dataesr/scientific-paragraphs-categorization huggingface dataset:
coli during acute extraintestinal infections. To this end, we have sequenced isolates, one of which was a strong mutator, from three different infections from various body sites using Illumina and Oxford Nanopore technologies in order to obtain high-quality genomic data. These data were processed using well-known bioinformatics tools and in-house scripts, giving a comprehensive catalogue of the mutations present in the collected isolates. We uncovered a high level of convergence both between and within the three infections as mutations found in genes hit multiple times represented more than 40% of the total. This suggests that adaptation is at play in these infections and that sufficient levels of diversity can be generated efficiently in E. coli in environments as diverse as the peritoneal fluid, the kidney, and the cerebrospinal fluid.
Isolate 5-36 from patient 17 was sequenced in 100-bp single reads later on equivalent equipment. Finally, the isolates from patient 11 were all sequenced in 100-bp paired-end reads on a HiSeq 4000 sequencer. For each patient, one nonmutator isolate was chosen as a reference and was sequenced on the Oxford Nanopore Technologies MinION platform using an R9. 4 flow cell, and samples were prepared using kits LSK-108 and NBD-104 for library preparation and barcoding. A hybrid assembly strategy with Unicycler software V0. 4. 4 (61) provided high-quality assembly on which to base further genomic analysis. Genomic analysis. For each patient, the Illumina reads for each isolate and the corresponding reference generated as mentioned above were independently fed to Breseq V 0. 39. 0 (62). Breseq outputs were processed using an in-house-made pipeline.
All the code is available as an R notebook in a repository at https://github. com/A-BN/acute_infect_evol. A table containing all the recovered mutations is available in Data Set S1 in the supplemental material as well as at an interactive webpage at https://a-bn. github. io/acute_infect_evol/2020-all_mutations. html. Mutation rate assay. Mutation frequencies of the isolates were estimated by monitoring their capacity to generate mutations conferring resistance to rifampicin in triplicates in at least three independent cultures (nine values minimum), as described previously  (22). rdar morphotype and H 2 O 2 resistance. Cells of the mutants of isolate 5-35 from patient 17 were tested by red, dry, and rough (rdar) morphotype and H 2 O 2 resistance assays, which are dependent on RpoS. For the rdar phenotype, cells were grown on LB agar plates without NaCl (LB0) at 28°C.
ACKNOWLEDGMENTS The author contributions as listed and described by CASRAI (https://casrai. org/credit/ [consulted on 20 November 2020]) are as follows: A. B. -N. , Software, Investigation, Visualization, Writingoriginal draft, and Writingreview & editing; A. L. , Software, Investigation, and Writingoriginal draft; A. B. , Methodology, Investigation, and Visualization; M. M. , Investigation; V. W. , Investigation; J. C. , Investigation; S. D. , Investigation; V. R. -S. , Investigation; O. C. , Investigation; F. N. , Investigation; E. D. , Project administration, Funding acquisition, and Writingreview & editing; O. T. , Supervision, Project administration, Funding acquisition, and Writingreview & editing. These contributions were the only factors considered in the ordering of the names of the authors.
The increasingly large amount of public proteomics data enables, among other applications, the combined analyses of datasets to create comparative protein expression maps covering different organisms and different biological conditions. Here we have reanalysed public proteomics datasets from mouse and rat tissues (14 and 9 datasets, respectively), to assess baseline protein abundance. Overall, the aggregated dataset contains 23 individual datasets, which have a total of 211 samples coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively. We compared protein expression between the different organs, including the distribution of proteins across them. We also performed gene ontology and pathway enrichment analyses to identify organ-specific enriched biological processes and pathways.
Author summary We have reanalysed 23 baseline public proteomics datasets stored in the PRIDE database belonging to various mouse and rat tissues and organs. Overall, the aggregated dataset contained 211 samples, coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively. We analysed the distribution of protein expression across organs in both species. Then we performed gene ontology and pathway enrichment analyses to identify organ-specific enriched biological processes and pathways. We also carried out a comparative analysis of baseline protein expression between mouse, rat and human tissues, observing a high level of expression correlation among orthologs in all three species, in brain, kidney, heart and liver samples. To disseminate these findings, we have integrated the protein expression results into the resource Expression Atlas.
Introduction High-throughput mass spectrometry (MS)-based proteomics approaches have matured significantly in recent years, becoming an essential tool in biological research  [1]. This has been the consequence of very significant technical improvements in MS instrumentation, chromatography, automation in sample preparation and computational analyses, among other areas. The most used MS-based experimental approach is Data Dependent Acquisition (DDA) bottom-up proteomics. Among the main quantitative proteomics DDA techniques, label-free approaches remain very popular, although labelled-approaches, especially those techniques based on the isotopic labelling of peptides (MS 2 labelling), such as iTRAQ (Isobaric tag for relative and absolute quantitation) and TMT (Tandem Mass Tagging), are increasingly popular as well.
Following the steps initiated by genomics and transcriptomics, open data practices in the field have become embedded and commonplace in proteomics in recent years. In this context, datasets are now commonly available in the public domain to support the claims published in the corresponding manuscripts. The PRIDE database  [2] , located at the European Bioinformatics Institute (EBI), is currently the largest resource worldwide for public proteomics data deposition. PRIDE is also one of the founding members of the global ProteomeXchange consortium  [3] , involving five other resources, namely PeptideAtlas, MassIVE, iProX, jPOST and PanoramaPublic. ProteomeXchange has standardised data submission and dissemination of public proteomics data worldwide. As a consequence, there is an unprecedented availability of data in the public domain, which is triggering multiple applications  [4] , including the joint reanalysis of datasets (so-called meta-analysis studies)  [5] [6] [7].
Indeed, public proteomics datasets can be systematically reanalysed and integrated e. g. , to confirm the results reported in the original publications, potentially in a more robust manner since evidence can be strengthened if it is found consistently across different datasets. Potentially, new insights different to the aims of the original studies can also be obtained by re-analysing the datasets using different strategies. It is the so-called re-purposing of public datasets  [8] , including for instance proteogenomics studies, for genome annotation purposes  [9] [10] [11] [12]. In this context of reuse of public proteomics data, PRIDE has started to work on developing data dissemination and integration pipelines into popular added-value resources at the EBI. This is perceived as a more sustainable approach in the medium-long term than setting up new bioinformatics resources.
One of them is Expression Atlas  [13] , a resource that has enabled over the years easy access to gene expression data across species, tissues, cells, experimental conditions and diseases. Only recently, protein expression information coming from reanalysed datasets has been integrated in the 'bulk' section of Expression Atlas. As a result, proteomics expression data can be integrated with transcriptomics information, mostly coming from RNA-Seq experiments. So far, we have performed two main meta-analysis studies involving the reanalysis and integration of: (i) 11 public quantitative datasets coming from cell lines and human tumour samples  [14] ; and (ii) 25 human baseline datasets coming from 32 different organs  [15]. The next logical step is to perform an analogous study of baseline protein expression in two of the main model organisms: Mus musculus and Rattus norvegicus.
To date, there are only a small number of bioinformatics resources providing access to reanalysed MS-based quantitative proteomics datasets, and even fewer if one considers only mouse and rat data. In this context, at the end of 2020, ProteomicsDB  [16]  released a first version of the mouse proteome, based on the re-analysis of five datasets. To the best of our knowledge, there is no such public resource for rat data yet anywhere. Additionally, although antibody-based human protein expression information is provided via the Human Protein Atlas  [17]  , their efforts are focused on human protein expression. Here, we report the reanalysis and integration of 23 public mouse (14 datasets) and rat (9 datasets) label-free datasets, and the incorporation of the results into the resource Expression Atlas as baseline studies.
Baseline proteomics datasets Overall, we quantified protein expression from 34 healthy tissues in 14 organs coming from 23 datasets. The analyses covered a total of 1,173 MS runs from 211 samples that were annotated as healthy/control/non-treated samples thus representing baseline protein expression. Non-control/disease samples associated with these datasets were also analysed but are not discussed here. Normalised protein abundances values (as ppb, parts per billion) from both control/healthy/non-treated and disease/treated tissue samples are available to view as heatmaps in Expression Atlas. The protein abundances along with sample annotations, sample quality assessment summary and experimental parameter inputs for MaxQuant can be downloaded from Expression Atlas as text files. Data selection and reanalysis protocol is shown in Fig.
Protein coverage across organs and datasets One of the main aims of this study was to study protein expression across various organs. To enable simplicity of comparison  [15]  we first grouped 34 different tissues into 14 distinct organs, as discussed in 'Methods'. We defined 'tissue' as a distinct functional or structural region within an 'organ'. We estimated the number of 'canonical proteins' identified across organs by first mapping all members of each protein group to their respective parent genes. We defined the parent gene as equivalent to the UniProt 'canonical protein' and we will denote the term 'protein abundance' to mean 'canonical protein abundance' from here on in the manuscript. (tendon, 273, 2. 2%), as highlighted above (Fig. 2B ).
Mouse proteome A We studied the normalised protein abundance distribution in organs (Fig. 2C ) and found that all organs, except tendon, had similar median abundances. However, one cannot attribute biological meaning to these observations, since the method of normalisation by definition fixes each sample to have the same "total abundance", which then gets shared out amongst all proteins. The normalised protein abundance distribution in datasets indicated a higher than median abundances detected in datasets PXD004612 (tendon) and PXD003164 (testis) (Fig. 2D ). A linear relation was observed with the number of canonical proteins detected in datasets and organs, when compared to the relative amount of their spectral data (Fig. 2E ). We found a large number of proteins uniquely present in one organ (Fig.
2F ). A C B D E F R 2 = 0. 61 PXD004496 (1) PXD004612 PXD009639 PXD009909 (  1 ) PXD019431 (1) R 2 = 0. 38 ArticularCartilage (6) Brain (66) Eye (2) Heart (  11 ) Liver (11) Lung (3) Pancreas (  1 ) Spleen (1) Tendon (  8 ) Testis (  1 ) TricepsMuscles (3) R 2 = 0. 51 PXD004496 (1) PXD004612 PXD009639 (  1 ) Distribution of canonical proteins identified across organs. PXD009909 (1) PXD019431 (1.
Datasets PXD016958 and PXD016793 consisted entirely of kidney and liver samples, respectively, and as mentioned above had the largest number of canonical proteins identified (Fig. 3B ). The normalised protein abundances were similar among the various organs and datasets (Fig. 3C, D ). We also observed a linear relation between the number of canonical proteins identified and the MS spectra identified (Fig. 3E ). As seen in mouse datasets, we also observed a large number of proteins uniquely present in one organ (Fig. 3F ). Liver (  8 ) Lung  (10)  SpinalCord  (18)  Tendon (  3 ) Testis (3) R 2 = 0. 88 PXD001839 (1) PXD003375 (2) PXD015928 Heart (  28 ) Kidney (  32 ) Liver (  8 ) Lung (  10 ) SpinalCord (  18 ) Tendon (  3  Distribution of canonical proteins identified across organs.
Mouse proteome We found that 1,086 (8. 6%) proteins were found with their highest expression in at least 3 organs, with a median bin value greater than 4 (Supplementary file 3). On the other end of the scale, 138 (1. 1%) canonical proteins were found with their lowest expression in at least 3 organs, with a median bin value less than 2. The bin transformed abundances in all organs are provided in Supplementary file 3. To compare protein expression across all organs in mouse, we calculated pairwise Pearson correlation coefficients (rp) across 117 samples (Fig. 4A ). We observed a good correlation of protein expression within the brain (median rp = 0. 55) and heart (median rp = 0. 82) samples. We performed PCA on all samples from mouse datasets for testing the effectiveness of bin transformation method in reducing batch effects.
Fig. 4B  shows the clustering of samples from various organs of mouse. We observed samples from the same organ clustered together. 242 For example, we can observe that brain samples all clustered together in one group, although 243 they come from different datasets (Fig. 4C ). However, we also observed that samples from 244 other organs such as liver, did not cluster according to their organ types but clustered together 245 with the dataset they were part of, indicating the presence of residual batch effects. 246 The colour represents the correlation coefficient and was calculated using the bin transformed 250.
Rat proteome Similarly, in rat, on one hand, 311 (4. 3%) proteins were found with highest expression in at least 3 organs with a median bin value greater than 4. On the other hand, 27 (0. 37%) canonical proteins were found with their lowest expression in at least 3 organs, with a median bin value of less than 2. The bin transformed abundances in all organs are provided in Supplementary file 3. The samples from rat datasets were also better correlated among each other (Fig. 5A ) than in the case of mouse. We observed a good correlation of protein expression within samples from liver (median Pearson's correlation rp = 0. 92), lung (median rp = 0. 84), spinal cord (median rp = 0. 80), heart (median rp = 0. 84) and brain (median rp = 0. 93). We also observed a good clustering in the PCA of samples coming from the same organ type (Fig.
5B ). Kidney, lung, spinal cord and heart samples all clustered together according to their organ type. Fig. 5C  shows the samples based on the dataset they are part of. However, most organ samples are part of individual datasets except samples from heart, which came from two datasets (PXD001839 and PXD013543). Fig. 5C  shows that the heart samples clustered into two groups, wherein each cluster included samples from a different dataset. Rat-Samples bin_values_batch-per-dataset-regions [filter: genes detected in at least 50% of samples] [number of genes: 1753].
Protein abundances across orthologs in three species We had previously analysed 25 label-free proteomics datasets from healthy human samples to assess baseline protein abundances in 14 organs following the same analytical methodology  [15]. We compared the expression of canonical proteins identified in all three organisms (rat, mouse and human). Overall, 13,248 detected human genes (corresponding to the canonical proteins) were compared with 12,570 genes detected in mouse and 7,116 genes detected in rat. The number of orthologous mappings (i. e. , "one-to-one" mappings, see 'Methods') between rat, mouse and human genes are listed in table  3. We only considered one-to-one mapped orthologues for the comparison of protein abundances. Table  3. Homologs identified in mouse and rat datasets when compared with the background list of genes identified in human datasets (Supplementary file 2 in  [15] ).
Discussion We have previously reported two meta-analysis studies involving the reanalysis and integration in Expression Atlas of public quantitative datasets coming from cell lines and human tumour samples  [14] , and from human baseline tissues  [15] , respectively. In this study, we reanalysed mouse and rat baseline proteomics datasets representing protein expression across 34 healthy tissues and 14 organs. We have used the same methodology as in the study involving baseline human tissues, which enabled a comparison of protein expression levels across the three species. Our main overall aim was to provide a systemwide baseline protein expression catalogue across various tissues and organs of mouse and rat and to offer a reference for future related studies. We analysed each dataset separately using the same software (MaxQuant) and the same search protein sequence database.
The disadvantage of this approach is that the False Discovery Rate (FDR) statistical thresholds are applied at a dataset level and not to all datasets together as a whole. However, as reported before  [15] , using a dataset per dataset analysis approach is in our view the only sustainable manner to reanalyse and integrate quantitative proteomics datasets, at least at present. As also reported in previous studies, one of the major bottlenecks was the curation of dataset metadata, consisting of mapping files to samples and biological conditions. Very recently, the MAGE-TAB-Proteomics format has been developed and formalised to enable the reporting of the experimental design in proteomics experience, including the relationship between samples and raw files, which is recorded in the SDRF-Proteomics section of the file  [41].
Submission of the SDRF-Proteomics files to PRIDE is now supported. The more well-annotated datasets in the public domain, the easier data re-use activities will become. CC-BY-NC-ND 4. 0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted December 21, 2021. ; https://doi. org/10. 1101/2021. 12. 20. 473413 doi: bioRxiv preprint The generated baseline protein expression data can be used with different purposes such as the generation of protein co-expression networks and/or the inference of protein complexes. For the latter application, expression data can be alone or potentially refining predictions obtained using different methods such as the recently developed AlphaFold-based protein complexes predictions  [42].
Mouse and rat are widely used species in the context of drug discovery, the latter especially, to undertake regulatory pre-clinical safety studies. Therefore, it is important to know quantitative protein expression distribution in these species in different tissues  [43]  to assist in the selection of species for such studies and also for the interpretation of the final results. In addition to the analyses reported, it would also be possible to perform correlation studies between gene and protein expression information. However, we did not find any relevant public datasets in the context of this manuscript where the same samples were analysed by both techniques, which is the optimal way to perform these studies. Future directions in analogous studies will involve: (i) additional baseline protein expression studies of other species, including other model organisms or other species of economic importance; (ii) the inclusion of differential datasets (e.
g. using TMT and/or iTRAQ); and (iii) include relevant proteomics expression data coming from the reanalysis of Data Independent Acquisition (DIA) datasets  [44]. As mentioned above, we performed a comparative analysis of baseline protein expression across human, mouse and rat. It was possible to perform this analysis for six common organs (brain, heart, kidney, liver, lung and testis). Ortholog expression across species is useful to infer protein function across experimentally studied proteins. This is particularly useful as evolutionarily closely related species are likely to conserve protein function. We could not find in the literature an analogous comparative study performed at the protein level. However, protein expression from closely related orthologs across tissues or organs has been compared at the transcriptomics level, providing a complete picture of gene expression.
In this context, many studies have compared gene-expression in mouse, rat and human orthologues and found that orthologues had generally a highly correlated expression tissue distribution profile in baseline conditions  [45] [46] [47] [48] [49]. Gene expression levels among orthologs were found to be highly similar in muscle and heart tissues, liver and nervous system and less similar in epithelial cells, reproductive systems, bone and endocrine organs  [47]. Studies have also shown that variability of gene expression between homologous tissues/organs in closely related species can be much lower than the variability between unrelated tissues within the same organism  [45, 46] , in agreement with the results reported here at the protein level. In conclusion we here present a meta-analysis study of public mouse and rat baseline proteomics datasets from PRIDE.
Datasets As of May 2021, there were 2,060 mouse (Mus musculus) and 339 rat (Rattus norvegicus) MS proteomics datasets publicly available in the PRIDE database (https://www. ebi. ac. uk/pride/). Datasets were manually selected based on the selection criteria described previously  [15]. Briefly, we selected datasets where baseline expression experiments were performed on (i) label-free samples from tissues not enriched for posttranslational modifications; (ii) Thermo Fisher Scientific instruments such as LTQ Orbitrap, LTQ Orbitrap Elite, LTQ Orbitrap Velos, LTQ Orbitrap XL ETD, LTQ-Orbitrap XL ETD, Orbitrap Fusion and Q-Exactive, since they represent a large proportion of datasets in PRIDE and to avoid heterogeneity introduced by data from other vendor instruments; (iii) had suitable sample metadata available in the original publication or it was possible to obtain it by contacting the authors; and (iv) our previous experience in the team of some datasets deposited in PRIDE, which were discarded because they were not considered to be useful (data not shown).
Overall, 14 mouse and 9 rat datasets were selected from all mouse and rat datasets for further analysis. Table  1  lists the selected datasets. The 23 datasets contained a total of 211 samples from 34 different tissues across 14 organs (meaning groups of related tissues, more details below), comprising 9 different mouse and 3 rat strains, respectively. The sample and experimental metadata were manually curated using the information provided in the respective publications or by contacting the original authors/submitters. Annotare [50] was used for annotating the metadata and stored using the Investigation Description Format (IDF) and Sample-Data Relationship Format (SDRF) file formats  [41] , which are required for integration in Expression Atlas. An overview of the experimental design including experimental factors, protocols, publication information and contact information are present in the IDF file, and the SDRF includes sample metadata describing the relationship between the various sample characteristics and the data files contained in the dataset.