dataesr/scientific-paragraphs-categorization huggingface dataset:
Total RNA extracted from fly heads from three independent experimental assays was used for library preparation and paired-end Illumina mRNA sequencing (RNA-seq). The raw sequencing dataset, composed of 24 libraries with an average number of (See figure on next page. ) Fig. 2  Identification of differentially expressed neuronal transcripts in response to RNAi-mediated silencing of Caz, Smn or TBPH in adult flies. A Schematic representation of the experimental set-up for RNA-seq analysis. Hormone-dependent, adult-onset expression of short hairpin (sh) RNA was used to induce RNAi-mediated gene silencing of caz, Smn or TBPH. RNA was prepared from fly heads five to seven days post induction of shRNA expression, quality checked and subjected to mRNA-seq. Fly lines with shRNA against the always early (ae) embryonic gene served as control.
B Bar graph showing the RNA-seq log2 fold change of the siRNA target genes plus the Gapdh housekeeping gene in each RNAi fly line. Statistically significant differences to the ae siRNA fly line are indicated as ** (adjusted p value < 0. 05) and * (adjusted p value = 0. 06 and p value < 0. 02). C Bar graph showing the number of upregulated (red), downregulated (green) and differentially spliced mRNAs (yellow) in each RNA-seq dataset. Note that the kind of splicing change was not considered for this analysis. D Venn diagrams and corresponding bar graphs showing the overlap in upregulated (top), downregulated (middle) or differentially spliced (bottom) mRNAs in flies with RNAi-mediated silencing of caz, Smn or TBPH. The bar color compares the expected (black) and observed (red) overlap given the total transcripts altered in response to the silencing of caz, Smn or TBPH, respectively.
The size of randomly expected overlaps and the statistical tests comparing observed with expected overlaps were calculated using the SuperExactTest R package. All shown overlaps are significantly larger than expected by chance (observed and expected overlap sizes and respective p-values are presented in Supplementary data file 5 50 million reads (Supplementary Data 1), was submitted to ENA with the study accession code PRJEB42797. Following quality control and filtering, reads were aligned to the Drosophila reference genome, mapping to ~ 13,600 expressed genes. The RNA-seq dataset was analyzed to determine the overall changes in transcript abundance and splicing patterns induced by the knock-down of each protein. Exploratory analysis of the normalized RNA-seq dataset revealed that the samples clustered primarily according to genotype, followed by treatment (Supplementary Fig.
S2B ), an observation consistent with the expected leakage from the siRNA locus. However, hormone-treated samples exhibited a better separation between genotypes than the corresponding untreated controls, as expected from shRNA-expressing samples (Supplementary Fig. S2C, ). As hormone treatment induced a significant number of common changes across all sample types, explaining up to 7% of the variance in the dataset (Supplementary Fig. S2C ), differential gene expression (DE) analysis was performed between hormone treated Caz, Smn and TBPH shRNA-expressing target and control fly lines (see Supplementary Data 3). Confirming the robustness of our dataset and DE analysis, the specific shRNA target genes were found to be significantly down-regulated exclusively in the corresponding fly line (Fig.
This result is in stark contrast with the strong functional signature that was observed for GO enrichment analysis of the subsets of mRNAs captured in the RIP-Seq assays. Furthermore, the identification of functional signatures was not improved by imposing more stringent fold change cut-offs on the DE gene sets. Indeed, the majority of DE genes showing common changes across all knockdowns presents very small variations in expression (Supplementary Fig. S2D ). The use of more stringent fold change cut-offs on our dataset leads to a big reduction in the size of the gene sets (Supplementary Fig. S2E ), with an even larger impact on the number of common DE genes that are retained (Supplementary Fig. S2F ). These observations support our choice to consider all genes showing statistically significant changes in expression in our analysis, independently of their fold change level.
However, we were unable to link this group of genes to a coherent set of molecular processes that are directly influenced by the three proteins, which might provide novel insights into the human disease context. Nevertheless, given the clear presence of a shared transcriptome signature, we hypothesized that putative underlying functional connections might be obscured by independent, albeit converging phenomena, taking place at the protein network level. To obtain insights into these potential connections, we proceeded to a more in-depth network-based analysis of our datasets.
To do so, we began by reconstructing the entire Drosophila neuronal interaction network using protein-protein interaction (PPI) and adult fly brain RNA-seq datasets available in the APID and FlyAtlas2 repositories, respectively  [53, 56]. Notably, 45. 5% of the 5 353 proteins found in this neuronal network are encoded by transcripts whose levels and/or splicing were altered in response to caz, Smn and/or TBPH knockdowns. Next, we defined functional modules in the neuronal network by selecting groups of physically interacting proteins annotated under the same enriched functional term. Of the 232 modules with associated GO terms, we focused on the subset of 122 modules composed of 10 to 100 proteins (Supplementary  Data 7). These modules retained 1541 proteins in total, maintaining the high percentage of Caz, Smn and/ or TBPH-dependent genes found in the original network (43.
4e-2, Fig. 3D ), being even lower for transcripts that do not integrate any functional module (18% of 2280 transcripts, p value = 3. 1e-8; Supplementary Data 7). Together, these results suggest that our integrated data analysis approach was able to identify key functional processes that are commonly and directly regulated by the three proteins. Our findings point to the ability of Caz, Smn and TBPH protein dysregulation to elicit a convergent functional impact through distinct individual targets. The connection to Fig. 3  Characterization of functional modules impacted by reduced abundance of Caz, Smn and TBPH proteins. A Left panel: workflow used to generate and select functional modules. The adult brain interactome was obtained from the APID protein-interaction network after filtering for proteins expressed in the adult Drosophila brain (i, see Methods).
Regulation through altered splicing (Altered Splicing, Fig. 5 , right panel), on the other hand, is more frequent in modules related with behavior, NMJ, stress and cytoskeleton. Given the critical link between MNDs and the physiology of NMJs, we focused on the NMJ super-module for a more in-depth analysis. The NMJ super-module comprises 104 proteins, of which 49% (51 nodes) are encoded by genes differentially expressed and/or displaying altered splicing in at least one knockdown condition (Supplementary Data 8). 38 of these genes establish direct interactions, forming the subnetwork represented in Fig. 6A. To assess the degree to which the NMJ "super module" functionally interacts with Caz, Smn and TBPH in vivo, we cross-referenced it to genetic modifiers of Drosophila Smn, Caz or TBPH mutants identified in genome-wide screens for modulators of degenerative phenotypes using the Exelixis transposon collection  (Kankel et al.
B Categorical heat map summarizing data concerning the proteins/genes within the network represented in A. Essential proteins were defined according to the FlyBase repository. Proteins labeled as "Dubious" display a lethal phenotype after induction of RNAi. Thus, it is likely that flies homozygous for amorphic mutations would result in lethality during development. However, since this might result from off-target effects, they were not considered essential. MND-associations were retrieved from the DisGeNET repository. Caz, Smn and TBPH columns indicate in which knockdown models the corresponding transcripts were found altered; different codes are used to highlight the DE, DE + AS or AS only nature of the change. Last 7 columns indicate whether the protein is also found in other super-modules have been associated with Ataxia-like phenotypes and mental retardation.
Funding This work is part of an  EU Joint   Programme -Neurodegenerative Disease Research (JPND)  project with the acronym ' Fly-SMALS '. The project is supported through the following funding organisations under the aegis of JPND -www. jpnd. eu:  France ,  Agence Nationale de la Recherche; Germany ,  Bundesministerium für Bildung und Forschung (BMBF) ;  Portugal ,  Fundação para a Ciência e a Tecnologia and Spain ,  Instituto de Salud Carlos III (ISCIII). Associated to the JPND, the group of JDLR was funded for this work by the  ISCIII and FEDER  through projects  AC14/00024  and  PI15/00328. Work in MGC's group was supported by the grant  JPND-CD/0002/2013  and by  UIDB/04046/2020  and  UIDP/04046/2020  Centre grants from  FCT, Portugal  (to BioISI). Work in F. B. 's group is supported by the  ANR  (through the  MEMORNP research  grant and the ' Investments for the Future' LABEX SIGNALIFE program  #  ANR-11-LABX-0028-01 ).
Availability of data and materials The datasets generated and/or analyzed during the current study are available in the European Nucleotide Archive repository under the umbrella study FlyS-MALS, with accession numbers PRJEB42797 (https:// www. ebi. ac. uk/ ena/ brows er/ view/ PRJEB 42797) and PRJEB42798 (https:// www. ebi. ac. uk/ ena/ brows er/ view/ PRJEB 42798).
2 affected these signals. We then focussed on the changes of these signals produced by fully blocking voltage-gated Ca 2+ channels (VGCCs) and/or Ca 2+ -activated K + channels (CAKCs) in order to correlate Na v 1. 2 signalling with the activation of these channels. To assess whether the role of Na v 1. 2 was exclusive, we performed control experiments using the partially selective Na v 1. 6 inhibitor 4,9-anhydrotetrodotoxin. Finally, in order to reconstruct the sequence of activation of the channels underlying the shaping of the generating AP, we built a NEURON model starting from the experience of existing models of the AIS of L5 pyramidal neurons  [7, 12, [20] [21] [22] [23] [24] , available in the NeuroDB database. The strategy, already used to realistically reconstruct the activation of the diverse dendritic ionic currents following a climbing fibre synaptic potential in cerebellar Purkinje neurons  [25] , consisted in reproducing the experimental profiles of ionic and V m transients and their modifications following selective blocks of individual channel types.
The Na v 1. 2-BK channels interaction is mimicked by simulations in a NEURON model To mimic the experimental AP generation in the AIS, we built a NEURON model on the base of our dataset. Starting from the model reported in Hallermann et al. [23]  and already utilised in our previous article  [15] , we adapted the AIS morphology to the mouse L5 pyramidal neuron and replaced the Na v 1. 2 and Na v 1. 6 channel models with those proposed in Mainen et al. [28] , distributed consistently with the results reported by Hu et al. [7]  (see Fig  6a ). From this starting model, we modified the channel functions, as described in the Methods, in order to match the experimental Na + and V m signals in the AIS. We then introduced Ca 2+ permeability to Na v 1. 2 corresponding to 0. 4% with respect to the permeability for Na + (as suggested by Hanemaaijer et al.
This work was supported by the  Agence Nationale de la Recherche  through three grants ( ANR-18-CE19-0024 -OptChemCom ;  Labex Ion Channels Science and Therapeutics: program  number  ANR-11-LABX-0015  and  National Infrastructure France Life Imaging "Noeud Grenoblois ") and by the  Federation pour la Recherché sur le Cerveau  ( FRC -Grant Espoir  en tête, Rotary France). We are indebted to  Fondation Leducq  and the  FEDER  resources for financing the automated patch clamp setup. Author contributions: L. F. , L. A. B. and M. C. designed the research, L. F. and L. A. B.
RNAseq and data analysis After transfer of samples to Novogene Genomics Service (Cambridge, UK), the RNA sample integrity was checked with a Bioanalyzer 2100 (Agilent). After samples passed the quality check, the service laboratory proceeded with the library construction and RNA sequencing (PE150) on Illumina NovaSeq™ 6000 platforms as described in a previous study  [52]. The RNAseq libraries were filtered and quality-trimmed with fastp (v0. 23. 2)  [53] , i. e. , read ends were truncated to achieve a Phred quality score of 30 or more. Reads below 15 nt length or those comprising at least 2 ambiguous N bases were removed from the dataset. Read qualities were monitored by FastQC (v0. 11. 3; https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Hisat2 (v2. 2. 1)  [54]  was used with default parameters to map the quality-trimmed RNAseq libraries to the A.
thaliana reference genome (TAIR10, Ensembl release 51). The mapping allowed spliced reads and single-read mapping to multiple best-fitting locations. FeatureCounts (v1. 5. 3)  [55]  was applied to perform read counting based on the A. thaliana reference annotation (TAIR10, Ensembl release 51). The Bioconductor DESeq2 (v1. 10. 0) package  [56]  was utilized to identify DEGs in the different pairwise mutant and wild type comparisons. Benjamini and Hochberg's False Discovery Rate (FDR) approach  [57]  was employed to adjust the calculated p-values for multiple testing. To identify DEGs of transporters predicted to transport major N compounds, the obtained results were in a first step filtered according to their p-value (p<0. 05). Next, DEGs were sorted according to their log2-fold change, here only changes with numbers >= +1.
Analysis of fungal colonization by qPCR 1 mg of RNA was used for the synthesis of cDNA. The Omniscript RT Kit (Qiagen, Hilden, Germany) was used according to the manufacturers protocol with the oligo(dT)18 primer (ThermoFisher Scientific, Waltham, USA). qPCR was performed with fifty nanograms of the synthesized cDNA as template in a Bio-Rad CFX96 Real-Time PCR Detection System (Feldkirchen, Germany) by use of DreamTaq Polymerase (ThermoFisher Scientific, Waltham, USA) and Evagreen (Biotium, Fremont, USA). The data were normalized with respect to the Arabidopsis RPS18B (At1g34030) gene by the 2 -∆∆CT method  [58]. To quantify the P. indica colonization level of Arabidopsis roots, the expression of PiTEF1  [59]  was analysed in comparison to the plant´s housekeeping gene RPS18B (At1g 34030).
P. ; and grant JPMJSC16C3 from the Japan Science and Technology Agency (JST) to H. S. This work was further supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) CRC1127 ChemBioSys (project ID: 239748522) to R. O. This work has benefited from the support of IJPBʹs Plant Observatory technological platforms. The IJPB benefits from additional support of Saclay Plant Sciences-SPS (ANR-17-EUR-0007).
Moreover, more detailed descriptions and comparative studies are also reported in the relevant literature  [44]. Finally, note that all models were developed in Python, using the Scikit Learn library. GB and RF are ensemble ML methods as they consist of combining the outputs of a large number of prediction models to obtain an improved ensemble prediction. The difference between GB and RF lies in the way the outputs are combined as well as the construction of the constitutive prediction models. More specifically, they, respectively, belong to boosting and bagging (or bootstrap aggregation) categories. In boosting, simple weak estimators (in underfitting) are combined sequentially in a way that at each iteration, a new weak learner is trained by considering the errors of the previous one (i.
e. , ±0. 1 mg of the precision balance). At the same time, the average standard deviation of three measurement repetitions for a total of seven samples was found to be equal to 0. 3 mg. Finally, other sources of introduction of random error to the measurements, such as the balance drift, influenced by air stream, temperature or humidity, were not quantified. During the experiments, the temperature inside the reactors was investigated to ensure the reliability of the data for latter modeling. In the small-sized system, a thermocouple was kept inside three reference tubes (corresponding to GTR/(GTR + styrene) contents of 10%, 40% and 70% wt, the ratio BPO/styrene being set at 5. 5% wt) during the whole reaction for the three temperatures of interest. An example of the recorded temperature evolution, for 90 °C, is shown in Figure  4  and in Table  3.
For the latter, experimental procedures enabling the collection of sufficient data of good quality are therefore required. Finally, an effort to share experimental data and accompany each experimental result with precise and detailed conditions is crucial, as demonstrated in this work. where: • ∆ 1 is the loss of styrene during polymerization reaction due to evaporation; • ∆ 2 is the loss of styrene during the transfer of the reactor content to the gravimetry cup at the end of the polymerization; • ∆ 3 is the styrene remaining in the glass tube at the end of the polymerization reaction. All the above losses were identified by meticulous weighing of the used equipment before and after each individual step, using a Mettler Toledo XP504 precision balance. During these measurements, the ratio of styrene to GTR was considered to be identical and constant, as defined in the recipe of each experiment and measured during the initial material introduction step.
Acknowledgments: The authors acknowledge the " Service Etudes et Réalisation Mécanique" of LRGP as well as the "Service de soufflage du verre " of  University of Lorraine  who, respectively, designed and built the medium-sized system and the glass parts. The authors also acknowledge the following masters students who contributed in setting up and collecting experimental data:  Redouanne Chenna ,  Justine Guerry ,  Bahia Lamari ,  Betty Soulet  and  Jérémy Lanoizelée.
sunshinyii contains a gene cluster for DMSP synthesis. The ~30 kb insert in pBIO2208 was sequenced and found to contain only one methyltransferase gene, termed dsyGD, adjacent to a reductase gene, predicted to encode a 4-methylthio-2-oxobutyrate (MTOB) reductase enzyme (Fig. 1a  and Fig. 1c ). DsyGD is a 494 amino acid protein with two domains. The N-terminal methyltransferase (PF08241. 15) domain, termed DsyG, phylogenetically clusters away from all known S-methyltransferases involved in DMSP synthesis (Fig. 2 ) and had < 33% amino acid identity to TpMMT from Thalassiosira pseudonana  [40]. The DsyGD C-terminus contained a predicted ureidoglycolate lyase domain (PF04115. 15), termed DsyD, likely acting as a DMSHB decarboxylase. Cloned G. sunshinyii dsyGD conferred in vivo MSM (177. 42 ± 3.
5b , Supplementary Fig. 6a, b, c ). Further supporting the role of DMSP and DsyGD in osmoprotection, cloned Gs dsyGD significantly enhanced the growth of an osmosensitive E. coli strain FF4169  [44]  under increased salinity in the presence of MTHB (which has limited osmoprotective properties  [38]  ) or, especially, DMSHB, compared to control strains lacking cloned dsyGD (Fig. 3b ). This osmoprotection phenotype was likely due to the DMSP produced from MTHB and DMSHB (5. 49 ± 0. 99 and 10. 13 ± 0. 63 pmol DMSP µg protein -1 h -1 , respectively), since E. coli strain FF4169 lacking cloned Gs dsyGD produced no DMSP from MTHB or DMSHB (Fig. 3c ). This is the first demonstration of any known DMSP synthesis genes conferring osmoprotection. Although no DsyGD proteins were predicted in eukaryotic algae, we did identify single domain DsyG-like proteins with < 38% amino acid identity to Gs DsyG from sequenced algal genomes (Fragilariopsis cylindrus CCMP1102 and Nitzschia inconspicua strain hildebrandi) and the Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP) database  [45].
It is also worth noting that bacterial DsyB is as efficient an enzyme as algal DSYB, despite bacteria generally producing low intracellular levels of DMSP  [9, 39, 57]  , and that there are many examples of organisms with DSYB that are considered low DMSP producers (e. g. , F. cylindrus, Chrysochromulina tobin). For these reasons and as  [8, 9, 39, 40]  shows, we propose that DMSP synthesis gene transcript and to a greater extent, protein levels, are more robust indicators of an organism's potential DMSP levels than which DMSP synthesis gene/s it contains, since it is these expression levels that are guided by varying environmental conditions, e. g. , nitrogen and salinity levels, and govern DMSP synthesis potential along with substrate availability. Irrespective of whether one can infer high or low DMSP production according to an organism's genotype, it was possible to obtain a better understanding of the global distribution and significance of DsyG/GD and DSYE compared to DsyB, DSYB, MmtN and TpMMT genes in marine waters by examining their abundance and expression profiles in Tara Oceans datasets  [58].
Initially, the ocean microbial reference gene catalogue (OM-RGC) metagenomic dataset, generated from samples fractionated to < 3 µm  [59]  and apportioned to bacterioplankton was analyzed. As previously described  [8]  , alphaproteobacterial dysB and its transcripts were far more abundant than those for mmtN in Earth's oceans, and these dsyB genes/transcripts were >2-fold more abundant in the surface (SRF) and deep chlorophyll maximum (DCM) than in mesopelagic (MES) waters (Fig. 4 ; Supplementary Table  6 ). dsyGD/dsyG genes and transcripts were not detected in any OMRGC sample, consistent with this system being largely irrelevant to marine DMSP cycling or that some species, notably filamentous cyanobacteria containing these genes, aggregated and were not captured by the bacterioplankton sampling methods.
Bacteria with dsyGD/dsyG were not detected in any TARA metagenomic or metatranscriptomic data, consistent with them having a negligible role in marine DMSP production. Furthermore, dsyGD/dsyG could not be detected in metagenomic data from Spartina rhizosphere samples in which G. sunshinyii was present  [14]  , suggesting that dsyGD may not even be universal in this species. In contrast, DSYE genes, particularly from Pelagophyte and Chlorophyta algae were more abundant than DSYB from Haptophyta and Dinophyta and orders of magnitude more abundant than TpMMT from diatoms in Earth's surface waters. However, DSYE transcripts were ~2-fold less abundant than for DSYB in these samples, which is likely a better indicator of DMSP production. Even with these reduced transcript levels, Pelagophyte and Chlorophyta algae with DSYE should be considered as potentially globally important DMSP producers, especially given many of these algae are known to form large blooms and were shown here to produce medium to high levels of DMSP.
G. sunshinyii YC6258 DMSP cycling gene analysis The G. sunshinyii YC6258 genome sequence and protein annotation data were downloaded from NCBI (PRJNA233633) and interrogated for the presence of DMSP synthesis cycling gene/proteins. G. sunshinyii DMSP cycling genes/proteins were predicted using local BLAST, with ratified DMSP cycling genes/proteins as probes (Supplementary table  11 ) and thresholds of E-value ≤1×10 -5 , amino acid identity ≥40% and coverage ≥70%.