of MS instrument was among the parameters with the greatest impact on identification rates.
Some of our initial investigation showed significant increase in peptide and protein identification rate when using the Q Exactive HF (faster scanning, improved sensitivity) versus the Orbitrap Elite (data not shown, but downloadable from ProteomeXchange). Both LSC and nLSC methods have merits for the metaproteomic analysis of murine faecal samples Our study confirms previous observation with regard to increased peptides or proteins identification, which is dependent on laboratory preparation method and specifically the usage of differential centrifugation  [24]. The LSC approach also leads to more consistent identifications and as a result fewer missing values, which is a general and extensive problem in metaproteomic datasets. Regarding the topic of reproducible protein identification and quantification, a recent metaproteomic study demonstrated the use of Tandem Mass Tag (TMT) approach in human stool samples  [22].