dataesr/scientific-paragraphs-categorization huggingface dataset:
Cellular composition changes and cell-autonomous expression changes can both explain the 10 divergence-convergence pattern. Ageing-related transcriptome changes observed using bulk tissue samples may be explained by temporal changes in cell type proportions within tissues, by cellautonomous expression changes, or both. To explore whether the observed inter-tissue DiCo patterns may be attributed to changes in cell type proportions, we used published data from a mouse singlecell RNA-sequencing experiment (Tabula Muris Consortium 2020). For each of the four tissues in our original experiment, we collected cell type-specific expression profiles from 3-month-old young adult mice in the Tabula Muris Senis dataset. We deconvoluted bulk tissue expression profiles in our mouse dataset using the corresponding tissue's cell type-specific expression profiles by regression analysis (Methods), and studied the relative contributions of each cell type to tissue transcriptomes and how these change with age.
Repeating the analysis considering DiCo genes only yielded a similar trend  (30/54 [56%]  decrease in correlation among the most similar cell types, permutation test with re-sampling all genes, p>0. 1; and 47/54 [87%] increase in correlation among the most distinct cell types, permutation test, p>0. 1). These results suggest that cell-autonomous changes also contribute to inter-tissue convergence during ageing and provide evidence for age-related cellular identity loss. Finally, we tested the possibility of intra-tissue convergence of cell types in the Tabula Muris Senis dataset, by calculating expression variation among cell types using the CoV measure for each individual. However, we did not observe a consistent trend of increasing similarity among cell types within tissue from 3m-to 24m-old mice (Figure  5 -figure supplement 6).
The second observation is the lack of significant overlap among reversal genes among tissues. These imply that the parallel changes among tissues observed during development and ageing, described earlier, do not include reversals. Therefore, we hypothesised that reversals might instead be linked to inter-tissue divergence and tissue specialisation during development, and possibly convergence during ageing. We indeed observed that the tissue-specific genes show an up-down reversal pattern in their native tissue. Studying inter-tissue similarity across mouse lifespan, we further found that the four tissues' transcriptomes diverged during postnatal development, and we further detected a trend, albeit weak, towards inter-tissue convergence during ageing. Studying two additional datasets from mice and humans, we observed that convergence during ageing is common, although possibly not ubiquitous.
Importantly, these results reliably show that developmental inter-tissue divergence does not continue into ageing. We predict that future work on ageing with datasets, including multiple distinct tissues and larger sample sizes may establish the prevalence of the DiCo phenomenon. Finally, we report three interesting observations on DiCo. We determine that tissue-specific genes tend to be down-regulated in the tissues that they belong to during ageing, while non-tissue-specific 13. CC-BY 4. 0 International license made available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is The copyright holder for this preprint this version posted March 10, 2021. ; https://doi. org/10. 1101/2021. 03. 02.
Functional associations: We tested the functional associations of age-related gene expression change in separate tissues for each period separately, following gene set over-representation analysis (GORA) procedure with Gene Ontology (GO)  (Ashburner et al. 2000)  Biological Process (BP) categories using the 'topGO' package (Alexa and Rahnenfuhrer 2019). We applied the classical algorithm and performed Fisher's Exact Test on categories that satisfy the criteria of a minimum 10 and maximum 500 number of genes. We used the whole set of expressed genes (n=15,063) as the background. P-values were corrected for multiple testing using Benjamini-Yekutieli (BY)  (Benjamini and Yekutieli 2001)  procedure, which does not assume independent tests and thus is suitable for multiple testing correction for GO enrichment with potentially overlapping gene sets.
Functional association: To test the functional associations of the genes showing divergent-convergent pattern among tissues, we performed GSEA using GO BPs. We retrieved developmental divergent genes (CoV-age ⍴>0, n=9,058) and multiplied these correlation values with the ones calculated in the ageing period. Therefore, the genes with a negative value represent a divergent-convergent pattern quantitatively, while the ones with a positive product represent a divergent pattern throughout lifetime. We then ranked the genes according to the calculated product values and sought enrichment for the upper and lower tail of the distribution using the Kolmogorov-Smirnov (KS) test implemented in the 'clusterProfiler' package  (Yu et al. 2012). 'gseGO' function was used with parameters: nPerm=1000, minGSSize=10, maxGSSize=500 and pValueCutoff=1.
Preprocessing We used Tabula Muris Senis dataset (Tabula Muris Consortium 2020) for scRNA-seq analysis as it is the only dataset to our knowledge that includes time-series samples covering old age, and the tissues present in our dataset. Seurat processed FACS data of the tissues: lung, liver, skeletal muscle and non-myeloid brain, were downloaded from figshare database  (Pisco 2020). Seurat package  (Stuart et 23 al. 2019 ) was used to retrieve the expression matrix of the cells that are annotated to cell types in the original article. Each tissue contains samples from three time points; 90 (3m), 540 (18m) and 720 (24m) days old mice, totaling 14 samples each in lung, liver and brain and 9 samples in liver. We excluded cell types with less then 15 cells among all samples, and excluded genes if the expression level is 0 for all cells at a given age.
This resulted in a median number of 99-382 cells assigned to cell types, 6-24 cell types and 16,951-22,122 genes across tissues. Using 3 month old mice, we calculated cell type specific expressions in each tissue such that we first calculated the mean expression levels among cells of an individual mouse for each cell type and then calculated the mean among individuals to obtain an average expression value for each cell type. Uniprot gene symbols were converted to Ensembl gene ids using biomaRt R package  (Durinck et al. 2009).
Deconvolution We used cell type specific expression profiles of 3-month-old mice to estimate relative contributions of cell types to the transcriptome profiles of tissues in our mouse dataset. We used a linear-regression based deconvolution method for each tissue using three genesets; all genes  (n=[12,492, 12,849] ), DiCo genes  (n=[4,007, 4,106] ) and non-DiCo genes  (n=[8,485, 8,743] ). Regression coefficients were used as relative contribution of cell types. We then tested the effect of age on cell type contributions using Spearman correlation test in development and ageing.
"Obs:" number of genes having the same age-related change trend among tissues. P value was calculated as the proportion of permutations that are higher than the observed value. a) Shared age-related genes among 2, 3 and 4 tissues in development. b) Shared age-related genes among 2 tissues in the ageing period. 38         (Fisher"s Exact Test; ORcortex=1. 66, ORlung=4. 41, ORliver=1. 47, ORmuscle=2. 12, p<10 -6 for each test). 57 Deconvolution of bulk tissue expression profiles of the mice in our dataset with regression analysis using the single-cell expression profile of the 3-month-old mice in the Tabula Muris Senis dataset. Contribution of each cell type was measured using three gene sets; all genes  (n=[12,492, 12,849] ),  DiCo (n=[4, 007, 4, 106] ) and non-DiCo genes  (n=[8,485, 8,743] ).
Likewise, transcripts related to "cell wall"  (47)  and "lipids" (80) categories were also differentially regulated under this condition (Figure  3B ). A more detailed analysis of highly enriched terms in the MapMan categories, such as transcription factors (TFs), transcriptional regulators (TRs), and protein kinases (PKs), was also conducted, using specific Arachis transcriptome databases. These functional categories are largely involved in the transcriptional reprogramming of the enhanced primed phase of plants upon stress and could be considered common priming state indicators  [8]. As expected, many TFs (332) showed an altered expression in response to UV stress, representing 18% over the 1854 gene models coding for TFs in A. duranensis. These UV-responsive TFs belong to 38 families, being MYB (14%), WRKY (10%), and bHLH (9%) the most represented (Figure  4A ).
Comparative Transcriptome Meta-Analysis of A. stenosperma under Different Stresses Aiming to identify genes that are commonly responsive to a broad spectrum of primary stimulus, we performed a meta-analysis comparison of abiotic and biotic stress responses in A. stenosperma by analyzing our previous transcriptomic datasets (Table  S3 )  [28, 46, 53]. For meta-analysis, the six RNA-Seq libraries from abiotic stress treatments (UV exposure, dry-down, and dehydration) were analyzed separately from those of the four biotic stress treatments (nematode inoculation) (Table  S3 ). Overall, we found 4513 metaDEGs commonly regulated, showing the same expression trend (up-or downregulation) in response to abiotic stresses and 5034 upon biotic stresses (Figure  6 ). Of these, 1460 (18%) metaDEGs were shared among all biotic and abiotic analyzed studies.
They were thus considered here as common metaDEGs responsive to a wide range of stresses in A. stenosperma (Figure  6 ), being ten of them further identified as 'multistress-robust metaDEGs' (see below). This result indicated that most DEGs were uniquely modulated by either abiotic or biotic stress, and A. stenosperma may use distinct transcriptional mechanisms to deal with different stresses, as previously reported in wild Arachis species plants submitted to individual and combined stresses  [53]. This metaDEGs dataset was further screened for putative plant priming genes responsible for initiating non-specific defense responses in many species.
A. stenosperma MetaDEGs Involved in Responses to Different Stress and Putatively Associated with Defense Priming To explore the core A. stenosperma transcript dynamics under biotic and abiotic stress conditions, we conducted a meta-analysis by combining the UV RNA-Seq data produced here with our previous transcriptomic datasets  [28, 46, 53]. Considering that UV-C exposure is an abiotic stress, we analyzed this data together with the transcriptome of two abiotic stress treatments, i. e. , severe (dehydration) and moderate (dry-down) drought imposition, as previously reported by  [89]  for wild Arachis plants submitted to water-limited conditions. This analysis revealed 4513 DEGs commonly modulated by these abiotic stress treatments in A. stenosperma. Likewise, we found that 5034 DEGs were shared by transcriptome of A.
Functional Analysis of A. stenosperma DEGs A. duranensis gene models functional annotation (http://peanutbase. org/ (accessed on 20 September 2021)) was used to assign the A. stenosperma DEGs into Gene Ontology (GO) categories. The hypergeometric test implemented in the FUNC package  [108]  was performed to test for significantly overrepresented GO terms among the DEGs using default parameters, as previously described  [28]. Only terms with a family-wise error rate (FWER) < 0. 05 for overrepresentation were considered in the analysis. The functional classification of the DEGs was inferred using the online tool Mercator v3. 6 (https://www. plabipd. de/portal/mercator-sequence-annotation (accessed on 15 October 2021)), searching against the A. thaliana reference database and SwissProt/UniProt, with the default settings.
These results were further submitted to MapMan software (http://mapman. gabipd. org/ (accessed on 15 October 2021)) to visualize the gene expression data in different pathways involved in stress responses. Plant transcription factors (TFs), transcriptional regulators (TRs), and protein kinases (PKs) were then identified from DEGs and classified into gene families by using the A. duranensis database of the iTAK program  [109]. The expression profiles of A. stenosperma DEGs identified as TFs, TRs and PKs were then subjected to heatmap construction in ggplots package  [110]  using the transcriptome data here obtained (UV exposure) and those previously published by our group from roots of A. stenosperma plants submitted to biotic (M. arenaria infection) and abiotic (dehydration and dry-down treatments) stresses  [28, 46, 53].
Identification of Orthologs of Plant Priming Markers To find A. duranensis orthologs of the 44 universal A. thaliana gene markers of plant priming  [8] , we examined a set of 35,238 protein orthogroups, previously characterized by our team  [54]. These groups of putatively orthologous genes (orthogroups) were inferred by the comparative analysis across the predicted proteomes of 22 plant species, including A. thaliana and A. duranensis, using the OrthoFinder v1 software  [111]. The A. duranensis orthologs of the plant priming markers were then found by searching the corresponding A. thaliana gene ID  [8]  in this previously characterized set of protein orthogroups  [54].
qRT-PCR reactions were conducted in technical duplicates for each sample on a StepOne Plus Real-Time PCR System (Applied Biosystems, Foster City, USA), according to  [52]. No template control (NTC) samples were included as negative controls. The online real-time PCR Miner tool  [112]  was used to estimate the primer efficiency and optimal cycle of quantification (Cq) values. Expression ratios of transcripts from the UV-stressed samples relative to nonstressed control samples (Relative Quantification; RQ) were determined and statistically tested using the REST 2009 v. 2. 0. 13 software  [113]  and normalized with two Arachis reference genes (60S and GAPDH), in accordance with  [114].
The present article reports the creation and usage of a general natural product database for structural dereplication of natural products. This database, acd_lotusv7, is based on the LOTUS natural products database as the unique source of chemical structures. Database construction and use for dereplication relies on the commercial ACD/Labs C+H Predictor and DB software. The linkage of each natural compound with a Wikidata resource identifier accelerates the access to the primary literature data such as biologic origin and bibliographic references.
Structural dereplication is achieved within the CaraMel procedure by looking into a locally developed and enriched database for compounds that match with a list of  13  C nuclear magnetic resonance (NMR) chemical shift values. The database contains structures that were accumulated through bibliographic searches driven by the taxonomic classification of the successively studied organisms. Each compound in this database is linked to a list of  13  C NMR predicted chemical shifts. The structural and spectroscopic data management is ensured by a software acquired from Advanced Chemistry Development, Inc. (hereafter, ACD/Labs) called C+H Predictors and DB (hereafter referred to as the ACD/Labs software, even though the ACD/Labs company provides many other software products). [2] pplementing a collection of small molecule structures with predicted NMR data by means of the ACD/Labs software is a tedious operation because there is by design no simple way to deal with batches of structures.
Prediction quality is high but operations on large amounts of structures is unpractical. More precisely, prediction in ACD/Labs software may be carried out by two different procedures. The accurate and slow one relies on the search of molecular fragments within an internal database. Besides, a predictor is dedicated to the verification of user supplied NMR chemical shifts values assigned to used supplied molecular structures. This predictor (hereafter, the validation algorithm) operates in an unsupervised way on collections of molecular structures for which a previously user-supplied NMR assignment exists. The output of the validation algorithm can be reused by a dedicated software to update the initially given structure assignments so that the predicted ones appear as if they were experimental.
This way of supplementing a collection of structures by means of the ACD/Labs validation algorithm was already proposed in the framework of the constitution of smallsized, taxonomically focused databases. [3] e present article reports the creation and usage of a general natural product database for structural dereplication. This database, acd_lotusv7, is based on the LOTUS natural products database. [4] The choice of  13  C NMR spectroscopy as primary identification data source and the resorting on predicted chemical shift values have already been discussed in previous works. [1, 5].
Material and methods All calculations were carried out on a DELL Precision 3530 laptop computer with 16 GB of RAM memory and an Intel® Core™ i5-8400H CPU @ 2. 5 GHz running Windows 10 Education version 20H2. The ACD/C+H NMR Predictors and DB 2021. 1. 0 software was purchased from ACD/Labs (Toronto, Ontario, Canada). The library of cheminformatics functions RDKit version 2021. 03. 2 was run in the conda environment (anaconda. com) with python 3. 6. 13 as programming language. [6] Additional chemical structure operations were carried out by functions implemented in the KnapsackSearch GitHub repository (https://github. com/nuzillard/KnapsackSearch). [7] The LOTUS database version 7 in file 220525_frozen_metadata. csv. gz, was downloaded from the zenodo data repository, https://zenodo. org/record/6582124.
[8]  The unpacking of 220525_frozen_metadata. csv. gz provided the text file 220525_frozen_metadata. csv from which each line was scanned to constitute triplets made of the line number, of a wikidata identifier (QID, see wikidata. org) and of a SMILES chain. [9] There may be more than one line related to the same QID and SMILES; in this case only the first line was considered for future use. The text produced by the python script csv2smi. py from the triplets was redirected to file lotusv7. smi; it contained two columns, one for the SMILES chain and the other one for a compound character string formed by the QID and the line number joined by an underscore character, such as in Q43656_2, and used as compound name. Wikidata compound Q43656 is cholesterol and it appears in file 220525_frozen_metadata.
csv at line 2. The file lotusv7. smi contains a minimal description for 201022 compounds. The script smi2ACD. py applied to lotusv7. smi resulted in file fake_acd_lotusv7. sdf in which a fake chemical shift value was assigned to each carbon atom (99. 99). The resulting chemical shift lists, one per compound, were formatted to be understood by the ACD/Labs software as if they were experimental chemical shifts. File fake_acd_lotusv7. sdf contains 201022 compounds. Action of tautomer. py and rdcharge. py scripts achieved in-place tautomer correction and charge/valence correction. The necessity of using these scripts was reported in a preceding work. [3] Operations on structure files make use of the RDKit cheminformatics function library for SMILES chain interpretation, 2D structure diagram generation, tautomer correction and SDF structure file handling.
The splitter. py script applied to file fake_acd_lotusv7. sdf created 21. sdf files stored in a dedicated subdirectory. These pieces were named fake_acd_lotusv7_xx. sdf with xx ranging from 00 to 20, the 20 first ones contained 10,000 compounds each and the last one contained the remaining structures. Chemical shift prediction by ACD/Labs software using the validation algorithm was carried out on these sub-files. Each sub-file was imported in an ACD/Labs database, validated for chemical shifts and exported as fake_acd_lotusv7_xx_exported. sdf. Action of the script CNMR_predict on the latter produced the files true_acd_lotusv7_xx. sdf in which initial fake chemical shift values were replaced by predicted ones. The content of these files was assembled into a single file, acd_lotusv7_tmp. sdf, containing 201,010 compound descriptions.
The supplement. py script collected chemical taxonomy data present in file 220525_frozen_metadata. csv and appended them to compound metadata in file acd_lotusv7_tmp. sdf in order to produce file acd_lotusv7. sdf. Chemical taxonomy data were obtained by the authors of LOTUS from NPclassifier and Classyfire. [10, 11] The chemical identifiers SMILES, InChI and InChIKey  [12]  were recalculated from the structures in acd_lotusv7. sdf and the resulting InChIKeys compared to those provided by 220525_frozen_metadata. csv. File acd_lotusv7. sdf was finally imported in the ACD database file acd_lotusv7. NMRUDB for future structural dereplication works. Three natural product structures and their corresponding list of experimental chemical shift values were selected from published literature in order to illustrate the use of the acd_lotusv7 database for structural dereplication from  13  C NMR data.
Results All the structures reported in LOTUSv7 file 220525_frozen_metadata. csv but 12 were imported in acd_lotusv7. NMRUDB, thus constituting a very high success rate due to the careful design of the LOTUS database. However, compounds Q105187773 and Q105007277 were manually discarded as they were erroneously supposed to contain divalent helium atoms. The other discarded compounds were left out by the ACD/Labs software at the time of fake_acd_lotusv7_xx. sdf file importation. Discrepancies between initial InChIKey descriptors and recalculated ones arose in 4167 compounds and originated from wrong writing or reading of configurational features in 2D structure drawings. The overall chemical shift prediction task took about 30 hours, corresponding to an average processing rate of 2 compounds per second.
The size of the final acd_lotusv7. NMRUDB file was 706 MB. The acd_lotusv7. sdf file can be read by any cheminformatics tool and its compressed version, of size 219 MB, was stored in a zenodo repository at https://zenodo. org/record/6621129. Compound 1 is characterized by a list of 15  13  C NMR chemical shifts: 36. 4, 22. 3, 29. 7, 33. 5, 44. 9, 31. 8, 161. 4, 78. 7, 41, 75, 120. 5, 176, 8. 3, 14. 7, and 16 ppm. [13] Searching in database acd_lotusv7 was carried out with these values as chemical shift targets, requesting at least the matching of 15 chemical shift values with a looseness of 3 ppm and solution ranking based on minimal distances. The list of results contained 177 structures, among which the one ranked first did fit will all constraints on chemical shift values and was identical to the published structure, the one of an eremophilane sesquiterpene isolated in our group from Hertia cheirifolia, an Asteraceae collected in the North of Africa.
[13] Searching Wikidata for the identifier related to the selected compound, Q105172965, lead to list of three plants in which it was found. Selecting Hertia cheirifolia led to the reference of the original publication, referenced Q57818186 in Wikidata, and then to the its DOI: 10. 1016/0031-9422(90)83039-4, thus providing an incredibly quick way of establishing a relationship between a list of chemical shifts and an article published in a phytochemistry journal in 1990. This structure is the first one of a natural product that was determined in 1989 by the author of the present article, when there were no computers in most of academic chemistry laboratories and at a time this story would have been considered as pure science-fiction. Scheme. Structure of compounds 1, 2, and 3 Compound 2 was reported with 43  13    4, 128.