We find that mPing bursts are restricted to recent domestication and is likely due to the accumulation of two TE components, Ping16A and Ping16A_Stow, that appear to be critical for mPing hyperactivity. Ping16A is a variant of the autonomous element with reduced activity as shown in a yeast transposition assay. Transposition of Ping16A into a Stowaway element generated Ping16A_Stow, the only Ping locus shared by all bursting accessions, and shown here to correlate with high mPing copies. Finally, we show that sustained activity of the mPing/Ping family in domesticated rice produced the components necessary for mPing bursts, not the loss of epigenetic regulation.Transposable elements (TEs) shape genome evolution through periodic bursts of amplification. In this study prior knowledge of the  Transposable element (TE) bursts shape genome evolution but their origin remains unclear. Here, the authors show that a burst is restricted to only a few domesticated rice accessions and is associated with the acquisition of two TE variants, Ping16A and Ping16A_Stow, not the loss of TE silencing. For a TE to successfully burst, it must be able to increase its copy number without killing its host or being silenced by host surveillance3. However, because the vast majority of TE bursts have been inferred after the fact—via computational analysis of whole-genome sequence—the stealth features they require for success have remained largely undiscovered4.Eukaryotic genomes are populated with transposable elements (TEs), many attaining copy numbers of hundreds to thousands of elements by rapid amplification, called a TE burstmPing from rice6. MITEs are nonautonomous DNA transposons that are the most common TE associated with the noncoding regions of plant genes1. To understand how MITEs attain high copy numbers, a computational approach was used to identify mPing, and its source of transposase, encoded by the related autonomous Ping element  ent Fig. 5.Fig. 1AmPing were discovered in four temperate japonica accessions: EG4, HEG4, A119, and A123, whose genomes were sequenced, and insertion sites and epigenetic landscape determined8. These analyses uncovered two features of successful mPing bursts. First, although mPing, like other DNA TEs, prefers genic insertions, de novo insertions in exons were only 14% of expected for random insertions, thus minimizing harm to the host8. Exon avoidance arises from mPing’s extended 9-bp adenine-thymine (AT)-rich insertion preference8 coupled with rice exon sequences that are significantly more guanine-cytosine (GC) rich than rice introns (51% vs. 37%)9. Second, because mPing does not share coding sequences with Ping  at position 16 ( + 16 G/A) that distinguishes mPing and Ping sequences  that is the only Ping locus shared by all bursting accessions7. Comparative sequence analysis of two of the four bursting accessions (A123 and A119) revealed that they were derived by self or sibling pollination about a century ago from a common ancestor that had not yet undergone Ping or mPing amplification7. Significantly, this common ancestor had only a single Ping locus, which was Ping16A_Stow7.The contributions of two other genetic components to the success of the bursts could not be assessed previously and are a focus of this study. First, all ces Fig. . The secPing and mPing elements in the genomes of 3000 domesticated rice accessions and 48 genomes of their wild progenitor, Oryza rufipogon. Rice has been divided into five major subgroups  that are thought to have originated from distinct populations of the wild progenitor O. rufipogon that arose prior to domestication11. Rice genomes are very stable: all analyzed genomes are composed of 12 chromosomes13, and rice subgroups share high sequence identity ( > 98.9%)13. However, the genomes also exhibit extensive presence–absence variation both within (5%) and between (10%–19%) subgroups14, with TEs representing more than half of this variation. In addition, significant gene flow from japonica to indica and aus has been noted previously, reflecting the more ancient origin of japonica15.To understand the origin of these genetic components and their possible role in the burst, we analyzed the presence, sequence, and copy numbers of O. rufipogon have been utilized in this study to better understand the identity and origin of the components necessary for mPing bursts. Of particular interest was whether (i) mPing bursts could be detected in other accessions of wild and/or domesticated rice, (ii) the + 16 G/A Ping SNP and Ping16A_Stow could be detected in wild rice or first appeared in domesticated rice, and (iii) the presence of + 16 G/A Ping SNP and Ping16A_Stow correlated with higher mPing copy numbers.Knowledge of the relationships between the major subgroups of rice and the populations of mPing bursts, Pong, a related transposase-encoding element, is a focus of this study  has been dramatically reduced. A transposition event of Ping16A into a Stowaway element created Ping16A_Stow whose presence correlates with accessions that have high mPing copies. We reject the hypothesis that a loss of global epigenetic regulation has occurred as no other TEs have amplified, indicating that these new Ping loci are the primary driver of the observed mPing burst in domesticated rice.Our analysis show that mPing, Ping, and Pong were identified from genome sequences of 3000 rice accessions using RelocaTE219 (see Methods). The paired-end DNA libraries had an average insert size of ~ 500 bp and were sequenced to a depth of 14-fold genome coverage20, which allowed clear distinction between mPing, Ping, and Pong elements . Outputs from the RelocaTE2 and read depth methods were well correlated  in the midst of mPing bursts7. Of the 3000 rice accessions, 2780 (92.7%) contain mPing, with an average of about 9 elements per accession  than tropical japonica (~2.6/accession), indica (~8.2/accession), or aus/boro (~3.8/accession)  with Tukey’s honest significant difference (HSD) test, adjusted P-value < 2e–16; Supplementary Table O. rufipogon accessions have mPing elements with copy numbers ranging from 1 to 11  in domesticated rice  were previously detected in O. rufipogon accessions22. Here we detected all four subtypes of mPing elements in O. rufipogon accessions  than the four accessions undergoing mPing bursts7. Ping elements were detected in only 199 of 3000 accessions (6.6%)  of the O. rufipogon accessions analyzed , located adjacent to the 15-bp terminal inverted repeat (TIR)  and Ping16A. First, all 21 O. rufipogon accessions with Ping have only Ping16G, which has the same sequence at + 16G/A as mPing  Fig. , may be mPing  + 6 G/A, lomPing  + 6 G/A, lomPing  + 6 G/A, lomPing  + 6 G/A, lo24. Because the SNP distinguishing Ping16A from Ping16G is adjacent to the 15-bp 5ʹ TIR  catalyzes transposition of mPing inserted in an ADE2 reporter gene, thereby allowing growth of yeast cells25. The results indicate that both the mutations adjacent to the TIRs (G16A and G17T) and 12 of 14 mutations in the TIR significantly reduced mPing transposition  was independently confirmed using Ping transposase  have many (7–10) Pings, and all share only a single Ping, Ping16A_Stow7. This correlation suggests that acquisition of Ping16A_Stow may have initiated the burst. Ping16A_Stow, located on chromosome 1 (2640500–2640502), is comprised of the Ping16A variant inserted in a 769-bp Stowaway element  7, there is no evidence to date that Pong has an impact on Ping or mPing copy number or distribution.7 and suggest that Pong was silenced in O. rufipogon and has been stably silenced in domesticated rice. Pong elements are present in the genomes of almost all of the analyzed rice accessions , and Pong copy numbers vary little within or between subgroups . As in domesticated rice, there is minimal Pong copy number variation among the O. rufipogon accessions examined  were analyzed to determine if this resulted from Pong activation. First, because active Pong elements produce proteins that catalyze mPing transposition, we tested if the genomes of these lines contained more mPings. However, all six accessions had the same range of mPing copies as accessions with few Pongs  were generated prior to domestication, probably in O. rufipogon, by internal deletion from Ping16G. Furthermore, Ping16G, but not Ping16A, was detected in 21 of 48 O. rufipogon accessions. The fact that only 31 of the 3000 extant domesticated accessions examined have Ping16G suggests that there has been a massive loss of this element in domesticated rice. In contrast, the Ping16A variant was identified in the majority of the domesticated accessions with Ping (154/199). Its absence in O. rufipogon genomes indicate that it was either very rare in wild populations or that it arose in japonica after domestication. Ping16A has experienced limited but significant proliferation in japonica and has even been introgressed into a small number of indica accessions  to + 16A (Ping16A) polymorphism significantly reduces transposition frequency. Thus, Ping16A encoded proteins  are more likely to catalyze the transposition of mPing (with its + 16G) than Ping16A. This situation is reminiscent of other autonomous elements that harbor sequences that reduce transposition frequency27. It has been hypothesized that autonomous TEs enhance their survival by evolving self-regulating mutations that reduce both host impact and epigenetic detection and silencing27.Yeast assays testing the functional impact of several mutations in and adjacent to the Ping16A have only one Ping (105/154 accessions) and a moderate number of mPing elements (mean = 28). One of these accessions is the reference accession Nipponbare where the inability to detect transposition of Ping or mPing was initially attributed to Ping silencing28. In fact, Ping is not silenced in Nipponbare nor in any other accession analyzed to date7. Rather it is transcribed and catalyzes (infrequent) transposition of mPing7. We speculate that accessions with a single copy of Ping16A may be experiencing a balance, perhaps under stabilizing selection, between host survival and the maintenance of an active TE family in the genome.The vast majority of accessions with Ping16A and mPing elements and the host was perturbed in the subset of temperate japonica accessions experiencing mPing bursts7 and it was suggested that the shared Ping16A_Stow locus may have been responsible7. Based on the evolutionary inventory presented in this study, it follows that Ping16A_Stow was generated in a temperate japonica accession when Ping16A transposed into a Stowaway element on chromosome 1. The Stowaway element (without the Ping insertion) was also present at this locus in O. rufipogon  and Ping (7–10) elements as the four previously characterized accessions. Further, analysis of the 3000 rice genomes and wild progenitors indicates that two variants of the autonomous Ping element, Ping16A and the subsequently evolved Ping16A_Stow locus appear to be critical for mPing hyperactivity. Other studies have shown that domestication can be associated with the loss of epigenetic regulation29, which may lead to the activation of TEs. However, our data indicate that Pong element copy number has been stably maintained from the wild ancestor through the generation of the thousands of domesticated accessions, suggesting that epigenetic regulation was unaffected. In contrast, Ping activity has been sustained during domestication, resulting in the spread and amplification of the Ping16A variant and the generation of the Ping16A_Stow locus in rice. Yet, the spread of Ping activity associated with exceptional mPing activity has been very limited in rice, likely due to its high level of self-fertilization.In conclusion, the results of this study demonstrate that 20. The raw reads of 48 O. rufipogon accessions were obtained from NCBI SRA under project accession numbers listed in Supplementary Data O. rufipogon accessions was extracted from prior studies30. The raw reads of Oryza glaberrima, Oryza glumaepatula , and Oryza meridionalis were obtained from NCBI SRA projects accession numbers SRR1712585, SRR1712910, and SRR1712972.Illumina DNA sequencing reads of 3000 rice accessions were obtained from NCBI SRA project PRJEB6180. The metadata incorporating name and origin of the 3000 rice accessions was extracted from previously published Tables S1B31 (http://snp-seek.irri.org). A subset of 270,329 SNPs was selected by removing SNPs in approximate linkage equilibrium using plink v1.09 (--indep-pairwise 1000 kb 20 kb 0.8)32. Population clustering analysis was performed by ADMIXTURE v1.3.033 (-s 2) with K from 2 to 10. Most rice accessions clustered into five subgroups : ARO, temperate japonica: TEJ, and tropical japonica: TRJ) when K is 5. Using the ancestral analysis of ADMIXTURE under the K = 5 model, a rice accession was assigned to one of these five subgroups if it had ≥ 80% of its ancestral component from a given subgroup. Any accessions that had no major ancestral component ( < 80%) were categorized as admixed (ADM) accessions. During the preparation of this study, Wang et al. published an analysis of the same dataset14. The subgroup classifications were compared between the two studies and the results are consistent except that Wang et al. identified additional subgroups in indica and japonica.The genotyped SNP dataset (release 1.0 3 K RG 4.8 million filtered SNP Dataset) of the 3000 rice genomes was obtained from SNP-Seek Database34 using default parameters (impute = true imp-states = 1600 imp-segment = 6.0 cluster = 0.005). A total of 768 accessions with major ancestral component over 99.99% were used as reference panels for five rice subgroups . Local ancestry assignment was performed on accessions of interest with RFMix v2.0335 using default parameters. Regions of interest were manually inspected in the results of RFMix. Introgression was defined as an allele that is present in one subgroup but has originated from another subgroup.The 4.8 million filtered SNPs were imputed and phased with BEAGLE v5.0mPing, Ping, and Pong insertion sites across the 3000 rice genomes were characterized with RelocaTE2 19 using raw reads obtained from NCBI SRA. Element-specific sequence differences were identified and used to distinguish Ping and Pong from mPing insertions  and the mate matched to a unique genomic region of the Nipponbare reference genome (MSU7) were used to differentiate Ping insertions. Similarly, paired-end reads where one read matched the internal Pong element sequence (23–5320 bp) and the mate matched to a unique genomic region of MSU7 were used to identify Pong insertions. An equivalent approach was undertaken with mPing sequences but the prior identified Ping and Pong insertion sites were removed from the mPing RelocaTE2 results to generate final mPing insertions. RelocaTE2 analysis was performed in 48 O. rufipogon genomes to identify mPing, Ping, and Pong insertions. However, the short insert size and insufficient read depth of O. rufipogon sequencing libraries prevented distinguishing Ping and Pong insertions from mPing.The ons Fig. . Three smPing, Ping, and Pong elements were estimated from the ratio of the element read depth to the genome-wide average read depth using the script “Rice3k_copy_number_depth_window_mPing/Ping.py”. The genome average sequence coverage for each genome was calculated using qualimap v2.1.236. The element read depth was calculated using a window-based approach to capture read depth variation across the element. All sequencing reads associated with a given repeat element were extracted from the RelocaTE2 results. The reads were aligned to the element using BWA v0.7.1237 with default parameters (mem -k 19 -w 100 -T 30). Alignments with ≤ 2 mismatches were retained for further analysis. The sequence coverage of each position in the element was calculated using mpileup command in SAMtools v0.1.1938 (mpileup -d 8000). Positions 1–430 bp of mPing element was divided into 50-bp windows with 40-bp of overlapping sequence between adjacent windows. The read depth of each 50-bp window of mPing was extracted from mpileup results. The copy number of each 50-bp window was defined as the ratio of the depth of each 50-bp window to the genome-wide average read depth. Approximate estimation of mPing copy numbers was from an average copy number of all 50-bp windows. Ping and Pong copy numbers were calculated using positions 260–3260 bp so that unique regions in the targeted element were considered for the assessment. To confirm the statistical differences a one-sample t-test was performed to determine whether the average read depth of 50-bp windows of a given element was equal to genome-wide average read depth.Copy numbers of mPing elements into rice chromosome 3 (OsChr3) and chromosome 4 (OsChr4) using custom scripts. Copy numbers of 1, 10, 100, and 1000 mPings were simulated to evaluate the performance of the read depth on TE copy numbers. Three replicates were generated for each dataset. Sequencing datasets were simulated with pIRS39 at varying depths of 1, 2, 3, 4, 5, 10, 20, and 40 to evaluate the performance of the read depth method on sequencing depths. Sequencing reads were aligned to OsChr3 and OsChr4 with SpeedSeq40 , which uses BWA (mem -k 19 -w 100 -T 30) to align reads, Sambamba41 (-M 20) to sort alignments, and SAMBLASTER42 (-c 2 -m 20) to mark PCR duplicates. Genome-wide sequencing depths were obtained with qualimap using BAM files generated by SpeedSeq. mPing-related reads were obtained with RelocaTE2  and were aligned to mPing sequence with BWA (mem -k 19 -w 100 -T 30). Alignments with ≤ 2 mismatches were retained for further analysis. The sequence coverage of each position in the element was calculated using mpileup command in SAMtools (mpileup -d 100000). mPing copy numbers were characterized with the script “Rice3k_copy_number_depth_window_mPing.py”. The results indicate that the read depth method can estimate approximate mPing copy numbers with a wide range of sequencing depth  v2.3.043. Only accessions showing sequence coverage across a given element  were defined as accessions having this element. This approach was also used to identify four Aus/boro accessions that have a Ping locus (Chr11: 25822230–25802232) that was not identified with RelocaTE2.The presence and absence of http://s3.amazonaws.com/3kricegenome/Nipponbare/”Accession_Name”.realigned.bam) were analyzed to determine if a Stowaway element was present at the Ping16A_Stow locus Chr1: 2640500–2640502. A total of 199 rice genomes with signatures of TE insertions at the Ping16A_Stow locus  were analyzed to confirm the Stowaway insertion. A pseudogenome was built of a single Stowaway element and its 2-kb flanking sequences at position Chr1: 2640500–2640502. The sequencing reads from each of the 199 rice genomes were aligned to the pseudogenome using BWA with default parameters (mem -k 19 -w 100 -T 30) followed by analysis of the BAM files to identify junction reads covering both the Stowaway and its flanking sequence. All of these 199 accessions were confirmed to have the Stowaway element at position Chr1: 2640500–2640502.The pre-aligned BAM files of 3000 rice genomes . Analysis of junction reads covering both Ping element and its flanking Stowaway element identified eleven accessions having a Ping insertion in the Stowaway element at the Ping16A_Stow locus . Alignments with ≤ 2 mismatches were analyzed using mpileup command in SAMtools (mpileup –d 8000) to generate a read depth profile, which includes base composition information at each position. The nucleotide counts at the + 16 G/A SNP were obtained from the read depth profile. A Ping with two or more reads supporting G was genotyped as Ping16G, whereas a Ping locus with two or more reads supporting A was genotyped as Ping16A. The genotypes of three Ping loci, including Chr6: 23521641–23526981 (Nipponbare Ping), Chr1: 264050–2640502 (Ping16A_Stow), and Chr11: 25822230–25802232 (a Ping locus in Aus/boro), were assigned through manual inspection because these loci were either reference Ping (53 accessions with Nipponbare Ping) or nonreference Ping but have not been identified with RelocaTE2 (11 accessions with Ping16A_Stow and 4 accessions with the Aus/boro Ping locus).A locus-specific approach was used to analyze the genotype of the + 16 G/A SNP of the O. rufipogon, all reads aligning to mPing, Ping, and Pong were pooled to analyze the base composition at the + 16 G/A SNP because mPing, Ping, and Pong insertions could not be efficiently sorted. An O. rufipogon genome was categorized as a genome having Ping16G or Ping16A based on whether they had two or more reads supporting G or A. Accessions that have two or more reads supporting both G and A were further analyzed to clarify whether the Ping16A is present in these genomes. For example, accession W1230 had both G (288 reads) and A (23 reads) at the + 16G/A SNP. These A-supporting reads and their mates were extracted from W1230 sequences and aligned to pseudogenomes that have W1230 mPing or Ping inserted in MSU7. All of these A-supporting reads were uniquely aligned to mPing locus Chr3: 25526483–25526485 that contains a 430-bp mPingC element successfully assembled from locus-specific paired-end reads, suggesting these A-supporting reads were from mPing not from Ping.For mPing sequences from rice sequences. The sequencing reads matching mPing were obtained using RelocaTE2, assembled using velvet v1.2.09 (MAXKMERLENGTH = 31 -ins_length 500 -exp_cov auto -scaffolding yes)44. The flanking non-mPing sequences were removed from the assembled sequences. Any mPing candidate loci containing sequence gaps were removed from the analysis. The remaining full-length mPing sequences were compared using BLAST v2.2.26 to build an undirected graph with python package NetworkX (https://networkx.github.io). Each node in the graph is an mPing sequence and each edge is a connection, which requires two mPing sequences are properly aligned (number of gaps or mismatches ≤ 4). The mPing sequences in each subgraph represent a subtype of mPing. Representative sequences were extracted from each mPing subtype and aligned with four canonical defined mPing subtypes  from the prior study5 using MUSCLE v3.8.42545 with default parameters (-maxiters 16). The multiple sequence alignment in MSA format was converted into VCF format using msa2vcf.jar tool (https://github.com/lindenb/jvarkit) to identify polymorphic sites. The assembled mPing sequences were classified into subtypes based on their breakpoints and point mutations compared with the four canonical mPing subtypes.A locus-specific assembly was performed to recover full-length O. rufipogon accessions were aligned to four canonical defined mPing subtypes  using BWA with default parameters (mem -k 19 -w 100 -T 30). Alignments with ≤ 2 mismatches were manually inspected using IGV v2.3.0 to determine if the reads cover breakpoint of each mPing subtype in each accession. An accession with two or more reads covering the breakpoint of an mPing subtype was identified as an accession containing this mPing subtype.The reads of 46. The phylogenetic tree of rice accessions was built using a neighbor-Joining method implemented in FastTree v2.1.10 (-noml -nome)47. The sequencing reads for the 48 O. rufipogon accessions were analyzed to obtain a SNP dataset. Briefly, paired-end reads were aligned to MSU7 using SpeedSeq v 0.1.0 . The resulting BAM files were analyzed with GATK UnifiedGenotyper to perform SNP calling. Filtering parameters ) > 0.1), QUAL < 30.0, DP < 6, DP > 5000, HRun > 5) were used to retain high-quality SNPs using GATK VariantFiltration. Only homozygous SNPs that did not overlap the repetitive sequences were used in the phylogenetic analysis. These high-quality SNPs were extracted and converted into PHYLIP format multiple sequence alignment for phylogenetic analysis with RAXML v8.2.848 under a GTRGAMMA model (-m GTRGAMMA). Bootstrap was performed using 100 iterations (-f a -# 100). O. glaberrima, Oryza glumaepatula, and O. meridionalis were treated as outgroups. Graphical representations of the phylogenetic trees were generated in R using “APE” libraries49.The 270,329 SNPs used for ADMIXTURE analysis were used to genotype HEG4, EG4, A119, and A123 using GATK UnifiedGenotyper v3.4-46mPing was amplified with Phusion High-Fidelity PCR Master Mix (Thermo Fisher Scientific) using the control mPing primers (mPing F and mPing R) or mutation containing primers  and transformed into Escherichia coli for plasmid purification and sequence validation.S. cerevisiae strain CB10150 containing previously described pAG413 GAL ORF1 Shuffle1 NLS and pAG415 GAL Pong TPase L384A, L386A plasmids25. The transposition rate was measured as described in the prior study18. Briefly, 3 ml cultures were grown in CSM-His-Leu-Ura (dextrose) for 24 h at 30 °C, and 100 µl was plated onto 100 mm CSM-His-Leu-Ura-Ade  plates. The total number of yeast cells was calculated by plating a 10-4 dilution of the cultures onto YPD plates. The numbers of colonies on the galactose plates were determined after 10 days of incubation at 30 °C. The transposition rate was determined by dividing the galactose colony count by the total number of cells plated.Sequence verified plasmids were transformed into P-values are indicated in figures or figure legends. Linear regression, two-tailed Pearson’s correlation, two-tailed Wilcoxon–Mann–Whitney, one-way ANOVA and Tukey’s HSD test were performed with “lm”, “cor.test”, “wilcox.test”, “aov”, and “TukeyHSD” functions in R. One-sample t-test was performed with ‘ttest_1sample’ function in Python module ‘sci.stats’.Sample sizes, statistical tests, and https://github.com/stajichlab/Dynamic_rice_publications or 10.5281/zenodo.1492794.RelocaTE2 and other code used in this study are available at Supplementary InformationPeer ReviewDescription of Additional Supplementary DataSupplementary Data 1Supplementary Data 2Source DataReporting summary


The molecular docking described the microenvironment of the interaction sites, rich in apolar residues. The stability of the RSA-PPL complex was checked by molecular dynamics.Piperlongumine (PPL) has presented a variety of important pharmacological activities. In recent pharmacokinetics studies in rats, this molecule reached 76.39% of bioavailability. Although PPL is present in the bloodstream, no information is found on the interaction between PPL and rat serum albumin (RSA), the most abundant protein with the function of transporting endo/exogenous molecules. In this sense, the present study elucidated the mechanism of interaction between PPL and RSA, using in conjunction spectroscopic and computational techniques. This paper shows the importance of applying inner filter correction over the entire fluorescence spectrum prior to any conclusion regarding changes in the polarity of the fluorophore microenvironment, also demonstrates the convergence of the results obtained from the treatment of fluorescence data using the area below the spectrum curve and the intensity in a single wavelength. Thermodynamic parameters revealed that PPL binds to RSA spontaneously (ΔG < 0) and the process is entropically driven. Interaction density function method (IDF) indicated that PPL accessed two cooperative sites in RSA, with moderate binding constants (2.3 × 10 This natural compound has been calling attention of scientific community because of its biological activities such as antitumor, antimetastatic, antiplatelet aggregation and anxiolytic2. Piperlongumine (PPL) is a lipophilic molecule, with logP = 1.63 calculated through the software Marvin . This molecule is characterized by the presence of electrophilic frames3 and α,β-unsaturated amide as Michael acceptor4. In addition, piperlongumine has no Lipinski’s and Lead-like rule violations5, which makes this molecule an excellent candidate to be launched as a drug. Published pharmacokinetic studies report that oral, intravenous and intraperitoneal administration in rats results in different times of PPL permanence in plasma2. Considering the lipophilic characteristics of PPL, its transport through plasma must be assisted by carrier proteins, such as albumin. A few works present in the literature bring the first results of the interaction of PPL with human albumin7. Nevertheless, no information about the interaction between PPL and rat serum albumin (RSA) is known. RSA has 584 amino acids with just one tryptophan residue8, which enables the use of fluorescence spectroscopy9. The crystallographic structure of RSA has not yet been determined. However, RSA modeling has been performed and published in a recent article10. Therefore, based on the modeled RSA, it can be compared with the HSA structure deposited in PDB (1AO6) and several similarities can be found between them. The comparison presents that RSA has 73% of sequence similarity  method. The microenvironment of each site was described using molecular docking and the stability of the complex was verified by means of molecular dynamics.12. Nevertheless, in this work it was verified that in the case in which ligand absorption spectrum overlaps to some degree the fluorophore emission spectrum, the shifts may be only a false positive caused by the inner filter effect. Figure exc = 295 nm and λem = 305–500 nm, keeping the maximum intensity at 340 nm. Figure exc = 240–350 nm and λem = 300–410 nm, keeping the maximum intensity at the excitation wavelength of 283 nm. The application of inner filter correction in both fluorescence emission and synchronous spectra eliminated the apparent red shift observed at Fig. The intrinsic fluorescence property of Trp214 was used to comprehend the interaction between PPL and RSA. Figure How is it possible for inner filter to cause such a shift on the maximum intensity of the spectra? This is due to two concurrent events that occur in the sample. The first requires that some overlap exists between the absorption spectrum of the ligand and the emission spectrum of fluorophore. The second consists of some asymmetry of the profile of the line shape of the absorption spectrum of the ligand in relation to the maximum emission of fluorophore spectrum see Fig. .SV) at different temperatures13. The analysis of the bimolecular quenching rate constant (kq) is another method that can also be used to indicate the quenching mechanism. In general terms, for collisional quenching process kq cannot be larger than 1010 M−1s−1 14. Usually, KSV is obtained from plots of the ratio of fluorescence intensities at the maximum emission wavelength (sine qua non condition that the entire spectrum has been previously corrected to avoid the inner filter effect.Quenching mechanism can be defined as either static (ground state complex) or dynamic (diffusive encounters) process. The most common method reported in literature to determine the quenching mechanism is by comparing Stern-Volmer constants  concerning the interaction between RSA and PPL was calculated through the binding equilibrium model17 using the double-log equation (Eq. (The binding constant (Kion (Eq. ), where ion  remained close to one, which is expected due to the first order binding equilibrium model, and the increment of Ka as the temperature rises, indicates its influence on the stability of the complex. According to binding constant values, the complex strengthened its interaction with the rise in temperature.Table Thermodynamic parameters, such as Gibbs free energy variation (∆G), enthalpy variation (∆H) and entropy variation (∆S), are important clues to interpret the intermolecular forces involved in the interaction between small ligands and proteins. The thermodynamic parameters mentioned are related to each other through Eq. , where \fore Eq.  can be rfore Eq. ). The liFigure 18.Table a priori19. The advantage of applying IDF is the possibility of not only determining the number of binding sites but also identifying cooperativity occurrence among them. IDF considers that, if the free ligand concentration ([PPL]free) is the same for two or more solutions at different concentrations of total protein ([RSA]), the average interaction density (Συi) will also be the same and consequently the system will have the same variation on the percentage of quenching (ΔF). The percentage of fluorescence quenching is given by Eq. . By meai and [PPL]free were obtained from IDF method, Scatchard plot21 can reveal some aspects of RSA-PPL interaction. Figure Once the values of Σν21, which relates the average interaction density (Σνi) and the free ligand concentration [Lfree] through Eq. (j represents the number of sites of each set (j), kj represents the binding constants for the sites, wj the affinity indexes.The number of sites and their respective binding constants were obtained by means of Hill equationough Eq. . Where ni) versus [PPL]free, where the solid line represents the best fit calculated with all the parameters explicit on Eq. 105 M−1 and k2 = (1.3 ± 0.1) 105 M−1. Figure 22. The binding constants obtained for this system are moderate constants with order of magnitude of 105M−1. It is found in the literature6 a study of the interaction between PPL and human albumin, whose results are different from the present investigation in terms of the number of binding sites, affinity constant and thermodynamic balance. In this case, it is noticeable that the microenvironment of each protein is decisive to differentiate the interaction.According to the results, PPL binds to 2 sites with different binding constants: kCircular dichroism (CD) was carried out to follow the secondary structural changes of protein with the addition of PPL at different temperatures Fig. .Figure 622. The calculated α-helix content remained very close to 63% even at different temperatures , the molecule has high negative charge density around the oxygen atoms and positive charge densities at the extremities of the molecule, while the scaffold is predominantly neutral. The oscillator strength Fig.  shows th24. The number of sites is in agreement with IDF analyses. Site 1 is constituted by the apolar amino acids: Leu446, Met219, Trp214, Ala443, Ala291, Cys448, Pro447, Gln196, Gln444 and polar amino acids Glu292, Arg 222 and Asn242, such composition suggests that hydrophobic interactions are predominant  of RSA along 50 ns of molecular dynamics showed that the free protein remained stable after the first 20 ns of molecular dynamics, as well as the protein with PPL in Site 1 and Site 2Fig. . The temThe local stability of secondary structures around PPL in site 1 and 2 was investigated Fig.  and compThis work reveals the importance of the role of inner filter correction when dealing with fluorescence data. The approach performed through this correction results in the elimination of the shift of the spectrum that is commonly interpreted as a change in polarity in the fluorophore site. One of the novelties presented is the inclusion of the area of the emission spectrum band instead of the intensity, whose results are more complete taking into account that the emission curve simultaneously contains intensity, bandwidth and corrected baseline.The RSA-PPL complex was characterized by a molecular biophysical approach with the fluorescence technique and computational methods. The thermodynamic balance from the experimental point of view indicated that the major energetic contribution came from entropy. This insight is corroborated by the result of molecular docking that confirms the largest number of apolar amino acids in the interaction sites.Among the methods used to treat the fluorescence data, IDF showed to be more accurate, since in this method the formation of the complex meets the law of masses and therefore is not limited to a reaction of any order. IDF analyses together with Scatchard and Hill’s model brought up the most surprising result: the cooperative effect. This configures some allosteric mechanism in RSA, however protein allosterism in principle occurs in proteins with more than one subunit with conformational change. RSA is a monomeric protein that did not present conformational change when interacting with PPL. This result highlights the need for further studies to identify the routes by which sites communicate, and how the intrinsic dynamics of the protein is understood.2HPO4 (>99%), C6H8O7 (>99%), NaCl (>99%) were bought from Sigma-Aldrich . CH3OH was purchased from Dinâmica Química Contemporânea . Ultra filtered water was obtained from - Direct-Q UV-3 . The preparation of the samples followed the previously described procedures10 and the concentration of the PPL stock solution was determined based on the molar extinction coefficient of 18700 M−1 cm−1 (at 326 nm).Piperlongumine (>97%), RSA (>99%), Nacorr and Fobs are the corrected and observed fluorescence intensities, and Aex and Aem are the absorbance at the excitation wavelength (295 nm) and at emission wavelengths (from 305 to 500), respectively13.The equipment for measuring emission fluorescence was ISS PC1  with temperature controlled at 288, 298, 308 K, and the bandwidth set to 8 nm. The quartz cuvette has optical pathway of 10 mm. The sample was excited at 295 nm, and the emission spectrum was obtained in the region of 305–500 nm, with 10 reruns. The experiments of binding equilibrium were carried out with PPL increments of 2 μM. The baseline was corrected by subtracting the signal of the scattering of the buffer. The inner filter effects were rectified with Eq. 10.When adopting the approach of the areas, the numerical integration of each fluorescence spectrum was calculated from 305 to 400 nm in order to reduce the influence of PPL fluorescence signal. In the IDF analyses, PPL was titrated with increments of 2 μM at three different concentration of RSA  with temperature at 288 K. The final volume of methanol in the experiments did not influence the RSA fluorescence emission as verified in our previous workcorr and Fobs are the corrected and observed fluorescence intensities, and Aex and Aem are the absorbance at the excitation wavelengths (from 240 to 350 nm) and the emission wavelengths (from 300 to 410 nm), respectively.The equipment for measuring synchronous fluorescence was Lumina  with temperature controlled at 298 K, and the bandwidth set to 10 nm. The quartz cuvette has optical pathway of 10 mm. Excitation wavelength varied from 240 to 350 nm, the excitation and emission wavelengths interval (Δλ) was 60 nm, with 15 reruns. PPL was titrated with increments of 2 μM. The baseline was corrected by subtracting the signal of the scattering of the buffer. The inner filter effects were rectified with Eq. i is the lifetime of each component, and αi is the contribution of each component to total fluorescence decay. The average lifetime τavg was calculated using Eq. 25 (Table The equipment for measuring fluorescence lifetime was Mini-τ coupled to a Time-Correlated Single Photon Counting (TCSPC) . PPL was titrated varying the concentration from 0 to 32 μM, at 298 K. The excitation was done at 295 nm and the decay collected at 340 nm. The fluorescence decay profile  using Eq. CD experiments were carried out with Jasco J-815  at three different temperatures 288, 298, and 308 K, with a 0.01 cm optical pathway demountable quartz cell. The range of measurement was 200–260 nm with 15 reruns scanned at 20 nm/min and with 0.1 nm of resolution. The molar ratios employed for rat serum albumin and PPL were 1:0 and 1:8, with buffer spectrum subtracted. The ellipticity θ collected in millidegrees was converted to mean residue ellipticity [θ]  formalism29 and functional density theory (DFT)30 The first set of base 6–31 + G  was chosen which result was refined by the new set of bases 6–311 + G, M11 functional and Polarizable Continuum Model (PCM) solvent model31 for water. The oscillator strength related to each electronic transition was calculated by TDDFT excite method with a set of bases 6–31 + G  and functional revTPSS. The geodesic method32 was applied to determine MEP and the partial charges. The facility of GRID UNESP was the computational center to perform all the calculations. The results visualized by wxMacMolPlt software33 and SciDavis .PPL structure optimization was calculated with Gamess201310, except for grid box spacing of 0.436 Å, with a dimension of 126 × 126 × 126 points, and grid center coordinates of 59.236, 108.4, and 50.661 for x, y, and z coordinates, respectively. The final conformations were visualized by VMD34.All data obtained with respect to molecular docking followed the procedures previously described in our study35, with GROMOS96/53a force field36. Simple Point Charge(SPC) water model37 was used to solvate the complex, which was centered in a box with dodecahedral geometry. In order to neutralize the system, Na+ and Cl− were added with 150 mM of concentration. A tolerance of 10 kJ/mol was set to the energy minimization step, through steepest descent algorithm. The NVT and NPT ensembles worked to reach the equilibrium along 100 ps of simulation where the temperature was maintained at 298 K by coupling to the V-rescale thermostat38 and the pressure was kept at 1 atm by coupling to the Parrinello–Rahman barostat39, respectively. Steps of 2 fs were set using the leap-frog algorithm to integrate the motion equations. The facility of GRID UNESP was the computational center to perform all the calculations. Hydrogen bonds were calculated through Visual Molecular Dynamics (VMD), OH and NH were regarded as donor, O and N were acceptors. The cut off for the angle hydrogen-donor-acceptor was 20 degrees and the cut off for the distance donor-acceptor was 0.3 nm34. The calculations of secondary structures of RSA were performed by VMD-TIMELINE plug-in employing STRIDE algorithm40.The molecular dynamics of the complex were executed in triplicate by GROMACS/5.1.4Supplementary Information


Arabidopsis thaliana) leaves and potentially links starch turnover to leaf sucrose status and demand from sink organs . To investigate this relationship further, we compared diel patterns of starch turnover in ethanol‐inducible Tre6P synthase (iTPS) lines, which have high Tre6P and low sucrose after induction, with those in sweet11;12 sucrose export mutants, which accumulate sucrose in their leaves and were predicted to have high Tre6P. Short‐term changes in irradiance were used to investigate whether the strength of inhibition by Tre6P depends on starch levels. sweet11;12 mutants had twofold higher levels of Tre6P and restricted starch mobilization. The relationship between Tre6P and starch mobilization was recapitulated in iTPS lines, pointing to a dominant role for Tre6P in feedback regulation of starch mobilization. Tre6P restricted mobilization across a wide range of conditions. However, there was no correlation between the level of Tre6P and the absolute rate of starch mobilization. Rather, Tre6P depressed the rate of mobilization below that required to exhaust starch at dawn, leading to incomplete use of starch. It is discussed how Tre6P interacts with the clock to set the rate of starch mobilization.Trehalose 6‐phosphate (Tre6P), a sucrose signaling metabolite, inhibits transitory starch breakdown in Arabidopsis ( In many species, including Arabidopsis, foliar starch is the major C reserve  and that this information is integrated with a measure of the starch content (S) to set the rate of starch mobilization (R = S/T). The robust pacing of starch mobilization to the coming dawn in the face of sudden perturbations (see above) is explained well by the molecular division model, but less easily by models that invoke changes in clock periodicity. However, it remains unclear which molecular outputs from the clock regulate the rate of starch mobilization, and how the amount of starch is measured. The biochemical mechanism that sets the rate of mobilization is also not known. There is some evidence that changes in the rate of starch mobilization may be linked with changes in phosphorylation of starch  mutants were recently described in which premature exhaustion of starch is associated with altered granule structure and shape  lines to investigate whether Tre6P regulates the rate of mobilization of leaf starch. iTPS plants were grown in a 12‐hr photoperiod for 3 weeks and then induced at the end of the day (ED) to increase Tre6P levels in the following night. This led to slower starch mobilization and strongly decreased levels of maltose  has been proposed to act as a sucrose signal to regulate sucrose homeostasis  rather than rising. The results strengthen the evidence that Tre6P plays a key role in the feedback regulation of starch mobilization. Rather surprisingly, they also reveal that there is no simple relationship between the Tre6P level and the rate of starch mobilization. Rather, Tre6P depresses the rate of mobilization below that required to exhaust starch at the coming dawn. This finding points to a close link between the mechanisms by which Tre6P and the clock act on starch mobilization.In the following experiments, we first asked whether the elevated levels of sucrose in leaves of 22.1Arabidopsis thaliana (L.) Heyhn. accession Columbia‐0 (Col‐0) was provided by in‐house collection . The Arabidopsis (Col‐0) ethanol‐inducible TPS‐overexpressor (iTPS) and empty‐vector control AlcR lines were the same as those described in  at constant 20°C, with an irradiance of 160 μmol m−2 s−1. At 2 weeks after sowing, seedlings were transplanted into 10‐cm pots (5 plants per pot). For the continuous light experiment, seeds were sown on soil in 10‐cm pots and placed directly in a phytotron under continuous light with a constant irradiance of 160 μmol m−2 s−1 and constant temperature of 20°C. At 2 weeks after sowing, plants were thinned out inside the phytotron, leaving five plants per pot.Seeds were sown in soil mixed with vermiculite (1:1) in 10‐cm pots and placed in a phytotron under long‐day (LD) conditions, 16 hr (20°C) light/8 hr (4°C) dark, with an irradiance of 160 μmol msweet11;12 mutant plants was transferred at dawn to either a LD , or high light  or to combined LD and HL . Similarly, on the day of the experiment, some of the empty‐vector control AlcR and iTPS plants were transferred at dawn to HL; 12 hr  or to LL  Plants were induced by spraying the mutant and the empty‐vector control AlcR 2 hr before dusk with 2% (v/v) ethanol.Short‐term responses were evaluated at 21 days after sowing by submitting a batch of plants to different growth conditions. On the day of the experiment, some of the wild‐type Col‐0 and AlcR and iTPS plants were germinated, thinned to four plants per pot, and grown in continuous light at 20°C for 3 weeks. On the day of the experiment, plants were sprayed with 2% (v/v) ethanol and then darkened 2 hr later.For the continuous light experiment, empty‐vector control In all experiments, plants were harvested just before darkening and at various times during the night, as indicated for each individual experiment. Whole rosettes were excised under the ambient conditions and immediately quenched in liquid nitrogen. For sampling in the light, care was taken not to shade the leaves at any time before quenching. Plants from one or two pots  were pooled to form one sample, ground to a fine powder at −70°C using a robotized ball mill . Absorbances were determined using a Synergy, an ELx800 or an ELx808 microplate reader . For all the assays, two technical replicates were determined per biological replicate. Tre6P and ADP‐glucose (ADPGlc) were extracted in chloroform‐methanol and measured by high‐performance anion‐exchange chromatography coupled to tandem mass spectrometry (LC‐MS/MS) as described by Lunn et al. . cDNA was synthesized using a SuperScript III First‐strand Synthesis System Kit . Quantitative real‐time PCR was performed using the SYBR Green PCR kit (Qiagen). Data were collected using SDS software . Concentration of target gene transcripts in the samples was calculated using standard curves generated for each sample, based on Ct values and spike‐in controls added to the samples before RNA extraction  at rosette level for 30 days and then sampled at dusk and 4‐hr intervals during the night ).Wild‐type Col‐0, the t Figure . Two Col−2 s−1 m 20°C; Annunziata et al., sweet mutants, but was elevated two‐ to threefold in the double mutant  resembled those seen previously in our standard growth conditions  for 21 days, and batches were then either left in standard conditions  or shifted for a single day to a 15‐hr photoperiod (LD) with unaltered light intensity (160 μmol m−2 s−1), or to high‐light . Sucrose levels were fourfold higher in sweet11;12 than wild‐types in Ctrl, and about threefold higher in the LD, HL, or LD + HL treatments. Average night‐time Tre6P levels in sweet11;12 were about twofold higher than wild‐type in all treatments. In wild‐type plants, starch content at dusk  and larger at dawn (76%–244%), with the relative increase being larger in Ctrl than in HL, LD, or LD + HL.In wild‐type plants, the average night‐time levels of sucrose Figure a and Trek Figure . Starch sweet11;12 did not lead to an increase in dusk levels of ADPGlc. This is consistent with the similar rates of starch accumulation between dawn and dusk in sweet11;12 and wild‐type plants. It is also consistent with the lack of effect of elevated Tre6P on ADPGlc levels and starch accumulation previously reported by Figueroa et al. (2017). ADPGlc levels were negligible in the dark in wild‐type plants. They were also negligible in the sweet11;12 mutant, despite its high sucrose levels during the night.The first dedicated and regulated reaction in starch synthesis is catalyzed by ADPGlc pyrophosphorylase, leading to formation of ADPGlc. The higher rates of starch accumulation in HL and HL + LD were accompanied by a twofold increase in the dusk level of ADPGlc . This increase in HL and LD + HL reflects the increased dusk starch content. Comparing across treatments, a higher dusk starch content led to a faster starch mobilization in sweet11;12 , marginally faster in HL and marginally slower in LD and HL + LD, withir none of differences being significant . Nevertheless, due to the differing starch content at dusk, sweet11;12 plants did not completely degrade their starch reserves in any of the treatments whereas wild‐type plants remobilized almost all their reserves by dawn in all treatments except LD + HL. The starch content at the EN, expressed as a percentage of the dusk starch content, was about twofold higher in sweet11;12  than in wild‐type plants  and lower in LD (8.77 compared to 6.95 μmol [Glc] g−1 FW hr−1) and HL (9.03 compared to 6.56) μmol [Glc] g−1 FW hr−1; Supporting Information Figure sweet11;12 showed a large and highly significant twofold increase in the percentage of the dusk starch content that remained at dawn  line  and another batch to a single LL  light period. All plants were sprayed with 2% (v/v) ethanol 2 hrs before dusk to insure the inducible mutants would enter the night with increased Tre6P levels. Rosettes were harvested in the light just before the onset of darkness (ED) and at 2‐hr intervals for the first 10 hr of the night to quantify starch, sugars, Tre6P, and other metabolites.Figure iTPS than control AlcR plants  when averaged over all times between ZT14 and ZT20 both in the LL and the HL treatment. Time resolved plots revealed that in the LL treatment  that could be supported by the dusk starch content under the provisos that breakdown is linear and that starch is not exhausted before dawn. Rmax is equivalent to the slope of a linear regression through the measured starch content at ED and a hypothetical null starch content at EN (ZT24). We then estimated the extent to which starch mobilization was depressed relative to Rmax (Rr = Ra/Rmax) and compared Rr with the average night‐time Tre6P level. As shown in Figure Rr . This strong relationship was found even though the starch content at dusk and absolute rate of starch mobilization varied considerably between the various treatments. Importantly, a similar relationship was found for sweet11;12 and iTPS, even though the former had elevated sucrose and the latter had lower sucrose than control plants. This consistent match across genotypes strongly indicates that the negative relationship in Figure Irrespective of whether we used 3.5T’). This information is integrated with a measure of the starch content (‘S’) to set the rate of starch mobilization (‘R’ = ’S’/’T’). The term Rmax in the calculations underlying Figure R’ in the arithmetic division model. The negative correlation in Figure The arithmetic division model , TIMING OF CAB EXPRESSION 1 (TOC1), EARLY FLOWERING3 (ELF3), ELF4, and LUX ARRHYTHMO (LUX)) as well as two output genes (PHYTOCHROME‐INTERACTING FACTOR 4 (PIF4) and PIF5) in AlcR and iTPS lines in the last 24 hr of continuous light and the first 12 hr after darkening  was rather low compared to that found in wild‐type, AlcR, or iTPS in a light‐dark cycle (60–130 μmol [Glc] g−1 FW). After darkening, Tre6P declined in AlcR controls but remained high in induced iTPS plants  that would partly compensate for the inhibition by Tre6P. We modeled this interaction, using a numerical Tre6P input that would lead to 0%, 30%, or 60% inhibition of the initial rate of starch mobilization .In contrast, there was no consistent relation between Tre6P levels and  Figures . In expeh Figure . OverallRr across genotypes underlines the importance of Tre6P in the feedback regulation of starch mobilization. Importantly, elevated Tre6P restricts starch mobilization, irrespective of whether sugars are rising or falling, implying that other C‐signals play only a small role in the feedback regulation of starch mobilization.We conclude that Tre6P plays a major role in the feedback regulation of starch mobilization. In particular, elevated Tre6P prevents complete mobilization of starch. The similar relationship between Tre6P levels and 4.2Many earlier studies showed that the rate of starch mobilization in Arabidopsis is fairly constant during the night  were when starch levels at the start of the night were high and similar to those in the control  in the Tre6P‐inhibited case and the control. This new balance can be explained if the inhibition by Tre6P is offset by a higher basal rate of starch mobilization due to the higher starch content. This pattern is seen in the sweet11;12 mutant in growth conditions  appears to be incompatible with the observed linear decline in starch content during the night (see above). However, this inconsistency can be resolved if a further factor promotes starch mobilization as the 24‐hr cycle progresses, that is, as the remaining time to dawn decreases. One simple and plausible factor is the parameter ‘T’ as defined in the arithmetic division model  even under conditions in which dusk starch content was low and mobilization was already slow in the control and, by implication, strongly restricted by the clock. (c) Tre6P overrides the clock, that is, modifies ‘R’. This also seems unlikely, because the largest inhibition of the absolute rate was seen in a treatment   Figures c and 5e t Figure . (d) TreS’. This provides a simple explanation for the consistent relationship between the level of Tre6P and the proportion of starch that is predicted to be mobilized during the night  when dusk starch levels are the same in the control and treatments with elevated Tre6P. Furthermore, it is consistent with the finding that Tre6P inhibits starch mobilization in plants with a desynchronized and non‐entrained clock, without requiring additional hypotheses that there is a default measure of time to dawn (‘T’) in such conditions. However, it should be noted that within the context of the arithmetic division model, modification of ‘T’, and modification of ‘S’ will be mathematically similar with respect to the effect on ‘R’.The most plausible hypothesis may be that Tre6P interacts with the measure of starch content, ‘Starch is located in the plastid, but TPS1 lacks a plastid transit sequence and non‐aqueous fractionation indicates that much or even all the Tre6P in the Arabidopsis rosette is located outside the chloroplasts (Martins et al., iTPS lines enter the night with high starch content (Figure The interaction between the clock and Tre6P in regulating starch mobilization is summarized in Figure t Figure . For exaLdA: Performed experiments and measurements, data analysis, contributed to writing the manuscript; PKP: Performed experiments and measurements, data analysis; TAM: Data analysis; RF: Performed measurements; JEL: Designed experiments, contributed to writing the manuscript; MS: Designed experiments, contributed to writing the manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Scientific advisory committees (SACs) are a critically important part of global environmental policy. This commentary reviews the role of SACs in six global and regional environmental regimes, defined here as the set of rules, norms, and procedures that are developed by states and international organizations out of their common concerns and used to organize common activities. First, SACs play a critical role in putting issues on the political agenda and the creation of an overarching regime. Second, the effectiveness of a given SAC and the associated regime is highly variable. Third, there is also considerable variation in the extent to which the regime is driven by an overarching scientific consensus, for example, high in the case of climate change, lower in the case of whaling. Fourth, the role of science in a given regime is also a function of whether the problem being addressed is relatively benign or more malign, that is to say, marked by deep political disagreements . Finally, the cases examined here suggest that the institutional design of the SAC matters and can influence the overall effectiveness of the SAC and by extension, the regime, but it is seldom decisive. The Scientific Committee of the International Whaling Commission (IWC) is but one example of scholars coming together to form a global scientific advisory committee (SAC), in part, to encourage the role of science in promoting an orderly development of the whaling industry.In the study of international relations theory, we define a regime as the set of rules, norms, and procedures that are developed by states and international organizations out of their common concerns and are used to organize common activities. Thus, a regime is much broader than a SAC but it is likely these committees will be an integral part of a given regime, particularly in areas like health and environment where scientific research is critically important to establishing the agreed‐upon rules and procedures.The theoretical groundwork provided by Underdal in the book has been a noteworthy analytical point of reference for many of the subsequent works on the science—policy nexus in international environmental policies. Since its release, additional publications have delved into new important areas and regimes such as technology and biodiversity, respectively. The empirical data have also been updated since then, leading to the creation and discussion of novel theoretical approaches. The purpose of this commentary is nevertheless not an exercise in the synthesis of these new findings; rather, we hope to provide some useful lessons for current and emerging international health regimes to maximize their overall impact.At the outset, the most important and sobering lesson to note is the following: the role and influence of science as well that as of various scientific advisory committees should not be overtly exaggerated. This is because scientific evidence is but one of several legitimate premises that policy makers need to consider during the decision‐making process. Realistically, more weight is usually attributed toward economic and political considerations. Simply put, politics is very rarely driven by science alone. Clever institutional designs of scientific advisory committees may make a difference in terms of the extent of the influence they hold but this is not always the case. Often, their influence (or lack thereof) is contingent on the nature of the issue being scrutinized. That is, the higher the intensity of political conflicts and scientific uncertainty, the less likely it is that scientific advice will be adhered to—and vice versa.With this caveat, we have organized this article as follows. First, we review various approaches to understanding the role of science in policy‐making as well as the relationship between science and the effectiveness of these international regimes. Next, we examine the establishment of international environmental agreements and associated scientific committees: what actors and interests are needed for them to emerge? Last, we reflect on the lessons that can be learned with regards to their role and influence.Empirical examples are mainly drawn from the global regimes studied; considering the high number of participants and the  strongly divergent interests, challenges of reaching effective solutions are most severe here. The two regional regimes will also be addressed to a lesser extent.2There are various approaches to the study of the science–policy nexus. One such method is to elevate the status and the influence of science by making it appear more legitimate via a broad participatory approach. This strand of research claims that the necessary knowledge needed to secure “good governance” goes beyond the stipulations of the traditional and narrower scientific community. In order to achieve this, they recommend diversifying the key stakeholders by inviting private actors and members of the broader public in the assessments of a variety of scientific disciplines. This approach asks that both normative and political elements be accepted in the decision‐making process.This all‐inclusive approach is quite frequently applied at the local level but presents logistical challenges in its application at the international level. For example, even when only traditional scientists are involved, there may be hundreds or thousands represented at various stages of the decision‐making process, as seen in the International Panel on Climate Change (IPCC).Another approach and associated discussion (and controversy) relates to the extent to which science should be independent or embedded in the political process. The traditional position has always been one where science is seen through a value‐free and objective lens, coined by the phrase, “Speaking truth to power.”There has been much debate concerning the fruitfulness (or lack thereof) of separating the “scientific” from the “political” sphere.What is the conceptual relationship between scientific input and the effectiveness of regimes? International relations scholars have studied this connection for more than two decades.Scientific expertise is an important factor that determines both the nature of the problem and the strength of the problem‐solving capacity of a regime. Regarding the nature of the problem, the simple assumption is that, all things being equal, the higher our knowledge and consensus on a topic, the higher the likelihood of a regime with increased effectiveness. This subsequently impacts the problem‐solving capacity of a regime through its involvement in the design of the science–policy nexus and the question of independently developed research as compared to those embedded in politics. Our rationalist and positivist approach implies a highly independent approach to science, but when confronted with the highly contested political realities globally, this is too simplistic. There is a need to create a balance between scientific integrity and political involvement to ensure that scientific experts recognize the needs of decision‐makers and present legitimate scientific results that can be utilized effectively in practice.3What are the role and responsibilities of scientists and of other actors in communicating pressing problems to policymakers, setting agendas, and developing international committees?Based on previous experiences in international environmental affairs, the short answer is that the role of scientists is extremely crucial. First, had it not been for the “whistle‐blowing” from the scientific community, many environmental problems would not have been acknowledged until it was long overdue. Second, the amount of time elapsed between the initial identification of the problem by scientists and its subsequent inclusion in policy makers' agenda is considerable. Third, in order for action to be taken, recommendations and warnings given by scientists need support from other stakeholders. For example, environmental NGOs are known to be experts in simplifying and amplifying often complicated “ivory‐tower” academic research. Similarly, international organizations and key states can be powerful contributors in paving the ground for political action. Fourth, “external shocks” and strong media exposure may also spur regime creation.3.1We will illustrate these general points by relying on some examples of international regimes. Global warming was first brought to attention by US scientists in the late 1950s and for more than two decades it remained almost exclusively within the sphere of various international scientific committees. It only reached the international political agenda in the mid‐1980s through the establishment of the IPCC in 1988. Key players such as the United Nations Environmental Program (UNEP), the World Meteorological Organization (WMO), green NGOs, activist policy‐makers, and scientists were all instrumental in drawing attention to this issue. The United States, with its dominant research experience in the field combined with its political clout, played a crucial role in launching the IPCC.Ozone depletion was another major environmental issue brought to the forefront in 1974 by US scientists. The WMO and the UNEP were also important actors in drawing attention to this problem. As a result, political negotiations began in 1981, and the Vienna Convention was adopted four years later .In the case of the whaling issue, the newly established International Council for the Exploration of the Seas (ICES) released warnings in 1900 regarding the overexploitation of large whales. However, whaling nations ignored ICES's counsel and whaling increased steadily. Alarm bells from scientists alone are therefore not sufficient to trigger policy action or the creation of regimes, particularly when strong economic interests are at stake.What was the reason behind the delay in the establishment of a permanent scientific panel for the biodiversity convention? One reason may simply be a lack of scientific uncertainty and disagreement on this issue. While some uncertainty exists with regards to estimating the number of species disappearing, there is practically no scientific discord concerning the severity of the loss of biodiversity. This is in part due to a number of already existing key scientific advisory committees, all agreeing on the severity of the problem . Another explanation is the instrumental role of the UN Millennium Ecosystem Assessment in providing rigorous scientific advice. However, the main reason behind the delay in establishing a permanent panel was probably due to a lack of powerful stakeholders championing such a body, in contrast to ozone depletion and global warming. The USA as well and other developing states, for various reasons, saw no need for such an institution .3.2Scientists from Sweden and Norway, some of the most negatively affected states, were instrumental in setting the agenda for the acid rain regime. By sounding the alarm on their respective governments, they were instrumental in rapidly setting up the regime in 1979.44.1Before exploring how influential science has been on adopted policies, we should address the effectiveness of these regimes in managing the problems they have been designed to address. Previous research indicates that the ozone regime is a rare success story in international environmental politics, as ozone depletion no longer presents an imminent threat to the environment . Nevertheless, although its message has become increasingly alarmist and unified over time, climate change skeptics from various camps have continued to distrust the IPCC. This was amplified by the so‐called “Climategate” in 2009, where it became known that renowned climate scientists had attempted to minimize the influence of critical views and failed to disclose their disputes.Overall, it is apparent that the IPCC has provided solid scientific recommendations to policy‐makers. Communication with the broader public has also improved significantly over time. While one of IPCC's key features is its intergovernmental nature, there has been criticism that its influence may be reduced given that its scientific independence can be questioned.The IPCC has significantly contributed to the reduction of scientific uncertainty, and has thereby reduced the complexity of the nature of the environmental problem to be solved. The design of the science–policy intersection has also contributed in increasing the problem‐solving ability of the regime. Thus, using the counterfactual argument, were the IPCC nonexistent, the climate change regime would in all probability have scored (even) lower in terms of effectiveness. But why is the climate change regime still deemed to be ineffective? The primary reason lies in the malign nature of the problem of climate change when compared to most other environmental issues. That is, the climate change problem is much more than an environmental problem alone, as it affects the economy, the trade, the energy and the development of countries significantly. Vested interests and conflict between nations makes political action very challenging. The problem‐solving ability of the regime has also been hampered by conflicting choices in the approach to take as well as the mostly negative stance taken by the US in the process. Still, there is no doubt that the IPCC has contributed significantly to an increased understanding among the public about the severity of this complex problem.5Some of the conditions needed for scientific expertise to influence decision‐makers and enhance the effectiveness of international environmental regimes are addressed in this commentary. First, scientists are crucial in the setting of agendas: their scientific discoveries are after all the core upon which frameworks can be created to confront environmental challenges. However, the creation of regimes requires active efforts from other stakeholders such as environmental groups, international organizations as well as powerful states. The conditions under which scientific advisory committees can influence policy‐makers and contribute to more effective international environmental regimes are several. Scientific expertise represents an important premise for decision‐makers in international environmental regimes, as scientific bodies are generally included in their institutional framework. The increased range of scientific input introduced in the regimes is another important factor. In addition, governments rarely dispute scientific consensus, which typically leads to some level of subsequent collective action to address the environmental concern. However, action alone is very seldom sufficient to solve the problem at hand.Generally speaking, scientific expertise seems to be most important in the agenda‐setting phase, and gradually declines in significance as politics take control over the relevant decision‐making processes. This is particularly true in global regimes such as biodiversity and climate change with highly complex political problems. The political action taken in these two regimes to this point has been too weak to effectively solve the problems they were designed to address. Given that political interests are usually deemed to be more influential than scientific input, it is clear that scientific advisory committees alone are intended to be but one contribution in the larger dialogue. Still, using the counterfactual, effectiveness would have likely been even more modest in the complete absence of scientific input. The ozone regime is the only global regime in our sample where decisions followed scientific advice. However, this was primarily due to the presence of other contextual factors aligning toward the direction paved by the scientific counsel. In contrast, the majority of IWC members reject the advice from the overwhelming majority of scientists in the Scientific Committee on the crucial issue of commercial whale catching practices: this is due to conflicts over value articulation which differ vastly at the global scale. That is, when there are strong conflicts over values, room for rational input represented by science tends to be small.Finally, with regards to the design of the science–policy nexus, in highly contested global regimes like climate change, experience indicates a need to strike a balance between scientific autonomy in the production of knowledge and some degree of political control in the finalization of the policy making process in order to reach scientific consensus. This may be one of the main reasons why the more recent IPBES for the biodiversity regime has replicated the intergovernmental approach of the IPCC. In contrast, where political conflicts feature less prominently, such as the case of the ozone regime, more autonomy is given to the scientific advisory bodies.To conclude, we argue that scientific advisory committees are but one variable in explaining the effectiveness of international regimes. However, these expert committees serve more than one purpose. Beyond simply informing a decision, SACs can help set an agenda, emphasize a particular value articulation, and promote stakeholder engagement to address environmental challenges with added clout.The authors declare no conflict of interest.


The starch percentage increased from 2.29% up to 46.18% after 14 days of treatment, with a total of starch level 2.6-fold higher than that of the control. Moreover, the level of endogenous ABA, zeatin-riboside (ZR) and indole-3-acetic acid (IAA) increased, while gibberellins (GAs) decreased. Notably, ABA content in treated samples reached 336.5 mg/kg (fresh weight), which was 7.5-fold greater than that of the control. Importantly, the enzyme activities involved in starch biosynthesis increased while those catalyzing starch degradation decreased after ABA application. Taken together, these results indicated that ABA can promote biomass and starch accumulation by regulating endogenous hormone levels and the activity of starch metabolism related key enzymes. These results will provide an operable method for high starch accumulation in duckweed for biofuels production.Duckweed is a valuable feedstock for bioethanol production due to its high biomass and starch accumulation. In our preliminary experiment, we found that abscisic acid (ABA) could simultaneously increase starch and biomass accumulation of duckweed, but the mechanisms are still unclear. The results showed that the biomass production of duckweed reached up to 59.70 and 63.93 g m Renewable and clean bioenergy will be an alternative resource to solve the energy crisis and environmental pollution. Bio-ethanol is a gasoline alternative that can reduce greenhouse gas (GHG) emissions when it is blended as an additive2. Therefore, it is urgent to explore novel and sustainable feedstocks (starch sources) for bio-ethanol production.With an ever-increasing demands for energy, energy shortages have received increasing attention worldwide. Moreover, some nations still rely on the consumption of traditional resources, such as coal and crude oil, which may cause air pollution and other environmental problems3. Under ideal conditions, duckweed can double its biomass in 16–48 hours. The growth rate of duckweed can reach 12.4 g/m2/day dry weight, and its yield has been documented to reach 55 tons/hectare/year dry weight4. Duckweed can absorb nutrients such as nitrogen and phosphorus from wastewater, assimilating CO2 through photosynthesis and converting it into high-quality raw materials. Moreover, duckweed can accumulate starch up to 64.9% dry weight5. High starch accumulation but low lignin content has allowed successful conversion to bioethanol in recent years6. Therefore, duckweed is a promising new non-grain feedstock with starch that is expected to partially solve the problem that the fuel ethanol industry faces due to the lack of starchy materials.Duckweed, as the smallest flowering plants in the world, grows faster than most other higher plants7, CO2 concentration8, plant hormones and light intensity11. High starch content can be realized under nutrient starvation stress, but cannot be used to treat sewage. It has been reported that elevated CO2 can also promote starch accumulation in duckweed. However, it is difficult to achieve large-scale outdoor production8. In addition, plant-growth regulators (PRGs) play an important role in plant growth and yield. In particular, ABA is a universal phytohormone involved in various physiological processes of plants, including seed development, improved growth and abiotic stress tolerance, and increasing carbohydrate accumulation in higher plants12. For example, the dry biomass yield was increased up to 2.1-fold, and saturated fatty acid content was promoted 11.17% compared with the control in Scenedesmus quadricauda after ABA supplement13. ABA seems to play an essential role in starch accumulation, but few studies have reported the application of ABA in duckweed. Wang14 has reported using ABA (added to cultures) to induce turion formation in Spirodela polyrhiza. However, turion is a special structure of Spirodela polyrhiza with high starch content, found at the bottom of water bodies, and would be difficult to harvest in future applications. Importantly, through a large number of pre-experiments, high starch accumulation of Landoltia punctata was obtained after spraying ABA on the surface of fronds.The rapid accumulation of high-quality and high-quantity duckweed is considered the key step in its conversion to bio-ethanol. Previous studies have reported that many factors can affect duckweed biomass and starch accumulation, such as nutritional conditions (starvation stress)Landoltia punctate increases starch accumulation in fronds. The objectives of this research were to determine the effect of ABA on the growth and the carbohydrate metabolism of duckweed and the underlying mechanism. The biomass of duckweed, starch accumulation, endogenous hormone content and key enzymes of carbohydrate metabolism were discussed. This work provides biochemical and physiological insights into L. punctata high-starch accumulation after ABA treatment. It also shows the dual benefits of duckweed: clean-up of sewage and production of potentially high-quality feedstock for biofuels.This study shows for the first time that foliar spraying of ABA on the surface of L. punctata. The results showed that ABA can promote biomass yield compared with the control samples . By the 6th day, the biomass production of duckweed reached up to 59.70 and 63.93 g m−2, respectively, with an increase of 7% (P < 0.05) compared to that of the control (P < 0.05). At 14th day, the dry weight has reached a steady-state condition, with the dry weight of 79.51 and 79.98 g m−2, respectively.Application of ABA significantly affected biomass accumulation of les Fig. . The dryP < 0.05). The starch content reached 19.26% (DW) by the 3rd day and finally reached 46.18% (DW) after ABA treatment on the 14th day. However, in the control samples, the starch content remained steady at 4.78% by the 3rd day and 17.76% on the 14th day. On the last day, the content of treatment samples was 2.6-fold higher than that of the control (P < 0.05).Besides biomass production, starch accumulation is another key consideration for duckweed application in biofuel production. As shown in Fig. The effects of ABA application on photosynthetic pigments were also investigated in this study. As shown in the Fig. 2/m2/s and decreased to 2.44 CO2/m2/s in the control and the ABA treatment samples, respectively. During this period, ABA treatment obviously decreased the net photosynthesis rate of duckweed by 4.5-fold when compared with the control.To investigate the effect of ABA on photosynthesis, net photosynthesis rates were also measured, as shown in Fig. In this study, to analyze the response of endogenous hormones after ABA treatment, the levels of endogenous phytohormones, including ABA, GA1 + 3, ZR and IAA, were measured.Endogenous ABA content rapidly rose from 36.3 to 262.0 mg/kg (FW) after ABA application. On the other hand, ABA levels were 37.0 mg/kg (FW) in control samples during the 1st day Fig. . By the ZR (zeatin-riboside) content increased from 6.1 to 8.5 and 9.6 mg/kg (FW) in the control and ABA treatment samples, respectively Fig. . The levSeveral enzymes participate in the process of starch accumulation, and the activity of these enzymes seriously affects the accumulation of starch in plant. The key enzymes involved in starch biosynthesis (ADP-glucose pyrophosphorylase (AGPase) and soluble starch synthase (SSS)) and key enzymes  and beta-amylase (β-amylase)) related to starch catabolism were assayed in this study Fig. .Figure 6The activity of AGPase increased from 8.37 to 10.98 U/mgprot (units per milligram protein) in the first 4 days in the control sample. It immediately rose from 8.37 to 27.53 U/mgprot after ABA application, which is a 2.5-fold increase compared with control samples Fig. . The actFigure 15. In this study, both the biomass yield and starch accumulation were increased by ABA application compared to the control. Therefore, ABA application may be regarded as an effective method to increase starch and biomass accumulation for industrial duckweed cultivation.Biomass production and starch accumulation are two key consideration in duckweed biofuel production. High-starch duckweed is a valuable feedstock for biofuel production. In this study, after culturing for 14 days, the starch content reached 46.18% in the treated samples and 17.76% in the control samples from an initial content of 2.30%  is the most important photosynthetic pigment in the chloroplast of plants, which plays an essential role in absorbing light and transferring light energy in the antenna systemsPn) in this study. These results are in agreement with those obtained by Li et al.19, who reported that the net photosynthesis rates decreased from 13.8 to 2.2 μmol CO2/m2/s after ABA treatment compared to the control of 13.5 μmol CO2/m2/s. In this study, the change of the net photosynthetic rate is consistent with the decrease of chlorophyll content.Exogenous ABA application also decreased the net photosynthetic rate  is considered to be the primary auxin in plants and stimulates growth through cell elongation and plant growth. In this study, the levels of IAA remarkably increased from 21.9 to 107.6 mg/kg (FW) after ABA application. The increase of ZR and IAA may be another important factor that influence duckweed biomass accumulation.Cytokinins are a group of phytohormones that promote cell division and cell differentiation in plant roots and shoots but that also affect biomass and starch accumulationL. punctata. The endogenous hormone content changed dramatically following ABA application  during turion formation under ABA treatment in Spirodela polyrhiza. The results showed that the expression of SpAPL2 and SpAPL3 (subunits of ADP-glucose pyrophosphorylase) was dramatically increased by 2-and 10-fold in 1–3 days and leveling off after 5 days by ABA treatment, respectively. Thus, it is probably true in Landoltia punctata as well given that our data indicate that ABA treatment increased the activities of the enzymes involved in starch biosynthesis. Therefore, the increase of ABA content plays a pivotal role in starch biosynthesis by regulating the activities of key enzymes involved in starch biosynthesis. Therefore, the increase of ABA content plays a pivotal role in starch biosynthesis by regulating the activities of key enzymes involved in starch biosynthesis.The alteration of the endogenous ABA, GAs, ZR and IAA co-regulates starch metabolism in ion Fig. . In thision Fig. , and the27. Moreover, in this study, the activities of α-amylase and β-amylase were decreased dramatically by ABA treatment  is a-1,4 endoglycolytic enzyme, responsible for the mobilization of stored starch reserves by initiating the degradation process. These genes encoding α-amylase have been shown to be differentially expressed at various developmental stages and environmental conditions through the action of plant hormones such as ABA. The expression levels of a-amylase is down-regulated by plant hormone, abscisic acid (ABA), during the process of starch metabolisment Fig. . ReportsL. punctata was achieved by ABA treatment. The increase of endogenous ZR and IAA enhanced the biomass yield of duckweed, and high levels of ABA promoted starch accumulation. Moreover, alterations of endogenous hormone levels also regulated the activity of enzymes involved in starch biosynthesis and catabolism in duckweed. These results evidenced the dual benefits of duckweed: clean-up of sewage and production of potentially high-quality feedstock for biofuels. It also provide an operable method for high starch accumulation in duckweed for biofuels production in the future.In this study, high biomass yield and starch accumulation of L. punctata, a widely distributed duckweed species with great potential to accumulate enormous amounts of starch, originally isolated from Sichuan Province, China, was cultivated in standard 1/6 Hoagland’s E + solution in illuminating incubator (Guangzhi Corp. China) for 3 to 5 days (16 h 130 μmol/m2/s 25 °C: 8 h 0 μmol/m2/s 15 °C). Then 9 g of fronds were transferred into 1,400 mL 1/6 Hoagland E + culture plastic containers (25*18*4.5 cm) for further cultivation over a period of 14 days treatment. 5 mL solution of 0.5 mM ABA  was sprayed evenly on the surface of fronds. For the control group, there was no plant hormones application on the surface of the fronds. Three biological replicates were performed in the experiment, and the experiment lasted for 14 days.31. To determine the dry weight (DW), the samples were dried at 60 °C until the weight was constant.To measure the fresh weight (FW), the fronds were measured with a balance using Bergmann’s method32. Dry duckweed powder (0.03 g) was hydrolyzed with 2.6 mL 1.2 M HCl in a boiling water bath for 2 h. Then adjusting the pH to 7.0 with NaOH, adding PbAc to precipitate protein and diluting the solution with deionized water to a final volume of 10 mL. Thereafter, the solution was filtered and treated with a C18 extraction column. Finally, the hydrolyzate was analyzed by HPLC  with an Evaporative Lightscattering Detector . The starch content was determined using the total sugar content (starch content = glucose content × 0.909).The starch content was determined according to previously published methods33. Weigh 0.5 g of duckweed sample in a 10 ml plug seal centrifuge tube, 10 ml of 80% acetone was added and kept it 72 h at room temperature in dark condition. After the incubation, the supernatant was collected and then optical density was measured at 645 and 663 nm for chlorophyll a and chlorophyll b respectively.The chlorophyll content was expressed as chlorophyll mass on a fresh weight (FW) basis (mg/g FW).The chlorophyll content was estimated according to Aron’s methodThe net photosynthesis rate was measured by Li-6400xt  using the whole chamber (6400–17) and the RGB light source (6400–18).34 and Yang35 with appropriate improvements. 0.5 g of fresh duckweed samples were homogenized in 2 ml of 80% methanol (containing 40 mg l−1 butylated hydroxytoluene as an antioxidant). The extract was incubated at 4 °C for 48 h, and then centrifuged at 4000 rpm for 15 min at 4 °C. The supernatant was passed through C18 Sep-Pak cartridges , and the phytohormone fraction was eluted with 10 ml of 100% (v/v) methanol and then 10 ml of ether. The eluate was dried down by pure N2 at 20 °C, and then stored at −20 °C until use for endogenous hormone assay.Duckweed was rinsed with distilled water using a strainer for three times. After free water stopped dripping, duckweed was blotted dry with paper towels, and then measured with a balance. The extraction, purification, and determination of endogenous levels of ABA, GA1 + 3, ZR and IAA by an indirect ELISA technique were performed as described by Wang2, 2 mmol/L EDTA, 2%(w/v) polyvinylpyrrolidone-40, 12.5% (w/v) glycerol. The homogenate was centrifuged at 10,000 g for 5 min. The supernatant extract was used as crude enzyme solution stored at minus 20 °C. All the procedures were carried out at 0–4 °C. Then, the supernatant was used to measure the enzyme’s activity. The activities of ADP-glucose pyrophosphorylase , soluble starch synthase , alpha-amylase  and beta-amylase  were analyzed using the method from a previous report38. Activity was expressed as units per milligram protein (U/mgprot). Accordingly, one unit of AGPase is defined as the amount of enzyme that causes the increase of 0.01 OD at 340 nm of the finally reacted solution per minute. The AGP units are then divided by the total protein amount. One unit of amylase is defined as the amount of enzyme in the presence of excess thermostable a-glucosidase required to release 1 μmol of p-nitrophenol from non-reducing-end blocked p-nitrophenyl maltoheptaoside (BPNPG7) per min under the defined assay conditions. One unit of SSS is defined as the amount of enzyme that causes the increase in absorbance at 340 nm of the finally reacted solution per minute.For the assays of enzymes, 1 g fresh weight duckweed was homogenized with a ceramic pestle in an ice-cold mortar in 5 ml of 50 mmol/L HEPES-NaOH (pH = 7.6), 5 mmol/L DL-Dithiothreitol, 8 mmol/L MgClp < 0.05) was analyzed by one-way analysis of variance39 using SPSS software .All samples were investigated in triplicate. All the results are presented as means ± standard error in the Figures. Statistical significance (Supplementary table and figure


Correction to: Semin Immunopathol (2018) 40:113–12410.1007/s00281-017-0662-9The presentation of Fig. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.