dataesr/scientific-paragraphs-categorization huggingface dataset:
Next, we analyzed the activity of respiratory complexes and related enzymes individually. Consistent with the divergent respirometry data for complex I substrates, we found reduced enzymatic activity of complex I in hearts from KO mice as compared to CTR (Figure  8E ). AMPK KO mice also showed reduced enzymatic activity of complex IV (Figure  8F ). In agreement with respirometry data for palmitoylcarnitine being similar for both phenotypes, activity of carnitine:palmitoyltransferase (CPT1 + 2) was unchanged (Figure  8G ). In spite of alterations in mitochondrial function observed in KO mice, the abundance of respiratory chain complexes in total heart extracts was unchanged as verified by immunoblotting (data not shown). This suggests differences at the post-translational level. There was neither a detectable effect of KO on calcium retention capacity linked to mitochondrial permeability transition and its sensitivity to cyclosporine A (Figure  8H ).
ACKNOWLEDGMENTS We thank  Amel Achouri  for excellent technical assistance,  Pauline Durrenbach  for animal care as well as the master students  Mylene Verney ,  Salma Guerfallah ,  Clémence André ,  Anton Kolarov ,  Cédric Dos Santos , and  Namita Bhyravbhatla  for participation in experiments during their internships. We also thank  Elise Belaidi-Corsat  and  Sophie Moulin  for helpful discussion concerning cardiomyocyte isolation.
FUNDING This work was supported by the  French National Research Agency  within the  Investissements d'Avenir program  ( CDP SYMER ,  ANR-15-IDEX-02 ) to US, the federal research structure  BEeSy (Université Grenoble Alpes ) and the  Institut Universitaire de France  (both to US), as well as the  Germaine de Stael Program for Franco-Swiss collaboration  ( 32771QE-2015/GdS 2015-13  to US and CZ). Radiopharmaceutiques Biocliniques , Inserm  U1039 , is member of the  France Life Imaging network  ( ANR-11-INBS-0006 ). Animal genotyping was performed by the genotyping.
This result is consistent with a role of ROS1 in limiting the spreading of DNA methylation at TE boundaries  (Tang et al. , 2016). To determine whether such hyperDMR could be directed by siRNAs, we monitored the accumulation of siRNAs at the RMG1 promoter by analysing small RNA sequencing (sRNA-seq) datasets generated in rosette leaves of untreated Col-0, ros1-3 and ros1dcl23 mutants. While no siRNA was retrieved at the 3' boundary of AtREP11 in Col-0 plants, we detected the accumulation of siRNA species of different sizes in the ros1-3 mutant, ranging from low levels in the 21-22 nt species to high level in the 23-24 nt species (Figure  2DE ). All these siRNA species were no longer produced in ros1dcl23 mutants, a molecular effect which was associated with an absence of methylation at this ROS1-targeted region (Figure  2E ).
These heatmaps were generated from BS-seq datasets obtained from 5-week-old rosette leaves of Col-0, ros1-3 and ros1dcl23 mutants. D. Increased 24 nt siRNA levels in ros1-3 at the whole set of genes exhibiting hyperDMRs in ros1-3 versus Col-0 (left panel) and at the 102 less-induced genes (right panel) are restored in the ros1dcl23 triple mutant. Heatmap representing 24-nt siRNA levels within regions comprising 2Kb upstream of the transcription start site (TSS), the gene body, and 2 Kb downstream of the transcription end site (TES) of the whole set of genes exhibiting hyperDMRs in ros1-3 versus Col-0 (left panel) and of the 102 less-induced genes (right panel). These heatmaps were generated from sRNA-seq datasets obtained from 5-week-old rosette leaves of untreated Col-0, ros1-3 and ros1dcl23 mutants.
ROS1 facilitates the flg22-triggered induction of three distinct targets by preventing hypermethylation at discrete regions in their promoters We next selected, from our RNA-seq datasets, candidate genes that were strongly induced by flg22 in Col-0 plants and showing pronounced compromised flg22 inducibility in elicited ros1-3 mutants (Figure  3B ; Figure  S4 ). These genes include the TIR-only resistance gene RBA1 (AT1G47370), which was previously shown to be repressed by methylation in Col-0 and to recognize the bacterial effector HopBA1 in the Arabidopsis thaliana Ag-0 accession  (Nishimura et al. , 2017) ; the orphan Receptor-Like Protein 43 (RLP43) (AT3G28890), which presents typical features of RLP PRRs  (Steidele and Stam, 2020) , and a phosphoglycerate mutase gene (AT1G08940) (Figure  3B ).
WRKY TFs bind to a single W-box element embedded in the middle of the demethylated promoter region of RLP43, thereby ensuring a proper flg22-triggered induction of this gene Given that the ROS1-directed demethylation of a subset of defence gene promoters was required for their flg22-triggered induction (Figure  3 ), we hypothesized that these demethylated promoter sequences might contain functional binding sites for PAMP-responsive TFs. To test this possibility, we selected 26 flg22-induced ROS1 targets exhibiting discrete and dense hypermethylated regions in ros1-3 mutants, along with a strong impaired induction in ros1-3-elicited mutants, and subjected their hyperDMR sequences to Genome Association Tester (GAT) (Figure  S5A ;  Heger et al. 2013 ). This approach interrogates DNA Affinity Purification sequencing (DAP-seq) datasets of whole-genome Arabidopsis TF factor binding sites and assess whether any DAP-seq peak would overlap more with the input DNA sequence than expected by chance  (O'Malley et al.
, 2016). By using this analysis, we found in vitro DNA binding of WRKY TFs at 14 out of 26 promoter-derived sequences tested (Figure  4A ; Figure  S7 ). Among these candidates, we retrieved RMG1, RBA1 and RLP43, which exhibit in vitro DNA binding of several WRKYs in their promoter regions subjected to ROS1-directed demethylation (Figure  4A /B; Figures  S7-9 ). Five out of these 14 candidate promoters also exhibit in vivo binding of the AtWRKY18 and AtWRKY40 based on chromatin immunoprecipitation sequencing (ChIP-seq) datasets A. A subgroup of the ROS1 targets exhibit an over-representation of WRKY DNA binding at the promoter regions corresponding the hyperDMRs that were retrieved in ros1-3. GAT analysis performed on publicly available DAPsequencing data  (O'Malley et al. , 2016)  identified WRKY transcription factors with significant enrichment in the regions corresponding to the hyperDMRs observed in ros1-3 for the 14 stringent ROS1 primary targets.
Bacteria growth assays Four plants per condition were dip-inoculated using Pto DC3000 at 5 x 10 7 cfu/ml supplemented with 0. 02 % Silwet L-77, and immediately placed in chambers with high humidity. Bacterial growth was determined 3 days post-infection. For the quantification, infected leaves were harvested, washed for one minute in 70 % (v/v) EtOH and one minute in water. Leaf discs with a diameter of 4 mm have been excised, grinded and homogenized in 100 µl of water. Each data point consists of four-leaf discs. Ten µl of each homogenate were then plated undiluted and at different dilutions. Bacterial growth was determined after 36 h of incubation at 28°C by colony counting.
B. GAT analysis performed on publicly available DAP-seq data  (O'Malley et al. , 2016)  identified transcription factors with significant enrichment at the promoter demethylated regions from the 26 stringent less-induced ROS1 targets. 8. WRKY DNA binding peaks are present at the RMG1 promoter region that is subjected to ROS1-directed demethylation. Snapshots representing, from top to bottom, RNA-seq, BS-seq and DAP-seq datasets at the RMG1 locus (left panel) with an enlargement surrounding the hyperDMR and the WRKY-binding peaks (right panel).
Acknowledgements We thank  Imre Somssich  for providing the pGEX-TM-WRKY40 plasmid and the ChIP-seq raw datasets ahead of publication;  Robert Fischer  for providing the ros1-3 and ros1-4 alleles;  Florence Jay  for its contribution to the selection of the ros1-3 dcl2-1 dcl3-1 mutants, and all the members of the  Navarro lab  for their inputs, discussions and critical reading of the manuscript. This work was supported by a  European Research Council (ERC)   Starting Grant  " Silencing and Immunity " ( 281749 ), and an  Agence Nationale pour la Recherche (ANR)  " NEPHRON " ( ANR-18-CE20-0020 ), both granted to LN. TH received additional support by a  Marie Curie Individual Fellowship  ( EU  project  661715 -BASILA ) and a post-doc funding from the " Fondation Pierre-Gilles de Genes ". JW and MSR received support from the programme " Investissements d'avenir" implemented  by  ANR  ( ANR-10-LABX-54 MEMO LIFE ,  ANR-10-IDEX-0001-02PSL ).
Merged data from two independent biological replicates are presented. C. Increased global DNA methylation levels observed in untreated ros1-3 compared to Col-0 at the whole set of genes exhibiting hyperDMRs (left panel) and at the 102 less-induced genes (right panel) are restored in the ros1dcl23 triple mutant. Heatmap representing global DNA methylation levels within regions comprising 2Kb upstream of the transcription start site (TSS), the gene body, and 2 Kb downstream of the transcription end site (TES) of the whole set of genes exhibiting hyperDMRs in ros1-3 versus Col-0 (left panel) and of the 102 lessinduced genes (right panel). These heatmaps were generated from BS-seq datasets obtained from 5-week-old rosette leaves of Col-0, ros1-3 and ros1dcl23 mutants. D. Increased 24 nt siRNA levels in ros1-3 at the whole set of genes exhibiting hyperDMRs in ros1-3 versus Col-0 (left panel) and at the 102 less-induced genes (right panel) are restored in the ros1dcl23 triple mutant.
Heatmap representing 24-nt siRNA levels within regions comprising 2Kb upstream of the transcription start site (TSS), the gene body, and 2 Kb downstream of the transcription end site (TES) of the whole set of genes exhibiting hyperDMRs in ros1-3 versus Col-0 (left panel) and of the 102 less-induced genes (right panel). These heatmaps were generated from sRNA-seq datasets obtained from 5-week-old rosette leaves of untreated Col-0, ros1-3 and ros1dcl23 mutants. Average of the two replicates are represented. E. Methylation levels at RBA1 are partially restored in ros1dcl23 whereas RLP43 retains methylation levels similar to methylation levels observed in the single ros1-3 mutant. IGV snapshots showing siRNA levels and methylation levels at the DMRs of RBA1 and RLP43 in Col-0, ros1-3 and ros1dcl23.
Introduction High-throughput sequencing (HTS) technologies have become an integral part of research and diagnostics toolbox in life sciences, including phytopathology and plant virology  [1]. HTS enables the untargeted acquisition of extremely large amounts of sequence data from diverse sample types and thus represents an ideal and unique solution for the generic detection of highly diverse viruses. In the past decade, sequencing prices have significantly decreased, and the technology has become accessible to many more research and diagnostic labs. From the first uses of HTS for detection of plant viruses in 2009  [2] [3] [4] [5] , the use of this technology for detection of known and new plant viruses and the characterization of viromes in different plant species has intensified dramatically.
Many different bioinformatics tools have been developed and different pipelines have been used to detect and identify plant viruses represented in HTS datasets. The variation in results associated with the use of different pipelines in different labs has highlighted the significance of understanding different approaches  [6]. Arguably, one of the main challenges for less experienced users of HTS is to understand, select, and properly use tools for the analysis of HTS data intended for detection and identification of plant virus sequences. In this review, we aim to present the different and often complementary approaches used for analysis of HTS data for the detection of plant viruses. We provide a short introduction to the laboratory work required and then describe the possible steps in data processing for the detection of plant viruses, including quality control and trimming of the sequences, de novo assembly, sequence similarity searches, and taxonomic classification of the identified viral sequences.
Considering the number of steps with the average HTS analysis pipeline and the number of samples studied, automation quickly becomes a priority. This can be achieved by writing scripts as well as grouping and ordering all the steps of the analysis, which also require expertise in programing languages (e. g. , shell, Python, R). Finally, for the interpretation of the analysis results, skills beyond pure bioinformatics are needed. A close collaboration between a bioinformatician and a plant virologist (or a plant virologist trained in bioinformatics) is needed to achieve a meaningful interpretation of the results. Beyond the skills of users, IT resources must also be addressed. The amount of data generated by each project must be anticipated in order to have raw data storage space available beforehand and to ensure that data is safely stored at least for several years after the end of projects.
Starting the Project: How Do I Prepare Samples and Sequence Nucleic Acids? Sampling, nucleic acids extraction, viral enrichment, and sequencing library preparation are essential steps before HTS itself. Since these steps can influence the sequencing results, we briefly summarize here the most important considerations for some of these processes. An extensive description of how to control all of these steps is in preparation in forthcoming international guidelines for the use of HTS tests for the diagnostic of plant pests  [9]. After obtaining the nucleic acids suitable for further analysis using HTS, the approximate amount of sequence data required per each sample should be estimated according to the goals of the study. If an external sequencing provider will perform HTS, this number, together with some general characteristics of the samples, should be communicated with the provider.
How Do I Analyze the Data? Figure  2  outlines typical steps that can be followed once the fastq file has been obtained. The first is a quality control (QC) check. This is followed by pre-processing steps, including trimming low-quality bases, removing adapter sequences, and discarding very short and low-quality reads, followed by further QC filtering (Section 4. 1). Then, reads passing QC are ready for analysis either directly or after assembly into contigs (Section 4. 2). Reads or contigs can optionally be mapped to a host reference genome, and, in this way, host sequences can be removed (Section 4. 3. 3). Then, reads or contigs are used to query a database of known viral sequences or motifs (Sections 4. 3. 2-4. 3. 5). Results need to be carefully inspected for correct taxonomic classification (Section 4.
Demultiplexing, Quality Control, and Trimming Each sequencing platform produces a series of quality metrics associated with the data produced from each sequencing run. A discussion of the metrics with the sequencing data provider is important before accepting any sequencing data. If the run was successful, the first step is the demultiplexing of barcoded samples, which is usually carried out using the sequencing platform software or performed by the sequencing data provider. In the event that data has not been demultiplexed, third-party tools such as Cutadapt  [48]  can be used to demultiplex the Illumina data by looking for specific barcode sequences present in the samples. Alternatively, demultiplexing tools developed by the sequencing platform provider are frequently accessible as stand-alone tools, such as Illumina's bcl2fastq software  [49] , or Oxford Nanopore Technologies' guppy scripts  [50].
With Illumina data, values such as Q20 (1% error) and Q30 (0. 1% error) are often used when trimming data, but this value depends on the application and the sequencing platform used. If accuracy is of the utmost importance (e. g. , for detection of SNPs), selecting a higher quality score will be beneficial. If accuracy is less important (e. g. , for detection of virus), then relaxing constraints on quality when trimming will allow more data to be available for downstream applications. Quality control reports can be generated by tools such as FastQC  [58] , MultiQC  [59] , or, specifically for nanopore sequencing data, Poretools  [60]  or NanoStat  [56]. This allows for the visual inspection of metrics such as per base sequence quality, sequence length distribution, and GC (guanine-cytosine) content.
qiagen. com, accessed on 13 April 2021) and Geneious Prime (https://www. geneious. com, accessed on 13 April 2021). The latter has the advantage of providing a graphical interface for command-line assembly programs such as Velvet and Spades. Different factors can positively influence the quality of the de novo assembly, e. g. , a preliminary filtering step to eliminate the genomic host plant reads  [23]  or the selection of appropriate k-mer values based on the read length  [6]. Moreover, approaches in which de novo assemblies using different k-mer values are generated and then reassembled can generally improve the completeness of de novo genome assemblies, but this can be a laborious and computationally lengthy process. Usually, higher sequencing depth and a higher fraction of viral reads in the dataset will positively affect the completeness of assembled viral genomes; however, extremely high coverage might have a negative effect on the completeness of the assembly when using some assemblers; thus, in such cases, the assembly of subsampled data might give better results  [15].
When the presence of specific viruses is already known, viral genomes can be reconstructed by mapping the reads (explained in Section 4. 3) to the closest reference sequences obtained from sequence databases (after initial similarity searches, Section 4. 3). Then, this is followed by the extraction of new consensus sequence from the mapping, which is an approach known as reference guided assembly. Sometimes, parts of the viral genomes are obtained by de novo assembly and other parts are obtained through reference guided assembly; such an approach is also known as combined assembly.
How Do I Find and Classify Viral Sequences in My Data? Identification of viral reads/contigs in massive HTS datasets is most frequently performed by comparing sequences against known and annotated sequences in databases. This can be done on the level of reads or contigs de novo assembled from the reads. Since longer sequences in almost all cases improve the ability to identify similarities regardless of the method or databases used, an assembly of quality checked raw reads is generally recommended prior to similarity searches. At the same time, a prior assembly will also generally reduce the computing time needed for the similarity search steps, as up to millions of reads can be assembled in a single contig. The annotation of HTS reads, or contigs, on the basis of similarity with known viral sequences can be performed using three main strategies: homology searches with tools such as Basic Local Alignment Search Tool -BLAST [83], read/contig mapping against reference viral genomes using tools such as BWA  [84] , and the search for encoded, conserved protein motifs using tools based on Hidden Markov Models (HMMs) such as HMMER  [85].
Databases The database(s) against which sequences are compared is/are of utmost importance for the efficiency and completeness of the annotation process. The more complete the collection of viral sequences, the greater the likelihood of detecting and identifying the presence of a virus. For BLAST and BLAST-like approaches, the most used databases are the non-redundant nucleotide database (nr/nt, named also just nt) hosted by the NCBI, the non-redundant GenBank protein database (nr) or the viral RefSeq database. The GenBank non-redundant nucleotide and protein databases are the most comprehensive and most frequently updated public databases, limiting the time from discovery of a novel virus to its availability for comparisons (provided the local version of these databases is also regularly updated).
However, the size of these databases has the drawback of increasing the computing time/power needed to perform a comparison. The reduced viral RefSeq database has the benefit of a better annotation/curation at the expense of the number of included sequences and of less frequent updates. For read mapping approaches, smaller dedicated databases are generally used, such as a subset of all viral sequences from the NCBI nt database, viral RefSeq, or a smaller, locally developed and curated database (for example, one or several isolates of every virus known to infect the crop of interest). For conserved protein motifs searches, the most common databases are PFAM  [87]  and CDD  [88]. The identification of viral sequences is critically dependent upon the quality of the database(s) used. For example, some plant-derived proteins might also be misidentified as viral if only a virus sequence database is used for similarity searches, because some viral proteins are related to plant encoded proteins.
Typical examples are heat shock proteins (i. e. , Hsp70h) found in closteroviruses  [89]  or reverse transcriptase proteins of Caulimoviridae that have homologs among retrotransposons. Wrongly annotated sequences in the public databases can also lead to erroneous annotations. Although this is generally not implemented at the moment, comparing the identified viral sequences with databases of retrotransposons  [90]  or to databases created from the systematic screening of plant genomes for integrated viral sequences  [91] [92] [93]  may provide an efficient strategy to differentiate transcripts derived from integrated viral elements from autonomously replicating viruses.
BLAST and BLAST-Like Approaches BLAST programs are the most widely used and among the most accurate in detecting sequence similarity  [94]. The BLAST suite  [95]  comprises different algorithms, each with its own use: 1. BLASTn can be used to compare a nucleotide sequence with a nucleotide database. It is less computationally intensive than BLASTx, but because of the higher divergence rate of nucleotide sequences, it is less efficient for the annotation of novel viruses not represented in the database used.
6. MegaBLAST can be used to compare nucleotide sequences expected to be already present or closely related to those in a nucleotide database. It can be much faster than BLASTn and is able to handle much longer sequences but deals less efficiently with very divergent sequences. Short sequences may lead to false positives in BLAST searches, and for this reason, other approaches should be preferred for very short reads or contigs. All BLAST programs return a table of results, which contain several parameters, among which some are particularly important to check: the identity threshold (threshold for the percentage of identical nucleotides between the query sequence and a hit in a database), e-value (expected number of random hits in the used database for a given query sequence), and query coverage (percentage of the query sequence covered by the database hit).
It is very important to consider that some of these values depend on the size of the database used and that the use of too stringent parameters (e. g. , identity threshold >85% and e-value smaller than 10 -10 ) may lead to a failure to detect some divergent viruses  [6]. BLAST is very widely used, but it remains, in the case of millions/billions of reads analyses, a time-consuming algorithm. Restricting the database used to specific taxa (e. g. , viruses) can speed up BLAST searches, but care should be taken, as this frequently leads to the identification of viral reads that on closer examination, using complete databases, are in fact host sequences (e. g. , plant sequences). An extremely fast but considerably less sensitive alternative to BLAST is BLAT (BLAST-Like Alignment Tool)  [96]. Another faster alternative to BLASTx is DIAMOND  [97] , which runs at 500-20,000× the speed of BLAST while maintaining a high level of sensitivity, especially if using the sensitive mode.