dataesr/scientific-paragraphs-categorization huggingface dataset:
Molecular cloning of Tmem120a Primers were designed from cDNA sequence of Tmem120a from the NCBI database (NM_172541). An 1. 096 kb fragment was amplified from cDNA libraries generated from adult C57BL/6J DRG total RNA using primers fwd (5' GATTATGCATGCCGTGGACAAAGACATGCAGT 3') and rev (5' GATTATGGATCCTCAGTCCTTCTTGTTCCCGT 3') and cloned into pIRES2-AcGFP1 vector (Clontech, #632435) with NheI and BamHI restriction sites. The protein sequence of Tmem120a that we cloned is identical to NCBI Reference Sequence NP_766129.
After ethanol dehydration and a Protease IV pretreatment for 30 minutes at room temperature, sections were incubated with a mouse Tmem120a probe (ACD; #513211, accession number #NM_172541. 2) for 2h at 40°C. The negative control, designed by ACD Bio, contains probes targeting the DapB gene from the Bacillus subtilis strain SMY (ACD; #320871, accession number #EF_191515). The signal was amplified with a succession of four pre-amplifiers supplied in the kit and detected with Alexa 488.
Gene Ontology (GO) Gene Ontology (GO) annotations  (Ashburner et al. , 2000; Gene Ontology, 2021)  were determined using the R package limma (v. 3. 44. 3) and custom R scripts. GO terms were taken from the Bioconductor annotation package org. Mm. eg. db (v. 3. 11. 4 ) released on 2020-05-02. The following GO terms were selected: detection of mechanical stimulus (GO:0050982), ion channel activity (GO:0005216) and integral component of membrane (GO:0016021). C was set to the baseline value of current once the mechanical stimulus is removed. Inactivation time constant τ was used to identify the type of MS current components. To note, τ measurements of ultra-SA currents are approximave due to the relative short lasting (150 ms) of the mechanical stimulus. When fitted with bi-exponential equation, the contribution (in percentage) of each component to the whole current is determined from the ratio of Ai to the amplitude of the peak current (see Fig.
Differential expression analysis Analysis of differential gene expression was made between the cells expressing a given type of MS current (RA, IA, SA or ultra-SA current) versus cells that do not express this specific current (control group). LogP value is the Pvalue of Fold Change determined with exact test for negative binomial distribution implanted in the R package edgeR between two groups of count libraries. We set up a 2fold enrichment threshold (with Pvalue < 0. 05) to consider genes displaying expression enrichment in the group of interest compared to the control group. For graphical representation, fold change is displayed as Log2 values (corresponding to Log2FC) and Pvalue as -log10 values (corresponding to -Log10 Pvalue). Abundance of transcripts is expressed by the Transcript Per kilobase Million value (TPM).
Image Acquisition and Analysis Images were acquired using a LSM 780 laser-scanning confocal microscope (Zeiss) with Ar and He-Ne lasers using a 63X oil-immersion objective. Images were treated and analysed with ImageJ software (v 1. 50b, NIH, United States). The "analyze particle" function was used to quantify in situ hybridization signal. Neurons containing at least two dots were considered positive.
Acknowledgements This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (Grant agreement No. 678610 ). RNA sequencing was done by  Profilexpert , " Génomique & Microgénomique " service, Lyon, FRANCE. We thank  Virginie Penalba  and  Axel Fernandez  for expert technical assistance, and  Dr. Nancy Osorio ,  Dr. Françoise Padilla  and  Dr. Nicolas Grillet  for critical reading of the manuscript.
Genotyped Animals Genomic data were obtained from the breeding company NUCLEUS (Le Rheu, France) from a three-way crossbreeding protocol (Figure  1 ). Animals from three pure lines were genotyped: 80 Pietrain (PI) boars, 240 Large White (LW) sows, and 89 Duroc (DRC) boars. Crossbred animals were also genotyped: Pietrain × Large White crossbred offspring (442 PLW sows), Duroc × PLW crossbred offspring (69 G0 boars and 471 G0 sows) and G0 × G0 crossbred offspring (472 G1 boars and 473 G1 sows). Genotyping was carried out by the Labogena laboratory using the Illumina Porcine Chip, Porc_XT_60K. We used a reference map based on the Sus scrofa 11. 1 pig genome assembly. Quality control of genotypes was performed with PLINK v1. 9 software  (Chang et al. , 2015). Only markers on autosomes were kept.
Population Structure Analysis First, a multidimensional scaling analysis (MDS) was conducted to visualize the genetic distances between animals and the structure of the pig population using PLINK v1. 9 software. After this we computed  Cockerham and Weir (1984)  F ST analysis with PLINK v1. 9 software to quantify genetic differentiation among pig groups. Finally, an admixture analysis was performed with ADMIXTURE v1. 3. 0 software  (Alexander et al. , 2009). Here, the number of genetic populations considered was 3 (for K parameter), the number of pure breeds involved in the crossbreeding.
Detection of Runs of Homozygosity ROH were detected with PLINK v1. 9 software. First, to choose the minimum size to define an ROH (in terms of SNP and kb) and the minimum SNP density in an ROH, we selected a range of minimum numbers of SNP and minimum size in kb according to  Peripolli et al. (2017). Tests of parental populations (Pietrain and Large White) were then performed to choose the values that neither underestimated nor overestimated the number of ROH detected  (Ganteil et al. , 2020). The values selected to define an ROH were 30 SNP and 1,000 kb and the minimum density was set at one SNP per 100 kb. Regarding the parameters for the number of SNP in the sliding window,  Curik et al. (2014)  recommended using a sliding window equal or larger than the minimum size used to define an ROH.
Genomic Annotation Genomic annotation was performed in G0 crossbreds, the first generation of the new line. In this generation, ROH hotspots mean frequent haplotype sharing between Pietrain, Large White, and Duroc. Genes in ROH hotspots in G0 animals were extracted using Biomart on the Ensembl website (https://www. ensembl. org/biomart/martview/fbef5263e 7166fc734235c9325399e4d, version 100 released in April 2020). As dataset, we used the current pig genome assembly (build 11. 1), and the regions of interest on the chromosomes were used as a filter to extract gene symbols.
Arctic algae possess expanded genomes with distinct composition Possible genomic trends underpinning the biology of Arctic algal species were inferred across a dataset of 24 sequenced algal genomes  (Grigoriev et al. , 2021)  and 296 MMETSP transcriptomes  (Keeling et al. , 2014)  from eight groups (chlorophytes, chrysophyte-related species, cryptomonads, diatoms, dictyochophytes, dinoflagellates, haptophytes and pelagophytes) with at least one sequenced Arctic species, henceforth referred to as the "pan-algal dataset" (Table  S1 , sheet 2). The Arctic algal genomes were typically larger than sequenced non-Arctic relatives: most dramatically, the Baffinella sp. CCMP2293 genome (534. 5 Mbp total content) is 6. 13 times that of the non-Arctic cryptomonad Guillardia theta (87. 2 Mbp; Table  S2 , sheet 1)  (Curtis et al.
, 2012) , although more moderate expansions (between 1. 17 and 2. 11 times the size of nearest sequenced relatives in the dataset) were observed for other Arctic algae. A broader analysis of the pan-algal dataset (Table  S2 , sheets 2, 3) revealed higher ratios of genes to PFAM protein domains (i. e. , suggesting an expansion in genes without known PFAM functions), in Arctic than non-polar sequence libraries. This enrichment was statistically significant (one-way ANOVA, P < 0. 05) for Arctic cryptomonads and chrysophytes compared to non-Arctic relatives (Table  S2 , sheet 3). The Arctic species within the pan-algal dataset encoded greater proportions of small hydrophobic residues (alanine, glycine, and valine, one-way ANOVA, P= 0. 013), and smaller proportions of charged residues (aspartate, glutamate, lysine and arginine, P= 0.
Convergence in protein domain content between distantly related small Arctic algae Next, we considered the global similarity of Arctic algal genomes and transcriptomes to one another, considering PFAM domain distributions as a proxy (Fig. 3 ;  (Mistry et al. , 2020) ). Phylogeneticallyaware principal component analyses (phylPCA) of genomes (Fig. 3 -Figure  Supplement 1i ) and transcriptomes (Fig. 3 -Figure  Supplement 1ii ) revealed clusters of some Arctic species (e. g. , Pavlovales sp. CCMP2436, the novel pelagophyte CCMP2097, and Ochromonas sp. CCMP2298), albeit with others (e. g. , Baffinella sp. CCMP2293) forming outliers. To determine if Arctic species have converged on similar PFAM contents, pairwise Bray-Curtis indices were calculated between the PFAM distributions across the pan-algal dataset, considering genome and transcriptome libraries separately (Fig.
002); and between pairs of non-Arctic species (genomes P = 0. 01; transcriptomes P = 0; Fig. 3 ). Greater similarity was noted between pairs of Arctic species than between Arctic and Antarctic species (e. g. , Fragilariopsis cylindrus)  (Mock et al. , 2017)  in both genomes (mean = 0. 527, P = 0. 0047) and transcriptomes data (mean 0. 569; P = 0. 049), indicating that this convergence is specific to the Arctic Ocean (Fig. 3 ). Statistically significant convergences were also observed between Arctic genomes considering the BUSCO-adjusted number of PFAMs shared between each library, suggesting it was independent of the completeness of Arctic and non-Arctic libraries in the pan-algal dataset (Fig. 3 -Figure  Supplement 3A ); and from the Spearman rank index of PFAMs in the transcriptomes dataset (34)  (Choi & Kim, 2007) , indicating that this was not due solely to expansions in Arctic algal PFAM contents (Fig.
3 -Figure  Supplement 3B ). Convergences involved diverse species, with Pavlovales sp. CCMP2436 having greater convergence to the novel pelagophyte CCMP2097; and Arctic chlorophytes and dinoflagellates likewise having greater convergence to one another than to non-Arctic equivalents, for all metrics tested (Fig. 3   S2 , Sheet 10). Exclusion of diatoms from the PFAM dataset indeed strengthened the separation between Arctic-Arctic species pairs and other biogeographical categories of species pairs in Bray-Curtis, BUSCO-corrected Bray-Curtis, and Spearman correlation calculations  Table S2, sheet 10). This may point to separate trends in PFAM content between Arctic diatoms, and smaller size fraction picophytoplankton in the Arctic.
Arctic-associated PFAMs are spread by within-Arctic horizontal gene transfer Next, we determined which PFAMs were principally responsible for the convergence in PFAM content amongst Arctic algae (Fig. 4 ). We considered both occurrence in Arctic species (Table  S2 , sheet 6) and expansions or contractions in copy number in Arctic species compared to their closest non-Arctic relatives (Table  S3 , sheet 1) (X. Zhang et al. , 2021) , performed through a phylogeneticallycalibrated CAFE (Computational Analysis of gene Family Evolution) across the pan-algal dataset  (De Bie, Cristianini, Demuth, & Hahn, 2006). Multiple PFAM domains were more frequently detected, or more frequently underwent expansions (two-tailed chi-squared P < 10 -05 ) in Arctic species within the dataset (Fig. 4 ; Table  S3 , sheet 2), with exemplar distributions of eight such PFAMs shown in Fig.
4 -Figure  Supplement  1. The most highly Arctic-associated PFAM domain, both considering presence and expansion frequencies (P < 10 -25 ), was PF11999  (Figs. 4, 5) , encoding ice-binding domains  (Raymond, 2011; Raymond & Kim, 2012; Vance, Bayer-Giraldi, Davies, & Mangiagalli, 2019). Ice-binding domains, which typically complex with water through six threonine-rich domains within a larger hydrophobic manifold, play key roles in cryotolerance, variously allowing cold-adapted species to avoid osmolysis during freezing transitions within the cell, or allowing membrane surface transporters to be kept open, or even adhesion of cells to the ice surface if secreted  (Raymond, 2011; Raymond & Kim, 2012;  previously assembled dataset of 151 combined genome and transcriptome libraries covering the entire tree of life; i.
Discussion We have harnessed newly sequenced genomes alongside a densely sampled dataset of genomes and transcriptomes, as well as environmental data from Tara Oceans to unveil evolutionary trends in algae isolated from the Arctic Ocean. Our data suggests convergence in the coding content of diverse small Arctic algae, linked to the presence of Arctic-specific genes, although with the exclusion of Arctic diatoms. We further show that within-Arctic HGT, exemplified by genes coding for ice-binding domains, is an important component of this convergent evolution. The overall results position within-ocean HGT as a mechanism of environmental adaptation, which may occur via biotic interactions characteristic of Arctic species such as photo-mixotrophy  (McKie-Krisberg & Sanders, 2014; Søgaard et al.
Both genome and transcriptome libraries were quantified using a next-generation sequencing library qPCR kit (KAPA Biosystems) and run on a LightCycler 480 real-time PCR instrument (Roche). The quantified libraries were prepared for sequencing utilizing a TruSeq paired-end cluster kit (v3), and a cBot instrument (Illumina) to generate a clustered flow cell, and sequenced on an Illumina HiSeq2000 sequencer using a TruSeq SBS sequencing kit, v3, following a 2x100 or 2x150 indexed run recipe. After sequencing, the genomic fastq files were screened for phix contamination. Reads composed of >95% simple sequence were removed. Illumina reads <50bp after trimming for adapter and quality (q<20) were removed. The remaining Illumina reads were assembled using AllPathsLG  (Gnerre et al. , 2011)  parameters: DATA_SUBDIR=data RUN=run SUBDIR=assem1 TARGETS=standard OVERWRITE=True THREADS=6 CLOSE_UNIPATH_GAPS=False).
The resulting assembled scaffolds were screened against all bacterial proteins and organelle sequences from GenBank nr  (Wheeler et al. , 2006)  and removed if found to be a contaminant. Illumina transcriptome reads were QC filtered to remove contamination and assembled into consensus sequences using Rnnotator v. 2. 5. 3  (Martin & Wang, 2011). Each genome was annotated using the JGI Annotation Pipeline, which detects and masks repeats and transposable elements, predicts genes, characterizes each conceptually translated protein with sub-elements such as domains and signal peptides, chooses a best gene model at each locus to provide a filtered working set, clusters the filtered sets into draft gene families, ascribes functional descriptions (such as GO terms and EC numbers), and creates a JGI genome portal in PhycoCosm (https://phycocosm.
A second round of cleaning was performed by reciprocal tBLASTn/ BLASTx searches of each peptide sequence against the corresponding MMETSP nucleotide transcriptome libraries  (Keeling et al. , 2014)  that had been decontaminated through a previously published pairwise BLAST similarity analysis  (Marron et al. , 2016)  (Table  S4 ,  Sheet 1). Only gene models that retrieved a reciprocal best hit with bidirectional threshold value 10 -05 were retained for downstream analyses. Between 3,056 (Ochromonas sp. CCMP2298) and 11,568 gene models (Pavlovales sp. CCMP2436) were confirmed to be of unambiguously non-contaminant origin for each genome, and retained for analysis of within-Arctic HGT (Table  S4 , sheet 1). Full details of the decontamination pipeline employed are provided in the linked supporting database https://osf.
io/3pmxb/  (Dorrell et al. , 2021b) I n the folder "Within-Arctic HGTs". Comparisons of key properties between the assembled genomes, and their closest assembled relatives (accessed January 2018) in cryptomonads, haptophytes, pelagophytes, and chrysophyterelated species  (16)  are provided in Table  S2 , Sheets 1-5. Per-gene analyses of the amino acid compositions of each genome and transcriptome included in this study, as defined below, are provided in the linked supporting database https://osf. io/3pmxb/  (Dorrell et al. , 2021b)  in the folder "Genome quantitative analysis".
Multigene phylogeny A pan-algal dataset of 21 genomes, including the four sequenced in this study (accessed January 2018)  (Grigoriev et al. , 2021)  and 296 decontaminated MMETSP transcriptomes  (Keeling et al. , 2014; Marron et al. , 2016)  was assembled for eight algal groups for which at least one sequenced Arctic genome or transcriptome has been completed: these being chlorophytes, chrysophyte-related species (including synchromophytes, pinguiophytes and eustigmatophytes  ( Dorrell et al. , 2021a) ), cryptomonads, diatoms, dictyochophytes, dinoflagellates, haptophytes and pelagophytes. The isolation sites of each sequenced species, and minimum and maximum recorded viable growth temperatures, were manually confirmed by comparing to the accession records in public culture collections, considering synonymous culture identifiers where present  (Gachon et al.
, 2013; Guiry et al. , 2014; Vaulot, Le Gall, Marie, Guillou, & Partensky, 2004) , and are provided in Table  S1 , Sheet 1. Reference sequences from a previously assembled eukaryotic multigene tree  (Burki et al. , 2016; Strassert et al. , 2021) w ere enriched using diamond v0. 9. 30. 131  (Buchfink, Xie, & Huson, 2015)  with orthologues from all members of this dataset. To infer phylogenies, single-gene datasets were aligned by MAFFT v7. 407  (Katoh, Rozewicki, & Yamada, 2017)  using the L-INS-i refinement and a maximum of 1000 iterations, then trimmed by trimAl v1. 4 (Capella-Gutiérrez, Silla-Martínez, & Gabaldón, 2009) using the -gt 0. 5 setting. ML trees were inferred by IQ-TREE v 1. 6. 12  (Nguyen, Schmidt, von Haeseler, & Minh, 2015)  using the LG+F+G model. Paralogs were manually removed from single-gene datasets, considering their branching and alignment coverage.
The concatenated multi-gene alignment was finally trimmed to remove sites with more than 20% gaps with trimAl (-gt 0. 8) and sites exhibiting the fastest exchange rates with TIGER(-exc 10 -b 10;  (Cummins & McInerney, 2011) ) analogously to the reference multi-gene matrix  (Burki et al. , 2016). The final ML tree was reconstructed using the Posterior Mean Site Frequency (PMSF) model of IQ-TREE on a LG+F+G guide tree, with the multi-gene matrix treated as a single partition to eliminate small sample LBA bias(H. C. Wang, Susko, & Roger, 2019). Incorporated gene and tree topologies are provided in Table  S1 , sheets 2-3; and single-gene alignments and topologies, and concatenated alignments are provided in the linked supporting database https://osf. io/3pmxb/  (Dorrell et al. , 2021b)  in the folder "Multigene tree topologies".
18S and 16S phylogeny 18S rDNA and chloroplast 16S rDNA sequences corresponding to cultured isolates, and uncultured environmental isolates from the Arctic and Antarctic/Southern Oceans were downloaded for four algal groups (cryptomonads; haptophytes; chrysophyte-related taxa, including chrysophytes, synchromophytes, pinguiophytes and eustigmatophytes; and pelagophytes/ dictyochophytes)  (Dorrell et al. , 2021a) , from GenBank nr (March 2020)  (59). These were supplemented with orthologues searched for each group from MMETSP nucleotide sequence libraries by BLASTn, using a randomly selected downloaded query from the group considered, and a threshold e-value of 10 -05. Homologous sequences were identified by a reciprocal BLASTn search against a complete copy of the Arabidopsis thaliana chloroplast, mitochondria and nuclear genome enriched with the query sequence; only sequences that retrieved the query as the BLAST best hit were retained  (Sato, Nakamura, Kaneko, Asamizu, & Tabata, 1999).
Tara Oceans calculations Ribotypes corresponding to the 18S rRNA (V4 and V9 variable regions) and 16S rRNA (V4V5 regions) sequences of each species were identified from version 2 (Arctic-inclusive) of Tara Oceans data  (Ibarbalz et al. , 2019)  using the 18S and 16S trees defined above as a guide. Briefly, the complete 18S or 16S sequence of the species in question was searched by BLASTn against all 18S or 16S plastidial sequences previously assigned to the same taxonomic group as the query (Baffinella sp. CCMP2293: cryptomonads; Pavlovales sp. CCMP2436: haptophytes; Ochromonas sp. CCMP2298: chrysophytes; novel pelagophyte CCMP2097: pelagophytes), with threshold e-value 10 -05. Matching sequences were then searched against the complete curated 18S or 16S alignments for each group using BLASTn, and only sequences that yielded BLAST best hits against either the query sequence or its immediate sister groups in the 18S or 16S tree topology were retained for subsequent analysis.
Curated alignments and tree topologies are provided in Table  S1 , sheets 8-9; tabulated individual and total read abundances are provided in Table  S1 , sheets 10-11; and all raw data pertaining to the identification of matching ribotypes by BLAST and phylogeny, and calculation of quantitative abundance trends, are provided in the linked supporting database https://osf. io/3pmxb/  (Dorrell et al. , 2021b)  in the folder "TARA Oceans calculations".
Quantitative analysis of PFAM content PFAM distributions for each algal genome in the pan-algal dataset (both newly and previously sequenced accessions) were reannotated for this study using InterProScan and an updated (December 2020) version of the PFAM database from the constituent fasta files for each genome  (Jones et al. , 2014; Mistry et al. , 2020). PFAM annotation files for each MMETSP transcriptome were manually downloaded from the source accession; and cleaned using a previously defined pipeline which compares the relative BLAST similarity between pairs of nucleotide sequences in each MMETSP library to identify transcripts of potential contaminant origin  (Dorrell et al. , 2019; Marron et al. , 2016). Tabulated PFAM outputs are provided in Table  S2 , sheet 6; and complete PFAM lists per gene for each decontaminated library are provided in the linked supporting database https://osf.
io/3pmxb/  (Dorrell et al. , 2021b)  in the folder "PFAM Bray-Curtis distributions". Phylogenetically-aware PCA was performed on both genome and transcriptome datasets  (Revell & Graham Reynolds, 2012) , due to strong taxonomic effects observed using crude phylogenyindependent analyses. Edited versions of the multigene tree topology (Fig. 1 ) retaining only genomeor transcriptome branches, were generated using MEGA version X as phylogenetic templates  (Kumar, Stecher, Li, Knyaz, & Tamura, 2018). Input and output data for PCA are provided for user exploration in Table  S2 , sheet 7; and in the linked supporting database https://osf. io/3pmxb/  (Dorrell et al. , 2021b)  in the folder "CAFE and phylPCA". Similarity in PFAM content between different algal PFAM libraries were calculated using Bray-Curtis  (Anderson et al.
3  For Bray-Curtis and Spearman calculations, algae were divided into three functional categories: Arctic (species isolated from > 60°N); Antarctic (species isolated from > 55°S); and all remaining taxa from intermediate latitudes grouped as "Other". To avoid introducing biases due to comparing taxa of unequal phylogenetic distance, Bray-Curtis and Spearman calculations were only compared between pairs of algal libraries from different taxonomic groups, and to avoid biases due to comparing taxa with highly divergent life-strategies, all freshwater and secondarily non-photosynthetic taxa were removed from the final dataset. These values were then used to calculate mean values, and perform one-way ANOVAs of difference, within and between different groups of algae in the dataset, separated by biogeography into "Arctic", "Antarctic" and "Other" taxa, as above.