Ectocarpus siliculosus, whereas one ESL was identified in the genome of Klebsormidium nitens providing evidence for the early emergence of these transporters in Streptophytes. A phylogenetic analysis using the 519 putative ESL proteins identified in the genomes of 47 Embryophyta species and being representative of the plant kingdom has revealed that ESL protein sequences can be divided into three major groups. The first and second groups originated in the common ancestor of all spermaphytes [ζ: 340 million years ago (MYA)] and of angiosperms (ε: 170–235 MYA), respectively, and the third group originated before the divergence of rosids and asterids (γ/1R: 117 MYA). In some eudicots , the ESL family presents remarkable expansions of gene copies associated with tandem duplications. The analysis of non-synonymous and synonymous substitutions for the dN/dS ratio of the ESL copies of the genus Arabidopsis has revealed that ESL genes are evolved under a purifying selection even though the progressive increase of dN/dS ratios in the three groups suggests subdiversification phenomena. To further explore the possible acquisition of novel functions by ESL MSTs, we identified the gene structure and promoter cis-acting elements for Arabidopsis thaliana ESL genes. The expression profiling of Arabidopsis ESL unraveled some gene copies that are almost constitutively expressed, whereas other gene copies display organ-preferential expression patterns. This study provides an evolving framework to better understand the roles of ESL transporters in plant development and response to environmental constraints.Carbon management by plants involves the activity of many sugar transporters, which play roles in sugar subcellular partitioning and reallocation at the whole organism scale. Among these transporters, the early response to dehydration six-like (ESL) monosaccharide transporters (MSTs) are still poorly characterized although they represent one of the largest sugar transporter subfamilies. In this study, we used an evolutionary genomic approach to infer the evolutionary history of this multigenic family. No ESL could be identified in the genomes of rhodophytes, chlorophytes, and the brown algae  Sugar carriers belong to a major facilitator superfamily (MFS), including disaccharide transporters [sucrose transporter (SUC)/suc transporter (SUT)]   and pea   and polyol transporter (PLT) subfamilies were greatly expanded, whereas the ESL subfamily was much larger in Arabidopsis , 14 VvESL open reading frames were found to be located in tandem on chromosome 14 , has been identified by using a differential screening of the complementary DNA (cDNA) library from Arabidopsis thaliana plants exposed to dehydration stress  is a low affinity facilitator, which is able to transport different hexoses  across the tonoplast. Its expression is highly upregulated by high salinity and ABA in roots and slightly induced by drought  are two tandem duplicated genes located on chromosome 5. These two genes displayed different expression patterns during leaf development. AtSFP1 was detected in seedlings 9 days after germination but not detected in mature plant organs , whereas AtSFP2 was expressed in all tested plant organs and in seedlings. Further analysis showed that only SFP1 is induced during leaf senescence but the expression of AtSFP2 remains stable during this process  compared to the wild type (86%) , the screening of a leaf cDNA library using a degenerated PCR led to the identification of AcMST1, an ESL localized in the tonoplast . Indeed, ERD6-like7, ERD6-like12, and ERD6-like16, as well as ERD6-like18, were downregulated in shoots and roots, respectively, whereas ERD6-like13 and ERD6-like15 were upregulated in roots in response to PGPR  were grown in ArasystemTM  containing an autoclaved mix of compost/vermiculite (3/1) into a growth chamber under a 10-h light (22°C)/14 h dark (18°C) photoperiod, with 50% (light) to 90% (dark) relative humidity. Fully expanded leaves, roots, flower buds, flowers, and siliques were used for sampling. The roots were separated from soil by multiple careful washings with water before sampling.A. thaliana, Brachypodium distachyon, Cucumis melo, Musa acuminata, O. sativa, Populus trichocarpa, Solanum lycopericum, V. vinifera, and Z. mays were collected from the Aramemnon online database (http://aramemnon.uni-koeln.de). The ESL sequences from other species were identified through the protein basic local alignment search tool (BLASTp) analysis  with the exceptions of P. taeda (http://congenie.org), Galdieria sulphuraria, Chondrus crispus, Pyropia yezoensis, Chlorella vulgaris, Phoenix dactylifera, Nelumbo nucifera, Cicer arietinum, Genlisea aurea, Ectocarpus siliculosus (https://www.ncbi.nlm.nih.gov), Lotus japonicus (http://www.kazusa.or.jp/lotus/), and Klebsormidium nitens (https://phycocosm.jgi.doe.gov/Klenit1/Klenit1.home.html). Only full-length sequences were collected, and the potential isoform sequences were eliminated from the study. The species and the identified sequences are summarized in Previously identified ESL proteins of https://bioedit.software.informer.com) and the ClustalW method. A phylogenetic analysis was performed by using the MEGA v.6.0 software  protein sequence and one referent of each monosaccharide transporter (MST) subfamily  were used as an outgroup to root the phylogenetic tree. From the inference of a phylogenetic tree, all dates, periods, and evolutionary data used to reconstruct the evolutionary history of the ESL family in land plants were obtained from the Time Tree of Life (http://www.timetree.org)  from A. thaliana, Arabidopsis lyrata, Arabidopsis halleri, and Capsella rubella were aligned by MEGA 6.0 using the neighbor-joining method, 1,000 bootstrap, and the JTT amino acid substitution model. This led to the definition of the 18 monophyletic groups of ESL sequences, each containing at least 1 A. thaliana ESL sequence as well as a sequence of C. rubella (when possible: 16 of the 18 groups). For each group, a second alignment was performed by using the BioEdit free software (https://bioedit.software.informer.com) and ClustalW method and was manually realigned to keep the same open reading frame for all the aligned CDS. In total, four sequences  were removed from the study due to non-possiblity of aligning the sequences correctly. The dN/dS (KaKs) ratio of ESL sequences was calculated by the Datamonkey online server (http://www.datamonkey.org), using the GA-branch method  developed by Beijing University's Bio-Informatics Center  suite 5.3.3 program (https://meme-suite.org/meme/tools/meme) with the following parameters: optimum width, 3–60; the maximum number of motifs 50, and an E-value of 10−60.The conserved motifs of AtESL genes were retrieved from The Arabidopsis Information Resource (TAIR) database. These sequences were then scanned for the presence of cis-regulatory elements by using the plant cis-acting regulatory DNA element (PLACE) database (https://www.dna.affrc.go.jp/PLACE/?action=newplace) . The results of the target gene expression were normalized with the average Ct value of the reference gene, AtPP2a (At1g13320) . The primer sequences used in this study are listed in Total RNA was extracted from the frozen ground y et al. . RNA quaStatistical analyses were performed by using XLStats 2011 .V. vinifera ESL protein sequences as queries against 63 proteomes from photosynthetic organisms representing the main groups of algae and the plant kingdom. BLASTp analysis of the genomes of 13 algae, including stramenopiles (E. siliculosus), rhodophytes , chlorophytes , and streptophytes (K. nitens), led to the identification of a single ESL protein in the genome of K. nitens. In the genomes of 50 embryophytes, a total of 526 sequences were identified, and mosses and lycophytes have <5 ESL copies . Gymnosperms and angiosperms have 5 to 19 ESL genomes, with a few exceptions of those having fewer ESL copies  or a very high number of copies . These data suggest that ESL transporters could have appeared with streptophytes and have considerably diversified in Embryophyta  analysis using 18 ryophyta .A. trichopoda. All the sequences identified in moss and fern (the tree sequences of P. patens ans the two sequences od S. moellendorffii) and 5 of the 9 sequences of the gymnosperm P. taeda are located at the basis of this group. Altogether, the 133 ESL protein sequences  form a cluster wherein the 47 analyzed species are represented by at least 1 protein sequence. The sequences of moss and fern form a monophyletic group and show a similarity higher than those of P. taeda and angiosperms. In the groupe ESL1 , which contains the 146 sequences identified in the genomes of 43 species. In this group (L. japonicus) and ESL2b . In each group, the protein sequences separate mainly according to the botanical families. There are two groups of ESL for Fabaceae, Rosaceae, Cucurbitaceae, Rutaceae, Malvaceae, Malpighiales, and Asterids, and three groups of ESL for Brassicaceae.The group ESL2  is formeis group , monocotA. trichopoda (one of the earliest divergent lineages within angiosperms) are present. This group contains the 232 sequences of 40 species . S. polyrhiza (one of the earliest divergent lineages within monocots), O. sativa, and G. aurea are not represented in this group. The group ESL3 can be subdivided into three subgroups, namely ESL3a, ESL3b, and ESL3c  form a single group in which the sequences are separated according to the botanical families. The subgroup ESL3b contains the 47 sequences  identified in V. vinifera and 14 Eurosids, and the subgroup ESL3c contains 123 sequences  from V. vinifera, Solanaceae (S. lycopersicum and S. tuberosum), and 17 Eurosids.The group ESL3  is specind ESL3c . The subP. taeda (56%) and to all the sequences of P. patens and S. moellendorffii. (2) The group ESL2 contains the highest number of ESL sequences of A. trichopoda , A. corulea , N. nucifera , most of the Fabaceae (39%), all Rosaceae (53%), and Cucurbitaceae (43%). (3) For V. vinifera (Vitaceae 56%), all Malpighiales (52%), E. grandis , Rutaceae (52%), Brassicales (71%), and Malvaceae (48%), the highest number of proteins is found in the group ESL3. This high number is generally correlated with the presence of a high number of sequences in group ESL3c (55 to 81% of ESL3 sequences) except for malvaceae and euphorbiaceae. (4) For Solanales, the groups ESL1 and ESL3 contain the same number of sequences (38% each), which is more than the group ESL2 (24%). In monocots and Fabaceae, this distribution varies according to the species.In conclusion, the distribution of ESL sequences in the three main ESL groups varies according to the botanical groups, and four distribution types can be observed : (1) TheESL genes for each species and studied their distribution. L. japonicus and G. aurea were not included in this analysis  only have two duplicated ESL2 genes. For monocots, P. dactylifera  contains two duplicated genes (one ESL2 and one ESL3a), whereas in Poaceae, all species have three to six tandem duplicated ESL belonging to ESL1 and/or ESL2. In addition, P. hallii, P. panicum, and S. bicolor have two duplicated ESL3. Therefore, Poaceae mainly have ESL1 (45%) and ESL2 (36%) duplicated genes. For eudicots, only three species have duplicated ESL1: E. grandis  with four duplicated ESL1 (16% of the EgESL) and two Fabaceae, G. max and M. truncatula, which have two and four tandem duplicated ESL1, respectively, representing 35% of the total Fabaceae sequences. The basal eudicots, A. coerulea and N. nucifera have more than 70% of the duplicated ESL2 genes and two Rosaceae only ESL2 tandem genes. V. vinifera, E. grandis, Rutaceae, and Brassicaceae have more than 85% duplicated ESL3, whereas Cucurbitaceae, C. papaya, and Solanaceae only have duplicated ESL3. In sum, among the 509 analyzed ESL, 246 are tandem duplicated genes, including 25 ESL1 from 10 species (Poaceae: 60% and eudicots: 40%), 51 ESL2 from 23 species (eudicots: 63% and monocots: 29%), and 170 ESL3 mostly from eudicots (96%). Furthermore, duplicated ESL3 genes are not homogeneously distributed among the ESL3 subgroups. The subgroup ESL3a contains 33 duplicated eudicot genes (82.5%) and only 7 monocot genes (17.5%) that are identified in 23 species. The subgroups ESL3b and ESL3c have 31 and 99 eudicot duplicated genes, respectively, all from Pentapetalae species, including V. vinifera, Malpighiales, E. grandis, Brassicales, and Malvales. ESL3c also contains duplicated genes from Rutaceae and Solanaceae (ESL genes per species (ESL3 and the number of ESL genes per species as well as the number of eudicot ESL (ESL3c and the number of ESL3 genes per species (ESL3c (123 genes) is presumed due to numerous eudicot tandem gene duplications, which have occurred in 20 species.The genome analysis has revealed the presence of tandem duplicated genes for many species, which could explain the heterogeneous distribution of ESL into three ESL groups. To test this hypothesis, we determined the number of tandem duplicated analysis . For a flanaceae . The gro species , which iicot ESL , which m species , indicatESL genes, we performed a comparative analysis of the intron/exon arrangement of 515 sequences from 46 species . As shown in ESL genes varies from 6 to 35. However, 14 exon/intron arrangements with each representing <4% of the total number of sequences might arise from incomplete sequences or erroneous CDS or represent the exceptional structures that would only concern a small number of ESL sequences. By contrast, the arrangements with 16, 17, and 18 exons have been identified in 8.35%, 24.08%, and 50.49% of the ESL sequences, respectively.To identify the potential structural specificities for S. moellendorffii. It is the only one found in the sequences of P. patens (moss), P. vulgaris (Fabaceae), and P. persica (Rosaceae). It is present in more than 80% of the sequences of the basal angiosperm A. trichopoda, the basal eudicot A. coerulea, the salicaceae P. trichopoda and S. purpurea, the Fabaceae C. arietinum, the Malvaceae T. cacao, and the Solanaceae S. lycopersicum. For the other species, it represents 20–80% of the ESL sequences, except in the cases of monocot Z. maize, the linaceae L. usitatissimun, and the lentibulatiaceae G. aurea, which have this structure for <20% of the sequences , and C. rubella (29.4%). For M. trucatula and M. guttatus, they represent 30 and 33%, respectively, whereas, for all other species, they represent between 5.6 and 25% of the sequences. Genes with 17-exon arrangements are equally split into ESL1 (41.1%) and ESL3 (40%) groups, whereas only 18.55% are ESL2 genes. In the ESL3 group, a quarter of them are ESL3c (ESL1 as it was identified in 28 monocot sequences from 9 Poaceae and from P. dactilyfera (arecaceae) and M. acuminata (musaceae). Only the ESL from the basal monocot S. polyrhiza does not have such a structure. Five 17-exon structures are more or less Brassicaceae specific. Structure 3 (the merging of exons 12 and 13) was identified in 22 sequences essentially from Brassicaceae (20 sequences) and Fabaceae (2 sequences) and concerned ESL1, ESL2a, and ESL3b genes. Structure 4 (the merge of exons 17 and 18) is specific to Brassicaceae ESL1 as it is found in only seven ESL1. Structure 5 (the merge of exons 14 and 15) is specific to Brassicaceae and concerns 14 ESL2a and ESL3c genes. Structure 6 (the merging of exons 15 and 16) is specific to ESL3c and was identified in four Brassicaceae and in one S. purpurea (Rutaceae) genes. Structure 7 (the merging of exons 4 and 5) is specific to ESL3c and is identified in eight Brassicaceae and in one V. vinifera genes. Finally, the structure 8 (the merging of exons 5 and 6) is specific to ESL2a and is identified in three Rutaceae genes.The arrangements with 17 exons are less widespread and have been identified in 124 embryophyte sequences (24.08%). They are present in more than 35% of the sequences of the fern re ESL3c . In factre ESL3c . StructuESL2 (37.21%) and in ESL3 (44%), especially in ESL2b (20.9%) and ESL3c (25.6%). They represent 33% of the ESL of P. taeda, 27% of those of M. domestica and S. lycopersicum, 20% of S. tuberosum and L. usitatissimum, and 3–12.5% of the ESL of Brassicaceae. Contrary to 17- and 18-exon structures, only the two types of 16-exon structures could be identified (ESL2b Brassicaceae and concerns six sequences in Brassicaceae. Structure 10 (the merging of exons 9 and 10 as well as 15 and 16) is observed for the three sequences of ESL2b of Solanaceae.The arrangements with 16 exons are less abundant. They have been identified in 43 sequences (8.35%) from 20 species . They arentified . StructuESL1 from monocots have a 17-exon structure (structure 2), whereas ESL2 and ESL3 have mainly an 18-exon structure (structure 1). Furthermore, 17-exon structures are highly represented in Brassicaceae, of which five structures have been identified, including one specific to ESL1 (structure 4), one specific to ESL2a (structure 8), two specific to the ESL3c (structures 6 and 7), and two others with less specificity (structures 5 and 3), to which a 16-exon structure specific to ESL2b is added (structure 9). Thus, we sought to determine whether there is a correlation between the observed structures and the different subgroups of monocots and Brassicaceae identified in the phylogenetic analysis, to highlight a possible link between ESL evolution and functional differences. In the phylogenetic analysis, 3 monocots (named as Mx) and 11 Brassicaceae (named as Bx) monophyletic groups were identified , 197 MSTs, and 31 SUCs. We have identified  2 motifsESL Brassicaceae family, we aimed at determining the selection regime experienced by the family. We performed an analysis of nucleotide substitutions or dN/dS ratio for the Arabidopsis genus, the only one for which in our study the ESL genes have been identified in three different species . The ESL sequences identified in C. rubella (another Brassicaceae) were used as an outgroup. To determine the number of alignments to be performed before calculating dN/dS ratios, a tree was built with the 65 CDSs of the three Arabidopsis and C. rubella (ESL sequences are under a strong purifying (negative) selection pressure, which tends to conserve the sequences and potentially their functions. However, small differences can be detected between the means of the dN/dS ratios relative to each ESL group (ESL1 (0.078) is the lowest one, whereas the value of the group ESL3 is the highest (0.244) and the values of the subgroups ESL2a (0.185) and ESL2b (0.146) are intermediate. Among the ESL3 subgroups, ESL3a shows the highest value (0.303). This suggests that even if the ESL genes are under a purifying selection, the selection pressure might be less stringent for the ESL3 genes than for another ESL, especially ESL1.For Brassicaceae, we identified 11 groups of ESL proteins (B1–B11), the presence of 68 tandem duplicated genes, and 7 different intron/exon structures. To better understand the evolutionary mechanisms involved in the genesis of the  rubella . 18 grou rubella –5. ConsiSL group . The valESL genes are conserved or, on the contrary, diverge, we analyzed the expression of the ESL genes from A. thaliana. First and foremost, we propose a new nomenclature for those genes according to the three main ESL groups we have identified in the present phylogenetic analysis. The nomenclature , as summarized in ESL name of the group they belong to. As two tandem duplicated genes AtESL3.09 (At5g05155) and AtESL3.12 (At5g05165) could not be analyzed because no specific primers could be designed, the expression level of 17 genes was measured in leaves, roots, buds, flowers, and siliques, by using real-time reverse transcription-PCR (qRT-PCR)  than in the other organs (0.11 ± 0.81). AtESL3 genes present very diverse expression levels. AtESL3.02, AtESL3.03, AtESL3.04, and AtESL3.13/SFP1 are the least expressed ESL3 genes in all organs, and their expression level is equivalent to that of AtESL2 genes. On the contrary, AtESL3.08/ERD6 is the most expressed gene in all tested organs. Its expression level is 3.5 and 11 times more important in leaves and roots than the mean of the expression values for all ESL in the corresponding organs. AtESL3.01 is expressed in all organs but shows a weaker expression in roots. The other AtESL3c present clear organ specificities: AtESL3.05/ESL3 expression level is 3.5 times higher in leaves (1.74 ± 1.04) than in other organs (0.37 ± 0.34), AtESL3.06/ESL2 is more expressed in leaves (1.33 ± 0.45) and siliques (0.74 ± 0.31), AtESL3.07/ESL1 is more expressed in roots, flowers, and siliques than in leaves and buds, AtESL3.10, AtESL3.11, and AtESL3.14/SFP2 are more expressed in siliques (1.16 ± 0.63), leaves (1.09 ± 0.26), buds (1.12 ± 0.46), respectively. The normalization of the total mean expression in each organ highlights that six ESL  are highly expressed in leaves, three  in roots, five  in buds, six  in flowers and five  in siliques.To determine whether some of the functions of qRT-PCR) . The twoESL2.02/ESL3.11, ESL3.02/ESL3.03, ESL3.05/ESL3.06, ESL3.07/ESL3.08, and ESL3.13 /ESL3.14). We therefore compared the expression patterns of tandem genes as well as that of the two ESL1  and siliques (14x) than ESL2.02 (ESL3.02 is significantly more expressed in leaves (16x), flowers (10.7x), and siliques (5x)  and roots (10x), whereas, in reproductive organs, this difference disappears as both are weakly expressed. On the opposite, ESL3.13 and ESL3.14 present more different expression levels in reproductive organs than in vegetative organs. Compared to ESL3.13, ESL3.14 is 71.6x, 111x, and 91.6x times more expressed in buds, flowers, and siliques, respectively.The bioinformatic analysis led to the identification of five tandem duplications (two ESL1 . The exp flowers . ESL3.11 ESL2.02 . Compareues (5x)  even if d organs . ESL3.07AtESL, and found 186 cis-acting elements that were classified into three main categories: growth and development, hormone response, and stress response, the last ones are the most numerous ones  or in response to biotic and abiotic environmental factors such as light and salinity. Interestingly, one motif (WBOXHVISO1: sequence TGACT) involved in sugar regulation, is present in all ESL promoters, in a few copies. Cis-elements regulating the expression in leaves or mesophyll cells are the most represented ones. In addition to the common cis-regulatory elements, we identified 30 cis-acting elements, which were only found in a single promoter, thereby suggesting expression specificity , abiotic stresses (5), cellular development (6), nutrition (3), and plant growth and development (1).We performed a PLACE analysis on the 1-kb promoter region of each of the 19 ous ones . 12 commSL genes . These mcificity . Their lIn silico analysis of AtESL promoters resulted in the identification of the 13 motifs that are potentially involved in sugar-regulated transcription  signal perception such as the GARC complex comprising the AMYBOX1 and 2, the MYBGAHV for gibberellins (GAs) induction and sugar repression, the PYRIMIDINE boxes for GAs, ABA, and TATCCOSAMY, a sequence enabling the binding of MYB proteins. It is noteworthy that the number and the distribution of these sugar elements are really different between the tandem duplicated copies with the exception of ESL3.13/SFP1 and ESL3.14/SFP2 and the two ESL1.cription . We founcis-acting elements involved in diverse stress responses  found in most AtESL with the exception of ESL1.01, ESL3.02, and ESL3.05/ESL2. Five elements involved in cold response are mainly present in the promoter of ELS3 genes as well as three others involved in wounding and axillary bud outgrowth after decapitation. The number and the distribution of these elements are really different between ESL1.01 and ESL1.02 as well as the two tandem genes ESL3.07/ESL1 and ESL3.08/ERD6. Differences could be observed between the two pairs of tandem genes: ESL3.02 only has an ANAERO1CONSENSUS element, whereas ESL3.03 also has cis-elements for hypo-osmolarity, CO2, and wounding responses. ESL3.13/SPF1 has cis-elements involved in cold response, whereas ES13.14/SFP2 has elements for CO2 response. Finally, we detected numerous drought-responsive cis-elements in all AtESL promoters , and chlorophytes (O. lucimarinus and O. tauri)  and rhodophyte (three species) genomes as well as in that of the brown algae . nitens . It is nK. nitens is in agreement with the generally accepted hypothesis that the ancestors of terrestrial plants are closely related to the charophytes  than in seed plants (3–37 copies), suggests that ESL carriers have undergone a significant expansion and have become a large multigenic family in angiosperms, particularly in eudicots. Our phylogenetic analysis of 519 ESL proteins identified in the proteomes of 47 Embryophytes provides evidence for the existence of three ESL monophyletic groups, namely ESL1, ESL2, and ESL3 (P. patens (bryophyte), S. moellendorffii (lycophyte), and half of those of P. taeda (gymnosperm)  specific subgroups. This suggests a third diversification event in the common ancestor of the Pentapetalae  event around 7.9 and 14.6 MYA  . Thus, g3 groups , 3 sugge3 groups . This ev3 groups . Similariosperms , which ciosperms . The ESLapetalae , possiblapetalae . This evapetalae  and thaton event . Howeveron event , Fabaceaon event  M. domesESL3c group compared with ESL3a and ESL3b. In this regard, we observed a good correlation between the number of tandem duplicated ESL3c genes and the total number of ESL3 per species . The observation is in agreement with the study of Myburg et al.  suggests that those are specific to Brassicaceae and therefore are unrelated to genome doubling events alpha, beta, and to the specific Brassica WGT. Based on the Time Tree of Life, this translates into 25.6–68 MYA. It has been reported that tandem duplicated genes, which are retained in a lineage-specific manner, are mostly responsive to environmental biotic  through the group ESL2 (dN/dS = 0.172) to the group ESL3 (dN/dS = 0.233), suggesting a slow decrease of the selection pressure. Even though the threshold of one is not reached, we speculate that the progressive increase of dN/dS ratio acts in favor of neutralization or subdiversification toward a possible acquisition of novel functions by ESL MSTs.The analysis of non-synonymous and synonymous mutations using the dN/dS ratios of the ESL copies of the genus ESL. We found two conserved protein motifs common to ESL and other sugar transporters and eight motifs conserved with the monosaccharide carriers. The different ESL groups discriminate in the presence or the absence of particular motifs located preferentially in the N-term region. We determined 10 major gene structure types, including 1 specific to ESL1 of monocots, 1 specific to ESL2a of Rutaceae, 1 specific to ESL2b of Solanaceae, and 6 specific to Brassicaceae. The first one, named as structure 1, is an 18-exon structure found in more than 50% of the ESL sequences, including those from early diverging species. Thus, it seems likely to be an ancestral type that has been preserved throughout evolution. The other 9 17-exon and 16-exon structures have probably appeared in the course of evolution by an intron loss. This hypothesis is in agreement with the literature as it was reported for Arabidopsis and rice, that after gene duplication, the rate of intron loss is higher than that of intron gain , and it has been demonstrated that the exon numbers of its 75 sugar transporter genes range from 2 to 18  and ESL3.12 (At3g05165) could not be amplified nor analyzed as no specific primers could be correctly designed. This is in agreement with the fact that no expression data are available in the Genevestator and BAR databases, suggesting that they are pseudogenes. At least for ESL3.09 (At3g05155), this is corroborated by its short sequence presenting only 13 exons. ESL1.01 and ESL1.02 are expressed more or less constitutively in all tested organs and in similar levels. The lack of organ specificity for these ESL1 genes potentially allows them to respond to a wide range of developmental and/or environmental signals. This is consistent with the hypothesis that ESL1 genes are related to the copies present in the genomes of P. patens (moss) and S. moellendorffii (fern). ESL2 genes are rather weakly expressed and only ESL2.03 shows a preferential expression in roots. In our experimental conditions, ESL2.01/ZIF2, is a weakly expressed gene, maybe slightly more expressed in roots, flowers, and siliques. This is in agreement with Remy et al. (ESL3 genes, our results highlight a low level of expression for ESL3.03, ESL3.04, and ESL3.1/SFP13 and the fact that ESL3.08/ERD6 is the most expressed ESL in all tested organs. We demonstrate a preferential expression pattern of ESL3.08/ERD6 in vegetative organs than in reproductive organs, and conversely, the highest expression of ESL3.10 in reproductive organs compared to vegetative ones, especially in siliques. The AtESL3c genes, whose dN/dS within the genus Arabidopsis suggests relaxed negative selection pressure, show different expression patterns in the five tested organs. This suggests a more rapid evolution and some subfunctionalization for these copies. Among the ESL tandem pairs, no or weak differences in the expression between the two copies could be detected for the two pairs ESL1.01/ESL1.02 and ESL3.05/ESL3.06. Inversely, a highly significant differential expression is clearly demonstrated for the following tandems: ESL2.02/ESL3.11 in leaves and siliques; ESL3.02/ESL3.03 in leaves, flowers, and siliques; ESL3.13/ESL3.14 in reproductive organs; and ESL3.07/ESL3.08 in vegetative organs. ESL3.13/SFP1 shows a weaker expression than ESL3.14/ SFP2 in all organs and is the weakest expressed ESL3c. This result corroborates the Northern blot analysis demonstrating that ESL3.13/SFP1 is undetectable in young and fully expanded leaves, stems, and roots, whereas ESL3.14/SFP2 is expressed in these organs (Quirino et al., ESL3.13/SFP1 is strongly expressed in seedlings and is highly induced in senescent leaves, whereas ESL3.14/SFP2 is not (Quirino et al., ESL3.08/ERD6 is more expressed than ESL2.08/ESL1 at least in leaves, roots, and buds. This result is in perfect accordance with the results of Yamada et al. (ESL3.08/ERD6 is more expressed than ESL2.08/ESL1 in the leaves from the plants growing under normal conditions. They also determined that the transcriptional activity conferred by the promoters of ESL3.08/ERD6 and ESL3.07/ESL1 is preferentially located in the same organs, i.e., shoots, sepals, and roots. In roots, ESL3.08/ERD6 is specifically expressed in epidermal and cortical cells, whereas ESL3.07/ESL1 is expressed in endoderm, pericycle, and xylemic parenchyma cells (Yamada et al., AtESL.Although y et al.  who showa et al. , who alsAtESL genes are evolving under a purifying selection, their highly variable gene structure and organ-preferential expression emphasize the onset of a plausible diversification. A plethora of promoter cis-acting elements tightly related to developmental and environmental cues opens novel perspectives for further elucidation of their biological functions.The authors performed an evolutionary analysis of plant ESL transporters using the protein sequences from various species representing the main groups of the plant kingdom and demonstrated that these genes arose with streptophytes and have considerably diversified in Embryophytes. Even though the The original contributions presented in the study are included in the article/FD, LS, ML, RA, and RC designed the experiments, discussed the data, and revised the article. AI, CP, FT, and LS performed the experiments. FD, LS, ML, and RA wrote the article. All authors approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The role of non-coding RNA, and particularly of circular RNA, in the DNA damage response and repair network is underappreciated. Given the vital role of this network in preserving the genomic integrity and consequently cellular homeostasis, the constantly increasing numbers of discovered circular RNAs and the increasing implication of these molecules in the function of this network unravel a new important field that may open new therapeutic opportunities, but also require detailed investigation.Circular RNAs (circRNA) comprise a distinct class of non-coding RNAs that are abundantly expressed in the cell. CircRNAs have the capacity to regulate gene expression by interacting with regulatory proteins and/or other classes of RNAs. While a vast number of circRNAs have been discovered, the majority still remains poorly characterized. Particularly, there is no detailed information on the identity and functional role of circRNAs that are transcribed from genes encoding components of the DNA damage response and repair (DDRR) network. In this article, we not only review the available published information on DDRR-related circRNAs, but also conduct a bioinformatic analysis on data obtained from public repositories to uncover deposited, yet uncharacterized circRNAs derived from components of the DDRR network. Finally, we interrogate for potential targets that are regulated by this class of molecules and look into potential functional implications. Chromosomal rearrangements following incorrect repair of DNA double-strand breaks (DSB) constitute one of the primary causes of tumorigenesis, setting the grounds for genomic instability . SeveralAn increasing body of evidence has demonstrated that several types of RNA species hold critical roles in various nuclear processes, such as DNA replication and repair, chromosome structure regulation, telomere elongation and chromatin organization ,7,8,9. AHerein, we focus on the various mechanisms through which circRNAs regulate the function of important genes of the DDRR network, thus exerting a direct impact on genomic stability and cancer progression. Moreover, we conducted a bioinformatic analysis to reveal potential circRNA isoforms that derive from the linear RNAs of key components of the DDRR network and explore their possible role in cell fate regulation.CircRNAs, together with microRNAs (miRNAs) and other ncRNAs, comprise about 95% of total RNA in eukaryotes, and an emerging body of evidence suggests their active involvement in gene regulation . CircRNAExonic circRNAs may be generated from single or multiple exons, and are the product of pre-mRNA splicing, where a 3′ splice donor is attached to a 5′ splice acceptor yielding a circular structure  15,18].,18.15,18Of note, highly expressed circRNAs have been shown to be produced from exons, particularly from those located closer to the transcription start of the host gene . MoreoveA recent body of evidence has suggested that circRNAs may be implicated in the initiation and progression of tumorigenesis ,30. OverThe role of circRNAs in cancer has been the object of recent research efforts showing a relationship between circRNAs produced by established oncogenes and oncogenic outcome. For example, cir-ITCH, which functions as an miRNA sponge to increase ITCH expression levels, is found downregulated in colorectal cancer tissues compared to control . This isInterestingly, it has been suggested that circRNAs may control the biological activity of a network of competing endogenous RNAs (ceRNAs) . AccordiZbtb7a gene which has a proto-oncogenic role in mesenchymal tumors, while the respective linear RNA acts as a tumor suppressor [Foxo3 gene, induce apoptosis, thereby restricting tumor growth [It is, thus, becoming clear that circRNAs are associated with cancer patient clinical outcomes, by exerting important functions in cancer cells ,42,43. Cppressor . Other cr growth ,46. Addir growth ,48. To enrich for circRNAs, ribonucleases can be used to remove rRNA, tRNA, poly(A)+ RNAs, and then preserve the circular forms with RNAse R treatment, which will degrade only the linear RNAs. To identify a putative site for RBP binding, RPA assays can be used using RNAse-H, which will digest the unprotected unbound-RNA site . To inve6A-driven translation of circRNAs along with IRES elements is widespread. To achieve this, a minigene reporter containing split GFP and a viral IRES that could be efficiently translated was generated.Experimental evidence of translation has been previously shown , where Chttps://genome.ucsc.edu/, accessed on 8 September 2021). CIRCpedia uses a different nomenclature which includes the species and a number derived internally as an identifier for circBase. Furthermore, as previously demonstrated [As new biochemical/NGS protocols are emerging together with novel bioinformatic approaches, more evidence will be provided towards the distinction and classification of circRNAs, thus posing the need for a standard nomenclature, as circRNAs become essential biomarkers of disease. The current nomenclature derives from circBase, which includes the species and a numeric code, while circBank and circAtlas use the gene symbol of the transcript that results in the generation of a circRNA annotation based on the genomic coordinates from UCSC  ,58. DSB Upon DSB formation, two primary repair pathways are triggered, the Non-Homologous End Joining (NHEJ) and the Homologous Recombination (HR) pathway ,60,61. OThe HR pathway occurs during the S and G2/M phases of the cell cycle and engages the MRN complex (MRE11-RAD50-NSB1), which functions as an intermediate link between DSB formation and cell cycle checkpoint activation . It was BRCA1 C-Terminal (BRCT) domains are important modules mediating protein–protein interactions in DDRR pathways . BRCT doThe hereditary breast and ovarian cancer biomarkers BRCA1 and BRCA2 are also pivotal in DSB repair in the HR pathway . FollowiCSNK1G1 gene and demonstrated higher stability than the respective linear RNA [The expression profile of circRNAs in colorectal cancer tissues compared to their normal counterparts showed that hsa-circ-101555 was markedly elevated in tumor samples, correlating with patient prognosis . Hsa-cirnear RNA . Interesnear RNA . Mechaninear RNA . MDM2 gene [The involvement of circRNAs in the p53 pathway was examined in the HCT116, RKO and SW48 colorectal cancer lines, which were either left untreated or treated with a DNA-damaging agent . InteresDM2 gene . MDM2 isDM2 gene ,75. CircDM2 gene . In lineDM2 gene . In a study investigating the link between circRNAs and tumor grade in bladder cancer patients, a fraction of circRNAs  were differentially expressed among various tumor grades compared to the respective linear transcripts . The stuSMARCA5 gene, circ-SMARCA5 (hsa_circ_0001445) is downregulated in clinical breast cancer samples compared to normal control tissues [Another study demonstrated that, contrary to the  tissues . SMARCA5 tissues ,79. Ecto tissues . The stu tissues . RecQ-mediated genome stability protein 1 (RMI1) plays an important role in genome stability maintenance as part of the BLM-Topo IIIa-RMI1-RMI2 complex . RMI1 knA recent study in glioblastoma cells showed that low-dose radiation triggered the secretion of exosomes containing high levels of circ-METRN, which in turn led to increased γH2AX . This suA number of studies have shed light on the role of miRNAs, lncRNAs and circRNAs in regulating genotoxic responses triggered by various environmental or internal stimuli. An interactive network among circRNA, lncRNA and miRNA has been identified in response to lead-induced neurotoxicity ,87. MiR-Currently and to the best of our knowledge, it is unknown if circRNAs are generated during the transcription of components of the DDRR network. Moreover, if such DDRR-derived circRNAs are produced, important unmet issues are if and how they affect the expression of the genes they originate from and/or of other genes as well as their overall impact on a cell’s fate. http://www.circbase.org/, accessed on 8 September 2021). This database encompasses public circRNA datasets obtained from NGS of transcriptome from various tissues and mainly cell lines, which were processed for the potential presence of circular RNAs derived from back-splicing events from the genes of interest. Given that the DDRR network is vast, consisting of many components [To identify whether circRNAs can be produced from gene loci encoding DDRR components, we interrogated the circBase database  [http://soft.bioinfo-minzhao.org/circvar/, accessed on 8 September 2021) and the Cosmic  databases that have been reported in various malignancies, and examined their overlap with the circular RNA transcripts derived from the investigated DDRR coding genes. Interestingly, we noted a significant overlap of reported SNPs within the sequences of the DDRR-related circRNA  and miRNA binding sites that are present within the sequences of the circRNAs originating from the above DDRR hallmark genes er 2021)   and finally searched for candidate IRES elements that can promote translation using the IRESite tool   elements, and therefore the possibility that the generated circRNAs are translated, we first used the circRNAfasta files from circBase, next extracted the secondary RNA structure with Vienna (er 2021) . AlthougTo investigate the role of circRNAs in cellular (patho)physiology, 2D and 3D cellular systems are being employed as adequate investigation platforms. In the case of 2D cellular systems, inducible systems overexpressing DDRR-related genes, like the human bronchial epithelial cells with Tet-ON inducible expression of the CDC6 replication licensing factor (HBEC-CDC6 TET-ON) , are excSimilarly, 3D cellular systems have already been implemented in circRNA research, such as iPSC-derived brain organoids . In thosCdr1ascircRNA locus that massively binds miR-7 and miR-671, displayed impaired sensorimotor gating, causing neuropsychiatric disorders from the inability of these animals to filter out unnecessary information [Other systems enabling the study of circRNAs are comprised by animal models in which loci encoding these molecules can be manipulated. Specifically, mice with a knockout of the ormation . Anotherormation . Of noteCircRNAs represent an abundant and highly expressed group of regulatory RNAs, as depicted in several normal adult and fetal human tissues . Moreove


N6-methyladenosine (m6A), the most prevailing modification in messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs) of higher eukaryotes, has attracted increasing attention. Growing studies have verified the pivotal roles of m6A methylation in controlling mRNAs and ncRNAs in diverse physiological processes. Remarkably, recent findings have showed that aberrant methylation of m6A-related RNAs could influence cancer metastasis. In this review, we illuminate how m6A modifiers act on mRNAs and ncRNAs and modulate metastasis in several cancers, and put forward the clinical application prospects of m6A methylation.Cancer metastasis is a symptom of adverse prognosis, a prime origin of therapy failure, and a lethal challenge for cancer patients.  Currently, the mechanism of m6A methylation on cancer metastasis remains elusive. Herein, we reviewed the progress of m6A-related mRNAs and ncRNAs in cancer metastasis, supplemented the profound pathways between methylated RNAs and cancer metastasis, and proposed the clinical value of m6A members as cancer biomarkers and therapeutic targets.Distant and multiple organ metastasis seriously reduces the overall survival of cancer patients –25. Comp6A, one of the most ubiquitous epigenetic modification in eukaryotic RNA, plays profound roles in many biological development  was found to be the largest molecule in the m6A methyltransferase complex (in vitro and in vivo (.The existing reports of methyltransferases are primarily focusing on methyltransferase-like 3 (METTL3), methyltransferase-like 14 (METTL14) and KIAA1429, which play a dominant role in the regulation between mtastasis , 41–43. tastasis , 44–49. tastasis . In canctastasis , 21, 51.tastasis . KIAA142 complex . Silenciin vivo . In addiin vivo –58  mediated methylation enhances the translation of mRNA. The binding of YTH N6-methyladenosine RNA binding protein 2 (YTHDF2) to m6A site increases mRNA degradation  cells in vitro and in vivo  and ENO2 expression, followed by the acceleration of glycolysis in gastric cancer (GC) cells, which subsequently results in liver metastasis   . Meanwhi (EpCAM) . Exposedf NANOG . Accordif NANOG  of aldehf NANOG  were sigf NANOG .6A in metastasis mechanism involves EMT, glycolysis, angiogenesis and cancer cell stemness, which may jointly provide distinguished indicators and drug intervention targets for the individualized diagnosis and medical care.Together, the discovery of m6A methylation on mRNAs and facilitates cancers invasion, metastasis, and drug resistance  (MYC)  serves as the substrate  methylation  , Myc pro1) (MYC) , and POU to act on the translation initiation factor and improve the translation efficiency  on the Forkhead box protein M1(FOXM1) transcript. Interestingly, FOXM1AS accommodates the interplay between ALKBH5 and FOXM1   . Since RA manner . In brie6A modification might provide new drug targets for the accurate therapy and early prevention of CRC metastasis.Accumulating studies have confirmed that CRC is no longer a single tumor type, and it is more accurate to describe it as a group of heterogeneous diseases due to genetic and epigenetic changes . These rSOCS2 mRNA and inhibits the chronic inflammation  drives the proliferation and metastasis of breast cancer. HBXIP upregulates METTL14 by inhibiting microRNA(miRNA) let-7g. However, METTL14 increases the expression of HBXIP, thus forming an HBXIP/let-7g/METTL14/HBXIP loop  is an endemic disease associated with Epstein-Barr virus infection, genetic element and environmental factors in Southeast Asian countries, and is also a high incidence in southern China , 155. Eapatients . Consenspatients . Thereby6A methylation regulators are posed as efficacious pharmacological targets for anti-cancer drug in solving clinical problems. To illustrate, METTL3 gives rise to unfavorable prognosis by maintaining the stable expression of SOX2 in CRC and glioma  and suppress GSC growth , Key Scientific Research Projects of Institutions of Higher Education in Henan Province (19A310024), The Medical Scientific and Technological Research Project of Henan Province (201702027), Youth Innovation Fund Project of The First Affiliated Hospital of Zhengzhou University (YNQN2017035), The China Postdoctoral Science Foundation , The National Natural Science Foundation of Henan Province (182300410342), and The Health Commission Technology Talents Overseas Training Project of Henan Province (2018140).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Leviviricetes class. 11% of viral contigs were found to be most closely related to members of the Ourmiavirus genus, suggesting that members of this clade of plant viruses may be far more widely distributed and diverse than previously thought. These results contrast with soil DNA viromes which are typically dominated by bacteriophages. RNA viral communities, therefore, have the potential to exert influence on inter-kingdom interactions across terrestrial biomes.The distribution and diversity of RNA viruses in soil ecosystems are largely unknown, despite their significant impact on public health, ecosystem functions, and food security. Here, we characterise soil RNA viral communities along an altitudinal productivity gradient of peat, managed grassland and coastal soils. We identified 3462 viral contigs in RNA viromes from purified virus-like-particles in five soil-types and assessed their spatial distribution, phylogenetic diversity and potential host ranges. Soil types exhibited minimal similarity in viral community composition, but with >10-fold more viral contigs shared between managed grassland soils when compared with peat or coastal soils. Phylogenetic analyses predicted soil RNA viral communities are formed from viruses of bacteria, plants, fungi, vertebrates and invertebrates, with only 12% of viral contigs belonging to the bacteria-infecting  Viruses are the most common and diverse biological entities on Earth  and can Riboviria and possess a universally conserved RNA-dependent RNA polymerase (RdRP) gene , 19Negarathogens , howevern plants .in-situ soil RNA viral community of soil ecosystems. We detected 3462 viral contigs across five sample sites, and observed site-specific variation in viral contig relative abundance. The viral contigs we detected are predicted to be from viruses of a range of hosts, including fungi, bacteria, vertebrates, invertebrates and plants. Therefore, RNA viruses have the potential to influence the grassland soil ecosystem at multiple trophic levels. From a technical standpoint, further development of both wet-lab and bioinformatics techniques is needed to further improve the detection and study of soil RNA viruses. Many RNA viruses have segmented and multipartite genomes, complicating the recovery of full RNA viral genomes from meta-transcriptomics and metaviromics datasets. This study found comparatively fewer putative mycoviruses compared to a previous study [Using an altitudinal primary productivity gradient as a source of soils with contrasting ecological properties for RNA virome analysis, this study is the first to apply a direct viromics approach to examine the us study  examininus study , the lacCaudoviricetes, the balance in RNA viromes is heavily skewed towards eukaryotic viruses [Whilst environmental DNA viromes are typically dominated by viruses of the prokaryote-infecting class  viruses . There a viruses , 64. Ref viruses  and so HThe impact that soilborne RNA viruses have on their host organisms has only just started to be explored, and future work is needed to establish the many influences they may have on global terrestrial ecosystems. Grassland soil bacterial communities show clear responses to the effects of climate change that are mediated by plant–soil–microbial interactions  and viruSupplementary Information


Millions of people worldwide are affected by neurodegenerative diseases (NDs), and to date, no effective treatment has been reported. The hallmark of these diseases is the formation of pathological aggregates and fibrils in neural cells. Many studies have reported that catechins, polyphenolic compounds found in a variety of plants, can directly interact with amyloidogenic proteins, prevent the formation of toxic aggregates, and in turn play neuroprotective roles. Besides harboring amyloidogenic domains, several proteins involved in NDs possess arginine-glycine/arginine-glycine-glycine (RG/RGG) regions that contribute to the formation of protein condensates. Here, we aimed to assess whether epigallocatechin gallate (EGCG) can play a role in neuroprotection via direct interaction with such RG/RGG regions. We show that EGCG directly binds to the RG/RGG region of fused in sarcoma (FUS) and that arginine methylation enhances this interaction. Unexpectedly, we found that low micromolar amounts of EGCG were sufficient to restore RNA-dependent condensate formation of methylated FUS, whereas, in the absence of EGCG, no phase separation could be observed. Our data provide new mechanistic roles of EGCG in the regulation of phase separation of RG/RGG-containing proteins, which will promote understanding of the intricate function of EGCG in cells. Neurodegenerative diseases (NDs) affect millions of people worldwide and therefore constitute a major public health threat. NDs occur when nerve cells in the brain or peripheral nervous system lose their function over time, leading to cellular death . The halSeveral studies have demonstrated the role of green tea catechins in neuroprotection, with epigallocatechin gallate (EGCG) being widely studied because it constitutes 65% of all catechins in green tea ,9,10,11.RGG3PY), thereby modulating its propensity to phase separate.To this end, we chose fused in sarcoma (FUS) as a model protein. FUS contains a N-terminal SYGQ-rich domain, with prion-like properties, as well as several RG/RGG-rich regions in its C-terminal disordered tail A. Both tRGG3PY and that FUS phase separation was inhibited by methylation. Unexpectedly, arginine methylation of FUS enhanced its EGCG affinity by several orders of magnitude, and EGCG restored RNA-driven phase separation of methylated FUS at low micromolar concentrations.Here, we used nuclear magnetic resonance (NMR) spectroscopy to obtain molecular details on EGCG binding to FUS and its regulation by FUS arginine methylation and RNA binding. We used turbidity assays and differential contrast microscopy (DIC) in order to decipher the role of EGCG, RNA, and arginine methylation on FUS condensate formation. We found that EGCG directly interacts with the RGG3-PY region of FUS in a non-specific and arginine methylation-independent manner, leading to the formation of FUS condensates at high micromolar EGCG concentrations. We uncovered that RNA can directly interact with both methylated and unmethylated FUSIn summary, our data provide unexpected evidence that EGCG can compensate for the inhibitory effect of arginine methylation on RNA-driven phase separation of RG/RGG regions. Although further studies are required to investigate the physiological significance of EGCG in the regulation of condensate formation, our studies shed light on the new mechanistic functions of EGCG that will promote a better understanding of the various roles of EGCG in human cells.6-protein A-tagged FUSRGG3PY (amino acids 454–526), His6-protein A-tagged FOXO4CR3 (amino acids 454–505), and His6-protein A-tagged axin-1 (amino acids 351–500) containing a tobacco etch virus (TEV) protease cleavage site after protein A were expressed from codon-optimized synthetic genes inserted into pETM11-based vectors . For the expression of recombinant protein, the construct was transformed into E. coli BL21(DE3) Star cells, and grown in a standard lysogeny broth (LB) medium  at 37 °C until an optical density (OD 600 nm) value has reached 0.6–0.8. Then, cells were induced with 1 mM isopropyl β-d-1-thiogalactopyranoside (IPTG),  and grown for 16 h at 20 °C. For the purpose of NMR experiments, 10 mL overnight precultures were transferred to minimal media 6Mo7O24, 17 mM EDTA) 6Mo7O24 from VWR International bvba, Leuven Belgium, CoCl2 from AppliChem GmbH - An ITW Company, Darmstadt, Germany) supplemented with 1 g of 15NH4Cl , and either with 6 g of unlabeled glucose or 2 g of 13C6H12O6 , followed by a growth as described for unlabeled protein. Cells were harvested , transferred to a denaturing lysis buffer , and sonicated . His6-protein A-tagged FUSRGG3PY, FOXO4CR3 and axin-1 were purified using nickel-nitrilotriacetic (Ni-NTA) agarose resin  and eluted into buffer containing 50 mM Tris-HCl pH 7.5, 1 M NaCl, 500 mM imidazole, 2 mM tris(2-carboxyethyl)phosphine (TCEP) , 0.04% NaN3. The eluted protein was desalted to buffer containing 50 mM Tris-HCl pH 7.5, 150 mM NaCl, 20 mM imidazole, 2 mM TCEP, 0.04% NaN3, and subjected to overnight TEV treatment at 4 °C. The cleaved FUSRGG3PY, FOXO4CR3, or axin-1 were then isolated by a second affinity purification using Ni-NTA beads. A final size-exclusion chromatography purification step was performed in the buffer of interest with the use of Superdex 75 Increase 10/300 GL column  at room temperature. Fractions corresponding to FUSRGG3PY, FOXO4CR3, or axin-1 were identified by SDS PAGE gel and either used immediately for experiments or stored at −80 °C until further use.Recombinant His6-PRMT1 (amino acids 11–353) was inserted into a pET28b-His6 vector , and the expression has been previously described - or -labeled FUSRGG3PY. All spectra were processed using TopSpin 4.0.9, and heteronuclear spectra were analyzed with the use of NMRFAM-Sparky 3.114, [CR3 titrations, were performed using protein samples prepared in 50 mM sodium succinate pH 6.0, 150 mM NaCl, 2 mM TCEP, 0.04% NaN3, and 10% (v/v) deuterium oxide  was added for the lock signal in all samples. Axin1-EGCG titrations were performed in 50 mM NaH2PO4/Na2HPO4 pH 6.5, 150 mM NaCl, 2 mM DTT and 10% (v/v) deuterium oxide. FOXO4 CR3-EGCG titrations were performed in 50 mM NaH2PO4/Na2HPO4 pH 6.5, 2 mM DTT and 10% (v/v) deuterium oxide. The following experiments were recorded: 1H-15N HSQC, 1H-13C HSQC, 1H-1H NOESY, heteronuclear 15N{1H} NOE, (H)CC(CO)NH, CBCA(CO)NH, HN(CA)NNH(N), and HN(CA)NNH(H). The assigned spectra were uploaded to Biological Magnetic Resonance Bank and are available under the accession codes 51314 and 51315. For the chemical shift perturbation (CSP) analysis, normalized values of FUSRGG3PY/meFUSRGG3PY 1H-15N cross-peaks upon binding to RNA and/or EGCG were calculated according to the following formula:Hδ corresponds to the 1H chemical shift difference between FUSRGG3PY/meFUSRGG3PY 1H-15N cross-peaks in the bound and free states, and Nδ denotes the 15N chemical shift difference between FUSRGG3PY/meFUSRGG3PY 1H-15N cross-peaks in the bound and free states, respectively.All NMR experiments were conducted at 25 °C on a Bruker 600 MHz spectrometer  equipped with a TXI probe equipped with z-axis gradients and using between 30 and 600 µM of [U-y 3.114,  and CcpNy 3.114,  softwareRGG3PY and PRMT1 to perform in vitro methylation. Using non-methylated FUSRGG3PY and methylated FUSRGG3PY (meFUSRGG3PY) samples, we carried out solution-based characterization of the (me)FUSRGG3PY interactions with EGCG and RNA as well as their implications in liquid-liquid phase separation.We successfully expressed and purified recombinant FUS15N isotope-labeled FUSRGG3PY in the absence or presence of EGCG. Stepwise addition of EGCG resulted in a progressive line broadening and chemical shift perturbation (CSP) of several 1H-15N FUSRGG3PY cross-peaks  [RGG3PY using NMR spectroscopy and then assess the effect of FUSRGG3PY arginine methylation on EGCG binding. We, therefore, carried out in vitro arginine methylation of FUSRGG3PY using purified recombinant PRMT1 and tested the effect of arginine methylation on EGCG binding employing NMR spectroscopy.Several studies have shown that FUS can be specifically methylated on several arginines by the protein arginine  (PRMT1) ,33,34. HRGG3 depending on their degree of methylation in three groups: (a) unmethylated arginines , (b) partly methylated arginines  (c) methylated arginines   B. ArginiUSRGG3PY . The obsRGG3PY alters its binding with EGCG, we performed an NMR-based titration by stepwise addition of EGCG in a solution containing meFUSRGG3PY. This resulted in a progressive line broadening  RNA binding affects EGCG binding of methylated or unmethylated FUSRGG3PY, respectively. We first tested the effect of FUSRGG3PY arginine methylation on RNA binding using NMR spectroscopy. Stepwise addition of (UG)12 RNA to solutions of either 15N-labeled methylated or unmethylated FUSRGG3PY caused CSPs of several 1H-15N (me)FUSRGG3PY cross-peaks , including FUS, CIRBP, and hnRNP family proteins ,25,36,37ss-peaks A–C. In aUSRGG3PY C. These RGG3PY with increasing amounts of EGCG using a fixed concentration of (UG)12 RNA. In both titrations, we observed CSPs and line broadening of several 1H-15N (me)FUSRGG3PY cross-peaks  of 21.7 ± 9.6 µM  and monomethylarginine (MMA) by adding two or one methyl group, respectively, to one of the nitrogen atoms of the arginine guanidinium group [1H NMR spectra physiological aggregates in cells ,27,28,29ormation A.RGG3PY and that methylation of full-length FUS impairs FUS LLPS in vitro and stress granules association in cells [RGG3PY to phase separate in the presence of RNA. Previous studies identified a higher LLPS propensity of unmethylated FUSRGG3PY with the presence of RNA [12 RNA led to a progressive increase in turbidity of FUSRGG3PY, it did not affect meFUSRGG3PY turbidity 12 RNA, at which FUSRGG3PY showed phase separation, and added increasing concentrations of EGCG to FUSRGG3PY or meFUSRGG3PY, respectively. For unmethylated FUSRGG3PY, the turbidity remained unchanged up to concentrations of 100 µM EGCG  had any impact on methylated FUS turbidity, this observation strongly suggests that EGCG restores phase separation of meFUSRGG3PY in the presence of RNA.Next, we aimed to unravel the effect of EGCG on RNA-driven FUS µM EGCG C. This rurbidity A. FurtheRGG3PY and meFUSRGG3PY in the presence or absence of 7.5 µM RNA and in the presence or absence of 25 µM EGCG, respectively. In line with the turbidity assays, no condensate formation could be observed for both FUSRGG3PY and meFUSRGG3PY after the addition of 25 µM EGCG 12 RNA led to the formation of small condensates that increased in size and number over time, as previously described [RGG3PY droplets  of FUS µM EGCG D, confirescribed  (Figure RGG3PY has no effect on its propensity to phase separate in the presence of RNA. In contrast, while FUSRGG3PY phase separation in the presence of RNA is impaired upon PRMT1-mediated FUS arginine methylation, it is restored by EGCG.To summarize, EGCG binding to unmethylated FUS1H-15N cross-peaks upon EGCG addition. Despite the non-specific trend of binding, we observed that EGCG seems to preferentially bind to the RG/RGG stretches within the FUS RGG3-PY region, as shown by the overall higher CSPs of these regions compared with non-RG-rich regions (In this study, we found that EGCG directly binds to the RGG3-PY region of FUS, with a non-specific/low-affinity behavior as observed by a linear increase in CSPs of FUS  regions F. The pr regions .RGG3PY, but it remains unclear how RNA binding to methylated RG/RGG regions in FUS could create a specific binding interface for EGCG. Binding of methylated arginines to Tudor domains that act as “methylation readers” has been described recently [In contrast, we observed that PRMT1-mediated methylation of the FUS RGG3-PY region in association with the presence of RNA allows specific binding of EGCG with low micromolar affinity. We showed that RNA can also directly interact with meFUSrecently . In protrecently ,44. TherEGCG function in neuroprotection is primarily associated with its inhibitory effect on amyloidogenic protein aggregation, redirecting the self-association of amyloidogenic peptides from toxic aggregates/amyloid fibrils to stable, non-toxic oligomers ,21,45,46Our data propose unexpected new functions of EGCG in the regulation of biomolecular condensate formation, targeting one of the most abundant regions in the human proteome. Through this, our work might set the base to promote a better understanding of the EGCG effect in neuroprotection, as well as putative other disorders linked to misregulation of RG/RGG-containing proteins.


RNA editing exerts critical impacts on numerous biological processes and thus are implicated in crucial human phenotypes, including tumorigenesis and prognosis. While previous studies have analyzed aggregate RNA editing activity at the sample level and associated it with overall cancer survival, there is not yet a large‐scale disease‐specific survival study to examine genome‐wide RNA editing sites’ prognostic value taking into account the host gene expression and clinical variables.In this study, we solved comprehensive Cox proportional models of disease‐specific survival on individual RNA‐editing sites plus host gene expression and critical demographic covariates. This allowed us to interrogate the prognostic value of a large number of RNA‐editing sites at single‐nucleotide resolution.ZNF264 indicates poor survival of uterine corpus endometrial carcinoma, with a hazard ratio of 2.13 and an adjusted p‐value of 4.07 × 10−7. Some of these prognostic RNA‐editing sites mediate the binding of RNA binding proteins and microRNAs, thus propagating their impacts to extensive regulatory targets.As a result, we identified 402 gene‐proximal RNA‐editing sites that generally predict adverse cancer survival. For example, an RNA‐editing site residing in In conclusion, RNA editing affects cis‐regulatory elements and predicts adverse cancer survival. RNA editing events have been shown to be associated with cancer, and drug sensitivity. We show that for the first time, RNA editing level is associated poor cancer prognosis after adjusting for host gene expression and other phenotypical variables. Of all 12 possible types of nucleotide substitutions, adenine‐to‐inosine (A‐to‐I) editing prevails with a clearly defined biological mechanism. In more detail, A‐to‐I RNA‐editing is mediated by adenosine deaminase acting on RNA (ADAR)AZIN1 in hepatocellular carcinoma can trigger more aggressive tumors by causing higher cell proliferation through the neutralization of antizyme‐mediated degradation of ornithine decarboxylase and cyclin D1.RNA editing has the potential to impact cellular processes and affect diseases such as cancers. For example, Peng et al. demonstrated experimentally that nonsynonymous A‐to‐I RNA‐editing can result in alternative protein sequences,The research field witnessed sporadic studies of RNA‐editing’s cancer prognostic value,We collected 99,071 distinct A‐to‐I RNA‐editing sites originating from patient samples of various cancers and explored their prognostic value with proper adjustment of crucial covariates. First, we showed that RNA‐editing level is positively associated with the expression of the host gene. This finding validated our intuitive hypothesis and justified our deliberate adjustment of gene expression in the next‐step survival analysis. Several thoughtful survival models were applied to nearly a hundred thousand A‐to‐I RNA‐editing sites, and, after multiple test adjustments, we fetched 402 gene‐proximal RNA‐editing sites that were significantly associated with survival, mostly in the adverse direction. Some of these prognostic RNA‐editing sites disrupted regulation cascades by modifying RBP binding sequences or miRNA‐matching 3′‐UTRs.22.1Technically, RNA‐editing events usually are detected with RNA‐Seq data. The RNA‐editing‐occurring genomic position is recognized as an RNA‐editing site (RES). A‐to‐I RNA‐editing events originating from RNA‐Seq data of The Cancer Genome Atlas (TCGA) were obtained as supplementary data from previous studies.We leveraged ANNOVARFrom Genomics Data Commons, we downloaded TCGA RNA‐seq gene expression data in the format of fragment per kilobase million; from the same source, we obtained clinical covariate information for the TCGA patient cohort, including age, gender, and cancer stage. From TCGA Pan‐Cancer Clinical Data Resource,2.2By definition, an RNA‐editing event must involve a RES, and one same RES may be involved in different RNA‐editing events where different subjects or patient cohorts were concerned. Therefore, we defined RNA‐editing level for a RES in one subject, RNA‐editing frequency for a RES in one cohort , and RNA‐editing density for a gene that hosts multiple RESs.L) of RES i in subject j (of cohort C) was expressed as the ratio of edited reads +) over total reads +andR‐). Edited reads support alternative, non‐reference allele at the particular genomic position. RNA‐editing level took value over interval .As indicated in Equation , RNA‐ediF) of RES i in a cancer type‐specific cohort (denoted as C) was expressed as the ratio of subjects showing RNA‐editing at RES i over total cohort size. RNA‐editing frequency took value over interval . In all cancer types and all genomic regions, we did observe RESs with a full frequency (1).As indicated in Equation , RNA‐ediD) of gene g was expressed as the rate of RESs within their host gene body, expressed as the number of RESs with non‐zero RNA‐editing level (in any subject of any cohort) divided by the length of the host gene. Here, G designated the gene body of the host gene g, interpretated as a set of continuous sites, and therefore ||G|| designates the gene length in nt; i was the location identifier of an RES, with i∈G meaning gene g was the host gene of RES i. I*(x) designated the indicator function where a logic expression x was evaluated and either 1 or 0 was returned. RNA‐editing density took value over interval .As indicated in Equation , RNA‐edi2.3L) on host gene expression (E), one continuous (Equation (L∗) that followed the standard normal distribution; in the binary model (Equation (L′). Logistic function was denoted as logit(x). Gene expression value was processed from initial fragment per kilobase million values with a state‐of‐the‐art protocol including logarithm and normalization. Linear regression and logistic regression were conducted to solve the coefficient (b) in the models.We conceptualized two models of dependence of RES level  was explained by RES level (L) only; in an intermediate model (Equation E); in the most comprehensive model (Equation Stg), age (Age), and sex (Sex). These Cox models were resolved within each cancer type separately, where data of all subjects relevant to one‐specific RES were utilized to resolve the coefficients . Of note, for gender‐specific cancer types , the sex term (csex·Sex) was omitted in the most comprehensive model . Because RES‐gene dependence analysis and survival analysis were conducted for each individual RES repeatedly, multiple‐test correction was applied to the 2.4When RNA‐editing takes place in cis‐regulatory elements, regulation of gene expression may be affected and the impact of a RES may be propagated to a large number of regulatory targets.2.5PID,p‐values. Adjusted p‐value <0.05 was considered statistically significant.A set of 379 cellular pathways,A series of methods are available to assess the functional impact resulting from a variation at a particular genomic position. These methods are generally based on multiple sequence alignment within a protein family, presuming that positions with a low conservation rate are likely to tolerate a mutation while positions with a high conversion rate are likely to be intolerant to a mutation. In light of such a evolutionary perspective, editing impact was predicted for each RES in our final report set, using eight algorithms: SIFT,33.1An overall description of the nearly one hundred thousand RESs, including information on TCGA cancer types, was provided in Table Considering the raw number of RESs, 3′‐UTR and intronic region are the most noteworthy genomic regions, as 44.2% of the 99,071 RESs occurred in 3′‐UTR regions followed by 30.7% in introns Figure . The heaIn light of RNA‐editing frequency Equation , 3′‐UTR HNRNPA1L2, SPN, and POLR2J4. HNRNPA1L2 is known to fuse with SUGT1 in cervicalSPN, a regulatory subunit of PP1A, is a known tumor suppressor.POLR2J4 is a non‐coding RNA and has recently been shown to be associated with survival in hepatocellular carcinoma by two independent studies.Lastly, we examined RNA‐editing density Equation  for gene3.2A majority of current methods seek to detect RNA editing from RNA‐seq data, where read counts for an allele are inherently correlated with gene expression. As reflected in the definition Equation , RNA‐edi3.3For gene‐proximal RESs, we modeled patients’ disease‐specific survival with three gradually more comprehensive Cox models: (1) Survival ~RNA‐editing level Equation ; 2) Sur Sur2). Wp‐value are available in Table The sign of (logged) RES coefficient, or hazard ratio, in the Cox model Equation  instructWe also exerted another procedure to validate the prognostic value of these recommended RESs, where we computed C‐index between two alternative models, one with RES term Equation  and the TRAPPC4 located at chr11:118,893,191  was associated with poor survival in kidney renal clear cell carcinoma (KIRC) (adjusted p = 0.0051)  (adjusted p = 0.0173)  (adjust p = 0.0159)  (adjust p = 0.0137)  Figure . There h) Figure . SPC24 h) Figure . SNORA40) Figure . DCAF16 For gene‐distal RESs which are located in the intergenic region, since they could not be allocated to a nearby host gene, the disease‐specific survival was modeled with a combination of RES and applicable clinical variables Equation . As a re3.4SOD2 caused a gain of binding sequence for RBP SRSF1  and statistically significant . Each RES that was applicable to a mutation impact prediction algorithm was assessed with a predicted functional impact score, and the average functional impact scores of the two separate groups were compared. For all attempted prediction algorithms except FATHMM, the average functional impact score of the significant group was higher than that of the non‐significant group  and liver hepatocellular carcinoma (adjusted p = 0.01). This pathway mediates estrogen receptor‐regulated gene transcription in breast cancerTP53 inactivation, DNA repair deficiency in ER‐positive, Her2‐negative breast cancer.EPHA2 forward signaling pathway which was significant in KIRC (adjusted p = 0.0008). EPHA2 expression has been shown positively associated with tumor size and Fuhrman nuclear grade in KIRCUsing the 402 significant RESs’ host genes, we conducted pathway enrichment analysis by cancer and identified 15 significant pathways after adjusting for false discovery Figure . Many ofAZIN1, BLCAP, COG3, COPA, FLNB, GRIA2, and NEIL1). In Table p‐values because we were focusing on a small list of predefined RESs. The editing level of 15 RESs had significant correlations with their host gene expression. One of these 15 RESs had significant prognostic values (COG3 I635V in LUSC). Of the other 11 RESs, four had significant prognostic values after adjusting for host gene expression and clinical variables . This analysis demonstrates that RESs with true association with cancer prognosis can endure rigorous statistical adjustment for host gene expression and basic clinical variables.Our initial RES data was previously analyzed by Han et al.4Previous studies touched upon RNA‐editing's implication in human diseases, but have not well elucidated RNA‐editing's involvement in cancer prognosis. In this study, we adopted a quantitative perspective to study RESs in 17 cancer types at single‐nucleotide resolution, addressing RES genomic distribution, RES‐gene correlation, RES survival association, and RES regulatory mechanism.The foremost interesting finding is that the highest RNA‐editing level is observed in splicing junctions, in any cancer type or the pan‐cancer scope. Previous studiesFor the first time, we demonstrated that RNA‐editing level tends to be positively correlated with host gene expression. This finding may be intuitive, but it casts doubt on RES analysis results where host gene expression is not properly adjusted – association relations identified through unadjusted RNA‐editing level may merely be reflecting the latent effect of host gene expression. Thus, we recommend that any analysis that revolves around RNA‐editing level should consider adjusting for host gene expression. In our survival analyses in this work, we elected to adjust for host gene expression and other available demographic covariates. After multiple‐test correction, we identified 402 gene‐proximal RESs that were significantly associated with disease‐specific survival in 11 cancer types. An overwhelming majority of these 402 significant RESs were associated with poor survival .The current work consolidated RNA‐editing’s crucial involvement in cancers. This was grounded in multiple lines of evidence. First, we showed RNA‐editing level has prognostic value for hundreds of RESs in a wide range of cancers. Second, a majority of the prognostic RESs exert functional repercussion by altering RBP/miRNA binding sequences, and many targets of these RBPs/miRNAs had known cancer relevance. Thirdly, quite a few cancer‐related cellular pathways emerged in the functional enrichment analysis of prognostic RESs’ host genes. Lastly, our refined prognostic RESs generally reside in more evolutionarily conserved genomic locations than the other RESs that failed our rigorous survival model analysis.Like the index of mutational burden, RNA‐editing number or level can be measured in aggregate as another sample‐level index. While early sporadic studiesNone.YMW and YG wrote the manuscript; YMW, YG, and HY performed formal analysis; TG supervised the project.All data used in this study are de‐identified public data. No ethical approval was a need from the institutional review board.Table S1Click here for additional data file.Table S2Click here for additional data file.Table S3Click here for additional data file.Table S4Click here for additional data file.Table S5Click here for additional data file.


In the past, deception detection research has explored whether there were specific personal characteristics that were related to lying and found that factors such as personality and morality are indeed related to lying. However, past research has usually focused on a variable-centered approach. Yet, a person-centered might be more suitable here as it allows for the study of people in an integrative manner. In this experiment, 673 students completed a questionnaire which included measures of the five factors of personality, the level of moral disengagement, the perceived cognitive load when lying, lying strategies, frequency of lying and the LiES scale, a tool measuring the tendency to tell self-serving, altruistic and vindicative lies. We performed a Latent Profile Analysis to integrate personality, moral disengagement, and perceived cognitive load scores into specific profiles. Then, we related profile membership to lying behavior. We obtained four profiles, and found that extraversion, moral disengagement, and the perceived cognitive load contributed most to profile differences. We also found that lying frequency did not differ across profiles, whereas lying tendency did. In conclusion, our results suggest that several facets of the individual play a joint role in lying behavior, and that adopting a person-centered approach might be a good strategy to explore the role of interpersonal differences in lie detection research. Lying is a common human behavior , and peoAlthough people find lying deplorable , researcFurther, factors other than personality also influence deceptive behavior. Indeed, interpersonal differences have been found to influence both senders and receivers in lie detection tasks , 2019. MThe cognitive load experienced when lying is another important factor. It has been suggested that, in general, people experience a higher cognitive load when lying than when telling the truth , a resulSummarizing, the available literature suggests an association between deceptive behavior and both personality and moral disengagement, but it has not explored how they interact nor how they relate to lying. Yet, such interactions, are important and can be studied via the person-centered approach. According to The person-centered approach has already been applied in marketing research , dementiThe aim of this study was to explore how the five factors of personality , the levWe did not have clear expectations concerning how many profiles would emerge, what would be the pattern of their scores on personality, moral disengagement, and the perceived cognitive load when lying, and what association the profiles would have with lying strategies. Indeed, (a) to the best of our knowledge, no previous research explored the interaction or relationship between such variables; (b) the correlational pattern of such variables in the whole sample are not very informative when using the person-centered approach, as different profiles are characterized by different correlational pattern, and (c) it is not possible to know such patterns within each profile before the profiles are obtained.However, research shows that there are both people with a high tendency to lie (sometime referred to as “prolific liars”) and people with low tendency to lie . FurtherTo provide an example, the (cor)relation between moral disengagement and extraversion may be different between individuals. Assuming that high moral disengagement and low extraversion are related to higher tendency to lie, those who show this pattern may be more likely to lie than those who score high on both or those who score low on moral disengagement and high on extraversion. Similarly, people with high moral disengagement and high perceived cognitive load when lying may have a lower tendency to lie than people who show high moral disengagement but low perceived cognitive load. Indeed, the first may be morally disengaged when lying, but they could still avoid it when possible if they feel that it is too cognitively demanding for them. On the contrary, those who are again morally disengaged but find lying cognitively simple, may lie more.Building on the above, we predicted that at least one profile would be marked by a combination of variables associated with high tendency to lie  (H1a) and one would be marked by a combination of variables associated with a lower tendency to lie  (H1b).Further, we predicted that: (i) the more a profile included characteristics associated with lying, the more frequently participants in that profile would lie (H2); (ii) profiles marked with a combination of variables associated with high tendency to lie would be associated with telling more vindicative and self-serving lies than the other profiles (H3).Unfortunately, due to the lack of previous research reported above, it is not possible to state more precise predictions, and indeed liberal, rather than strict, hypotheses are common in person-centered approaches .M = 32.63, SD = 15.14). About 73% were female, and about 26% were male. About 57% were students (about 17% of them were working students), about 1% was unemployed, about 5% were retired from their work and about 35% were active workers.The only inclusion criterion was that the participant must be over 18. In total, 673 participants took part in the study. Age ranged from 18 to 91 years old , euphemistic labeling , advantageous comparison , displacement of responsibility , diffusion of responsibility , disregarding the consequences , dehumanization , and attribution of blame (the victim is seen as responsible for the behavior adopted by the perpetrator). The total level of moral disengagement was calculated by averaging the scores of the 32 items.Morality was measured via the Moral Disengagement Scale, consisting of 32 items exploring the eight dimensions of moral disengagement  to five (very much). We decided to measure the perceived, rather than the actual, level of cognitive load because we were interested in what the participants experience when lying. Indeed, our prediction is that perceiving lying a cognitively demanding task may act as a deterrent of lying , “I lie in order to make people feel better” , and “I lie for revenge” (vindicative lies). The scores for each factor are calculated by averaging the answers to the items concerning each specific dimension. Due to the lack of validation of this scale in Italian language, the internal consistency of the three factors was calculated and a confirmatory factor analysis was run.For what concern the dependent variables, lying tendency was measured via the LiES scale, consisting of 16 statements which the participants rate from 1 (strongly disagree) to 7 (strongly agree). This tool examines three dimensions concerning lying behavior: self-serving lies, altruistic lies and vindicative/antisocial lies . Some ex“keeping the statement clear and simple,” “telling a plausible story,” “avoidance,” “embedding the lie,” “providing unverifiable details,” “matching the type of details between lies and truths,” “reporting from previous experiences,” “matching the amount of detail between lies and truths,” “use complete fabrications,” “other strategies.” Further, following For what concerns lying strategy, research suggests that people strategize and adopt information management in order to increase the odds of being believed when lying . People In the same way as done in previous research , particiFirst, due to the need for a translation, for some items, an Italian researcher with high proficiency in English who was unaware of the aims of the experiment translated the LiES scale and the definition of lying strategies of Then, an online Google Modules survey was shared with university students and on social media. After providing informed consent, the participants read an explanation of their task and then completed the survey. Participation was voluntary, no reward was offered. Filling the questionnaire took about 12 min. The study was conducted in accordance with the Declaration of Helsinki  and withf at 0.25, as we were interested in at least moderate effect sizes. We run two a priori power analyses: one assuming two emerging profiles (needed N = 210) and one assuming five . However, analyses such as LPA require large sample sizes , based on participants’ scores on the I-TIPI-R, the Moral Disengagement Scale and on the perceived cognitive load when lying. The aim was to obtain homogeneous groups and to explore whether they differed quantitatively and/or qualitatively. We evaluated five different models, from a one profile solution thorough a five profiles solution. As an estimator, we selected Robust Maximum Likelihood . With LPThe association between profile membership and the categorial preferred lying strategy was explored by contingency tables analyses.Post hoc analyses were conducted with Bonferroni correction. Last, to further illustrate the pros of the variable-centered approach, we conducted a multivariate general linear model with the five factors of personality, the level of moral disengagement, the perceived cognitive load when lying and gender as predictors and the LiES scores as the outcome variables.The association between profile membership and: (i) the tendency to tell altruistic, antisocial, and self-serving lies; (ii) the perceived importance of verbal lying strategies; (iii) the perceived importance of non-verbal lying strategies; and (iv) the frequency of various forms of lying was explored via MANOVAs and subsequent univariate tests. Screening of the data showed that, by adopting I do not lie in order to intentionally harm people”) was problematic. When excluding this item, internal consistency raised to 0.81. Hence, we excluded this item from the calculation of the vindicative lies factor. We then conducted a Confirmatory Factor Analysis using the Diagonal Weighted Least Squares (DWLS) estimator, which is considered to be more effective than Maximum Likelihood estimator when dealing with ordered variables (2(74) = 246.91, p < 0.001, CFI = 0.97, TLI = 0.96, RMSEA = 0.059, SRMR = 0.071].Since the LiES scale  is not variables . The fitd are interpreted . This profile was made up of 235 participants  and showed a pattern that may be related to low lying tendency. Indeed, high scores on openness , scored low on extraversion and openness, but high on perceived cognitive load when lying. Further, it also scored slightly lower than the grand mean on moral disengagement. Considering that this profile showed both features related to higher lying tendency , which partially supported H1a, in particular because of the level of perceived cognitive load.Profile 4 was made up of 95 participants , who scored low on extraversion, openness, and perceived cognitive load when lying, but higher than all other profiles on moral disengagement. Since low levels of extraversion, openness, and perceived cognitive load when lying, and high levels of moral disengagement are related lying, this profile support H1a.2(3) = 20.33, p < 0.001]. Less males and more females then expected belonged to profile 2, but more males than females belonged to profile 4.To explore the association between gender and profile membership, a chi-square test was run. However, since only six persons selected the option “other,” they were excluded from the analyses. The test was significant, .The Profiles obtained with the LPA where related to the dependent variables reported above. There was no significant difference when focusing on the preferred lying strategy, , and gender, Pillai’s trace . The interaction effect was not significant, Pillai’s trace . Concerning the univariate effects of profile membership, the difference was significant for the importance attributed to verbal strategies, , partial eta squared = 0.017, but not for non-verbal strategies, , partial eta squared = 0.003. However, none of the pairwise comparisons were significant.On the contrary, the importance of verbal and non-verbal strategies when lying showed a significant multivariate difference for both profile membership, Pillai’s trace , partial eta squared = 0.006, but significant for non-verbal strategies, , partial eta squared = 0.02. Female participants  attributed more importance to non-verbal strategies than male participants .Concerning the univariate effects of sex, the difference was not significant for the importance attributed to verbal strategies, , nor for gender, Pillai’s trace  or their interaction, Pillai’s trace . Hence, H2 was not supported.There was no difference in the frequency of lying, neither for profile membership, Pillai’s trace , gender, Pillai’s trace , and their interaction, Pillai’s trace .Last, when focusing on the LiES scores, a significant multivariate effect appeared for profile membership, Pillai’s trace , partial eta squared = 0.040 and vindicative lies, , partial eta squared = 0.030, but not for altruistic lies, , partial eta squared = 0.011. As g = 0.52. For vindicative lies, the difference between Profile 1–4 and Profile 2–4, g = 0.47. This supports H3.Concerning profile membership, a univariate significant effect appeared for self-serving lies, , partial eta squared = 0.020, but not for self-serving, , partial eta squared = 0.00, nor altruistic lies, , partial eta squared = 0.001. Female participants reported a lower tendency to tell vindicative lies  than male participants , g = −0.30. The Profile by sex interaction was not significant for self-serving, , partial eta squared = 0.005, altruistic, , partial eta squared = 0.008, nor for vindicative lies, , partial eta squared = 0.01.Concerning gender, a univariate significant effect appeared for vindicative lies, [z = 0.50) to that of Profile 1  based on the combination of personality, moral disengagement, and perceived cognitive load when lying. We obtained four profiles, one of which (Profile 4) showed a pattern of scores associated with a higher probability of lying. Indeed, Profile 4 showed low levels of extraversion, agreeableness, openness and perceived cognitive load when lying but high moral disengagement. On the other hand, profile 1 showed a pattern associated with a lower tendency to lie, as it showed high levels of extraversion, openness and cognitive load, but low scores on moral disengagement. The other two profiles showed a somehow mixed pattern, with scores on some variables which, according to the available literature are associated with low lying tendency, and some with a higher one. Our results thus support the idea that people should be seen in an integrative manner, as already suggested previously , and thaWhen looking at the association between profile membership and lying behavior, we found no differences between profiles for the number of lies told in the previous 24 h. However, our results showed several differences when focusing on the LiES dimensions. We found that participants belonging to profile 4 showed higher tendency to tell self-serving and vindicative lies. This makes sense when read in light of previous research showing that high moral disengagement, low extraversion and low perceived cognitive load when lying are associated with lying . SimilarWe did not find any association between profile membership and the preferred lying strategy, nor for the importance attributed to verbal nor non-verbal strategies when lying, yet we found an effect for gender. The fact that females attributed more importance to non-verbal behavior than males and showed a lower tendency to tell vindicative lies also fits with previous research .This experiment also supported the idea that there might be dispositional features that are related with a higher likelihood of lying, but it is important to note that labeling individuals as “liars,” or that some people were born liars while others not, must be avoided: others factors, such as contextual factors, play an important role on lying behavior and tendency .For what concern the practical applications of the present experiment, being this the first study exploring personality profiles in the deception arena, we focused our attention mainly on the exploration and the description of the emerging profiles. Future studies should, however, also focus on the practical application of this approach. For example, profile membership could be used to tailor the best interviewing technique according to each interviewee. Also, once stronger evidence that there are indeed specific profiles related to a higher tendency to tell lies is obtained, such profiles can then be used to select employees for jobs requiring high reliability, such as the intelligence operatives. In this regard, joint role in lying. Personal characteristics and their relationship with lying are no longer studied on their own but, rather, in an integrative manner as if they moderate each other. Here, people are considered as characterized by a constellation of traits , attitudes , and beliefs/perception (perceived cognitive load when lying) which are differently (cor)related within each emerging profile. For example, we found that people with similar levels of extraversion and openness, but opposite patterns of moral disengagement and cognitive load (see profiles 2 and 4) show different lying tendency. We thus believe that applying the person-centered approach in the investigative interviewing arena might be a useful line of research to reach such goals.Under a theoretical point of view, our study shed light on the importance of studying how several variables play a It is important to note, however, that our study had several limitations. First, the effect sizes we obtained were generally small or medium. This could be due to the variables selected to create the profiles. Second, our study was based on self-reported measures. Although relevant research in lying behavior is indeed based on measures such as survey data  or diaryDespite these limitations, future research should explore in more details what other factors and variables should be used to create such profiles, as well as whether different profiles: (i) are associated with different cues to deception, and (ii) impact on the efficacy of various types of interviewing techniques available to date. In short, a potential practical relevance of this approach would be to focus on different interviewing strategies and/or cues to deception according to the interviewee’s profile membership and to the specific context. Yet, research is still scarce and reaching this aim is likely difficult.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.NP and LuC conceived the idea of the study. NP, LeC, AG, and LuC designed the experiment, collected data, conducted the data analysis, interpreted the results, and wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.