dataesr/scientific-paragraphs-categorization huggingface dataset:
Ammonium Treatment Reduces NIA2 Methylation Levels MeDIP sequencing analyses indicated that the whole genome methylation level was lower in (NH 4 ) 2 SO 4 -treated plants than in control plants. Therefore, we examined the methylation level of NR genes, which encode the first enzyme in the nitrate reduction pathway. The Arabidopsis genome database contains two NR genes, NIA1 and NIA2, and their methylation levels were analyzed by bisulfite sequencing of genomic DNA isolated from control and (NH 4 ) 2 SO 4 -treated plants. The results showed that NIA1 and NIA2 CG methylation levels were lower in (NH 4 ) 2 SO 4treated plants than in control plants (Table  2 ; Supplementary Table  S2 ). CHG methylation of NIA1 was not detected in control and (NH 4 ) 2 SO 4 -treated plants, whereas CHG methylation of NIA2 was significantly reduced from 11.
, 2007 ; Table  1 ). It is worth noting, however, that the nomenclature for B -subunits in  Farkas et al. , (2007)  differs from that indicated in the GenBank database  1  and followed, e. g. , by  Leivar et al. (2011;  Table  1 ). Phylogenetic analysis of plant PP2A catalytic subunits from diverse plant species in turn clustered the C-subunits into two subfamilies designated I and II  (He et al. , 2004). Within these clusters, the Arabidopsis PP2A catalytic subunits C1, C2, and C5 belong to subfamily I while C3 and C4 belong to subfamily II  (He et al. , 2004). The trimeric holoenzyme compositions provide extensive variability and versatility for PP2A in regulatory networks. What determines the trimeric PP2A subunit composition of a specific holoenzyme is currently not well understood. Computational analysis indicated that the amino acids located on PP2A-C/A/B interfaces are highly conserved, and hence unlikely to determine the different trimeric compositions of A.
thaliana PP2A  (Rasool et al. , 2014). This conclusion is also supported by BiFC experiments in Nicotiana benthamiana cells where all three A. thaliana scaffold PP2A-A-subunits interacted with all five catalytic PP2A-C-subunits in vivo  (Waadt et al. , 2015). Spatiotemporal gene expression patterns and subcellular localization may instead provide important regulatory levels that determine the availability of the different PP2A subunits for holoenzyme formation  (Pernas et al. , 2007; Konert et al. , 2015a). Indeed, the regulatory PP2A-B subunits have been detected in distinct subcellular localisations, albeit most of them seem to reside in more than one compartment, at least in the over-expression systems currently available for studies on protein localization (Table  1 ). Clues to transcriptional regulation of genes encoding PP2A subunits can be obtained by querying publicly available microarray datasets of mRNA abundance available in Genevestigator 2  (Hruz et al.
SAM also serves as a precursor for the biosynthesis of the stress hormone ethylene, where the ACC synthases ACS2 and ACS6 and possibly also ACC oxidase 2 are controlled by PP2A. Demonstrated PP2A targets are indicated by violet, and enzymes with increased protein amount and/or phosphorylation in a pp2a-b γ mutant background are indicated by green. ICDH has been identified as a phosphoprotein in the Arabidopsis Protein Phosphorylation Site Database (http://phosphat. uni-hohenheim. de/) but its regulation has not been connected with PP2A and is hence indicated in blue. P denotes phosphoprotein.
, 1995). A recent study showed that OsIRL may behave as an antioxidant in response to reactive oxygen species (ROS) in suspension-cultured cells as well as during root development in rice  (Kim et al. , 2010b). In a previous study, a cDNA library enriched for mRNAs encoding ESTs that increased in abundance during infection with Phytophthora sojae was constructed by suppression subtractive hybridization from leaf tissues of a high resistant soybean cultivar "Suinong 10, " and an EST homologous to an isoflavone reductase from white lupin (Lupinus albus) was identified to be up-regulated by microarray and real-time PCR  (Xu et al. , 2012). In this study, the full-length EST, designated GmIFR (GenBank accession no. NM_001254100, NCBI protein no. NP_001241029), was isolated through RT-PCR from "Suinong 10" soybean, and the transgenic soybean plants over-expressing GmIFR gene under the control of 35S promoter were produced.
Isolation of the GmIFR Gene A suppression subtractive hybridization library coupled with cDNA microarrays was queried using a soybean EST encoding an EST homologous to an isoflavone reductase from white lupin (L. albus), previously shown to be upregulated in "Suinong 10" soybean inoculated with P. sojae  (Xu et al. , 2012). Here, the full-length cDNA (tremed GmIFR, GenBank accession no. NM_001254100, NCBI protein no. NP_001241029) of the EST was amplified by RT-PCR from cDNA of "Suinong 10" using the primer pairs GmIFRF and GmIFRR (Supplementary Table  S1 ). PCR was performed as follows: 94 • C for 5 min, followed 30 cycles of 94 for 30 s, and 72 • C for 90 s, with a final extension at 72 • C for 10 min. The amplification product was gel purified and cloned into the pMD18-T vector (TaKaRa, Dalian, China).
Quantitative RT-PCR Analysis Quantitative real-time PCR analysis was performed to determine the transcript abundance of GmIFR. Total RNA was isolated from "Suinong 10" soybean leaves using Trlzol reagent (Invitrogen, Shanghai, China). The synthesis of cDNA was conducted using an oligo(dT) primer and a M-MLV reverse transcriptase kit (Takara, Dalian, China) according to the manufacturer's instructions. qRT-PCR was performed on a CFX96 Touch ™ Real-Time PCR machine (Biobad, USA) using the real-time PCR kit (ToYoBo, Japan). DNA accumulation was measured using SYBR Green as the reference dye. The soybean housekeeping gene Gmactin4 (GenBank accession no. AF049106) was used as the internal control (see Supplementary Table  S1  for primer sequences). For tissue distribution analysis, the transcript level of GmEF1 gene (GenBank accession no.
Isolation and Bioinformatic Analysis of GmIFR The full-length cDNA sequence of GmIFR (GenBank Accession No. NM_001254100) was isolated from total RNA of "Suinong10" soybean by RT-PCR and cloned into pMD-18T vector. Sequence analysis showed that GmIFR has an open reading frame (ORF) of 939 bp and encodes a polypeptide of 312 amino acids (Supplementary Figure  S1 ) with a predicted molecular mass of 34. 92 kDa and a isoelectric point (pI) of 6. 33. The deduced protein has a central 107 amino acid NAD (P) domain (Supplementary Figure  S1 ). The predicted threedimensional model of the GmIFR consists of 13 α-helices and 10 β-strands (Supplementary Figure  S2A ). To further explore the evolutionary relationship among plant NAD (P) proteins, a phylogenetic tree was constructed using MEGA 4. 0  (Tamura et al.
, 2007)  based on the amino acid sequences. Alignment and phylogenetic tree analysis of leguminous plants sequences revealed that GmIFR has 44-94% identity for overall amino acid sequence to Phaseolus vulgaris PvIFR, Cicer arietinum CaIFR, Medicago truncatula MtIRF, Lotus japonicus LjIFR, Glycine soja GsIFR, Medicago sativa MsIFR, Pisum sativum PsIFR (Supplementary Figure  S2B ). Analysis of the conserved NAD (P) domain of 107 aa showed that GmIFR shared 86-90% amino acid identity with others (Supplementary Figure  S2C ). The analysis of homologs of GmIFR in the soybean genome, based on data obtained from the Phytozome database (http:// www. phytozome. net/ soybean), indicated that the two genes were clustered on two linkage groups, namely one each on Gm 01 and Gm 09 and had three introns.
Mapping Reads to the Genome and Identification of Differentially Expressed Genes (DEGs) The P. persica v1. 0 genome dataset was used as a reference. The abundance of each gene was normalized to reads per kilo bases per million reads (RPKM) for between-sample comparison purposes. The edgeR software was applied to identify DEGs. Fold change (|log 2 FC| ≥ 1) and p-value (p ≤ 0. 01) were used as statistical significance indexes.
Functional Analysis of DEGs in J and C The Database for Annotation, Visualization and Integrated Discovery (DAVID) online platform (https://david. ncifcrf. gov/) was used to analyze the function of the DEGs. The upand down-regulated cluster groups of the DEGs were subjected to gene ontology (GO) term analysis. Among the upregulated clusters, the DEGs were significantly enriched in "phenylpropanoid biosynthetic and metabolic process. " Glycosyltransferase, especially the relevant uridine diphosphate (UDP)-glucosyltransferase genes were highly enriched from 24 h in the inoculated shoots. At 12 HAI, the genes involved in JA biosynthesis were highly enriched. Among the downregulated clusters of the GO term analysis, chloroplast, plastid, and photosynthesis were significantly enriched (Supplementary Table  5 ).
Function Annotation and Pathway Analysis To understand function distribution of genes at macro-level, gene ontology (GO) annotation of DEGs was performed using software Blast2GO and used for GO functional classification of DEGs using software WEGO. Kyoto Encyclopedia of Genes and Genomes (KEGG) is the major public pathway-related database, the significant differences between different groups were calculated using the formula P = 1 - m=1 i=0 M i N-M n-i N n Where N is the number of all genes with KEGG annotation, n is the number of DEGs in N, M is the number of all genes annotated to specific pathways, and m is the number of DEGs in M. Besides the list of the most meaningful pathways, the detailed pathway information in KEGG database can be obtained by clicking the hyperlinks on KEGG pathways.
Function Ontology and KEGG Pathway Enrichment Analysis of DEGs To evaluate the potential functions of the DEGs between the two symbiotic systems, DEGs with > 2-fold expression change in Group 3 were assigned to different GO categories such as biological process, molecular function, and cellular location, and 44 functional GO terms were analyzed (Figure  3 ). For all the five tested time points, the biological processes associated with the DEGs mainly focused on metabolic process, cellular process,  not in USDA205-21dR vs. 113-2-21dR. Besides, 26 go function terms were indicated at all the five tested time points and revealed no high shift in the distribution (Figure  3 ). KEGG is the major public pathway-related database. Therefore, we analyzed 10 KEGG pathways (Figure  4 ). In these pathways, the metabolic pathways were most prominent, followed by biosynthesis of secondary metabolites.
truncatula and L. japonicus NF-related genes in soybean genome sequence database (Table  3 ;  Kevei et al. , 2007; Mbengue et al. , 2010; Schmutz et al. , 2010; Kim et al. , 2013). Because same proteins may be encoded by one or more DEGs, only 14 NF-related genes are listed (Table  3 ), and the identified orthologs in these three legumes were named according to the given nomenclature in cloning studies in M. truncatula, L. japonicus and soybean. Nodulins are legume genes whose expression is induced by rhizobium bacteria upon nodulation  (Denance et al. , 2014). Twenty-nine nodulin genes have been analyzed in M. truncatula and most of them play key roles in nodulation  (Gamas et al. , 1996; Denance et al. , 2014). In this study, 25 soybean nodulin genes were identified as DEGs in soybean roots (Table  4 ).
Verification of RNA-Seq Results by qPCR and Expression Analysis of 189 Candidate Genes in Roots and/or Nodules To verify the RNA-Seq results, the expression stability of five reference genes (ELF1b, Qact, G6PD, Fbox and Ubiquitin) was evaluated (Supplemental Figure  S3 ), of which, Ubiquitin, ELF1b FIGURE 5 | Differentially expressed genes associated with the plant-pathogen interaction pathways in soybean roots at five time points after inoculation with USDA205 or 113-2. DEGs in  comparison  between soybean roots inoculated with rhizobium strain USDA205 vs. inoculated with rhizobium strain 113-2 at 0. 5 h, 7-24 h, 5 d, 16 d, and 21 d post inoculation), associated to the KEGG plant-pathogen interaction pathway in the KEGG database. Up-regulated genes are boxed in red, down-regulated genes are boxed in green.
The red arrows point out the up-regulation of DEGs, the green arrows point out the down-regulation of DEGs. and Qact were most stable in all samples, while GmG6PD and Fbox were consistently unstable (Supplemental Figure  S3 ). Thus, Ubiquitin was selected as reference gene for quantitative real-time PCR (qPCR) experiment. A total of 11 DEGs were randomly selected based on the transcriptional profile analysis and measured by qPCR. The results were in agreement with the transcriptional profile data for 136 out of 165 (82. 42%) data points (Figure  7 ). Although, the fold-changes were not exactly identical, both methods yielded identical expression trends for most data points. The sequences of the specific primers used for qPCR are given in Supplemental Table  S5. To confirm the candidate genes whether or not play roles in nodulation, the expressions of 189 candidate genes (78 from Tables  1 2 3 , others are protein kinases and/or transcription factors) in roots and/or nodules were analyzed according to two databases (Phytozome v10.
Microarray dataset was submitted to Gene Expression Omnibus (GSE89228). Differentially expressed genes (DEGs) with p-value < 0. 05 were selected. Different fold change cut off values ranging from 0. 5 to 1. 5 were tried with intension of including genes with high fold change expression. Also we looked for the fold change value which did not eliminate genes related to hormone, biotic stress, abiotic stress and xylem modification. Consequently, upregulated genes with fold > 1 (log base2) and down-regulated genes with fold > -1 (log base2) were selected for further studies.
Annotation of Transcripts and In silico Expression Profiling Gene annotation data was retrieved from chickpea transcriptome database 1   (Verma et al. , 2015). Further, annotation of some of the genes was updated by performing BLASTN search against chickpea database (taxid: 3827) available in NCBI. Differentially regulated genes were clustered using hierarchical clustering based on Pearson coefficient correlation algorithm 2. Venn diagrams were generated to view the common and unique genes in various conditions (Venny 2. 0 3  ). Orthologous genes of Arabidopsis thaliana were obtained from chickpea transcriptome database 4   (Verma et al. , 2015)  and their GO enrichment was performed using AgriGO singular enrichment analysis 5  with default setting of Arabidopsis gene model (TAIR10) background and other parameters (statistical test method: Fisher; multitest adjustment method: Yekutieli FDR under dependency; significance level: 0.
solanacearum was observed under SD-combined stress compared to SD-pathogen stress (Supplementary Figure  S2B ). However, in planta bacterial count was unchanged under LD-combined stress compared to LD-pathogen stress treatments (Supplementary Figure  S2C ). Transcriptomic alterations in chickpea plants challenged with combined drought and R. solanacearum stress (SD-and LDcombined) and individual drought (SD-and LD-drought) and R. solanacearum stress (SD-and LD-pathogen) were studied by microarray analysis. Microarray data was submitted to Gene Expression Omnibus (GEO# GSE89228). The DEGs (Log 2 FC ≥ ± 1) under drought stress compared to absolute control, and under combined and pathogen stresses compared to mock control were screened using unpaired t-test (p ≤ 0. 05). Putative annotation of DEGs were obtained from chickpea transcriptome database 7   (Verma et al.
, 2015)  and also by homolog search using nucleotide BLAST 8  against chickpea (NCBI taxid: 3827) database. The DEGs for which putative annotation could not be obtained by BLASTN or BLASTX were termed as unannotated DEGs (Supplementary Table  S2 ). Under both SD-pathogen and LD-pathogen stress treatments, many genes involved in defense response (WRKY33, MAP KINASE 11, and DEFENSIN) were up-regulated. Similarly, under SD-drought and LD-drought stress treatments, genes involved in signaling, biosynthesis of abscisic acid (ABA) and osmo-protectants namely the genes encoding for LATE EMBRYOGENESIS ABUNDANT 5 (LEA5), LOW-TEMPERATURE-INDUCED 65 KDA PROTEIN were upregulated. Concordant with these observations, genes involved in both defense responses and abiotic stress tolerance (genes encoding for LEA and RESPIRATORY BURST OXIDASE HOMOLOG B) were differentially expressed in SD-combined and LD-combined stresses, which conforms to the nature of the stressors (Supplementary Figure  S5  and File S1).
BETA-GLUCOSIDASE 12 LIKE, ACETYLTRANSFERASE, PROLINE DEHYDROGENASE 2, and ASPARTYL PROTEASE 1 encoding genes exhibited high upregulation in both SD and LD combined stresses. DIRIGENT PROTEIN 22 and LACCASE 7 LIKE encoding genes had high expression under SD-combined, SD-pathogen and LD-pathogen stresses. Gene for VEIN PATTERNING 1 (VEP1) was equally upregulated in all treatments (Supplementary Figure  S6 ). With the qPCR results we show that except VEP1, all other genes selected from SD-combined dataset were either specifically expressed or had higher expression under SD-combined stress, LD-combined stress and LD-pathogen stress treatments as compared to rest of the treatments. We further categorized the DEGs under each stress treatments based on gene ontology (GO) using Arabidopsis orthologs of chickpea genes (Supplementary Table  S3 ).
Molecular Characterization of ACC Deaminase An 800 bp amplicon was obtained by PCR from genomic DNA of the isolate HSW-16 using AcdS gene specific primers (Figure  2A ). Sequence analysis of the amplicon confirmed its identity where it showed 98% similarity with AcdS sequence of B. cereus AcdSPB4. The obtained sequence was submitted under the accession number KM501059. Sequence analysis of AcdS gene obtained from HSW-16 and other bacterial species revealed that AcdS sequence of HSW-16 is closely related to B. cereus AcdSPB4, B. subtillis, and strains belonging to other genera including Klebsiella, Pseudomonas, Burkholderia, Achromobacter, and Serratia. It also showed 99% similarity with one of the most characterized ACCD bacteria Pseudomonas putida UW4 (Figure  2B ). Nucleotide sequence analysis suggests that AcdS gene found in B.
, 2014). Furthermore, the presence of ACCD in test organism was confirmed by amplification of AcdS, a gene encoding ACC deaminase in bacteria. The universal primers used in this study have also been employed for successful amplification of AcdS gene in different environmental isolates  (Jha et al. , 2012; Shrivastava and Kumar, 2013). The nucleotide sequence of 800 bp amplicons was confirmed by sequence analysis, which matched with the AcdS of other bacteria available in the database. Phylogenetic analysis suggested that the AcdS gene sequence of HSW-16 was similar to bacteria belonging to genus Bacillus as well as other bacterial genera. To the best of our knowledge, this is the first report where the presence of AcdS in B. licheniformis has been demonstrated. Phylogenetic analysis indicated similarity of AcdS sequences among diverse species including Pseudomonas sp.
ACKNOWLEDGMENTS ACKNOWLEDGMENTS The authors thank all lab members for helpful discussions and  Gaetano Guarino  for technical assistance. This work was supported by the  Italian Ministry of University and Research,  projects  GenoPOM  ( DM17732 ) and  GenoPOM-PRO  ( PON02_00395_3082360 ), and by the  Italian Ministry of Economy and Finance  (Legge n. 191/2009 ), to the  National Research Council , project " Innovazione e Sviluppo del Mezzogiorno -Conoscenze Integrate per Sostenibilità ed Innovazione del Made in Italy Agroalimentare " ( CISIA ).
ACKNOWLEDGMENTS This study was sponsored by the  National Science Foundation of China  ( 31101166 ,  31571695 );  Jiangsu Key Laboratory for Bioresources of Saline Soils  ( JKLBS2014002 ;  JKLBS2014006 ),  Shuangchuang Talent Plan of Jiangsu Province , the  Jiangsu Natural Science Foundation, China  ( BK20151364 ) and  Jiangsu Agriculture Science, and Technology Innovation Fund (JASTIF , CX( 15)1005 ).
ACKNOWLEDGMENTS The authors acknowledge  Dr. Warwick Easdown ,  Regional Director, World Vegetable Center, South Asia, Hyderabad , for suggestions and support, and thank  Maureen Mecozzi  and  Dr. Roland Schafleitner  for editing the manuscript. Core funding to support  World Vegetable Center  activities worldwide is provided by the  Republic of China (ROC) ,  UK Department for International Development (UK/DFID) ,  United States Agency for International Development (USAID) ,  Germany ,  Thailand ,  Philippines ,  Korea , and  Japan. ACKNOWLEDGMENTS This work was supported by the  National Natural Science Foundation of China  ( 31270719 ), the  Importation and Development of High-Caliber Talents Project of Beijing Municipal Institutions  ( CIT&TCD201304095 ) and the  Project of Construction of Innovative Teams   and Teacher Career Development for Universities and Colleges under Beijing Municipality  ( IDHT20140509 ).
ACKNOWLEDGMENTS We thank  Dr. Claudio Pasian  ( The   Ohio State University, Columbus, OH, USA ) for critical reading of the manuscript. This research was supported by the  National Natural Science Foundation of China  (Nos. 31160398  and  31560563 ), the  Post Doctoral Foundation of China  (Nos. 20100470887  and  2012T50828 ), the  Key Project of Chinese Ministry of Education  (No. 211182 ), the  Research Fund for the Doctoral Program of Higher Education  (No. 20116202120005 ), the  Natural Science Foundation of Gansu Province, China  (Nos. 1308RJZA179  and  1308RJZA262 ), the  Fundamental Research Funds for Universities in Gansu, P. R. China. ACKNOWLEDGMENTS This work was supported by a grant from the  Next-Generation BioGreen 21 Program (Plant Molecular Breeding Center  no. PJ01108701 ),  Rural Development Administration, Republic of Korea.
ACKNOWLEDGMENTS The research was supported through funding from the  Heilongjiang Province outstanding youth  fund ( JC201308 ),  NSFC Projects  ( 30971811 ,  31071439 ,  31171577 ,  31101167 ),  Natural Science Foundation of Heilongjiang Province  ( C2015010 ), the  Specialized Research Fund for the Doctoral Program of Higher Education  ( 20112325120005 ), the  Science and Technology Innovation Project in Harbin  ( 2012RFQXN011 ,  2012RFXXN019 ), and the  Research Fund for Young Teachers through NEAU  ( 2012 RCB 08 ).
ACKNOWLEDGMENTS We thank  Prof. Zhongming Zhang  ( Huazhong Agricultural University in China ) for USDA205 strain, and this work was supported by funds from the  China Agriculture Research System  ( CAAS-04-PS08 ), the  National Transgenic Project of China  ( 2014ZX08004-005 ), the  Agricultural Science and Technology Innovation Program of CAAS , and the  Scientific and Technological Support Project of Soybean Breeding and Propagation  ( 2011BAD35B06 ). The  National Natural Science Foundation of China  (grant no. 31400213 ).
7:61. doi: 10. 3389/fpls. 2016. 00061 FUNDING AS is supported by a  German Academic Exchange (DAAD)  fellowship ( A/11/75070 ). Research in our laboratory is financed by the  German Research Foundation  through the Collaborative Research Centre  SFB648/TP-B1  " Molecular mechanisms of information processing in plants. " The  BMBF  project  ProNET-T3  ( 03ISO2211B ) supported LE. FUNDING This study was supported by the  National Natural Science Foundation of China  (Grant No. 31471840 ) and  China Agriculture Research System  (Grant No. CARS-31-2-4 ). FUNDING This work was supported by the  Université Paul Sabatier (Toulouse, France) , the  CNRS (France)  and by the  French Laboratory of Excellence  project " TULIP " ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ). FUNDING  We thank  Science and Engineering Research Board (SERB) ,  Department of Science and Technology (DST)  for providing grant and fellowship for this study ( SB/YS/LS-237/2013 ) to RS.