Saccharomyces cerevisiae. A computer program, PeakFinder, enables flexible, automated identification and annotation of cohesin binding peaks in ChIP chip data. Cohesin sites are highly conserved in meiosis and mitosis, suggesting that chromosomes share a common underlying structure during different developmental programs. These sites occur with a semiperiodic spacing of 11 kb that correlates with AT content. The number of sites correlates with chromosome size; however, binding to neighboring sites does not appear to be cooperative. We observed a very strong correlation between cohesin sites and regions between convergent transcription units. The apparent incompatibility between transcription and cohesin binding exists in both meiosis and mitosis. Further experiments reveal that transcript elongation into a cohesin-binding site removes cohesin. A negative correlation between cohesin sites and meiotic recombination sites suggests meiotic exchange is sensitive to the chromosome structure provided by cohesin. The genome-wide view of mitotic and meiotic cohesin binding provides an important framework for the exploration of cohesins and cohesion in other genomes.In eukaryotic cells, cohesin holds sister chromatids together until they separate into daughter cells during mitosis. We have used chromatin immunoprecipitation coupled with microarray analysis (ChIP chip) to produce a genome-wide description of cohesin binding to meiotic and mitotic chromosomes of  Chromatin immunoprecipitation reveals that cohesin sites -- those responsible for holding sister chromatids together until they separate into daughter cells -- are conserved in meiosis and mitosis Saccharomyces cerevisiae, since mutation results in the precocious dissociation of sister chromatids.Sister chromatid cohesion ensures that daughter cells inherit complete copies of their genome. Cohesion in eukaryotic cells is mediated by a multisubunit protein complex called cohesin. Cohesin consists of four proteins: Smc1, Smc3, Scc1/Mcd1, which is called kleisin and is the target of the protease separase, and Scc3. These proteins have recently been proposed to form a ring structure that encircles sister chromatids . AlternaCohesion serves at least three roles in the cell with respect to accurate genome transmission. Firstly, cohesion proximal to the centromere facilitates biorientation of chromosomes with respect to the spindle . Secondlcis determinants of cohesin binding. Cohesin-associated regions have been identified in yeast using chromatin immunoprecipitation. In these studies cohesin association with chromatin was followed at low resolution along the entire length of Chromosome III  or high resolution  at limited regions on Chromosome III, V, and XII  and microarray methods, often referred to as ChIP chip technology. To aid identification of peaks of cohesin binding we developed a program, PeakFinder, for extraction of peaks from raw ChIP data. We further used this approach to map all the cohesin-binding sites on an “ectopic chromosome,” a yeast artifical chromosome containing a 334-kb insert from human Chromosome VII. Information from a large number of sites greatly facilitates the assessment of cohesin distribution and of the significance of correlations with many local properties of the genome, such as base composition and coding content. Furthermore, it allows us to evaluate the impact of several factors, such as strain background, transcription, and developmental programs like meiosis, on cohesin binding, and to test the predictions by engineering individual cohesin sites.One approach to better understand cis determinants of cohesin sites  much of the analysis has to be done manually. A better approach would be to use the local parameters of cohesin binding to identify the peaks. To aid such a task, we have written a Windows program, PeakFinder, which discerns and filters the peaks from a variable local background and maps the tip of the peak to a single array element. The program is freely available (http://research.stowers-institute.org/jeg/2004/cohesin/peakfinder).We used the genome-wide approach of ChIP chip to identify and evaluate anaphase . We werehip data . A singlCEN3 is the highest for the chromosome, (2) the height of the peaks declines towards the ends of the chromosome, and (3) peaks correlate with AT content , dense region of binding in pericentric domains  (p < 0.0001) even though intergenic regions make up only 27% of the genome length. Intergenic regions in S. cerevisiae are more AT-rich than open reading frames (ORFs). Therefore, we tested whether the AT bias could be explained by the bias towards binding intergenic sequences by comparing the AT content of all intergenic regions with the AT content of intergenic regions associated with cohesin . Thus, AT content appears to be a major determinant for cohesin association.Cohesin peaks were strongly associated with AT-rich regions . We foun 0.0001) A. CohesiWe observed local oscillations of AT content in a 5-kb sliding window, which corresponded to cohesin-binding peaks in chromosome arms see , thus exThe semiregular spacing of cohesin and the correlation with local oscillations of base composition suggested that AT content and/or a measuring mechanism might control cohesin distribution on the chromosome. In order to test these possibilities, we used a nonessential ectopic yeast artificial chromosome (YAC). We used ChIP followed by quantitative PCR to map cohesin-binding sites in a YAC containing 334 kb of human DNA from Chromosome VII. The pericentric region on the right end of the YAC shows a broad (approximately 35 kb), intense association with cohesin. This is similar to the cohesin association with endogenous pericentric regions. However, the spacing of cohesin does not have the same periodic nature as on endogenous chromosomes. For example, the leftmost 83 kb of the YAC contains only one peak of cohesin binding, resulting in two large gaps for cohesin of 38 and 45 kb A. These To further test the contribution of sequence to cohesin binding, we took advantage of the fact that none of the human sequences were essential for yeast survival. When we replaced the region from 156 to 162 kb, which contains a cohesin-binding site, with the gene encoding for geneticin resistance, this region was no longer associated with cohesin B. Neighbp < 0.0001). In fact, 39% of the convergent intergenic regions in the genome have peaks of cohesin binding, and nearly half of all cohesin-binding sites are in convergent intergenic regions. These percentages approach the predictive power of consensus sequences for identifying binding sites of their cognate transcription factor  location within 25 kb of a centromere, (2) above average AT content, or (3) location in an intergenic region with converging transcription. This leaves 230 sites unexplained. Of these, 43 are intergenic. Most of these are simply difficult to assign to a transcriptional category. Interestingly, of the 230 “unexplained” sequences, 187 are in ORFs, which is more than half of all the ORFs associated with cohesin. These ORFs do not appear to have any unifying theme with regard to function, dubiousness, transcription level, or essentiality. The 187 peaks have similar height to other peaks. Thus, unlike the 5 (p = 10−65) and (GAN)10 (p = 10−76). The p values reflect the significance of these sequences as calculated based on a Monte Carlo simulation for the yeast genome. These sequences were rare in intergenic regions in yeast. These sequences were not present in the YAC. Further experiments will be required to determine whether these repeats are targeted by cohesin. Interestingly, human cohesin has been localized in Alu repeats . We carried out ChIP chip analysis in parallel with monitoring gene expression in the same cells using ORF arrays. Of the regions where gene expression changed 5-fold or more, only two regions were associated with cohesin. One peak of cohesin binding in glucose was associated with the promoter of the GAL2 gene . Expression of YDL218W was induced 11-fold in the presence of glucose compared to galactose. This ORF is associated with cohesin in the presence of galactose, but this association is reduced when glucose is present (unpublished data). The peaks surrounding both regions were unaffected. These results demonstrate that high levels of transcription are incompatible with cohesin binding. It also supports the results observed with the human DNA that neighboring cohesin sites behave independently.We analyzed whether changing the transcriptional program could change the association of cohesin with a locus. We grew cultures with either 2% glucose or 2% galactose as the carbon source and arrested them in metaphase using nocodazole. The main difference between metaphase arrest in the presence of nocodazole and in AL2 gene A, which AL2 gene B. The prThere are a number of mechanisms that could account for the incompatibility between transcription and cohesin binding. Transcription of a region during G1 may prevent new association of cohesin, or transcription may displace cohesin in G2. We explored the mechanistic link between transcription and cohesin association at the previously characterized cohesin sites cohesin-associated region on Chromosome III (C) (CARC1) and cohesin-associated region on Chromosome XII (L) (CARL2)  by inserThe displacement of cohesin may be due to a competition between RNA pol II/chromatin–remodeling factors and cohesin for DNA, or transcript elongation may remove cohesin. We tested if the incompatibility was dependent on transcript elongation. A culture was arrested with nocodazole, and galactose-responsive transcription was induced by the addition of galactose, as described above. Thiolutin was added to half of this culture immediately prior to the addition of galactose. Thiolutin inhibits transcript elongation but presumably does not inhibit the binding of RNA pol II . The effcis determinants of this complex has been reported. We expressed Rec8-3HA in SK1, a rapidly and synchronously sporulating strain, and analyzed the location of the cohesin–DNA complex in ChIP experiments . This suggests that binding of the meiotic complex, like the mitotic complex, is not compatible with transcription.ing site . Bindinging site A. The tring site . Other gWe compared the location of meiotic cohesin to the location of the double-strand breaks (DSBs) that initiate meiotic recombination . We founROK1, RRS1, and EBP2  locus.  The rDNA is encoded in an approximately 1–2-Mb region on the right arm of Chromosome XII consisting of 100–200 tandem copies of a 9.1-kb repeat. There is a peak of cohesin binding that localizes to the left border of the rDNA repeat  that is and EBP2 , are dowand EBP2 , suggestand EBP2 , 2000. Tcdc16-ts or nocodazole, galactose versus glucose as a carbon source, and induction of meiosis) and in three different yeast-strain backgrounds . Using PeakFinder, a program that assesses cohesin binding by comparison of signal to variable local background, we find that the majority of cohesin-binding sites are remarkably constant under these different circumstances. Distribution of cohesins throughout the genome appears to depend on a combination of base composition, sequence, and transcriptional activity. We find evidence for three types of cohesin sites in the genome: (1) the centromere and pericentric domain, (2) intergenic regions in chromosome arms, and (3) ORFs in chromosome arms. The association of cohesin with these three types of sites is subject to different genomic parameters. Cohesin at centromeres and pericentric regions is spread over a broad domain with an elevated “baseline” level and is not affected by the natural transcriptional and coding status. Much of the cohesin in chromosome arms is located in transcriptionally converging intergenic regions. ORFs in chromosome arms where cohesin is found are enriched for repetitive sequences. This suggests that there may be three mechanisms to load cohesin, consistent with what has been proposed for cohesin in meiotic chromosomes for S. pombe . If cohesin and transcript elongation were incompatible, then we would also expect to find sites biased towards intergenic regions, which we do. However, we find a strong bias towards intergenic regions with converging transcription, and a bias against intergenic regions with surrounding unidirectional transcription or diverging transcription, suggesting that intergenic regions with converging transcription may have especially low transcription. These regions may have evolved particularly strong transcriptional stops since they are quite short on average and the cell may need to avoid transcription from one side extending to the other to prevent the synthesis of antisense RNA. The protection of sequence elements important for the replication and segregation of eukaryotic chromosomes from transcription may be a general necessity for their proper function in vivo. For instance, transcription through an autonomous replicating sequence  “pushes” cohesin into place or strips cohesin from inappropriate locations in each cell cycle. Secondly, transcript elongation may be indirectly responsible for localizing cohesins, for example by accumulation of “nonpermissive” chromatin in transcribed regions and “permissive” chromatin in nontranscribed regions. This type of genome-wide demarcation of transcription units has been shown to occur in revisiae  and may revisiae  and histrevisiae . The prelization . The poslization .Despite the subunit difference between the meiotic and mitotic cohesin complex, we find that the association of cohesin with DNA in meiotic cells is similar to that in mitotic cells. In addition, we find that the constitutive peaks of meiotic cohesin binding are negatively correlated with DSB sites. This negative correlation is consistent with the model proposed by S. cerevisiae will serve as a useful framework upon which to explore attributes of cohesin localization in higher eukaryotes.Genome-wide studies of protein–DNA complexes afford a better understanding of the role of these complexes in the biology of an organism and its genome. In the process of analyzing the first genome-wide map of cohesin in any organism, we developed PeakFinder, a program able to sensitively identify binding sites of protein–DNA complexes in their local genomic environment, and potentially useful for analysis of any other genome-wide measurements. While budding yeast appears to have largely opted for placement of cohesin in AT-rich, transcriptionally inactive regions, other organisms with much longer and more complicated transcriptional units, different base composition properties, or different levels of condensation may employ different strategies for the placement of cohesin, which may in turn affect the stability of those genomes. The genome-wide analysis of cohesin in ChIPs were performed as previously described . Semiquacdc16-ts cells.For the meiotic timecourse, cultures were grown in YPA, then transferred to SPM. Timepoints were removed for ChIP at 4, 6, 8, 10, and 12 h after transfer to SPM. Nocodazole-mediated arrest was accomplished by adding nocodazole to a final concentration of 15 μg/ml to the media. All cultures were grown at 30 °C. Shifting cultures to 37 °C in prewarmed media induced metaphase arrest in http://microarrays.org). The meiotic experiments were done in the SK1 strain background and although two independent timecourses were performed, the results from a single representative timecourse were used for analysis. The resolution of these genome-wide maps is limited by (1) the shear size of the DNA fragments (range of 200–1000 bp) and (2) the size of the elements on our microarrays (mean of 0.9 kb). We do not expect these fragment-size distributions to introduce a significant bias in our mapping effort since the previously estimated size of a cohesin-binding region at an arm site is 0.8–1.0 kb . PeakFinder allows filtering of peaks based on the parameters of the peak. For example, cohesin peaks analyzed in the cdc16-ts dataset were identified using the following set of parameters: (1) sampling log2-transformed ratios at 100-bp intervals, (2) smoothing over eight rounds using a nine-point Gaussian-weighted moving average, and (3) filtering of peaks with a left and right rise of less than 0.1 and a height less than 0.4 (log2 space). These conditions identified all peaks mapped to high resolution on Chromosomes III and XII  4000B scanner and quantitated using GenePix 4.0. Results were stored in the AMAD database. Data were normalized and filtered by requiring intensity to be 200 or more, and spots to have a correlation coefficient of 0.5 or more. For analysis purposes, any feature with less than two measurements was excluded (with the exception of the meiotic timecourse). Data were analyzed using PeakFinder, a program developed specifically for finding peaks in ChIP data, but generally applicable for plotting any measurement against genomic coordinates, smoothing the curves, and annotating peaks on the basis of local properties of the curve. Extensive documentation for PeakFinder is available at  and XII . A curreURA3. Nucleotides 326,702—332,707 contain vector sequences from pYAC4 including the centromere, TRP1, and ARS1. The YAC was introduced into the strain 1377 A1 4B, two independent cultures were grown to exponential phase, and nocodazole was added. After 3 h of growth at 23 °C, more than 90% of cells were arrested in metaphase. Cultures were processed for ChIP as described previously.PCR primers were designed to amplify 150–300-bp sequences, at 1-kb intervals along the entire length of the 1572 YAC . Nucleot6/ml. Cultures were arrested in G1 with alpha factor, released from G1 in the presence of nocodazole, and grown for 3 h to generate an M phase arrest. Cultures were split in two, and one half received galactose to a final concentration of 4%. One hour after addition of galactose, cultures were fixed and processed for ChIP. Experiments with thiolutin were performed as described above except thiolutin was added to a final concentration of 3 μg/ml just prior to galactose addition. Primers were generated that amplified 5′ and 3′ regions of CARC1 and CARL2 in the endogenous locus and on pCM34 and pCM38. Reduction in cohesin binding was expressed as the ratio of (1) the amount of cohesin bound to the 5′ or 3′ ends of the CAR on the plasmids to (2) the amount of cohesin bound to the 5′ or 3′ regions of the CAR in the genome.0.8 kb from CARC1 and 1.4 kb from CARL2 were cloned into pUNI and then recombined with pYCE to form pCM34 and pCM38. This places the pGAL1-10 promoter immediately adjacent to cohesin-associated regions. pCM34 and pCM38 were introduced into 1377 A1 4B by transformation. Strains with pCM34 and pCM38 were initially grown in complete medium lacking uracil with raffinose as a carbon source. This medium selects for retention of the plasmids and prevents transcription from the Gal-inducible promoter. These cultures were diluted approximately 100-fold in YEP raffinose and grown to 7 × 10Dataset S1File cdc16_Mcd1-18Myc_W303.(483 KB TXT).Click here for additional data file.Dataset S2File cdc16_Mcd1-6HA_A364a.(957 KB TXT).Click here for additional data file.Dataset S3File cdc16_Smc3-6Myc_A364a.(701 KB TXT).Click here for additional data file.Dataset S4File Mcd1_18Myc_W303_NZgalCHIP.(406 KB TXT).Click here for additional data file.Dataset S5File Mcd1-18Myc_W303_NZgal_exp.(196 KB TXT).Click here for additional data file.Dataset S6File Mcd1-18Myc_W303_NZglu_exp.(221 KB TXT).Click here for additional data file.Dataset S7File Mcd1-18Myc_W303_NZgluChIP.(694 KB TXT).Click here for additional data file.Dataset S8File Mcd1-3HA_SK1_NZ.(339 KB TXT).Click here for additional data file.Dataset S9File Rec8-3HA_SK1.(710 KB TXT).Click here for additional data file.Dataset S10cdc16-ts Mcd1-6HA in A364a; Cy5 = genomic DNA.Cy3 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S11cdc16-ts Mcd1-6HA in A364a; Cy5 = genomic DNA.Cy3 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S12cdc16-ts Mcd1-6HA in A364a; Cy5 = genomic DNA.Cy3 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S13cdc16-ts Mcd1-6HA in A364a.Cy3 = genomic DNA; Cy5 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S14cdc16-ts Mcd1-6HA in A364a.Cy3 = genomic DNA; Cy5 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S15cdc16-ts Smc3-6Myc in A364a; Cy5 = genomic DNA.Cy3 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S16cdc16-ts Smc3-6Myc Mcd1-6HA ChIP for HA in A364a; Cy5 = genomic DNA.Cy3 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S17cdc16-ts Smc3-6Myc Mcd1-6HA ChIP for Myc in A364a; Cy5 = genomic DNA.Cy3 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S18cdc16-ts Smc3-6Myc in A364a.Cy3 = genomic DNA; Cy5 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S19cdc16-ts Smc3-6Myc Mcd1-6HA ChIP for HA in A364a.Cy3 = genomic DNA; Cy5 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S20cdc16-ts Smc3-6Myc Mcd1-6HA ChIP for Myc in A364a.Cy3 = genomic DNA; Cy5 = ChIP (4.6 MB XLS).Click here for additional data file.Dataset S21cdc16-ts Mcd1-18Myc in W303.Cy3 = genomic DNA; Cy5 = ChIP (2.5 MB XLS).Click here for additional data file.Dataset S22cdc16-ts Mcd1-18Myc in W303.Cy3 = genomic DNA; Cy5 = ChIP (2.5 MB XLS).Click here for additional data file.Dataset S23Cy3 = ChIP 6h Rec8-3HA in SK1; Cy5 = genomic DNA.(2.6 MB XLS).Click here for additional data file.Dataset S24Cy3 = ChIP 8h Rec8-3HA in SK1; Cy5 = genomic DNA.(2.5 MB XLS).Click here for additional data file.Dataset S25Cy3 = ChIP 10h Rec8-3HA in SK1; Cy5 = genomic DNA.(2.6 MB XLS).Click here for additional data file.Dataset S26Cy3 = ChIP 6h Rec8-3HA in SK1; Cy5 = genomic DNA.(2.6 MB XLS).Click here for additional data file.Dataset S27Cy3 = ChIP 8h Rec8-3HA in SK1; Cy5 = genomic DNA.(2.6 MB XLS).Click here for additional data file.Dataset S28Cy3 = genomic DNA; Cy5 = ChIP 12h Rec8-3HA in SK1.(2.6 MB XLS).Click here for additional data file.Dataset S29Cy3 = genomic DNA; Cy5 = ChIP 4h Rec8-3HA in SK1.(2.6 MB XLS).Click here for additional data file.Dataset S30cdc16-ts Mcd1-18Myc in W303.Cy3 = genomic DNA; Cy5 = ChIP (2.6 MB XLS).Click here for additional data file.Dataset S31cdc16-ts Mcd1-18Myc in W303.Cy3 = genomic DNA; Cy5 = ChIP (2.6 MB XLS).Click here for additional data file.Dataset S32cdc16-ts Mcd1-18Myc in W303; Cy5 = genomic DNA.Cy3 = ChIP (2.6 MB XLS).Click here for additional data file.Dataset S33cdc16-ts Mcd1-18Myc in W303; Cy5 = genomic DNA.Cy3 = ChIP (2.6 MB XLS).Click here for additional data file.Dataset S34Cy3 = genomic DNA; Cy5 = ChIP 4h Rec8-3HA in SK1.(2.6 MB XLS).Click here for additional data file.Dataset S35Cy3 = genomic DNA; Cy5 = ChIP 10h Rec8-3HA in SK1.(2.6 MB XLS).Click here for additional data file.Dataset S36Cy3 = genomic DNA; Cy5 = ChIP 12h Rec8-3HA in SK1.(5.6 MB XLS).Click here for additional data file.Dataset S37Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest Mcd1-3HA in SK1.(2.6 MB XLS).Click here for additional data file.Dataset S38Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest Mcd1-3HA in SK1.(2.6 MB XLS).Click here for additional data file.Dataset S39Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303.(2.7 MB XLS).Click here for additional data file.Dataset S40Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303.(2.7 MB XLS).Click here for additional data file.Dataset S41Cy3 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303; Cy5 = genomic DNA.(2.6 MB XLS).Click here for additional data file.Dataset S42Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303.(4.4 MB XLS).Click here for additional data file.Dataset S43Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303.(4.4 MB XLS).Click here for additional data file.Dataset S44Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest galactose Mcd1-18Myc in W303.(4.4 MB XLS).Click here for additional data file.Dataset S45Cy3 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303; Cy5 = genomic DNA.(4.4 MB XLS).Click here for additional data file.Dataset S46Cy3 = ChIP nocodazole arrest galactose Mcd1-18Myc in W303; Cy5 = genomic DNA.(4.4 MB XLS).Click here for additional data file.Dataset S47Cy3 = ChIP nocodazole arrest galactose Mcd1-18Myc in W303; Cy5 = genomic DNA.(4.4 MB XLS).Click here for additional data file.Dataset S48Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest glucose Mcd1-18Myc in W303.(4.4 MB XLS).Click here for additional data file.Dataset S49Cy3 = genomic DNA; Cy5 = ChIP nocodazole arrest galactose Mcd1-18Myc in W303.(4.4 MB XLS).Click here for additional data file.Dataset S50Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest galactose Mcd1-18Myc in W303.(2.2 MB XLS).Click here for additional data file.Dataset S51Cy3 = polyA+ RNA nocodazole arrest galactose Mcd1-18Myc in W303; Cy5 = polyA+ reference RNA.(2.2 MB XLS).Click here for additional data file.Dataset S52Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303.(2.2 MB XLS).Click here for additional data file.Dataset S53Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest galactose Mcd1-18Myc in W303.(2.2 MB XLS).Click here for additional data file.Dataset S54Cy3 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303; Cy5 = polyA+ reference RNA.(2.2 MB XLS).Click here for additional data file.Dataset S55Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303.(2.2 MB XLS).Click here for additional data file.Dataset S56Cy3 = polyA+ RNA nocodazole arrest galactose Mcd1-18Myc in W303; Cy5 = polyA+ reference RNA.(2.2 MB XLS).Click here for additional data file.Dataset S57Cy3 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303; Cy5 = polyA+ reference RNA.(2.2 MB XLS).Click here for additional data file.Dataset S58Cy3 = polyA+ reference RNA; Cy5 = polyA+ RNA nocodazole arrest glucose Mcd1-18Myc in W303.(2.2 MB XLS).Click here for additional data file.Protocol S1(61 KB DOC).Click here for additional data file.Protocol S2(37 KB DOC).Click here for additional data file.Saccharomyces Genome Database (http://www.yeastgenome.org/) accession numbers for the genes and gene products discussed in this paper are CDC16 (SGDID S0001505), EBP2 (SGDID S0001655), GAL2 (SGDID S0004071), Mif2 (SGDID S0001572), NCE102/YPR149W (SGDID S0006353), PCD1 (SGDID S0004141), Pds1p (SGDID S0002520), Rec8 (SGDID S0006211), ROK1 (SGDID S0003139), RRS1 (SGDID S0005820), Scc1/Mcd1 (SGDID S0002161), Scc3 (SGDID S0001288), Smc1 (SGDID S0001886), Smc3 (SGDID S0003610), YDL218W (SGDID S0002377), YDL238C (SGDID S0002397), YER179W (SGDID S0000981), and YPR006C (SGDID S0006210).The


In August 2021, we detected highly pathogenic avian influenza A(H5N1) clade 2.3.4.4b viruses in poultry in southern Benin. The isolates were genetically similar to H5N1 viruses of clade 2.3.4.4b isolated during the same period in Africa and Europe. We also found evidence for 2 separate introductions of these viruses into Benin.  Highly pathogenic avian influenza (HPAI) viruses represent a major threat to animal and public health. HPAI A/Goose/Guangdong/1/96-lineage subtype H5N1 viruses first emerged in southern China in 1996, and their descendants have since evolved into different phylogenetic clades causing large outbreaks in poultry and wild birds worldwide  as described elsewhere  from among poultry at 6 infected farms and poultry markets. We performed one-step real-time reverse transcription PCR (rRT-PCR) targeting the influenza A virus H5 and N1 genes and detected avian influenza H5N1 virus RNA in 11 samples. We isolated viruses from positive samples in embryonated eggs, then performed molecular characterization by whole-genome sequencing using an Illumina MiSeq system (http://www.iqtree.org) with automatic model selection and 1,000 nonparametric bootstrap replicates , we conducted Bayesian Markov Chain Monte Carlo sampling in BEAST version 1.10.4 (https://beast.community) ; based ohttps://acmacs-web.antigenic-cartography.org). The antigenic map revealed 2 slightly distinct antigenic profiles for Benin H5N1 viruses. A/poultry/Benin/21-A-09-031-O/2021 was antigenically similar to most of the World Health Organization candidate vaccine viruses used in the HI assay, especially to A/Sichuan/26221/2014-like (IDCDC-RG42A) and A/duck/Hyogo/1/2016 (NIID-001). In contrast, the other 4 Benin isolates that clustered together in the phylogenetic trees were 2–3 log2 away from the candidate viruses, suggesting a good but lower level of cross-reactivity. The most antigenically similar candidate virus to these 4 Benin isolates was A/Astrakhan/3212/2020-like (CBER-RG8A). Amino acid sequence analysis showed that the H5N1 viruses from Benin had multiple basic amino acids motif (PLREKRRKR/GLF) at the HA cleavage site, characteristic of HPAI viruses. In addition, we detected a mammalian adaptation mutation in the polymerase basic 1 segment (113V) of the 5 Benin isolates, suggesting potential for these viruses to replicate more efficiently in mammalian cells  assay against reference ferret postinfection serum  Table 2,Although several outbreaks occurred in the region, relatively little information was available on the genetic and antigenic diversity of HPAI H5N1 clade 2.3.4.4b viruses in poultry in West Africa. We characterized 5 HPAI H5N1 clade 2.3.4.4b viruses from Benin highly similar to viruses detected earlier in neighboring Nigeria. It is therefore likely that the Benin H5N1 viruses originated from the region because of the movement of infected poultry and poultry products across neighboring countries  virus in poultry, Benin.


CAP-generated reactive species penetrated cells via AQP3 and suppressed RPS6KA3, a shared kinase of AQP3 and FOXO1. Reduced AQP3-19Y phosphorylation suppressed SCAF11-mediated AQP3-5K K48-ubiquitination that led to sabotaged FOXO1 stability. Inhibited FOXO1 phosphorylation retarded its regulatory activities in maintaining cancer stemness including ALDH1 and IL6. Enhanced anti-cancer efficacy was observed through combining CAP with Atorvastatin in vitro and in vivo. We propose CAP as a 'selective' onco-therapeutic against cancer stemness, with the AQP3/FOXO1 axis being one molecular mechanism. We report SCAF11 as an E3 ubiquitin ligase of both AQP3 and FOXO1, identify AQP3-5K as an AQP3 K48-ubiquitination site, and emphasize the essential role of AQP3-19Y in this process. We reposition Atorvastatin into the onco-therapeutic portfolio by synergizing it with CAP towards enhanced efficacy. We anticipate the efficacy of CAP in targeting malignancies of high stemness alone or as an adjuvant therapy towards the hope of ultimate cancer cure.Cold atmospheric plasma (CAP) is selective against many cancers with little side effect, yet its molecular mechanism remains unclear. Through whole transcriptome sequencing followed by assays  Cell phenotype switching between distinct states in response to environmental perturbations and mutational rewiring of the gene regulatory network is fundamental to cancer development and progression. Cancer therapy generally seeks to exploit this switching mechanism to force cancer cells into the apoptotic state. However, random and essentially uncontrollable transitions of highly-stressed surviving cells into the cancer stem cell (CSC) state often cause the failure of many therapeutic strategies.Breast cancer is comprised of heterogeneous cell cohorts, with the triple negative breast cancer (TNBC) subtype being one of the most difficult to treat as they are easily attracted in the CSC state that defies effective therapeutic approach with little side effect Cold atmospheric plasma (CAP), composed of reactive oxygen and nitrogen species (RONS) and electric fields in vitro, in vivo and using clinical samples, we propose CAP as a 'selective' onco-therapy targeting CSCs via the AQP3/FOXO1 axis. We report AQP3-5K K48-ubiquitination via SCAF11 with AQP3-19F phosphorylation being essential, and demonstrate synergies between CAP and Atorvastatin towards enhanced anti-cancer efficacy. We anticipate the feasibility of CAP in targeting other highly plastic cancers especially those lack safe cure, and its long-term success in synergizing with, e.g., immune- and chemo-therapies via arresting CSCs together with bulk tumor cells.Using TNBC cells as the model of malignant cells with high stemness, we aimed to explore the potential impact of CAP on cancer stemness, underlying molecular mechanism and possible drug synergies towards enhanced onco-therapeutic outcome in this study. Through whole transcriptome sequencing followed by assays ) for 2 to 5 minutes selectively reduced the viabilities of SUM159PT , SUM149PT , and MDAMB231  TNBC cells as compared with the quasi-normal MCF10A cells, whereas the viabilities of MCF10A and hormone receptor (HR)-positive MCF7 cells were slightly increased and HER2-amplified SKBR3 cells were not responsive to CAP treatment . Consistent with this, the Annexin V-FITC assay showed approximately two-folds increased apoptosis with significance in SUM159PT (p=1.75E-5), SUM149PT (p=0.009), MDAMB231 (p=0.005) cells on CAP treatment, whereas no visible apoptosis was detected in MCF10A, MCF7 or SKBR3 cells . CAP also reduced 40% migrative abilities with significance in SUM159PT (p=2.9E-4), SUM149PT (p=1.4E-3) cells, 20% in MDAMB231 (p=1.2E-3) and SKBR3 (p=0.036) cells, and 10% in MCF7 (p=0.01) cells without altering that of MCF10A cells 24 hours post CAP exposure . HoloMonitor imaging  showed that CAP-activated medium (PAM) substantially reduced the random mobility of SUM159PT TNBC cells in terms of both migration distance (p=2.56E-5) and speed (p=1.9E-3) . These results suggested that CAP selectively reduced the malignancy of TNBC cells without harming healthy breast cells, and with little effect on HR-positive and HER2-positive luminal breast cancer cells. TNBC cells showed higher glycolysis , and were more responsive to CAP in a dose-dependent manner on mitochondria ATP production rate . MCF10A cells exhibited higher glycolysis and mitochondria ATP production rates due to the supplements EGF, cholera toxin, insulin and hydrocortisone in the culturing medium that are necessary for MCF10A cells to grow and promote cell metabolism.CAP exposure , its enzyme activity significantly reduced from 11% to 2.66% (p=5.43E-3) in SUM159PT, from 7.01% to 1.73% (p=1.93E-4) in SUM149PT cells, from 2.95% to 0.4% (p=2E-4) in MDAMB231, and from 8.58% to 3.04% (p=5.73E-4) in SKBR3 cells, whereas those in MCF7 and MCF10A cells did not substantially vary on CAP exposure . Consistent with this, SUM159PT, SUM149PT and MDAMB231 cells formed larger tumoroids, the sizes of which were largely reduced after CAP treatment, whereas those of MCF10A and MCF7 were smaller and not suppressed on CAP exposure; SKBR3 cell tumoroids were not visible . These results consolidated the hypothesis that breast cancer cells harboring higher percentage of CSCs were more sensitive to CAP treatment. Indeed, breast CSCs isolated from TNBC SUM159PT cells conveyed the highest sensitivity to CAP than the bulk tumor cells, unsorted TNBC cells and re-mixed cells , directly supporting the statement that CSCs drove the selectivity of CAP against cancer cells. Remixed cells were more vulnerable than unsorted TNBC cells, presumably due to the sorting process. Electron microscope imaging showed numerous empty vacuoles and leaky organelle membranes in SUM159PT cells after CAP exposure, suggestive of CAP-triggered cell death .While ALDH1 . These observations suggested a deterministic role of higher basal ROS level in TNBC cells in the selectivity of CAP against TNBC cells, especially in light of the elevated cellular ROS level on CAP exposure . Through quenching each primary component of CAP using different ROS scavengers, we found that TNBC cell viability returned to normal levels if tiron, sodium pyruvate, mannitrol, uric acid or hemoglobin were used , suggesting the leading roles of superoxide anion (‧O2-), H2O2, hydroxyl radical (‧OH), ozone (O3) and nitric oxide (‧NO) in enabling the selectivity of CAP against TNBC/BCSC cells.Reactive oxygen species (ROS) levels were higher in TNBC cell lines  as compared with non-TNBC cell lines , and the quazi-normal breast epithelial cell line (MCF10A), which was substantially lower than malignant cells , further evidencing the efficacy of CAP in reducing breast cancer stemness in vivo.Mice inoculated with SUM159PT cells showed significantly reduced tumor size after weekly CAP exposure as compared with the control group that did not receive such a treatment , and validated by qRT-PCR . While AQP1 was over-expressed and AQP3 was under-represented in SUM159PT cells, expression of both were increased on CAP exposure from the whole transcriptome data .We assessed the expression profiles of AQP family members, which have been implicated in the uptake of reactive species by cancer cells AQP1 or AQP3 (efficiencies in ) led to increased cell viabilities , and enhanced cell migrative abilities according to the scratch wound closure assay . Reduced cell migration rebounded back from 34.6% to 45.3% reduction as observed at 24h post-CAP exposure if AQP3 was knocked down  in SUM159PT cells.Knocking down either AQP1-knockdown; p=1.4E-3 AQP3-knockdown), and knocking down AQP1 but not AQP3 significantly reduced the ROS levels both with (p=0.039) and without CAP exposure , suggesting the involvement of AQP1 in mediating CAP-induced cellular redox fluctuation. The lipid ROS level decreased in SUM159PT (p=6.33E-4) and AQP1-knockdown cells (p=2.1E-3) upon CAP exposure with statistical significance, but did not significantly vary in AQP3-silenced cells , suggestive of decreased cell sensitivity to CAP and CAP-triggered cell death once knocking down AQP3 but not AQP1.CAP treatment caused increased cellular ROS level in SUM159PT cells . Such a negative correlation at the transcriptomic level was also observed in the public datasets TCGA, GEO4450 and METABRIC . In consistent with this, the transcription of FOXO1 was significantly higher and that of AQP3 was lower in basal than non-basal samples in the METABRIC dataset  according to Breast Cancer Gene-Expression Miner v4.5 (bc-GenExMiner) Fig. ). While FOXO1 expression was reduced and translocated from cell nucleus to cytoplasm, AQP3 expression was boosted and distributed in cytoplasm and cell membrane upon CAP exposure . Similarly, AQP3 had a lower expression in FOXO1-transfected SUM159PT (SUM159PT-FOXO1+) xenografts than in the control SUM159PT cells, and such an opposite relationship was also observed in in vivo mouse tumor samples after CAP treatment but with elevated AQP3 intensity . In consistent with this, FOXO1 and AQP3 protein expression exhibited opposite profiles according to the Human Protein Atlas  Fig. , , Supplementary Table 2), with the p value from the chi-squared test being 4.63E-20 and the correlation score being -0.54 .Among AQP members available in GSE132083 and our whole transcriptome data, AQP3 showed a negative association with FOXO1 regarding the transcriptome profiles across CSC and non-CSC cohorts in HCC1937 or SUM149PT cells and before and after CAP treatment in SUM149PT but not MCF7 cells . We next used MS to assess whether FOXO1 directly or indirectly interacted with AQP3. We did not find AQP3 from the list of FOXO1 interactants (Supplementary Table 6), but identified one E3 ligase, SCAF11 , by comparing it with the list of E3 ligases (Supplementary Table 7) retrieved from the hUbiqutome database (http://bioinfo.bjmu.edu.cn/hubi/) Fig. ), suggestive of the mediating role of SCAF11 in the interactions between AQP3 and FOXO1. Indeed, knocking down SCAF11  substantially weakened interactions between AQP3 and FOXO1 , decreased their ubiquitination levels , elevated their protein expression , and reduced CSC percentage from 8.05% to 7.25% with statistical significance .Given the opposite correlation between AQP3 and FOXO1, we examined their potential physical interactions and regulatory relationships. FOXO1 physically interacted with AQP3 (Supplementary Table 8), we identified 16 shared kinases between FOXO1 and AQP3 . Among these kinases, RPS6KA3 and SIK3 showed similar transcriptomic profiles in SUM159PT cells 8h post-CAP exposure with those in MCF7 cells , suggestive of the relevance of these two kinases in mediating CAP-triggered rewiring of SUM159PT cells away from the malignant state. Knocking down RPS6KA3  or SIK3  both decreased FOXO1 and AQP3 phosphorylation (-4E), with RPS6KA3 showing a leading role. Knocking down RPS6KA3 resulted in reduced interactions between AQP3 and FOXO1 , decreased AQP3 ubiquitination  and enhanced FOXO1 ubiquitination .By predicting the site-specific kinase-substrate relationships of FOXO1 and AQP3 from phosphoproteomic data using iGPS  that contains four phosphorylation sites in the cytoplasmic domains  as predicted using TOPCONS Fig. ). The AQP3-Y19F mutant resulted in substantially decreased interactions of AQP3 with SCAF11  and with FOXO1 , dramatically reduced AQP3 phosphorylation , and suppressed AQP3 ubiquitination .We next aimed to identify the site of AQP3 that was subjected to RPS6KA3 phosphorylation, and its roles in mediating the interaction of AQP3 with SCAF11 and AQP3 ubiquitination. AQP3 is a protein with 3 transmembrane domains . Indeed, the AQP3-K5R mutant was associated with reduced AQP3 ubiquitination  and decreased FOXO1 protein expression , in consistence with the competitive use of SCAF11 for the ubiquitination of AQP3 and FOXO1.In addition, we identified AQP3-5K as the most promising site for AQP3 K48-ubiqutination given that it was located at the N terminal and intracellular region of the transmembrane protein . FOXO1 had a higher level in SUM159PT than MCF7 cells , and CAP effectively reduced FOXO1 expression in SUM159PT cells . BCSC percentage in SUM159PT cultures was positively associated with FOXO1 expression with statistical significance when transfecting cells with FOXO1 over-expression plasmids or siRNAs , which was consistent with the promotive role of FOXO1 on cancer stemness FOXO1 was knocked down, and substantially elevated (p=6.01E-3) when FOXO1 was over-expressed .FOXO proteins are known essential players in the maintenance of somatic and CSCs FOXO1 over-expression were constructed . It took 12 and 21 days, respectively, for SUM159PT-FOXO1 and SUM159PT tumors to grow to 5 ± 0.5 mm in diameter , suggesting that SUM159PT-FOXO1 tumors grew substantially faster than SUM159PT tumors. Though mice inoculated with SUM159PT-FOXO1 cells more easily developed tumors, they shared a similar progression rate with SUM159PT tumors . SUM159PT-FOXO1 tumors were bigger in size and weight than SUM159PT tumors at harvest, which were reduced on CAP exposure . Notably, CAP more effectively controlled the growth and size of SUM159PT-FOXO1 inoculated tumors than SUM159PT tumors .SUM159PT cells with stable Fig. ). The average spleen weight of mice inoculated with SUM159PT-FOXO1 cells was considerably larger than that of the mice carrying SUM159PT cells, which became similar to each other after CAP treatment . The average kidney size of mice carrying SUM159PT-FOXO1 tumors significantly shrunk after CAP treatment, whereas those in mice carrying SUM159PT tumors did not change . CAP treatment considerably altered the heart weight in mice carrying SUM159PT cells but not in mice inoculated with SUM159PT-FOXO1 cells . Although the weight of liver or lung in mice carrying SUM159PT tumors did not alter if FOXO1 was over-expressed, both of them were significantly reduced upon CAP treatment independent of FOXO1 expression level .The organs of mice carrying SUM159PT-FOXO1 tumors were, in general, larger than those of mice inoculated with SUM159PT tumors . Interestingly, CAP caused weight loss (red), implicating the role of CAP on body weight that is beyond the scope of here.The average weight of mice carrying SUM159PT-FOXO1 (blue) was lower than that carrying SUM159PT (black) and was slightly increased after CAP exposure (magenta). These results suggested enhanced cancer cachexia when FOXO1 was over-expressed, and alleviated symptoms such as weight loss, splenomegalia and organ enlargement upon CAP exposure .ALDH1, FOXO1, IL6 levels were all higher in SUM159PT-FOXO1 inoculated tumors than those inoculated with SUM159PT cells, which were effectively reduced on CAP treatment , but not in MCF7 cells. Consistently with this, FOXO1 mono- and di-methylation levels were significantly up-regulated after CAP treatment . On the other hand, the total amount and nuclear accumulation of FOXO1, as well as its phosphorylated status, were all considerably reduced , suggesting a decreased gene regulatory functionality of FOXO1 as a result of cellular translocation away from the nucleus that was caused by decreased FOXO1 phosphorylation and enhanced FOXO1 methylation.The expression of PRMT1 (protein arginine methyltransferase 1) that suppresses FOXO1 activity IL6, GADD45A, GADD45B were top genes in the enriched pathways when both 'presence frequency' and 'pathway score' were taken into account . In this network, IL6 was the hub of the network that showed the highest number of connections . IL6 was over-represented in TNBC cells , and supplementing cells with IL6 promoted the self-renewal ability of SUM159PT cells . Over-representation of IL6 in TNBC cells was also supported at the transcriptional level by the E-MTAB-181 dataset ) and the METABRIC dataset analyzed using bc-GenExMiner . On the other hand, FOXO1 expression was highly associated with that of ALDH family members according to our transcriptome data , whereas ALDH1 is a canonical marker that characterizes breast cancer stemness We further examined whether the most abundant genes in the top altered pathways upon CAP exposure were involved in cancer stemness regulation and whether these genes and canonical genes controlling cancer stemness could be regulated by FOXO1. Fig. -3P). Importantly, FOXO1 physically interacted with IL6 , and was predicted to be a transcription factor of IL6 using ConTra v3 http://bioit2.irc.ugent.be/contra/v3) , with its positive regulatory role on IL6 being reported Fig. ), further substantiating the role of FOXO1 in the inhibitory functionality of CAP on breast CSCs. ALDH1 was translocated from the nucleus to the cytoplasm upon CAP exposure , physically interacting with FOXO1  and, importantly, transcriptionally regulated by FOXO1 .Upon CAP exposure, more IL6 was accumulated to the cytoplasm from cell nucleus . Combined use of CAP and Atorvastatin enhanced the anti-cancer efficacy of either CAP or Atorvastatin alone regarding tumor growth , apoptosis , and migration . Importantly, synergistic use of CAP and Atorvastatin significantly reduced CSC percentage  and self-renewal ability  as compared with using each single agent alone. By applying CAP together with Atorvastatin, the dose response curve of Atorvastatin was significantly leftward shifted in SUM159PT but not MCF7 cells , suggesting that such a synergy was subtype-specific and probably solely existed in TNBC cells.Atorvastatin selectively halted the growth of TNBC cells at 40 μM/L . Moreover, tumor size and weight were decreased as compared with using CAP alone .When CAP and Atorvastatin were administrated together as compared with using CAP alone, tumor growth was further halted . Among the organs measured, the synergistic efficacy of CAP and Atorvastatin was most evident in kidney and lung , and both CAP and 'CAP plus Atorvastatin' reduced the weights of heart, liver and spleen in mice inoculated with SUM159PT cells . CAP caused significant weight loss of mice carrying SUM159PT tumors, and combined administration of CAP and Atorvastatin prevented such a weight loss . ALDH1, FOXO1 and IL6 expression were reduced on CAP exposure in SUM159PT tumors, and the reduction was escalated if CAP was used together with Atorvastatin .Organs slightly shrank in SUM159PT-inoculated mice receiving CAP treatment or joint exposure to 'CAP coupled with Atorvastatin'  that is featured by metastatic capacity and therapy resistance The significant anti-cancer efficacy of CAP has been demonstrated in approximately 20 cancer types 2O2, NO2-, NO3-, ONOO-) and short-  lived reactive species 2-, H2O2, ‧OH, O3 and ‧NO in CAP's selectivity against CSCs that also involves species derived from their interactions. This holds particularly true for short-lived species due to their short free diffusion path length (FDPL) and half-life span (HLS) 21, ~250 nm FDPL, ~1µs HLT) or H2O2  21 causes local inactivation of a few catalase molecules on tumor cell surface that triggers aquaporin-mediated H2O2 influx and tumor cell death, and H2O2 accumulates at the site of locally inactivated catalase together with ONOO- to generate additional O21 towards self-sustained auto-amplification and catalase inactivation The fact that CAP contains numerous long- . Interestingly, being under-represented in TNBC cells, AQP3 played a more important role in CAP-induced anti-cancer efficacies than AQP1 that was over-expressed in these cells. Therefore, it was not the amount of AQP under homeostatic condition but rather the increased amount of AQP upon CAP exposure that determined cells' sensitivity to CAP treatment. Knocking down AQP1 or AQP3 significantly reduced cellular H2O2 concentration and ROS level . These findings were in line with the previous report that aquaporins facilitated H2O2 transmembrane diffusion 2O2 generation and uptake.We focused on aquaporins that mediate the entry of long-lived species such as HFig. 3). FOXO factors promote cellular antioxidation We demonstrated that cancer stemness as represented by FOXO1 over-expression could characterize breast tumors likely to positively respond to CAP treatment . We uncovered one molecular mechanism explaining the reverse expression levels of both proteins. That is: 1) AQP3 competes with FOXO1 for SCAF11-mediated K48 ubiquitination, where AQP3-5K is the site for AQP3 K48 ubiquitination and AQP3-19Y phosphorylation is essential for the interaction of AQP3 with SCAF11 and SCAF11-enabled AQP3-5K K48 ubiquitination; 2) CAP suppresses AQP3-19Y phosphorylation by inhibiting its kinase RPS6KA3 that leads to reduced SCAF11-mediated AQP3 ubiquitination and enhanced AQP3 stability; and elevated level of free SCAF11 binds FOXO1 towards its ubiquitination and consequently protein degradation; 3) the suppressive role of CAP on RPS6KA3 also decreases FOXO1 phosphorylation that is associated with reduced FOXO1 nucleus translocation and recessed activation of its target genes relevant to cancer stemness such as IL6 and ALDH1 . We are the first to report AQP3 ubiquitination, its specific ubiquitination site AQP3-5K, and the associated mechanism including the E3 ubiquitin ligase SCAF11 as well as the essential role of AQP3-19Y phosphorylation in mediating this process.We revealed opposite expression patterns between AQP3 and FOXO1 at both transcriptional and translational levels as demonstrated using Besides ubiquitination and phosphorylation, this study also identified the involvement of FOXO1 methylation in driving its cellular localization away from the nucleus, suggestive of the regulatory complexity on the hub gene FOXO1 that plays major roles in cell fate dictation.Fig. 4). As CSCs are the key drivers of cancer recurrence, metastasis and drug resistance, we anticipate a long-term success by combining CAP with canonical approaches Fig. ). Organs of these tumor-carrying mice swell due to dystrophy or tumor constriction. As CAP was capable of considerably shrinking the size of tumors, the burden and symptoms caused by the tumor and associated dystrophy were reduced accordingly.Atorvastatin, a drug commonly used to treat cardiovascular diseases, was repositioned in this study as an onco-therapeutic approach capable of creating synergies with CAP. Our results demonstrated their synergistic efficacy in selectively killing TNBCs or other cancer cells with high stemness and plasticity , which might be caused by the significantly reduced weight of tumors and mice organs. However, CAP slightly increased the weight of mice inoculated with SUM159PT-FOXO1 cells, suggestive of a positive correlation between CAP efficacy and cancer stemness.Interestingly, CAP led to weight loss in mice inoculated with SUM159PT cells .TNBC lines , luminal breast cancer cell lines , HER2-positive breast cancer cell line (SKBR3) and the quazi-normal breast epithelial cell line (MCF10A) were used, which were purchased from American Type Culture Collection , and cultured following supplier's recommendation (Supplementary Table 2). This clinical study was approved by the Medical Ethics Committee of Affiliated Hospital of Jiangnan University, with informed consent obtained from the patients.Patients from the Affiliated Hospital of Jiangnan University were collected from January 2018 to July 2020 at the time of primary surgery for invasive breast cancer with AJCC (American Joint Committee on Cancer) stages I-III. Information on the status of canonical markers for tumor diagnosis were recorded for each sample by physicians and pathologists according to IHC staining results . The peak-to-peak voltage applied to the electrode was set in the range of 1.0kV to 1.4kV, the sinusoidal wave frequency was set to 8.8kHz, the flow rate of He was set to 1L/min, and the distance between the plasma source and the medium surface was fixed at 13mm. Plasma activated medium (PAM) was generated by setting the distance between the CAP nozzle and the medium surface to 13mm, the peak-to-peak electrode voltage to 1.1kV, the sinusoidal wave frequency to 8.8kHz, the He gas flow rate to 1L/min, and exposing 2mL of cell culture medium to CAP treatment for 1-5min for each well in 12-well plates.The home-made experimental setup for CAP generation consists of controlled power supply, helium (He) gas cylinder, rotor flow meter, and plasma jet (FOXO1 sequence (NM_002015.4) was synthesized, sequence-validated, and subcloned into the eukaryotic expression vector pcDNA3.1 by GenePharma . Cells were seeded in 12-well plates at a density of 3×105 cells/well followed by Lipo3000 (Gene Pharma) transfection. The medium was replaced with fresh medium containing 400μg/mL G418 24 h later for the first time, and the medium refreshment was repeated for every 2 to 5 days until one cell was left in a single well. Cells were successively cultivated under 50 μg/mL G418 selection till stable FOXO1 expression.The 5/well and cultured until 30-50% confluence. Cells were transfected with 50 nM annealed double-stranded siRNA  or negative control (targeting non-coding region) using Lipofectamine® 2000  following the manufacture's protocol. Cell medium was refreshed 6h later after transfection, and cultured for 36-48h before performing additional experiments. The siRNA sequences (Supplementary Table 3) were designed using Primer-BLAST (https://www.ncbi.nlm.nih.gov/tools/primer-blast/) and purchased from GENEWIZ .Cells were seeded in a 6-well plate at a density of 3.5×103 cells/well in 100 μL of complete culture medium followed by CAP treatment or incubation using 100 μL PAM for 24h. Cell viability was assessed by Cell Counting Kit-8  according to the manufacturer's protocol. The absorbance was measured at 450 nm using EZ Read 800 microplate reader .Cells were plated in 96-well plates at a density of 8×105 cells/well followed by 24 h incubation. The medium was replaced by 2 mL PAM and incubated for 24h. Cells were harvested and stained using annexin V and propidium iodide (PI). Flow-cytometric analysis was performed using FACSCalibur  and analyzed using FlowJo software .Apoptotic cells were quantified by annexin V-FITC apoptosis detection kit  according to manufacturer's protocol. Cells were plated in 6-well plates at a density of 4×10Gating: (a) select all particles, set FSC and SSC to logarithmic axis mode for standby; (b) set the horizontal axis and vertical axis to Annexin channel and PI channel respectively (7-AAD is similar to this), and select double negative cell population; (c) in the double negative cell gate, set the horizontal axis and vertical axis to FCS and SSC respectively, change the data presentation mode to contour graph, find cell fragments, draw out the fragment gate, and apply to all cells; (d) in all cells, invert the fragment gate to get all cells; (e) set the horizontal axis and vertical axis as Annexin channel and PI channel, respectively, refer to the untreated control group, draw the cross gate, and determine the ratio of different groups of cells.6 cells/well, and scratched by a p200 tip when cells attached to the bottom of the plate as a monolayer and reached the confluence of 90-100%. Cells in each well were incubated using PAM with 1%FBS added for 24 h. Scratches were visualized using inverted phase contrast microscope and images were captured using a digital microscope camera. Images were captured for each well immediately after the medium was replaced with PAM, and after 8h, 16 h and 24 h of incubation. The Image J software was used to quantify the cell migration area.Cells were plated in 6-well plates at a density of 1×10Cells were plated in 96-well plates at 5000 cells/well for 24 h before real-time monitoring. When the cell density was around 30%, the digital holograms of cells were set up using the HoloMonitor M4 Digital Holography Cytometer . The results were calculated using Hstudio M4 software .4 cells of each cell line were seeded in a 96-well Seahorse XF cell culture plate at 24 h prior to the treatment. After 0.5 h pre-treatment, cells were washed 3 times and cultured in a phenol red free DMEM medium  for 1 h at 37 °C in the atmospheric CO2 incubator. To determine the OCR and ECAR values, the ATP rate assay, including 1.5 μM Oligomycin and 1 μM Antimycin A Rotenone, were added during the test process separately. These values were normalized by the number of cells in each sample and counted by InCell HS6500.The ATP generation rate from glycolysis and mitochondria were measured using the Agilent Seahorse XFe96 Analyzer  following the manufacture's protocol. The oxygen consumption rate (OCR) value showed how much ATP was produced through Oxidative Phosphorylation (OXPHOS) from mitochondria. The extracellular acidification rate (ECAR) value represents ATP generated from glycolysis process. In brief, 2×105 cells were suspended in 500 μL ALDEFLUOR™ assay buffer containing 5 μL/mL ALDEFLUORTM substrate and incubated for 30 min at 37 °C in the darkness. As a negative control, cells were stained under identical conditions in the presence of the specific ALDH inhibitor 4-diethylaminobenzaldehyde (DEAB) . Cells were centrifuged, with the supernatant being removed. The remaining pellet was suspended in ice-cold ALDEFLUORTM assay buffer and kept on ice. Cells were immediately assayed with FACSCalibur  using DEAB controls as baselines to gate ALDH+ and ALDH- cohorts.ALDEFLUOR™ assay was performed according to the manufacturer's instructions . 5×10Gating: First, create a Forward Scatter (FSC) vs. Side Scatter (SSC) dot plot. (a) In setup mode, place the DEAB tube on the cytometer. (b) Center the nucleated cell population within the FSC vs. SSC plot. (c) Gate on all nucleated cells, excluding RBCs and debris (R1). Second, create a Fluorescence Channel 1 (FL1*) vs. SSC dot plot, gated on R1. (a) Adjust the FL1 photo-multiplier tube voltage. Line the rightmost edge of the stained DEAB control population with the second log decade on the FL1 axis. (b) Remove the DEAB tube from the cytometer. (c) Place the corresponding SAMPLE tube into the cytometer. (d) Gate on the ALDHbr population (R2).5 cells/well in 2 mL of complete culture medium, and incubated with PAM for 24 h, harvested and dosed. 200 cells were plated in 200 μL StemXVivo Serum-Free Media (R&D) containing 2 U/mL heparin (Tocris), 0.8 μg/mL hydrocortisone (Tocris), 100 U/mL penicillin , and 100 μg/mL streptomycin  in ultra-low adherent 96-well plates (Corning). Colonies were quantified after 7 days.Cells were plated in 6-well plates at a density of 4×10Cells were collected and fixed using 2.5% glutaraldehyde overnight, pre-stained using 1% osmic acid and placed in 4 °C for 2 h. Cells were dehydrated using gradient ethanol , 90% ethanol: 90% acetone (1:1) and acetone (90%), washed three times using 100% acetone, each time for 10 min. Acetone was gradually replaced by epoxy resin by soaking cells in acetone: epoxy resin (1:1) solution for 1 h, in acetone: epoxy resin (1:2) solution overnight, in epoxy resin for 3 h and in refreshed epoxy resin for another 4 h. Samples were polymerized in 60 °C for 48 h, and spliced into 50 nm ultrathin sections using ultramicrotome Leica EM UC7. Samples were stained using colloidal gold solution, and observed under biological transmission electron microscope Tecnai G2 Spirit Biotwin, and performed in the Instrumental Analysis Center of Shanghai Jiao Tong University.CellROX® Green Reagent  was added to cells at a final concentration of 5 μM and incubated at 37 °C for 30 min. Medium was removed and cells were washed 3 times using phosphate buffer solution (PBS), after which cells were stained using NucBlue™ Live ReadyProbes Reagent . Supplement cells with 2 drops/ml NucBlue™ Live ReadyProbes Reagent followed by cultivation for 15-30 min and signal detection under 360 nm ultraviolet using Nikon Eclipse Ti-U.2O2, O3, ·OH, ·O2-, ·NO and e-, respectively Sodium pyruvate (10 mM), uric acid (100 μM), mannitol (200 mM), Tiron (20 mM), hemoglobin (20 μM), monopotassium phosphate(1mM) were used to trap HSupplementary Table 3) were amplified using 96-well plate and Roche LightCycler 480. The relative mRNA expression levels were calculated by 2-∆∆Ct method.RNA was extracted according to the instructions of CWBIO cell RNA Extraction Kit. Reverse transcription of the extracted RNA and the system required for quantitative real time PCR (qRT-PCR) detection was performed following instructions of PrimeScriptTM reverse transcription  kit. The samples including primers (Supplementary Table 4) for at least 12 h at 4 °C, and further incubated with an appropriate horseradish peroxidase-conjugated secondary antibody at the RT for 1.5 h. Visualization of reactive protein bands was performed using High-sig ECL Western Blotting Substrate (Tanon), and the expression level of reactive protein bands was quantified using Image J software.Cells were washed twice using ice-cold PBS and lysed in RIPA lysis buffer  supplemented with Protease Inhibitor Cocktail  and Phosphatase Inhibitor Cocktail  for 1 min on ice followed by centrifugation at 12,000 g for 5 min and supernatants collection. The protein concentration was quantified by the BCA Protein Assay Kit . Proteins (50 μg) per lane were resolved by SDS-PAGE. The SDS-PAGE electrophoresis (pH=8.5) was run at 135V for 75 min followed by PVDF membrane transfer at 250 mA for 120 min. Non-specific binding sites were blocked using 1×TBS containing 0.05% (v/v) Tween 20 and 5% (w/v) skimmed milk for 1h at the room temperature (RT). After washing, the membranes were incubated with the indicated primary antibody  overnight at 4 °C. Corresponding secondary antibody was added and incubated for 1 h at the RT. Images were observed with Pannoramic MIDI .The 5 μm thick paraffin sections were deparaffinized in xylene and rehydrated in ethanol at different gradients . Tissue slices were incubated in 3% HSupplementary Table 4) under gentle shaking at 4 °C for 2 h, and after addition of 50 μL of 50% protein A-agarose beads tumbled overnight at 4 °C. The agarose beads were extensively washed with a lysis buffer without Triton X-100 and NP40. The samples were added to 2×Western blot loading buffer and the Western blot analysis was performed.The nuclear extracts were isolated and lysed in a RIPA buffer . The supernatants containing 1 mg protein were incubated without (input) or with monoclonal goat anti-human antibodies  for 1 h.Supplementary Table 4) or control IgG overnight at 4 °C. DNA-protein crosslinks were reversed and chromatin DNA was purified and subjected to PCR analyses. After amplification, PCR products were resolved using 3% agarose gel and visualized by ethidium bromide staining.Chromatin immunoprecipitation (ChIP) assay was performed according to the manufacturer's protocol  with slight modifications. Chromatin solutions were sonicated and incubated with monoclonal goat anti-human antibodies (5'-CCCGCCATGGGTCGACAGAAGGAGCTGGTGTCCCGCTGCGGGGAGATGCTCCACATCCGCTTTCGGCTGCTCCGACAGGCGCTGGCCGAGTGCCTGGGGACCCTCATCCTGGTGGTGAGTGGA-3') and that of CRISPR/Cas9 sgRNA plasmid (5'-GTTTTAGAGCTAGAAATAGCAAGTTAAAATAAGGCTAGTCCGTTATCAACTTGAAAAAGTGGCACCGAGTCGGTGC-3') were synthesized by FenghuiBio  and YunzhouBio , respectively.Cells were transfected with CRISPR/Cas9 and single-strand oligo-deoxyribonucleotides (ssODN) to establish stable cells harboring mutation AQP3-Y19F ('AQP3-Y19F' cells). The sequence of ssODN for AQP3-Y19F , negative control, drug-free negative control at each drug concentration were designed with 6 replicates. Atorvastatin (Sigma) was added to cells after they form confluent monolayers. 5 μL/well of CKK-8 was added 24 h after adding Atorvastatin, and luminescence was detected using EZ Read 800 microplate Reader after cell incubation at 37 ºC for 2 h. The dose-response curves of Atorvastatin treatment and IC50 values were obtained for each concentration in each cell line using the 'drc' package in R, where a four parameter log logistic model (LL.4) was used for data fitting. Statistical significance on IC50 alteration was evaluated by student T test using R.All animal experiments were performed in accordance with the National Institute of Health Guide for the Care and Use of Laboratory Animals and approved by the Animal Laboratory Center of Jiangnan University.6 cells. 15 mice carrying SUM159PT cells and 10 mice carrying SUM159PT-FOXO1 cells were recruited in this study. 15 SUM159PT cell inoculated BALB/c mice were evenly divided into 3 groups, i.e., 'SUM159PT_control' (receiving untreated medium), 'SUM159PT_CAP' (receiving CAP), and 'SUM159PT_CAP+ATO' (receiving CAP+Atorvastatin). 15 SUM159PT_FOXO1 cell inoculated BALB/c mice were grouped following the same strategy into 'SUM159PT-FOXO1_control', 'SUM159PT-FOXO1_CAP', and 'SUM159PT-FOXO1_CAP+ATO'. The first treatment was performed when a tumor reached 5±0.5 mm in diameter as measured using vernier caliper. Mice were anesthetized with ketamine (10 mg/mL) intraperitoneally before treatment. The injection volume was 10 μL/g of the mouse body weight. PAM was subcutaneously injected at two sites of the tumor for each mouse with 100 μL/site. This treatment was repeated every 6 days. Tumors were dissected after sacrificing mice on 39th day starting from the 1st day when mice were inoculated with tumor cells .SUM159PT and SUM159PT-FOXO1 cells suspended in PBS were injected subcutaneously in the right forelimbs of 50 female BALB/c mice aged 4 weeks with the weights of 16 ± 2g on the first day, and each mouse was injected with 1×10® 3.0 Flurometer (Thermo) and Agilent 2100 Bioanalyzer (Agilent). The sequencing library was prepared following manufacturer recommendations for VAHTS Total RNA-seq (H/M/R) Library Prep Kit for Illumina® and a total of 4 μg RNA was sequenced using HiSeq10 (Illumina) by Vazyme Company  (Supplementary Table 5).Total RNA was extracted from SUM159PT and MCF7 cells treated with CAP at time points of 0 h, 1 h and 8 h, each with three replicates. Total RNA was extracted using miRcute miRNA Isolation Kit extraction  following the recommended protocol. RNA purity, concentration and integrity were determined by Nanodrop 2000 (Thermo), QubitRaw reads were pre-processed using the in-house platform of Vazyme. CircRNAs were predicted from back splicing junctions extracted from unmapped reads using circRNAFinder GO enrichment analysis of differentially expressed genes or target genes of differentially expressed lncRNAs was implemented with perl module (GO::TermFinder) Supplementary Table 5). GSE132083 contains 3 lines of CSCs and 2 lines of non-CSCs isolated from 3 breast cancer cell lines, each with 3 replicates. GSE24450 contains 29445 genes for 183 breast cancer patient clinical samples.'METABRIC 2016' and 'METABRIC 2012' are short for METABRIC (Nature 2012 & Nat Commun 2016) and METABRIC (Nature 2012), respectively, which were retrieved from and analyzed using bc-GenExMiner v4.5 GSE132083, GSE24450 from Gene Expression Omnibus (GEO), METABRIC from cBioPortal for Cancer Genomics, The Cancer Genome Atlas Program (TCGA) for breast cancer transcriptome, and E-MTAB-181 Transcription factor prediction was performed using ConTra v3 Student's t-test was performed to assess the statistical significance of each experiment. The values were shown as the 'mean ± SD' of at least three experiments, and the p<0.05 were considered statistically significant.Supplementary figures and tables.Click here for additional data file.


Volvariella volvacea. Its biological activities include hemolysis, writhing reaction, neurotoxicity, and ventricular systolic arresting activity. The cytotoxicity of VVA2 was mainly considered due to its pore-forming activity. Here we report a novel biological activity of its variants VVA2 I82E/K86K as a duplex-specific nuclease. Recombinant VVA2 variant I82E/L86K (Re-VVA2 I82E/L86K), deprived of the oligomerization property, shows increased nuclease activity compared to VVA2. Re-VVA2 I82E/L86K converts supercoiled DNA  into nicked form (RF II) and linear form (RF III) in the presence of Mg2+ or Mn2+. Besides plasmid DNA, it also exhibits nuclease activity on E. coli genomic DNA rather than ssDNA or RNA. Re-VVA2 I82E/L86K preferentially cleaves dG-dC-rich dsDNA regions and shows the best performance at pH 6–9 and 55 °C. Our structure–function study has revealed amino acid E111 may take an active part in nuclease activity through interacting with metal ions. Based on the sequences of its cleavage sites, a “double-hit” mechanism was thereby proposed. Given that Re-VVA2 I82E/L86K did not exhibit the conserved nuclease structure and sequence, it is considered an atypical duplex-specific nuclease.VVA2 (volvatoxin A chain 2) is a cardiotoxic protein purified from  Volvariella volvacea was previously purified and designated as volvatoxin A (VVA) /[RF II + RF I]. GraphPad Prism was used to calculate the mean and SD (standard deviation), and to visualize the data.2+ or Mn2+) were added to the final concentration of 20 mM. A Tecan Spark 10M microplate reader was used to measure the intrinsic fluorescence. Excitation was set as 290 nm, while emission scan was set as 310–440 nm. The bandwidth was adjusted to 5 nm, with a step size of 2 nm. Background fluorescence was deducted using the buffer with a similar operation. Spectra with more than three repeats were analyzed and presented with GraphPad Prism.The metal ion binding capacity of VVA2 variants was conducted based on intrinsic tryptophan fluorescence (ITF) measurements ,41, as p


The formation and stability of the nanocluster complexes were experimentally and theoretically confirmed. Meanwhile, the chiral nanocluster complexes exhibited enhanced luminescence and induced CD signals at room temperature in the solid state, revealing the stable complexation and chirality transfer from the chiral macrocycles to the nanocluster Ag20.A pair of chiral nanocluster complexes were formed by the host−guest interaction between the enantiomeric 2,6-helic[6]arenes and nanocluster Ag Metal nanoclusters (MNCs) have emerged as an excellent choice for constructing functional nanomaterials in recent years because of their disparate microscopic structures and unique macroscopic properties ,2. Hence20 nanoclusters arenes (P/M-H6) and Ag20 nanocluster n were prepared according to the literature procedure n  was added under vigorous stirring at room temperature. Then, AgNO3  and the L1 ligand  were added successively. When L1 was completely dissolved, 40 μL of triethylamine was added. The mixture was allowed to react for another 30 min. Finally, the resultant solution was filtered with syringe filters and injected into a test tube. With the poor solvent tetrahydrofuran diffusing into the filtrate, light-brown block crystals of G were obtained in darkness after three days. Yield: 65% (based on Ag). Elemental analysis calcd. (%) for C71H138N12S10Ag20O33 (4144.77): C 20.47; H 3.34; N 4.03; S 7.70; found: C 20.48; H 3.37; N 4.07; S 7.66.G@P-H6: The host P-H6  and the crystals of G  were dissolved in 6 mL of mixed solvent of CH3CN and DMSO (v/v = 5:1). After stirring for 30 min, ether was added to precipitate complex G@P-H6. The turbid liquid was centrifuged, and its supernatant was discarded. Then, the white nanocluster complex was collected after the residual solvent was evaporated. The enantiomeric complex G@M-H6 was synthesized by the same method.P-H6/KBr = 1:77, mass ratio of G/KBr = 2:77) and then compressed to transparent discs.The solid-state samples for CD measurements were ground with KBr (mass ratio of


Biomaterial-based nanofibrous scaffolds are the most effective alternative to bone transplantation therapy. Here, two recombinant minor ampullate spidroins (spider silk proteins), R1SR2 and NR1SR2C, were blended with Poly(lactic-co-glycolic) Acid (PLGA), respectively, to generate nanofiber scaffolds by electrospinning. The N-terminal (N), C-terminal (C), repeating (R1 and R2) and spacer (S) modules were all derived from the minor ampullate spidroins (MiSp). The physical properties and structures of the blended scaffolds were measured by scanning electron microscopy (SEM), water contact angle measurement, Fourier transform infrared spectroscopy (FTIR), Differential scanning calorimetry (DSC), and Tensile mechanical testing. The results showed that blending of MiSp (R1SR2 and NR1SR2C) reduced the diameter of nanofibers, increased the porosity and glass transition temperatures of nanofibrous scaffolds, and effectively improved the hydrophilicity and ultimate strain of scaffolds. It is worth noting that the above changes were more significant in the presence of the N- and C-termini of MiSp. In cell culture assays, human bone mesenchymal stem cells (HBMSCs) grown on NR1SR2C/PLGA (20/80) scaffolds displayed markedly enhanced proliferative and adhesive abilities compared with counterparts grown on pure PLGA scaffolds. Jointly, these findings indicated recombinant MiSp/PLGA, particularly NR1SR2C/PLGA (20/80) blend nanofibrous scaffolds, is promising for bone tissue engineering. Bone defects or dysfunction caused by trauma, congenital bone malformations and other disorders affect up to 20 million individuals each year ,2,3. MeaUnlike permanent bone implants, scaffolds are implanted to provide effective temporary support for cell adhesion. Multiple reports have demonstrated nanostructured scaffolds are more efficient than macrosized scaffolds in promoting bone regeneration . BecausePLGA is a biodegradable copolymer that has been widely used for the preparation of nanofiber scaffolds. PLGA has had approval from the U.S. Food and Drug Administration (FDA) for human clinical trials . The matBased on the above substantial information, this study adopted the electrospinning technology to prepare nanofiber scaffolds blended with minor ampullate spidroins R1SR2 and NR1SR2C and PLGA, respectively, and prepared pure PLGA nanofiber scaffolds as the control. scanning electron microscopy (SEM), water contact angle measurement, Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and tensile mechanical testing. The physical features of nanoscaffolds were examined from multiple angles and the potential application of blended nanofibers in bone tissue engineering was assessed. The experimental results also provided theoretical evidence for the further application of minor ampullate spidroins in bone tissue engineering nanoscaffolds.Araneus ventricosus MiSp, referred to as R1SR2 and NR1SR2C, respectively (Spidroins were composed of repeat units (R1 and R2), spacer units (S), and N-(N) and C-(C) termini of ectively a. The R1ectively .No previous study has examined the influence of N- and C-termini on the characteristics of fibers during electrospinning. Herein, we selected recombinant MiSp containing N- and C-termini or not for comparisons, since during the polymerization of spidroins are controlled simultaneously by these two structures . The spiTwo recombinant spidroins were produced and blended with PLGA. We fabricated pure PLGA and spidroins (NR1SR2C and R1SR2)/PLGA electrospinning nanofibrous scaffolds with a weight ratio of 20/80 and analyzed their mechanical properties, respectively. The spinning process of R1SR2/PLGA (20/80) and NR1SR2C/PLGA (20/80) polymer solutions were the same, continuous and uniform, as that used for pure PLGA. We also fabricated blend nanofibrous scaffolds at different weight ratios to identify an ideal mix ratio of PLGA for electrospinning. However, it was hard to generate uniform nanofibers with spidroins (NR1SR2C and R1SR2) and PLGA mixed at weight ratios of 50/50 and 80/20. Therefore, all spidroins utilized here were blended with PLGA at 20/80 for the fabrication of electrospinning blend nanofibrous scaffolds.w/v) /PLGA (20/80) spinning dopes by routine electrospinning methods. Pure PLGA nanofibrous scaffolds were also prepared in the same process for comparison. The surface of all types of scaffolds were examined by SEM  and R1SR2/PLGA (20/80) blend scaffolds were about 300 nm to 700 nm , which wThe images in the middle column show magnified versions of the scaffolds. This appeared at the images in the middle column (multiples 5000), showing that the average diameters of the fibers gradually decreased as spidroins blended in. After blending spidroins into PLGA, the diameter decrease of the blend scaffold is generally caused by elevated electrical conductivity of the mixed solution . When PL−1 representing esters groups (C-O), as well as a typical carbonyl (C=O) peak at ~1754 cm−1, which are typical PLGA peaks [−1 for amide II and I bonds, respectively. The blend scaffolds had typical absorption peaks of spidroins, consistent with the peaks of pure NR1SR2C (R1SR2) scaffolds in amide I bands  were all greater than 90°, showing hydrophobic characteristics, while the water contact angle of scaffolds NR1SR2C/PLGA (20/80) was about 75°, which could provide a more suitable wettable environment for cell growth. Cell culture experiments were utilized to confirm this finding. BMSCs were cultured using hydrophobic scaffolds RISR2/PLGA (20/80) and hydrophilic scaffolds NRISR2C/PLGA (20/80) as substrates. Pure PLGA scaffolds were also implanted into cells for comparative assessments. To evaluate the adhesive and proliferation abilities of BMSCs on blended scaffolds, the activity of HBMSCs on different stents was evaluated by the CCK-8 assay and phalloidin staining, after 2, 4, 6, and 8 days of culture a,b. AdheCell proliferation and adhesion on the fibers were examined by scanning electron microscopy. As depicted in 1, 1, 1, 3, 3, 3 hexafluoro 2-propanol (HFIP) was provided by Shanghai Darui Finechemical . Poly(lactic-co-glycolic) acid  was provided by Jinan Daigang Biomaterial . Competent cells, chemicals and kits were provided by Sangon Biotech . Restriction enzymes were provided by Thermo scientific . Phusion PCR Master Mix and T4 ligase were provided by New England Biolabs . Human bone mesenchymal stem cells (HBMSCs) were provided by Shanghai Institute of Biochemistry and Cell Biology . They are commercialized cell lines. Dimethyl sulfoxide (DMSO) was provided by Changshu Hong sheng Chemical Reagent . Paraformaldehyde (POM) and Cell Counting Kit -8 (CCK-8) were provided by Biyotime Institute of Biotechnology . Dulbecco’s Modified Eagle’s Medium (DMEM), fetal bovine serum (FBS) and phosphate-buffered saline (PBS) were provided by Mesgen Company . Penicillin/streptomycin and trypsin were provided by Shanghai Yuanxiang Medical Equipment . A Milli-Q Plus 185 water purification system  was utilized to produce ultrapure water (18 MΩ cm). Araneus ventricosus minor ampullate spidroin (MiSp)  were constructed. For constructing another plasmid NR1SR2C harboring the MiSp, N- and C-termini also from MiSp were added to both ends of R1SR2, respectively, utilizing polymerase chain reaction (PCR), restriction enzyme cleavage and DNA ligation. Nucleotide sequencing was carried out for verifying sequence accuracy.Plasmids harboring a recombinant spidroin with a His-tag and a single repeat unit (R1), a spacer unit (S) and another repeat unit (R2) of Escherichia coli BL21 (DE3) cells  transformed with various plasmids underwent culture in an incubator at 37 °C with shaking at 220 rpm in Luria broth with 50 μg/mL ampicillin. At OD600 of 0.6–0.9, bacteria were treated with isopropyl β-D-1-thiogalactopyranoside (IPTG) at 0.3 mM, followed by incubation at 16 °C and 180 rpm for 16 h. Upon cell harvesting by a 15 min centrifugation , lysis was performed with lysis buffer  using a JN-3000 plus high-pressure homogenizer  at 1200 bar. The target proteins in the pellet upon a 40 min centrifugation  were re-suspended in washing buffer , followed by sonication for 30 min at 250 W (5 s ON and 8 s OFF) in an ice bath. After another 40 min centrifugation , the resulting pellets were resolubilized in a dissolution buffer . The sample was sonicated again and incubated at ambient for 30 min. Insoluble materials were cleared by a 30 min centrifugation . The protein solution next underwent gradient-dialysis at 4 °C for 4 h against dialysis buffers  containing 6, 4, and 2 M urea, respectively. Upon de-ionization in water for 72 h, the dialysate was lyophilized on a lyophilizer  and assessed by SDS-PAGE and Coomassie Brilliant Blue staining for purity evaluation.w/w) and mixed with pure 1,1,1,3,3,3-hexafluoro-2-propanol (HFIP) at 10% (w/v). Spinning dopes were then stirred for 24 h at ambient. Purified spidroin solutions underwent dialysis against deionized water at 4 °C overnight and lyophilization for the pulverization of spidroins. Spidroin powders and PLGA were blended /PLGA (20/80) blend nanofibrous scaffolds were fabricated with a routine electrospinning protocol. Spinning dopes were loaded into a 2 or 5 mL syringe with a 23G stainless steel spinneret and ejected at 0.6 mL/h by a LSP01-3A syringe pump . The voltage was 12 kV, and collection distance  approximately 15 cm. Pure PLGA nanoscaffolds were also electrospun under identical conditions. The procedures are summarized in The nanofibrous scaffolds were surface-coated with Au particles for 45 s and analyzed under a SEM  at 10 kV. Diameter distribution for the fibers was assessed with ImageJ  and >100−1 in the attenuated total reflection mode at ambient. For every spectrum, 64 scans were averaged at a resolution of 1 cm−1. Amide I regions (1600–1700 cm−1) of every specimen were next assessed for identifying the peaks of α-helix and β-sheet structures in spidroins.A Nicolet 6700 FITR spectrometer (Thermo Fisher) was utilized for recording spectra for nanofibrous scaffolds from 4000 to 600 cmDSC was performed on a TA instrument  for determining the thermal features of electrospun nanofibrous scaffolds, with Tg derived as previously described .The water contact angles of scaffolds were obtained via a drop-shape analyzer DSA30  using the sessile drop method. Data analysis was performed with ADVANCE-drop shape for determining contact angles. A minimum of 3 distilled water drops in various positions were assessed per sample.Nanofibrous scaffolds were cut at 20 mm × 10 mm for mechanical property measurements. Sample thickness was assessed by a screw micrometer and approximated 10–30 μm. A universal material tester H5K-S  was utilized for assessing the specimens at ambient with a cross-head rate of 20 mm/min. Average mechanical features for various scaffolds were obtained from a minimum of 6 specimens.2. Round electrospinning nanofibrous scaffolds (14 mm in diameter) were prepared as reported previously [4 cells/well and culture for 2, 4, 6 and 8 days at 37 °C with 5% CO2, respectively, with the culture medium refreshed at 48 h intervals.Prior to in vitro assays, HBMSCs underwent culture in RPMI-1640 containing 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin at 37 °C with 5% COeviously . Briefly4 cells/well, followed by culture for 2, 4, 6 and 8 days at 37 °C with 5% CO2, with the culture medium refreshed at 48 h intervals [Round electrospinning nanofibrous scaffolds (14 mm in diameter) were produced as reported previously  and placntervals . Next, sp < 0.05 indicated statistical significance.Analysis of variance (ANOVA) was carried out using Origin 8.5  to determine whether at least two data sets showed differences. In this study, recombinant MiSp and PLGA were blended at a ratio of 20/80 to generate a uniform nanoscaffold, which was utilized to evaluate the application potential of the recombinant MiSp/PLGA blended scaffold in bone tissue engineering. This was the first study blending recombinant MiSp with PLGA by electrospinning.The results showed blending the scaffold with recombinant MiSp could improve the mechanical features of the material, especially its ductility. Meanwhile, recombinant MiSp increased the hydrophilicity of the blend scaffold. It caused the water contact angle to decrease from 131.7 ± 2.1° to 74.0 ± 4.0° and slightly increased the Tg temperature of the material from 55.98 °C to 57.22 °C. With the addition of N- and C-termini, part of material performance optimization becomes more significant. Among them, NR1SR2C blend nanoscaffolds were superior to all nanoscaffolds in terms of performance.The average diameter of nanoscaffolds decreased with the addition of recombinant MiSp, 1437 ± 211 nm for pure PLGA to 349 ± 100 nm. It is worth noting that the average diameter of the recombinant Misp containing the N- and C-termini of the minor ampullate was reduced by nearly half (349 ± 100 nm) compared with that without the C- and N-terminal nanoscaffolds R1SR2/PLGA (20/80) of 661 ± 130 nm. NR1SR2C/PLGA nanofibers scaffolds have sizes ranging from 50–500 nm; therefore, the fibers sizes are similar to that of nanofibrous collagen (a nature extracellular matrix). In the present study, with the addition of recombinant MiSp, the mechanical properties of blend nanoscaffolds had a higher ultimate stress and tensile modulus than those of other composite PLGA blends the previous article reported In vitro, the blended scaffold showed enhanced adhesion compared with pure PLGA and significantly induced cell proliferation. This advantage was most significant in the NR1SR2C blended scaffold, which may be associated with the improved hydrophilicity of the material.Meanwhile, the above results revealed that the blend nanoscaffold with the recombinant MiSp with the N- and C-termini could significantly improve the hydrophilicity of the material, and suitable hydrophilicity compared with the hydrophobic structure of pure PLGA could provide a more appropriate environment for cell growth and proliferation.According to the above results, we produced blend scaffolds with potential properties and suitable for bone tissue engineering research. Additionally, the results predicted that the N- and C-termini of the MiSp could be utilized as functional modules to conjugate with other proteins to adjust the diameter, hydrophilicity and porosity of the blend nanoscaffolds. There are active groups on the surface of spider silk protein, which is easy for secondary modification , also prThese results indicate that the blend nanoscaffolds made from the recombinant MiSp/PLGA are potential materials for bone tissue engineering.