AR targets showed variable expression in patients with androgen insensitivity syndrome. We examined AR function at single-cell resolution using single-cell RNA sequencing (scRNA-seq) in male and female mesenchyme. Surprisingly, both AR and target genes were distributed throughout cell subsets, with few positive cells within each subset. AR binding was weakly correlated with target gene expression.Prostate development is controlled by androgens, the androgen receptor (AR) and mesenchymal–epithelial signalling. We used chromatin immunoprecipitation sequencing (ChIP-seq) to define AR genomic binding in the male and female mesenchyme. Tissue- and single-cell-based transcriptional profiling was used to define mesenchymal AR target genes. We observed significant AR genomic binding in females and a strong enrichment at proximal promoters in both sexes. In males, there was greater AR binding to introns and intergenic regions as well as to classical AR binding motifs. In females, there was increased proximal promoter binding and involvement of cofactors. Comparison of AR-bound genes with transcriptomic data enabled the identification of novel sexually dimorphic AR target genes. We validated the dimorphic expression of AR target genes using published datasets and confirmed regulation by androgens using  Summary: A study of how androgens lead to sexually dimorphic development of the prostate using transcription factor genome binding and transcriptome analysis in mesenchymal subsets. Testostosterone  activateosterone .Tfm mice, which express a mutant AR, showed that mesenchymal AR is required for prostate induction but epithelial AR alone is not sufficient to drive prostate development  that signal directly to the epithelium to induce growth and budding of the prostate  that are important for sexual dimorphism of the UGS. We used microdissected tissue subsets from both female and male UGS in order to derive mesenchymal-specific ARTGs and identify transcriptional differences between males and females. Using ChIP sequencing (ChIP-seq) of these tissue subsets, we were able to identify potential direct ARTGs. Comparison of these with RNA sequencing profiles of the same tissue allowed us to examine which of these genes were sexually dimorphic between female and male UGS. To address issues of cellular complexity within male UGS tissues, which were comprised of both mesenchyme and epithelium, we derived sexually dimorphic transcripts using single-cell RNA sequencing (scRNA-seq) of ventral prostate (VP) versus cells of a specific area of mesenchyme in female UGS termed the ventral mesenchymal pad (VMP) . We valiFig. S1). Rat testes and brain tissue (P0) were included as positive and negative controls for AR, respectively. In addition, WPMY1-AR stromal cells and LNCaP prostate cancer cells were diluted 1:100 in brain extract to serve as positive controls for AR. We observed lower levels of AR protein in female VMP and SU tissues than in male VP and DP tissues. AR protein levels in cell line positive controls were at least 100 times greater than in all four tissue types. To validate the distribution of AR in tissue, immunohistochemistry was performed to confirm western blot data suggesting enriched mesenchymal expression of AR. The VMP is mesenchymal and lacks epithelia, and there is little AR in the urethral epithelia within SU , indicating that these tissues were good models of mesenchymal AR. VP and DP samples were comprised of mesenchyme and significant epithelial content. Although it was not feasible to further microdissect VP and DP samples to separate epithelia and mesenchyme, there was relatively little AR expression in epithelial cells  suggesting that most of the AR signal originated in VP and DP mesenchyme. We propose that the four tissue types used for analysis are good models of mesenchymal AR action and target gene identification, albeit with some epithelial contamination in male tissues.Tissues were microdissected from day of birth (P0) rats to isolate the female ventral mesenchymal pad (VMP), female adjacent urethra , male ventral prostate (VP) and male dorsal/dorsolateral prostate (DP). AR protein levels in the four tissues were measured by western blotting using the AR H280 antibody .To assess the suitability of these tissues for ChIP sequencing analysis, ChIP was performed on tissue pools of VMP, SU, VP and DP followed by qPCR analysis of a known ARTG Zbtb16  to confiTable S1). The abundance of peaks in female samples (VMP and SU) suggested that AR protein level did not correlate with the number of AR ChIP-seq peaks, and that low AR levels led to high peak numbers in females. This was consistent with a previous study using human primary prostate stromal cells  and HOMER, despite the lower levels of AR in female samples. We defined 6992 peaks in VMP, 6456 peaks in SU, 2711 peaks in VP and 14,290 peaks in DP  and 41% (2662) of total peaks at gene promoters whereas VP and DP showed 16% (439) and 20% (2911), respectively D. The ende novo motif analysis on VMP, SU, VP and DP peaks using HOMER. We observed that AR in female tissues (VMP and SU) did not bind to the classical androgen-response elements (AREs) observed in male tissues (VP and DP)  tissues, respectively  and observed 9–30 times less ARE peaks in female tissues than males. In addition, the genomic location of ARE-positive peaks in all samples were enriched at intergenic regions . This suggests that the high proportion of AR peaks at proximal promoter sites in female tissues are largely devoid of AREs. A similar observation was reported in a previous study using human primary prostate stromal cells  A. We strectively B. We alsectively C and in al cells .Fig. 2.Next, we performed transcriptional profiling of our male and female tissues in order to identify genes that were differentially regulated by androgens during prostate development; we then compared these to ARTGs derived by ChIP-seq. To perform a comparison using tissue with the closest developmental homology, as well as reducing cellular heterogeneity, we focussed our analysis on VMP and VP tissues. VMP is the anatomic female equivalent of the VP and is known to possess prostate inductive activity if exposed to testosterone . For a gFig. S5A). A smaller subset of our genes were also co-identified as differentially regulated during prostate organogenesis in mice . Gene ontology analysis of the DEGs identified enrichment in pathways related to tube morphogenesis and development of branching structures, validating our ability to identify genes involved in developmental processes . For a more targeted approach to identifying androgen-regulated ARTGs, we next compared the DEGs to VMP and VP ARTGs . A similar analysis was conducted combining female VMP and SU samples and combining male VP and DP samples to identify global differences in gene expression and ARTGs between females and males. This identified fewer DEGs between females and males than for VMP versus VP analysis  of which 35 were associated with a female AR peak and 36 were associated with a male AR peak .To identify which of these AR target transcripts were androgen regulated, we used NOISeq  to ident in mice  .VP is composed of both epithelium and mesenchyme, and in whole tissue RNA-seq analysis it is possible that DEGs between VMP and VP are derived from differences in tissue composition, such as the inclusion of epithelia. Furthermore, VMP and VP mesenchyme are composed of multiple cell subsets . In ordeFig. S10. The average read and gene statistics as well as the numbers of cells that passed quality control are shown in Table S2. In total, 94 VMP and 91 VP single cells from two replicate batches were combined and used for further analysis. The landscape of cells from the combined dataset in two-dimensional space was shown by principal component analysis and demonstrated a clear separation of VMP and VP cell types, confirming that tissue identity was retained in dissociated single cells . Cell cycle differences were analysed and the proportion of cells in each cell cycle phase for all samples are shown in Fig. S12A. Data were examined for other technical confounding factors as well as cell cycle to detect unwanted variation within our data . Of these, only batch effects represented a median of >3% of gene expression variance, with other technical factors only having a minor confounding effect . We therefore regressed out batch effects in our downstream analyses. The average number of mapped reads, genes detected per cell and genes with read counts >10 TPM are shown in Our scRNA-seq data were quality controlled using Scater  to removIn a global comparison of mesenchymal-specific transcripts from scRNA-seq with ChIP-seq ARTGs, we took genes with an average read count of >10 TPM in VMP and VP single cells and compared these with our VMP and VP ChIP-eq ARTGs. We identified 2035 VMP and 645 VP genes associated with a VMP or VP AR binding site, respectively B.F-test adjusting for cellular detection rate and batch effects yielded 3740 DEGs with an FDR value of <0.05. Using MAST with counts per million expression values and adjusting for cellular detection rate and batch effects yielded 1098 DEGs with an FDR value of <0.05. We also used a non-parametric Wilcoxon test following normalizing, filtering and scaling of combined VMP and VP single-cell data using Seurat . We identified 24% of our DEGs as androgen regulated in murine prostate development in vivo  whereas 2% were identified as markers of prostate organogenesis . The lower degree of overlap compared with whole tissue RNA-seq derived DEGs probably reflects the underrepresentation of mesenchymal transcripts in these published microarray datasets .To identify mesenchymal-specific androgen-regulated transcripts, we used three methods to identify DEGs in our single cell data. Using an edgeR quasi-likelihood g Seurat . This yireviewed . We idenreviewed C. Visualreviewed D. Gene o in vivo  .To determine which of these androgen-regulated transcripts were also direct targets of the AR in mesenchyme, and to derive our final sexually dimorphic AR cistromes, we compared the 53 DEGs with both VMP and VP ARTGs which yielded 23 VMP and 10 VP genes associated with an AR binding site (ARBS), respectively F. ComparFig. S17A). This is also reflected in the position of the ARBS relative to gene TSS, consistent with our previous observations using total ARBSs for the same samples, and suggests a shift of AR binding to intergenic regions upon stimulation with androgens. In addition, we performed de novo motif analysis on the AR cistrome peaks using HOMER. Surprisingly, we did not observe AR binding to classical AREs in either VMP or VP . However, because of the low number of starting AR peaks, we were unable to identify enriched DNA-binding motifs that were statistically significant. Using our top hits, despite low numbers, our analysis identified Stat1 and Elf3 as possible cofactors for AR in VMP and VP, respectively .To assess the AR–DNA binding characteristics of these candidate genes, we evaluated the genomic distribution of ARBSs and observed a lower proportion of sites in intergenic regions in VMP (76%) than in VP (94%)  and genes equally expressed in females and males (Ebf3 and Meis2) . Overall, our candidate genes showed sexually dimorphic expression, and the majority were VMP-enriched compared with male tissues. A subset of these have been previously reported as sexually dimorphic in urogenital tract tissues  and male microdissected VP and DP tissues. Organ cultures were treated with 10 nM testosterone or ethanol for 24 h. Overall, we observed a reduction in expression of our candidates in response to testosterone in VP samples, with variable response in female VSU tissues  versus those that have a peak in both VMP and VP showed a more pronounced response to testosterone. Ptn and Fgf10 showed variable response to testosterone whereas Srd5a2 was upregulated in response to testosterone as expected, validating our experimental approach. These results identify new ARTGs, as defined by AR genomic binding, sexually dimorphic transcript expression and response to testosterone in organs ex vivo. Surprisingly, the majority of AR targets were repressed by androgens.To verify whether our candidate genes were also androgen responsive, we performed  tissues B. We notFig. S19). Importantly, there were no candidate genes showing a consistent or similar pattern of misexpression across all patients.To assess whether our candidate AR-regulated genes are involved in human reproductive development, we investigated transcript expression in fibroblasts derived from patients with disorders of androgen action. We examined a panel of genes by Taqman qPCR in primary genital fibroblasts from 13 patients with complete androgen insensitivity syndrome (CAIS). Of these patients, nine had wild-type AR and four had mutated AR, as defined by exon sequencing. We observed high variability in the expression of our candidate genes, although both over- and underexpression of candidate genes was noted among different individuals . Comparison of these published markers of human and mouse adult prostate stromal cell populations co-identified genes associated with adult smooth muscle cell populations; overall, our markers of developmental mesenchyme were poorly represented in adult prostate stroma .Our results had identified a group of candidate mesenchyme-specific ARTGs, and our next goal was to determine whether these were expressed uniformly throughout mesenchymal cells or within mesenchymal subsets. First, we used Seurat to assess the degree of cellular heterogeneity and subset composition in VMP and VP single-cell datasets. A previous study showed that the VMP is comprised of two subpopulations . ConsistFig. S22).Next, we investigated whether AR and ARTGs were expressed throughout the mesenchyme uniformly, or if androgen-responsive subpopulations could be defined. Our goal was to determine whether AR clustered within a population, and whether ARTGs were expressed within the same population or distributed among other subgroups. We observed that AR expression was not enriched in specific subpopulations of cells and was randomly distributed across all cells B. VisualThe role of androgens and AR signalling in sexually dimorphic development of reproductive tissues is well established, as is the requirement for AR signalling in mesenchymal cells . This leZbtb16. However, we showed that the majority of AR protein expression is mesenchymal in origin at the developmental stage used in our analysis and took steps to exclude possible epithelial AR targets in our downstream experiments and analyses. Further epigenetic profiling studies of these tissues are required to define the function of AR at these genomic regions, but we propose that these may be defining features of AR action between males and females. We also observed differences in the DNA sequences to which AR binds in females versus males. Female AR binding sites were enriched for sequences associated with members of the nuclear factor I (NFI) family of transcription factors, whereas male AR binding sites were enriched for the classical palindromic androgen response elements (AREs). This suggests that NFI transcription factors act as co-factors for the AR in females. NFI factors and AR have been shown to co-regulate known ARTGs such as PSA and FKBP5 in prostate cancer cell lines  or regulated by AR bound at distal enhancers that we were unable to characterize using ChIP-seq. Further experimentation incorporating techniques such as DNase sequencing , ATAC seogenesis  and is togenesis . Rspo2 i pathway , is expr pathway  and is a pathway . In addi pathway . This, cdromedin . Interesosterone , concordin vitro . The anain vitro , 2003. TFig. S21)  . This suig. S21) . It is pig. S21) .Overall, our study is the first to perform an in-depth AR genomic and transcriptomic analysis of mesenchymal tissues of the developing prostate. We document the differences in AR genomic binding profiles between males and females and the transcriptomic features of androgen-driven masculinization of the prostate. We are the first to combine AR ChIP-seq with scRNA-seq to further our understanding of how AR functions at the single-cell level and have found that AR and its target genes transcend cellular identity and heterogeneity. We suggest that further verification of AR binding patterns and how these relate to transcription of sexually dimorphic genes will eventually lead to a more complete understanding of how AR drives masculinization.Animal studies were approved by the McGill University Facility Animal Care Committee (FACC) and performed as per MUHC animal protocol number 2015-7670. Wistar rats were maintained under a 12-h light/dark cycle with a standard laboratory diet. P0 pups were sacrificed by cervical dislocation and decapitation. Urogenital tracts were extracted from both females and males and were microdissected and pooled to produce female ventral mesenchymal pad (VMP) and smooth muscle and urethra (SU) and male ventral prostate (VP) and dorsal/dorsolateral prostate (DP) tissue components with a Leica MZ6 dissection microscope. Testes from males and brain tissue from males and females were pooled to serve as western blot controls.LNCaP prostate tumour cells and immortalized human prostatic stromal cells overexpressing wild-type androgen receptor (WPMY1-AR)  were maiAR western blotting and AR immunohistochemistry of rat P0 tissues were performed as described previously .ChIP, ChIP-qPCR and Illumina ChIP-seq library preparation was carried out by Active Motif  as described previously . Reads w−8 M) or vehicle for 48 h before harvesting for RNA isolation.Ventral prostate, dorsal prostate and female urethra  were microdissected from P0 rat pups and placed in serum-free organ culture . Organs ex vivo organ cultures using Qiazol followed by the RNeasy™ Mini kit  following the manufacturer's instructions. Complementary DNA synthesis was performed using the High Capacity cDNA Reverse Transcription kit  according to the manufacturer's instructions. Duplicate biological samples were analysed using a custom TaqMan® qPCR array as per manufacturer's instructions on an ABI 7500 Fast machine . Transcript abundance was normalized to four housekeeping genes: Gapdh, Tbp, Gusb and Mt-atp6.Total RNA was extracted from pooled tissues and q-value of ≥0.9 were considered differentially expressed. Comparisons of RNA-seq transcriptomes and DEGs with ChIP-seq data and their visualization were performed using R packages VennDiagram  as described previously . SequencnDiagram  and NMF nDiagram , respectSingle-cell dissociation, RNA extraction, library preparation, RNA-seq, read alignment and read quantification was performed on VMP and VP dissociated cells as described  using thTable S2.The Scater R package  was usedF-test. MAST .Comparisons of single-cell transcriptomes and DEGs to ChIP-seq data and their visualization were performed using R packages VennDiagram  and NMF Gene ontology enrichment analysis was conducted using the clusterProfiler R package  on femal


PP2 (BnPP2) genes had been identified in ramie. Here, a total of 15 full-length BnPP2 genes were identified. These BnPP2 genes exhibited different responses to abiotic stresses. Interestingly, the BnPP2 genes are more sensitive to insect pests than to other stresses. A study of the BnPP2-15 promoter revealed that pBnPP2-15 could drive specific GUS expression in the petiole, root and stamen and could also be induced by mechanical wounding and aphid infection in transgenic Arabidopsis lines. The subcellular localization of six BnPP2 proteins showed that GFP-BnPP2-1, GFP-BnPP2-6, GFP-BnPP2-7, GFP-BnPP2-9, GFP-BnPP2-11 and GFP-BnPP2-12 were predominantly located in the cytoplasm. These results provide useful information elucidating the functions of BnPP2 genes in ramie.Phloem protein 2 (PP2) is one of the most abundant and enigmatic proteins in sieve elements and companion cells, which play important roles in the maintenance of morphology, photoassimilate transportation and wound protection in higher plants, but to date, no  However, the mechanism of plant response to external stimuli via phloem remains unclear.Normal growth and development of land plants rely on the coordination of various tissues and organs, and the phloem plays a role as a bridge in this process. In vascular plants, the phloem tissue not only plays a necessary role in transporting photoassimilates and in long-distance delivery of macromolecules11. P-proteins are a very complex component of the phloem tissue; so far, little is known about their function12. The soluble P-proteins and the structural P-proteins are two forms of P-proteins, both of which are found in phloem exudates15. The study of structural P-proteins has been more in-depth than that of soluble P-proteins. The structural P-proteins are divided into dispersive P-proteins and non-dispersive P-proteins depending on their chemical structure and function12.In angiosperms, the phloem mainly consists of sieve tubes, companion cells, sieve phloem fibers and phloem parenchyma cells. Mature sieve elements contain phloem-specific proteins (P-proteins), mitochondria, ER, and sieve element plastids16, which were the first dispersive P-proteins deciphered in detail with respect to structure and function at the molecular level17. Phloem filaments contain PP1 and PP2, which are linked by disulfide bridges. When plant phloem tissue is exposed to the air, the phloem filaments perform an anti-invasive function involving the oxidation of the phloem proteins cross-linked cysteine residues to form a sealed mechanical system18. Phloem protein 2 (PP2), a dimeric chitin-binding lectin19 and one of the most abundant and enigmatic proteins in the phloem sap20, plays important roles in the maintenance of morphology, photoassimilate transportation and wound protection in higher plants21.Previous reports have shown that phloem exudates contain two predominant P-proteins (phloem protein 1 and phloem protein 2)Cucurbita contain high concentrations of P-proteins, Cucurbita has been used as a model plant for studying P-proteins. As early as the end of the last century, an mRNA encoding the PP2 subunit of Cucurbita maxima had been cloned, and its specific localization in companion cells had been determined by in situ hybridization14. In addition, three genomic clones encoding PP2 were isolated from Cucurbita maxima in subsequent studies; meanwhile, they suggested that PP2 may be encoded by a small gene family (two to eight genes) by analyzing the copy number of PP2 genes17. Wang et al.22 also obtained two cDNA clones encoding PP2 in Cucurbita pepo. The PP2 genes were identified as one of the most abundant sequences in melon phloem sap, and at least five PP2 genes were identified to be associated with cellular responses to hormones, stress and defense23. In celery (Apium graveolens), AgPP2-1 and AgPP2-2 are predominantly expressed in the phloem24. As a model plant, Arabidopsis thaliana was the first species in which all PP2 genes were identified. In recent years, research into AtPP2-A1 has become more active. AtPP2-A1 and AtPP2-A2 are two tandem genes, and they share very high amino acid homology with CbmPP2, CmmLec26, AgPP2-1 and AgPP2-2; in addition, analysis of the promoter function of AtPP2-A1 showed that it could drive dominant GUS expression in the phloem of tobacco and Arabidopsis20. Beneteau18 suggested that AtPP2-A1 had different functions in the trafficking of endogenous proteins and in interactions with phloem-feeding insects; meanwhile, AtPP2-A1-overexpressing transgenic Arabidopsis thaliana had a deterrent effect on phloem-feeding activity25. Furthermore, AtPP2-A1 was found to perform dual functions, both molecular chaperone activity and antifungal activity26.As the phloem exudates of PP2 of Cucurbita have been studied in detail at the protein level, and all AtPP2 genes have been identified20, the expression patterns of the entire gene family under different stresses remain to be clarified. For vascular plants, the identification and expression pattern analysis of the PP2 genes will help to elucidate the mechanisms of phloem response to external stresses.Although the Boehmeria nivea L. Gaudich.), mainly grown in China, Philippines, Brazil, India and Viet Nam, is one of the most important phloem fiber crops in the world27. In view of the previous studies on the function of PP2 genes, the PP2 genes of ramie (BnPP2) may affect the yield and quality of phloem fiber, especially under stress conditions. Elucidation of the functions of the BnPP2 genes may have positive impacts on the improvement of ramie resistance and on deciphering the mechanisms regulating response to adversity. However, no studies have yet characterized the BnPP2 genes. In this study, fifteen BnPP2 genes were identified, and we analyzed their phylogeny, gene structures, distribution of conserved domains and subcellular localizations. Meanwhile, we also investigated the expression patterns of all the BnPP2 genes in various tissues or organs, as well as their expression profiles under biotic and abiotic stresses. In particular, we cloned and analyzed the promoter of BnPP2-15, which resembles AtPP2-A1 (accession no. AT4G19840) very closely in its genetic evolution. This fundamental study will not only facilitate the further investigation of BnPP2 genes with respect to their biological and molecular functions but also help us to understand the mechanism of phloem resistance, and provide a reference for the study of phloem protein 2 in other plants.Ramie   based on the results of the clustering analysis   Fig. , and subII) Fig. .20. To examine the conservation of PP2 proteins between ramie and other species, we selected eight PP2 proteins from six different species  for multiple sequence alignment. These PP2 proteins were CbmPP2-1 (accession number L31500), CmsLec26 (AF520581), CmmLec17 (AF520577), AgPP2-1 (AY114139), AtPP2-A1 (AT4G19840), BnPP2-2, BnPP2-5 and BnPP2-9. The result was presented by Web-Logo. The N-terminal extension of the PP2 proteins was poorly conserved, while the C-terminus contained four conserved motifs   D) Fig. . There w D) Fig. .To more intuitively present the conserved domain of the BnPP2 proteins and their distribution, all BnPP2 amino acid sequences were analyzed using the Conserved Domain Architecture Retrieval Tool (CDART). Figure BnPP2 genes in various tissues or organs were investigated by qRT-PCR using total RNA isolated from eight different tissues or organs  of ramie   Fig. , High-teLT) Fig. , and DroLT) Fig.  for the LT) Fig. ] and bioLT) Fig.  and FungLT) Fig.  for the BnPP2 genes were evaluated in four different tissues  under different treatments . The results are shown in Fig. BnPP2 genes were up-regulated under the LT treatments while nine BnPP2 genes were up-regulated under the HT treatments, and the relative expressions levels of five BnPP2 genes  were also up-regulated under the DS treatment  were significantly up-regulated under the DS treatment . The promoter sequence is shown in Fig. cis-acting regulatory DNA elements). Several putative cis-regulatory elements were deciphered in detail from the promoter sequence of BnPP2-15  leaves infected by aphids were used for GUS staining. GUS expression was detected in the wounded leaves of transgenic Arabidopsis lines containing pBnPP2-15-2086 and pBnPP2-15-1455  fused with each BnPP2 protein. Six of these fusions exhibited available results while the fluorescence signals of remaining fusions could not be effectively observed. The fluorescence signals of six GFP-BnPP2 fusion proteins  were captured by the Laser Scanning Confocal Microscope. As shown in Fig. To understand the role of BnPP2 proteins at the cellular level, it is helpful to decipher BnPP2 protein functions. We investigated the subcellular localization of all PP2 may play an important role in assisting vascular plants against environmental stresses. In this study, fifteen PP2 genes were identified in ramie, and the expression patterns of these genes in different tissues or organs were investigated as well as those in response to LT, HT, DS, MW, IF and FI. In addition, we cloned and analyzed the function of the BnPP2-15 promoter; the subcellular localization of BnPP2 proteins in tobacco epidermal cells and the distribution of BnPP2 mRNAs in the stem of ramie were also investigated.The importance of phloem in vascular plants is self-evident. BnNAC (NAC transcription factor) genes31, BnCOL (constans-like) genes32, BnEXP (expansin) genes33 and BnSAUR  genes34. However, the PP2 genes have not been studied in ramie. Although PP2 genes have been found in some angiosperms, the number of PP2 genes present in the genome varies greatly among different species. Early studies using DNA blot analysis suggested that PP2 was a small family in Cucurbita maxima, which contained two to eight genes17, whereas another study in the same period differed in this result; Wang et al. suggested that PP2 proteins were encoded by a gene family with a relatively large number of members (estimated as seven to fifteen per haploid genome) in Cucurbita pepo22. In addition, after the Arabidopsis thaliana genome sequence became available, a total of thirty PP2 genes were identified in Arabidopsis20. In ramie, we identified fifteen PP2 genes based on three transcriptome databases. Recently, a transcriptome study of the phloem and xylem in ramie was performed35. However, we did not find additional PP2 genes based on their published data. Furthermore, we have reason to believe that all BnPP2 genes will be identified for more comprehensive study with the completion of ramie genome sequencing.In recent years, the study of related gene families has rapidly progressed with the prevalence of transcriptome sequencing. The molecular biological study of ramie has developed rapidly in recent years as well. Some gene families have been identified and characterized, such as PP2 genes. Through CDART analysis, we found PP2 domains in thirteen BnPP2 proteins  to external mechanical wounding, except that of BnPP2-9, whereas most of the genes in subgroup II and subgroup III showed perfectly opposite responses in damaged leaves and bark, except that of BnPP2-11  are different in function. Furthermore, after scratching, GUS expression was induced in wounded leaves carrying the pBnPP2-15-2086 construct and pBnPP2-15-1455 construct  to use as the query to perform local TBLASTN searches against three pools of unigenes de novo assembled from the RNA-seq results of previous studies30. The principle of selecting candidate genes was based on the E-value cutoff, which was set as 1e−10. Subsequently, the obtained sequences were aligned based on the nucleotide sequence using Clustal X42. After alignment, the redundant sequences were removed, and the remaining sequences were used for further analysis. Open Reading Frame Finder (http://www.ncbi.nlm.nih.gov/projects/gorf/) and FGENESH (http://linux1.softberry.com/), the HMM-based gene structure predictor43 were used to confirm the initiation and termination codons for each gene. The theoretical molecular weight of each BnPP2 protein was determined via an online bioinformatics website (http://www.bioinformatics.org/sms/index.html).To identify phloem protein 2 genes in ramie, BnPP2 genes were cloned based on polymerase chain reaction (PCR) by using ramie genomic DNA as the template. The BnPP2 gene-specific primers are listed in Table BnPP2 genes. The exon-intron structure of each BnPP2 gene was displayed via Gene Structure Display Server 2.0 (http://gsds.cbi.pku.edu.cn/) by comparing the coding sequence and genomic sequence44.Genomic DNA was extracted from the young leaves of ramie cultivar Huazhu No. 5 using an Omega Plant Genomic DNA Extraction Kit , according to the manufacturer’s protocol. The http://www.ebi.ac.uk/Tools/msa/tcoffee/)45, and then we used Web-Logo (http://weblogo.berkeley.edu/logo.cgi) to represent the results46. The amino acid sequences were analyzed using the Conserved Domain Architecture Retrieval Tool (CDART)47. The multiple alignments of BnPP2 proteins were analyzed by the MAFFT algorithm48. Phylogenetic trees were constructed in MEGA 6.0 software with the default settings by using the maximum likelihood method with 1000 bootstrap replications49.The T-Coffee program was used to perform a multiple sequence alignment of the PP2 proteins from different species 50. The promoter element and motif predictions were based on the online analysis tool PLACE (Plant cis-acting regulatory DNA element) (http://www.dna.affrc.go.jp/PLACE/)51.The 5′-flanking region of The experimental materials were Huazhu No.5, which was obtained from the ramie germplasm resources garden at Huazhong Agricultural University and planted in pots with a diameter of 25 cm. All materials were tested after incubation in the artificial climate chamber for 30 days under a 16 h light/8 h dark cycle at 25 °C light/22 °C dark .52, we crushed the leaves and stem bark several times with a needle, which effectively wounded approximately 40% of the leaf and bark area. Plants were incubated for 90 min under normal conditions, and the leaves and stem bark were harvested subsequently. For the IF, the plant materials were used for feeding Cocytodes coerulea Guenee, which were captured from the ramie germplasm resources garden at Huazhong Agricultural University. The leaves were harvested after approximately 40% of the leaf area had been destroyed. For the FI, the a Colletotrichum gloeosporioides strain was isolated from ramie leaves and used to infect ramie leaves according to the method described by Zhang53. The leaves were harvested after obvious disease spots appeared on the infected ramie leaves. The corresponding untreated plant materials were used as the controls. All harvested samples were immediately immersed in liquid nitrogen and stored at −80 °C for further analysis. Each experiment was performed with three biological repeats.For the LT and HT, plant samples were incubated in the artificial climate chamber until the emergence of the phenomenon of stress under a 16 h light/8 h dark cycle at 5 °C light/5 °C dark and at 42 °C light/42 °C dark, respectively. For the DS, plant material growth conditions were consistent with the normal conditions, except for water supply, until severe drought symptoms appeared. Then, the leaf (without main vein), petiole, bark and stem were harvested for each treatment . For the MWTotal RNA from diverse tissues under different treatments was extracted with the RNAprep Pure Plant Kit . Subsequently, the total RNA was reverse transcribed by TransScript One-Step gDNA Removal and cDNA Synthesis SuperMix , according to the manufacturer’s instructions.eEF1α was used as the endogenous control; this gene has been proven to be a stable internal reference gene based on preliminary experiments (data not shown). The sequences of the gene-specific primers used for qRT-PCR are listed in Table eEF1α were defined as ΔCt = (Ct Target − Ct eEF1α). The relative expression level of each gene was calculated according to the 2−ΔCt and 2−ΔΔCt method54. Experiments were performed three times in triplicate. The data were analyzed statistically with SPSS 20.0 and visualized with the R programming language or GraphPad Prism 5.0.The qRT-PCR was conducted with iTaq Universal SYBR Green Supermix  on a Bio-Rad iQ5 Real-Time PCR System . The ramie elongation factor BnPP2-15 was truncated to form four segments according to the distribution density of its predicted cis-elements. Each of the four segments were introduced into the binary vector pBI12155 using a ClonExpress II One Step Cloning Kit . The relevant primers are listed in Table Agrobacterium tumefaciens GV3101, and then used to transform Arabidopsis thaliana using the floral dip method56.The promoter of Arabidopsis thaliana plants were analyzed by histochemical staining for GUS activity57. Samples were immersed in X-gluc reaction buffer at 37 °C for 3 to 12 h in a dark incubator. Then 90% ethanol and 70% ethanol were used for decolorization. GUS activity sites were observed by stereomicroscope (Olympus SZX16).Seedling and organs from transgenic T3 BnPP2 cDNAs were obtained via PCR. The PCR products were cloned into the binary vector pEGAD58 using the ClonExpress II One Step Cloning Kit. The relevant primers are listed in Table Agrobacterium tumefaciens GV3101. Agrobacterium tumefaciens containing the recombinant plasmid was injected into the leaf epidermis of Nicotiana benthamiana, which had been incubated in the greenhouse for 30 days at 25 °C in 14 h light and 10 h dark59. After injection, the tobaccos plants were replaced in the greenhouse for 48 h. Next, the inoculated leaves were separated for fluorescence microscopy by using the laser scanning confocal microscope (Olympus FV1200).The full-length BnPP2-1) MF362959, (BnPP2-2) MF362960, (BnPP2-3) MF362961, (BnPP2-4) MF362962, (BnPP2-5) MF362963, (BnPP2-6) MF362964, (BnPP2-7) MF362965, (BnPP2-8) MF362966, (BnPP2-9) MF362967, (BnPP2-10) MF362968, (BnPP2-11) MF362969, (BnPP2-12) MF362970, (BnPP2-13) MF362971, (BnPP2-14) MF362972 and (BnPP2-15) MF362973].The nucleotide sequence reported in this paper has been submitted to NCBI with accession numbers [(Supplementary Information


This paper provides a game-theoretic model of the effect of higher adversity on the evolution of cooperation. The focus lies on how this effect of higher adversity is impacted when there is transient, non-genetic heterogeneity in the form of differences in the players’ capabilities of contributing to the public good, in the benefits they obtain from the public good, or in their cooperation costs. A framework is provided that identifies the common mechanisms that are at work across two models of cooperation , and across the mentioned types of heterogeneity. With relatively small heterogeneity, higher adversity generates a common-enemy effect for large cooperation costs and a deterrence effect for small cooperation costs. Yet, these results on the effect of higher adversity are completely reversed for relatively large heterogeneity. Standard explanations for the evolution of cooperation include direct8 and indirect reciprocity9, kin selection10, group selection11, and network reciprocity12. An alternative explanation is by-product mutualism, which argues that organisms cooperate whenever it is in their individual interests to do so14. Specifically, following an argument that dates back to Kropotkin15, the common-enemy hypothesis of by-product mutualism argues that cooperation arises when organisms face the “common enemy of a sufficiently adverse environment” , where the common enemy may be biotic  or abiotic . This hypothesis thus provides a rationale for the phenotypic plasticity of cooperative behaviour17, which would be triggered by harsh environments.Explaining the evolution of cooperation is one of the key themes in evolutionary biology21. Part of the literature on reciprocity24, on group selection18, and on network reciprocity29 has already shown that heterogeneity can fundamentally affect predictions on the evolution of cooperation. Also, part of the literature on kin selection can be interpreted as investigating how heterogeneity mediates the effect of relatedness on cooperation30. An analysis of heterogeneity in the context of the common-enemy hypothesis is so far missing, and this report attempts to fill this gap. As will be shown, analysing the effect of heterogeneity in this context is important, as sufficiently large heterogeneity is predicted to fundamentally change the incidence of the common-enemy effect.At the same time, explanations of the evolution of cooperation often only model homogeneous organisms, whereas cooperating organisms may typically be heterogeneous31; a related concept is the one of social diversity25, defined as differences in the scaling factor relating payoffs to fitness). Such a focus is justified, first, simply because of the importance of transient heterogeneity (see23); second, because even if heterogeneous traits are non-transient and subject to evolution, these traits may evolve for reasons unrelated to the cooperative situation considered . Another account proposes that higher adversity in the form of a higher risk of predation can give potentially cooperating prey more reasons to repeatedly interact, and may thus promote reciprocity49. Finally, the common-enemy has been modeled as taking the form of a competing group, in a theory that includes elements of group selection50.The common-enemy hypothesis has been formulated in several disciplines51, payoffs in two-player games take the form of survival probabilities. Adversity can be measured by the number of iterated attacks a pair of players faces, where an attack eliminates each player with a probability that depends on the number of cooperating players in the pair. In a second theory52, which is the focus of this report, cooperating in a pair means contributing to a public good, in two modelling variants. In the production variant cooperating means providing inputs for the production of a public good, and in the defence variant cooperating means investing in the defence of an existing public good. In both variants, adversity is reflected by the extent to which a second cooperating player in a pair contributes more than a first cooperating player, and thus by the extent to which each player’s contribution is pivotal. For instance, in cooperative hunting54, the larger the prey the more may be lost when a second hunting predator in a pair fails to hunt, and the less may be gained when a first predator starts hunting. Also, in territorial defence of a common territory57, the harsher the environment in the form of a larger number of attacks on the territory, the larger the reduction in the probability of keeping the territory if a single of two animals fails to defend, and the smaller the increase in the probability of keeping the territory if only one of two animals starts defending. Thus, while in the most general sense adversity means a reduction in the payoffs27, the argument here is that specifically in contexts where players contribute to a public good, higher adversity makes each player’s contribution to the public good more pivotal. It is into these modelling variants that we introduce heterogeneity.Yet, in order to get insight in the mechanism underlying the common-enemy hypothesis, it is important to consider it separately from the standard rationales for cooperation. Two existing theories serve this purpose. In a first theorys), and the column player is the weak player (w). Each of the players can either contribute to a public good , or not contribute , with corresponding benefits and costs as defined in the literature on public goods61 . Alternatively, cooperating may mean producing a private good for oneself which happens to produce a by-product benefit for others, where benefits and costs are defined in a different manner63; as shown in Section 5 of the Supplementary Information, the introduction of heterogeneity in such a private-good model does not qualitatively affect the predictions, which is why it is not considered here.In both modelling variants, at any point of time, the population consists of a fraction of strong and weak players (where strong players benefit more from cooperating than weak players). These fractions do not vary across time, and players are randomly assigned to the strong and weak roles independently of the roles they had in the past. Also, players are randomly matched in pairs independently from their roles (see the Methods section for the exact details). The cases of interest are subgames where a strong and a weak player are matched. Such subgames can be represented by the asymmetric bimatrix game in Table bi denotes the benefit that player i = s, w obtains from the public good when player s adopts strategy x = C, D and player w adopts strategy y = C, D. Furthermore in Table c denotes cooperation costs, with c > 0. Define by i = s, w, when the other player cooperates (x = 0) or defects (x = 1), so that Specifically, a, in both modelling variants for minimal adversity it is the case that i = s, w), meaning that with minimal adversity, a player of a given type contributes the same to the public good, whether or not the other player contributes as well. Furthermore, in both modelling variants the effect of adversity on the added benefits is monotonic; specifically, it is the case that 38); at the same time a player who deviates from joint defection by cooperating benefits to a smaller extent.Denoting in general adversity by As detailed in the Methods section, in the production variant, adversity is measured by the degree of complementarity between the players’ efforts, which measures how much a second cooperating player in a pair contributes to the public good compared to a first cooperating player . In the defence variant, the degree of complementarity between the players’ contributions to the public good is instead fixed. Players in a pair face random attacks, and a player contributes to the public good when defending the public good (=cooperating), but also when not defending it (=defecting) but never being attacked. Adversity is now measured by the number of random attacks facing the players. For a large number of attacks, defecting means not contributing to the public good with probability approaching 1, and the extent to which a second cooperating player contributes to the public good compared to the first cooperating player is reflected by the fixed degree of complementarity. For a lower number of random attacks, a second cooperating player contributes less to the public good, because a defecting player is targeted with a probability lower than 1. The number of attacks therefore has a similar effect as the variable degree of complementarity in the production variant, in making each player’s effort more pivotal.x = 0, 1, it is the case that h, where for vanishing heterogeneity we have x = 0, 1, and where it is the case that The strong and the weak player differ in the sense that the strong player gains more from cooperating than defecting than the weak player, whatever the action taken by the other player; put otherwise, for 64) prescribe for players in subgames as Table The question we seek to answer is: what actions do evolutionarily stable strategies . A necessary condition for a Free-Rider game is that heterogeneity is sufficiently strong compared to adversity, such that a second weak cooperating player benefits less than a first strong cooperating player.From the previous it follows that the game in Table A . Figure 52. In this case, for minimal adversity we have symmetric common-enemy effect. Starting again from low adversity, when cooperation costs are instead small, joint cooperation takes place (Harmony Game). With higher adversity, a second cooperating player loses even more when failing to cooperate . As comparison of Fig. Starting from Fig. Figure relative to the degree of adversity. In Fig. While Fig. Figure These differences in results are explained as follows. First, the results differ between the production and defence variants for the following reason. In the production variant, where adversity is measured by the degree of complementarity, complementarity can become so large that a first cooperating player contributes vanishingly little compared to a second cooperating player; in this case, the effect of heterogeneity is undone, explaining why in the production variant symmetric effects continue to be obtained for high adversity. In the defence variant on the contrary, the fixed degree of complementarity between the players’ contributions need not be maximal. For this reason, even with maximal adversity (=large number of random attacks), the extent to which players’ contributions are pivotal is still not maximal. The consequence is that even high adversity does not undo the effect of heterogeneity, explaining why for large heterogeneity cf. Fig.  only theSecond, heterogeneity modelled as a difference in capability of contributing to the public good leads to different effects than the other two types of heterogeneity considered. Even if players differ considerably in their capabilities to contribute to the public good, when a second cooperating player’s effort becomes to a large extent critical, this undoes the effect of heterogeneity and the symmetric effects are obtained. However, when heterogeneity takes the form of players obtaining different shares of the public good or incurring different cooperation costs, heterogeneity can always be so large that a first cooperating player who is strong is more inclined to contribute to the public good than a second cooperating player who is weak, whatever the degree of adversity cf. Fig. . This exThe game-theoretic model in this report allows not only for a common-enemy effect, but also for a deterrence effect of a harsher environment. Moreover, how the incidence of these two effects of a harsher environment changes as a function of the cooperation costs, depends on the degree to which the players are heterogeneous: if heterogeneity is relatively small, the common-enemy effect applies for large cooperation costs, and the deterrence effect applies for small cooperation costs; if heterogeneity is instead relatively large, it is the deterrence effect that applies for large cooperation costs, and the common-enemy effect for small cooperation costs. The model thus provides detailed predictions for how the phenotypic plasticity of cooperative behaviour may be influenced by the harshness of the environment, the cooperation costs, and the degree of heterogeneity between the players.71, such as information-acquisition costs to determine the cooperation costs, the degree of adversity and the degree heterogeneity, were not considered (though experiments suggest that even simple organisms are able to overcome these costs17). In the model, players cooperate in pairs, whereas in reality cooperating groups may have any size , and congestion may take place as the number of cooperators in a group increases 73. Finally, while in the model investment in the public good is an all-or-nothing one-dimensional decision, in reality cooperators’ efforts may be continuous58. Though the expectation is that the stylised model in this report reveals mechanisms that are at work in much more complex reality, relaxing the mentioned simplifying assumptions deserves separate attention. Most of all, while in the model the focus is on the effect of harsher environments on the probability of cooperation, future research needs to investigate how this effect interacts with standard rationales for the evolution of cooperation, including crucially network reciprocity. Moreover, an avenue for future research is to explore the effect of harsher environments when players are able to collaborate, in being able to make coordinated moves towards strategies that are mutually beneficial75.It should be stressed that the model for which these predictions are obtained is extremely stylised, with several simplifying assumptions. Costs of plasticity Section  suggests16, and while heterogeneity may also have a direct biological interpretation, cooperation costs may be difficult to quantify . The level of the cooperation costs is moreover only defined relative to the benefits obtained from the public good, which also do not have a direct biological interpretation. Moreover, even if an appropriate proxy variable could be found to account for relative cooperation costs, such a variable need not relate in a linear way to fitness, and therefore to the payoffs in Table We end by exploring how the results of the model could be translated into testable hypotheses. While adversity may be quantified by predation risk in the defence variant, or by the size of a prey in the production variant 52. For each of these models, three types of heterogeneity are considered, namely heterogeneity in the capability of contributing to the public good, heterogeneity in the share of the public good obtained, and heterogeneity in cooperation costs. As reported in the Results section, qualitatively similar results are obtained for each of the cases. Formal descriptions of the results, and all the proofs, are contained in the Supplementary Information  = bw = V); if both players defect they both obtain payoff 0  = bw = 0). If one player cooperates and the other one defects, both players obtain value (1 − k)V from the public good  = bw = bs = bw = (1 − k)V). It is the case that k is the degree of complementarity between the players’ efforts80, and reflects the extent to which a second cooperating player in a pair is pivotal compared to a first cooperating player. With k = 1/2, pivotality is minimal, and a first and second cooperating player contribute the same . With k = 1 pivotality is maximal, and a second cooperating player contributes the entire value of the public good.The production variant may be seen as a stylised representation of cooperative huntingbs = bw = V and that bs = bw = 0. Yet, it is now the case that bs = bw = 2(1 − k)hVand that bs = bw = 2(1 − k)(1 − h)V, with h measures the degree of heterogeneity. Specifically, h reflects the extent to which the players have heterogeneous capabilities of contributing to the public good, where the larger h, the more a strong player that cooperates alone contributes to the public good compared to a weak player that cooperates alone. When h = 1/2, heterogeneity vanishes; when h = 1, heterogeneity is maximal and a weak player who cooperates alone does not contribute anything.It continues to be the case that h, with bs = h2V, bs = bs = h2(1 − k)V, bs = 0; also, bw = (1 − h)2V, bw = bw = (1 − h)2(1 − k)V, bw = 0. When h = 1/2, the case with vanishing heterogeneity is again obtained. When h = 1, heterogeneity is maximal, and e.g. when both players cooperate, of the total value that is produced for the players in the absence of heterogeneity , the strong player obtains 2V and the weak player obtains nothing.In this case, players have the same capability of contributing to the public good, and heterogeneity cs = 2(1 − h)c, whereas a weak player incurs costs cw = 2hc, with In this case, benefits are the same as with vanishing heterogeneity, but the strong and the weak player have different cooperation costs. In particular, we assume that a strong player incurs costs 81. Each player in a pair is positioned on one of the two sides of a common territory. Cooperating (C) means doing effort to defend one’s side, defecting (D) means not doing any effort. The players face a number of random attacks A (with A ≥ 1), taking the form of sampling with replacement of the two sides of the common territory.In the defence variant, the number of players that contributes to the public good relates to the value obtained from the public good in exactly the same way as in the production model, where the degree of complementarity is now kept fixed. Yet, cooperating or defecting now relates in a stochastic way to contributing or not contributing to the public good. In particular, the defence variant represents in a stylised way a situation of circular defenceA, the individual player is never attacked, and with probability [1 − 1/2A], this player is attacked at least once. The individual player contributes to the public good when defending, but also when not defending and never being attacked. With vanishing heterogeneity, it is the case that bs = bw = V. Furthermore, bs = bw = bs = bw = With probability 1/2bs = bw = V and bs = bw In this case, we have bS = h2V, bw = (1 − h)2V, Here, In this case benefits are the same as in the defence variant with vanishing heterogeneity, and heterogeneous cooperation costs are constructed in the same way as for the production model with heterogeneous cooperation costs.Supplementary information


The capacitation process was analyzed using four fluorescent techniques: 1. chlortetracycline (CTC) staining, 2. anti-acrosin antibody (ACR.2) assay, 3. anti-phosphotyrosine (pY) antibody assay, 4. fluorescein isothiocyanate-conjugated phalloidin  assay. All these methods were tested using fluorescent microscopy and flow cytometry.Capacitation involves physiological changes that spermatozoa must undergo in the female reproductive tract or in vitro to obtain the ability to bind, penetrate and fertilize the egg. Up to date, several methods have been developed to characterize this complex biological process. The goal of the presented study is to mutually compare several fluorescent techniques, check their ability to detect changes in molecular processes during the capacitation progress and determine their ability to predict the percentage of acrosome reacted (AR) sperm after the exposure to solubilized ZP-induced AR differ significantly (CTC and ACR.2 gave the best predictions).All selected methods are capable to detect the capacitation progress of boar sperm in vitro, but there are significant differences in their outcome when using fluorescent microscopy or flow cytometry experimental arrangements and subsequent statistical analysis (KW-ANOVA). Also, the ability to predict the absolute numbers of sperm which will undergo ZP-induced AR.Our study compared four largely used methods used to characterize capacitation process, highlighted their differences and showed that all are able to detect capacitation progress, CTC and ACR.2 are furthermore able to accurately predict the percentage of AR sperm after  Capacitation is a physiological process that spermatozoa must experience in the female reproductive tract or in vitro to obtain the ability to bind, penetrate and fertilize the egg –3. The cSince the discovery of capacitation, several methods have been developed to characterize this complex biological process. There are four major fluorescent methods, to be mentioned, and they all target different sperm characteristics: 1. CTC method detects the redistribution of the intracellular calcium in the sperm head during capacitation , 13; 2. zona pellucida (ZP) [All accounted methods can be used in various experimental protocols, e.g. CTC in fluorimetry, ACR.2 in ELISA, anti-Y in western blot etc. Fluorescent analysis is a general method suitable for all detection procedures and generally, there are two ways to perform fluorescent analysis on a cellular level: 1. by fluorescent microscopy and 2. flow cytometry. The physiological acrosome reaction (AR) is triggered by glycolytic extracellular matrix of the egg called ida (ZP) .ZP triggered AR and subsequently fertilize the oocyte.A standardize and reliable evaluation of capacitation and the selection of a reliable detection methods is a methodological prerequisite for the quality assessment of fertilizing potential of individual sperm and sperm population exposed to physiological or environmental factors. In our study, we focused in detailed on analyzing the capacitation process of boar sperm through fluorescent detection using both fluorescent microscopy and flow cytometry. The aim of this work was to assess the ability of individual methods to detect relevant molecular changes during sperm capacitation; to compare their advantages and disadvantages in order to select a suitable method for evaluation of sperm capacitation and estimate the potential of individual methods to predict sperm ability to undergo All chemicals were purchased from Sigma  unless otherwise specified.Sus scrofa) ejaculates  were supplied by Insemination Station, Kout na Sumave, CR as chilled (17 °C) and diluted samples [2, 2 mM TRIS, 1.1 mM glucose, 0.5 mM pyruvate). Sperm were resuspended in capacitation medium containing BSA (1 mg/mL) to concentration 5 × 107 sperm/ml. and suspension was incubated for 60, 120, 180, 240 min under paraffin oil at 37 °C, 5% CO2.Boar  for 60 min  [Pisum sativum agglutinin (PSA).After 240 min of incubation, selected samples incubated for 240 min were treated by boar solubilized  5% CO2)  to inducThe CTC was performed as described previously  using thACR.2 (Exbio 11–260-C100) immunofluorescent analysis was described previously . After tSamples were examined with a Nikon Labothot-2 fluorescent microscope equipped with 40x Nikon Plan 40/0.65 and photographed with a COHU 4910 CCD camera  using LUCIA imaging software . Sperm cells were classified according to their cellular  staining patterns into non-capacitated, acrosome intact sperm; capacitated, acrosome-intact sperm; and acrosome-reacted sperm , then centrifuged and washed in PBS  and fixed by 96% ethanol at 4 °C for 30 min. After ethanol fixation, sperm were re-fixed in ethanol-acetone mixture at 4 °C (10:1) for 30 min. CTC treatment was carried out as described previously. Sperm intended for other analysis were washed three times in PBS and incubated with anti-acrosin ACR.2 antibody (50 μg/ml), anti-pY antibody  and FITC-phall  at 37 °C for 60 min. After the incubation with the primary antibody , sperm were washed three times in PBS and incubated with a FITC-conjugated anti-mouse IgG antibody ) for 60 min. FITC-phall samples were only kept in incubation chamber. After the incubation, all sperm samples were intensively washed in PBS (five times for 5 min) and subsequently 100 μl of the suspension was placed on 96-well plate. Flow cytometry data acquisition was performed on BD LSR II instrument , excitation laser 488 nm, emission filters 530/40, measurement of fluorescent intensity in FITC channel. Analysis was performed using FlowJo 7.5.4. software  and GraphPad 5.03. The statistical differences in the number of sperm cells with specific acrosomal status among control and experimental samples were assessed by the Kruskal–Wallis one-way analysis of variance (KW-ANOVA). Post hoc analysis was performed by the Newman-Keuls test and multiple comparisons of mean ranks. The Bland-Altman method was used to calculate the bias and its variance between the number of capacitated cells detected by individual methods after 240 min of incubation and the number of acrosome-reacted sperm after n = 20). At the beginning of the capacitation process (time 0 min), there was 5–8% of sperm with specific fluorescent pattern evaluated as capacitated and 7% of sperm evaluated as acrosome reacted and there were no significant differences between individual methods. At 120 min, a significant increase in the number of sperm with capacitated fluorescent pattern was observed in all the methods with the highest increase in ACR.2 and CTC. Moreover, at 240 min, all the methods detected significant increase in the number of capacitated sperm. After ZP-induced AR, all the methods detected strong significant decrease in the number of capacitated sperm, which correlated with the sperm evaluated for the specific fluorescent staining pattern after AR.Figures ZP-induced AR. Fluorescent intensity increased in all methods during capacitation progress with the exception of the CTC assay, where fluorescent intensity was highly stochastic and was not subjected to subsequent gating and analysis . The ACR.2 detection method  with exception of flow cytometry for CTC. The highest correlation was observed between fluorescent microscopy detection of the capacitation state by CTC and ACR.2 antibody. Even though there was a high Pearson correlation coefficient (r = 0.81) for fluorescent microscopy data, the individual methods expressed different sum of correlation coefficients  with the highest r total for CTC and ACR.2 fluorescent methods. In contrast, flow cytometry with CTC and fluorescent microscopy with FITC-phall had the lowest r total . In general, individual data sets from fluorescent microscopy expressed higher in-between correlation compared to coefficients between fluorescent microscopy (FM) and flow cytometry (FC) data and oppositely.Table ZP-induced acrosome reaction. The Pearson correlation coefficients (r) between number of cells with specific acrosomal pattern and means of fluorescent intensity  and numbers of cells after AR (detected by PSA fluorescent microscopy (PSA FM) and flow cytometry (PSA FC)) are presented. The strongest correlation was observed between a number of capacitated cells detected by CTC fluorescent microscopy (CTC FM) and a number of cells after AR detected by fluorescent microscopy with PSA (PSA FM). Almost the same result was obtained by fluorescent microscopy with ACR.2 antibody (ACR.2 FM) and PSA FM. All other methods and approaches expressed correlation at different levels of significance with exception of flow cytometry with CTC. In general, individual data sets from fluorescent microscopy expressed higher in-between correlation compared to coefficients between FM and FC data.Table ZP-induced AR detected by PSA method. The highest Pearson correlation coefficient was observed by CTC analysis (r = 0.93) and ACR.2 method (r = 0.92) while FITC-phall assay and pY assay presented moderate positive correlations . All the correlation coefficients were statistically significant (p ≤ 0.01). Inserted bar charts  and their differences (Δ).Figure rts Fig.  represenZP-induced AR was calculated for ACR.2 and CTC method (5.2 ± 1 and 5.35 ± 0.87), the highest bias was calculated for pY method (46.78 ± 2.15).Data from FM experiments are finally represented as a Bland-Altman plot , which are accessible for subsequent statistical analysis  , 25–27. The combination of the data from fluorescent microscopy and flow cytometry enables us to describe the temporal changes and succession of the molecular processes detected by individual analytical methods. According to our results, the first observable change is the redistribution of calcium ions , accomzona pellucida, we tested the ability of individual methods to predict number of physiologically capacitated sperm. According to the results presented in Fig. zona pellucida induced AR, but there are major differences in their ability to predict the percentage of cells undergoing acrosome reaction in the presence of zona pellucida in boars. FM CTC and FM ACR.2 are best in prediction of status physiologically capacitated sperm showing the lowest bias in Bland-Altman analysis and thus can be used as a useful tool for optimization of capacitating media [ZP-induced acrosomal reaction and therefore in our arrangement highly underestimate the % of cells which will undergo the ZP-induced AR.Due to the fact that capacitation is the physiological process, which results in the ability of sperm to undergo AR in the presence of ng media  or for sng media . On the ZP. The presented ratios and agreements of cells detected as capacitated by CTC and ACR.2 and cells detected as AR by PSA after ZP-induced AR suggest that cells detected as capacitated by these two methods will undergo AR after exposure to solubilized ZP. However, such conclusion can not be drawn for anti-pY and FITC-Phall methods. A possible approach to probe this in further detail would be to induced AR by ZP during several times of incubation where the ratios of cells detected as non-capacitated and capacitated are different and using FC observed what population of cells (non-capacitated/capacitated) will undergo AR. However, there are again several technical limitations, since ACR.2 antibody displays intermediate fluorescent intensity peaks in earlier stages of the incubation and there are gates overlaps for anti-pY and FITC-phall, as shown in Fig. Despite the fact, that our experimental approach enabled to compare four methods used for characterization of capacitation process in boar sperm, and broaden up the knowledge on interpretation of obtained data, there are still several limitations, which need to be addressed in future studies. The first is related to the evaluation of individual cells in a sample by multiple analytical methods. The co-staining of the individual samples by for example ACR.2 and anti-pY antibody would enable to conclude if individual cells are detected by both methods as non-capacitated, capacitated or AR and rigorously calculate methods agreement on the level of individual cells. This approach would not be technically possible for the CTC method since sample processing and evaluation by FM differs from antibody or FITC-phall staining. The second limitation is similar but related to AR prediction. The experimental approach used in the current study also does not allow to determine if individual cells detected as capacitated by individual methods would be exactly those undergoing AR when exposed to solubilized ZP-induced acrosome reaction was shown, that described methods are able to predict the number of spermatozoa undergoing AR after exposure to ZP but there were major differences among individual methods. The detailed knowledge of limits of these methods commonly used for evaluation of capacitation status and prediction of sperm ability to undergo the AR should help to standardize individual results and lead to production of good comparable data among scientific laboratories.To summarize, the multiple fluorescent methods used in our study to monitor boar sperm capacitation proved to be able to detect the temporal changes of the capacitation process. However, for some methods, flow cytometry is more appropriate than fluorescent microscopy and vice versa, and this should be considered in an experimental design. Data from individual analytical methods significantly correlates, albeit there are notable differences in the correlation coefficient between them. Furthermore, a change in the temporal dynamics in individual molecular processes detected by appropriate methods were observed. These individual observations and assessments are crucial, as the differences in temporal changes allow us to make rough model of chronological succession of processes underlying capacitation. Finally, using a correlation analysis with data from ZP-induced AR. Our study thus contributes further to better characterization of the important step in the mammalian reproduction such as capacitation.Capacitation is one of the most crucial steps sperm has to undergo before being able to fertilize egg. Therefore, the proper characterization of its dynamics has great importance for many studies addressing sperm physiology. In this article, we have studied capacitation of boar sperm using four largely used methods, compared their experimental outputs using fluorescent microscopy and flow cytometry and highlighted their limits and differences when detecting capacitation progress. Furthermore, we show that CTC and ACR.2 methods are able to accurately predict the percentage of acrosome-reacted sperm after Additional file 1:Figure S1. Example of the fluorescent microscopy analysis of the sperm capacitation status. The microphotograph shows 14 boar sperm stained by anti-pY antibody (green) and counterstained by DAPI. Eleven cells detected as capacitated (C), two cells detected as non-capacitated (NC), one cell as AR. Time 240 min, 400x.Additional file 2:Figure S2. Example of the flow cytometry analysis of the boar sperm capacitation progress by ACR.2 antibody. The dot plots represent the gating of the analyzed sperm population using FSC and SSC detection and the histograms of the FITC channel with the gating of the non-capacitated (N), capacitated (C) and AR cells (%) for the individual time points  and AR. The data from 0, 240 and AR from the 20 individual boars were subsequently used for the preparation of Fig. Additional file 3:Figure S3. Bland-Altman plots separately for individual methods. Bland-Altman plots .


Asparagus officinalis roots (ARs) native to New Zealand (green and purple cultivars) and China  using ultrasound-assisted, solid-phase extraction (UASE-SPE) coupled with ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The method was validated in terms of linearity, limit of detection (LOD), limit of quantification (LOQ), accuracy (expressed as recovery %), and precision (expressed as relative standard deviation (%RSD)). The retention times, ultraviolet visible (UV-vis) data, and mass spectral patterns of the detected peaks matched those of commercial standards, allowing characterization of the target compounds. The LODs and LOQs were 23 ng/mL and 70 ng/mL, 50 ng/mL and 150 ng/mL, 10 ng/mL and 30 ng/mL, 18 ng/mL and 54 ng/mL, 14.4 ng/mL and 43.6 ng/mL, and 7.5 ng/mL and 22.5 ng/mL for caffeic acid, quercetin, apigenin, ferulic acid, baicalein, and kaempferol, respectively, and the mean recovery rates were 85.8%, 73.0%, 90.2%, 80.6%, 76.7%, and 74.5% for the six compounds, respectively. The levels of the target compounds were significantly different (p < 0.05) among the six cultivars. The Chinese yellow AR had the highest levels of bioactive compounds: 6.0, 3.9, 0.4, 1.0, 0.86, and 0.8 mg/g for caffeic acid, quercetin, apigenin, ferulic acid, baicalein, and kaempferol, respectively. The AR extracts showed protective effects against oxidative stress in the HepG2 and L929 cell lines. The results indicate that AR extracts contain high flavonoid levels that provide protective functions against oxidative stress and support the potential commercial application of AR extracts.A simple, rapid, specific, and sensitive method was developed for the simultaneous identification and quantification of six major bioactive compounds, namely, caffeic acid, quercetin, apigenin, ferulic acid, baicalein, and kaempferol, from  Asparagus officinalis L. (green asparagus) is consumed worldwide as a popular fresh vegetable due to its nutritional value and high levels of bioactive phenolic compounds + of the analyte determined the EIC signal. The response factors (RFs) and regression coefficient (R2) were used to assess linearity.DR is defined as the detector response in area counts, and C is the concentration of the injected analyte. The best linear calibration curve produced by the detection signal for individual analyses was subsequently selected for quantitative analysis.Five concentrations of external standards  were used for quantification using least-squares linear regression. 2 vented cell culture flasks . Aliquots were placed in 50-mL Falcon tubes  and stored at −4 °C. All procedures were conducted under sterile conditions using sterile instruments in a laminar flow hood . Both cell lines were cultured in a humidified incubator  at 37 °C with 5% CO2. Growth medium was replaced every three days. Once they reached the desired 70–80% confluency, the cells were prepared for sub-culturing under sterile conditions. After the third passage, the cells were used in the experiments + of caffeic acid . The ferulic acid content in all six AR varieties was lower than that reported in the literature [The developed method was employed for detection and quantification of the six compounds  in the AR samples from New Zealand and China. The levels of the six compounds are shown in terature , which mterature ,30. The terature  who usedA. officinalis spears from Alcala del Rio and green, bronze, and purple A. officinalis spears from Huetor-Tajar and obtained total flavonoid content (TFC) values of 1.62 mg rutin equivalents (RE)/g of fresh product (FP), 2.25 mg RE/g of FP, 6.40 mg RE/g of FP, 5.26 mg RE/g of FP, and 5.94 mg RE/g of FP, respectively. Kulczyński, et al. [A. officinalis spears from Nowy Tomysl, Poland (Miedzichowo), and obtained total phenolic content (TPC) values of 270–430 mg quercetin equivalents (QE)/100 g of dry weight of extract (DW), 450–730 mg QE/100 g of DW, and 260–420 mg QE/100 g of DW, respectively. These results suggest that different species of AR, and even different parts of AR, contain different phytochemical levels. There were no significant differences in the levels of ferulic acid, baicalein, and kaempferol among the six AR cultivars, except in Chinese yellow AR (p < 0.05).The phytochemical content in AR matrices is dependent on type, region, climate, age, and other environmental factors ,32. Rodr2O2-induced oxidative damage than the Chinese white, purple, or green AR extracts (p < 0.05). The effects of low concentrations of AR extracts (≤0.25 mg/mL) were similar to those of the untreated control and the control containing 0.1% DMSO (p > 0.05). The likely reason for these observations is the differences in qualitative and quantitative compositions of the phytochemicals in the six AR cultivars . The number of viable cells in H2O2 treated L929 cells by New Zealand green AR at a concentration of 0.25 mg/mL provides protection similar to that of Chinese white AR given at a concentration of 1 mg/mL. Moreover, Chinese green and purple AR significantly protected the L929 cell viability against H2O2-induced damage (p < 0.05). We found that pre-incubation with the highest concentration of Chinese purple AR substantially increased the viability in H2O2 treated L929 cells. In an effort to understand the effect of protection on the membrane integrity of L929 cells, an LDH membrane leakage assay was used and compared with untreated (negative) counterparts. We found that high concentration of Chinese yellow and New Zealand green AR treatments significantly improved the protective effects in L929 cells. In contrast, low concentrations of Chinese green and white AR extracts did not have a significant effect on cell viability compared with positive control H2O2 of both cells (p > 0.05). This finding indicates that the biological activity of phytochemicals is generally a function of their concentrations, which affect the membrane permeation and interaction with the cellular systems [2O2 were enhanced; these results are in line with the literature [Osbeckia aspera leaves was found to exert a more protective effect against induced damage  to HepG2 cells than lower concentrations (0.25 and 0.5 mg/mL), as reported by Thabrew et al. [2O2. Herein, we found that caffeic acid (in Chinese yellow AR) at a concentration of 5.97 mg/g significantly decreased the LDH leakage on H2O2-induced damage in HepG2 and L929 cells. Interestingly, New Zealand green and purple AR  exhibited similar levels of LDH release in both cell lines induced by H2O2 exposure. However, Chinese yellow AR displayed a better bio-protective effect at the highest tested concentration (1 mg/mL), the concentration that completely protected the membrane integrity against oxidative stress in H2O2-induced HepG2 and L929 cells. This difference could be attributed to the higher photochemical constituents of Chinese yellow AR. Caffeic and ferulic acids protected against oxidative membrane damage owing to their highly conjugated double bond structure and the ability to aggregate in the lipid bilayer located on the cell membrane. Collectively, these findings suggest that phytochemicals in AR have a significant protective influence on cell membrane against H2O2-induced oxidative damage [Chinese green AR exhibited no enhancement effects in protecting L929 and HepG2 cells against H25 mg/mL . On the  systems . As the terature . In thise damage .A simple and rapid method for the analysis of phytochemicals in AR cultivars using SPE-UPLC-MS/MS was developed. All six analytes were identified and quantified based on UV-vis data, mass spectral characteristics, and retention times. MS detection exhibited a higher sensitivity for the measurement of low analyte concentrations than DAD detection, and the qualitative establishment of structural features of phytochemical compounds was also performed using MS. In addition, MS facilitated the determination of qualitative fragment patterns of the bioactive compounds, leading to the identification of caffeic acid, quercetin, apigenin, ferulic acid, baicalein, and kaempferol in six different types of AR, suggesting that this method exhibits high sensitivity at low concentrations. Furthermore, the establishment of a biological and physiologically relevant HepG2 and L929 cell model system was important for measurement of the bio-protective capacity of AR by-products by taking into consideration variations in the phytochemicals being obtained from the AR cultivars. The six phytochemicals from AR cultivars that determine the protective capacity towards HepG2 and L929 cells and act against oxidative stress should be isolated and purified in future investigations.