C14MC cluster that is subject to maternal imprinting. This assessment was not significantly affected by common confounders such as age, BMI or time to centrifugation nor alternative methods to data normalisation. Our analysis provides important benchmark data supporting the development of miRNA biomarkers for the impact of life-course exposure as well as diagnosis and prognostication of chronic disease.Understanding the normal temporal variation of serum molecules is a critical factor for identifying useful candidate biomarkers for the diagnosis and prognosis of chronic disease. Using small RNA sequencing in a longitudinal study of 66 women with no history of cancer, we determined the distribution and dynamics  of the miRNA profile over 3 time points sampled across 2–5 years in the course of the screening trial, UKCTOCS. We were able to define a subset of longitudinally stable miRNAs (ICC >0.75) that were individually discriminating of women who had no cancer over the study period. These miRNAs were dominated by those originating from the  They guide RNA-induced silencing complexes (RISCs) to the 3′-untranslated region (UTR) of mRNAs with complementary sequences, leading to the downregulation of gene expression either through translational repression or mRNA degradation2. miRNAs are ubiquitously expressed across different cell types, and help control a wide range of physiological processes, including cell-cycle control, cell differentiation, metabolism, and apoptosis6. Distinctive signatures of microRNA dysfunction have also been observed in a range of conditions, including neurodegenerative disease7, cardiovascular disease8, and cancer10.MicroRNAs (miRNAs) are a class of highly conserved, small non-coding RNAs  that play a role in post-transcriptional regulation of gene expression11, and are remarkably stable despite the presence of endogenous RNAses in circulation. This presents opportunities to develop non-invasive diagnostic or prognostic tests, as biofluids are easier to sample than tissue. Circulating miRNA biomarkers have been identified for conditions such as various cancers12, brain injury13, and diabetes14, and the literature on the matter continues to expand rapidly.The characteristic microRNA profiles associated with specific pathologies has led to extensive investigation into microRNA biomarkers of disease. Significant amounts of miRNA have been found in extracellular human biofluids including blood plasma15. These limitations contribute to the poor track record of reported biomarkers entering clinical practice, as few achieve the anticipated performance in follow-up studies18. While there are notable exceptions that have been successfully validated, such as the use of circulating miR-371a-3p and other members of the miR-371–373 cluster in germ-cell tumors20, there are currently still no circulating microRNA-based diagnostic or prognostic tests approved for use by the U.S. Food and Drug Administration. A fundamental cause of this failure to translate is insufficient consideration of variability in the healthy population. Underestimating the extent of natural variation can lead to the selection of candidate biomarkers that are either insufficiently specific for the disease, or are too temporally variable to precisely reflect the biological state. This is particularly important in early detection, where candidate markers need to be sufficiently robust to achieve the necessary diagnostic performance in a population screening environment that may involve repeat testing over several years.Most published work on circulating miRNA biomarkers use a case-control study design, often with a single timepoint, small sample sizes and varying levels of case-control matchingIn this study, we aimed to characterise the longitudinal stability of circulating microRNA profiles in serum from women with no history of cancer over an extended period. We calculated intraclass correlation coefficients (ICC) from microRNA measurements by small RNA sequencing. We proceed to investigate the impact of normalization, age, BMI, and other sample collection factors on these measurements. Our investigation reveals that the most abundant circulating miRNAs, which are frequently reported as biomarkers for chronic conditions, are not necessarily the most stable and discriminatory over time. We also observed that a known maternally imprinted genomic region was a major contributor to the portion of the serum microRNAome with the greatest longitudinal stability.Our sample set consisted of 66 women with no cancer from the multi-modal arm of the UKCTOCS cohort for whom 3 serum samples over a ~5-year period was available. Characteristics of the sub-sample are listed in Table The utility of any biomarker candidate depends heavily on our ability to consistently and reliably detect the marker. We examined levels of 2,576 microRNAs annotated in miRBase v20, expressed as read counts per million (cpm). The abundance of each microRNA in circulation was calculated by taking the percentage contribution of each microRNA to total circulating miRNA levels. The top 20 miRNAs by abundance are shown in Fig. th most abundant, miR-92a-3p22.Overall, the circulating microRNA profile is dominated by a small number of microRNAs Fig. . The topAs most microRNAs are detected at very low levels, we considered the rate of inclusion in analyses applying a detection filter at thresholds of >0 (i.e. present or absent), 1, 3, 5, and 10 cpm. The cumulative distribution of detection rates at these thresholds are shown in Fig. For further analysis, we focused on the 671 miRNAs that are detected (>0 cpm) in at least 90% of samples. This filtering criterion was selected to focus on miRNAs that are very consistently measured in serum, even if they are detected at very low levels.We examined contributions of the confounding factors age, BMI at recruitment, regional centre of collection, and time to centrifugation to microRNA levels. We found no significant associations to age Fig.  or BMI aWe found significant associations to regional centre in 12 microRNAs, explaining between 14.4% and 27.3% of variance Fig. . These mTime to centrifugation exerted more significant effects on the microRNA profile Fig. . More thWe examined the impact of other normalization on the calculated contribution to variance by examining the effect of using a frequent alternative to total depth normalization, trimmed mean of M (TMM)-normalization  to determine the stability of microRNA levels over time Fig. . The med2 of only 0.214. The Pearson Correlation coefficient was 0.460, also indicating a weak correlation.We found no strong relationship between miRNA abundance and ICCs, as the log-linear regression of mean cpm against ICCs for each miRNA examined Fig.  had an RWe also examined the effect of other normalization on the calculated ICCs. The distribution of ICCs calculated from TMM-normalized data (median 0.350) were very similar to those using total depth-normalized data , we calculated ICCs for 528 miRNAs that fulfil the inclusion criteria specified by the authors . Overall, the ICCs we calculated from the Keller et al. data were substantially lower than those we observed, ranging from 0 to 0.690, with a median of 0.136 , distributed primarily in two neighbouring segments spanning ~40 kb that are part of a maternally-imprinted region of the genome. 47 of these 55 miRNAs possessed an ICC >0.5 in our study, 1/3 of all miRNAs with moderate to high ICC. The median ICC for the cluster was 0.672, significantly higher than that for the whole serum miRNAome (p = 3.3 × 10−22 after a Mann-Whitney Test), with the highest ICC for the cluster (miR-412-5p) observed to be 0.863.An analysis of the ICC values by genome distribution indicated that the long arm of chromosome 14 was enriched in miRNAs associated with high ICC values Fig. . Of the The search for circulating microRNA biomarkers of disease continues to be a very active research space. This is largely due to the potential afforded by the presence and apparent stability of microRNA in a range of biofluids, the availability of sensitive detection technologies, and the physicochemical properties that keep microRNAs stable over prolonged periods of time. While hundreds of research papers are published yearly on microRNAs that distinguish individuals with a range of different diseases from healthy controls, these are typically carried out on relatively small case-control studies and have yet to be validated and applied in a broader disease testing context. A key unanswered question is whether we can generalize findings from these case-control study designs to the larger, more diverse general population. A greater understanding of the factors affecting microRNA profiles in healthy individuals over time would allow us to better prioritize miRNAs more likely to be robust discriminants in a population context.27 but not all28 serum or plasma miRNA sequencing studies. miR-451a is a DICER-independent miRNA30 required for erythropoiesis31 and highly abundant in erythrocytes. Accordingly, high levels of miR-451a, the closely associated miR-14432 or the miR-451a/miR-23a-3p ratio from qPCR measurements33 in serum or plasma have been suggested to be markers for haemolysis. There are no measures comparable to the miR-451a/miR-23a-3p ratio that have been validated for sequencing data. In our dataset, we calculated the log2 miR-451a/miR-23a-3p ratio and found that it was not correlated to pre-analytical factors such as time to centrifugation that might indicate a significant influence of haemolysis in our dataset . This is a dependent normalization, and any shift in the read counts of one microRNA will adjust the normalized value for all the other remaining miRNAs even if there has been no change in their absolute expression37. As such, any large shifts in levels of certain miRNAs caused by changes in blood cell numbers may substantially influence the miRNA sequencing measurements in serum. This would require much more careful examination of the extent by which blood cell populations (and other contributing tissues) influence the circulating miRNA profile. Future work should also review the longitudinal stability of miRNA isoforms (isomiRs), as these may well encode further information about cellular origin, as shown in a recent study of blood cells38.The cell origin of circulating miRNAs is also key to understanding the amounts of miRNA secreted by different cells and tissues into circulation. Levels of a large number of circulating miRNAs have been shown to correlate with blood countsWe found no significant associations to serum miRNA levels for BMI at recruitment. While other studies identify links between circulating miRNA and BMI, our study is limited by the fact that we only have records of BMI at recruitment. Using this assumes that the BMI of subjects remain in a small range of BMI over the period of sampling, and can only inform on relationships to an individual’s tendencies around a certain BMI rather than on changes relating to weight loss or weight gain within individuals.39 from a cohort of 372 individuals ranging in age from 22–79 years. This may relate to differences in the age range explored, as our study is limited to ages between 53 and 85. The identification of stronger age-related effects in a cohort including young individuals may relate to developmental miRNA signatures that differentiate between younger and older individuals. However, as most population screening programmes for diseases such as cancer are likely to be carried out in people above 50 years old, the absence of a strong age-related effect indicates that miRNA measurements in this age group may be robust to impacts from the aging process.We also identified no significant associations to age at sampling, which accounted for under 10% of explained variance in the miRNAs examined. This contrasts with previous work where age was associated with 7 out of 179 miRNAs measured by RT-PCR in plasma40 and vitamin E41, and to environmental chemicals like lead and cadmium42, arsenic43, and Bisphenol A44 have been shown to alter miRNA expression in different cell types, but the impact on circulating miRNA profiles has not been explored. Most miRNA biomarker studies have been carried out as small single-center studies with single point measurements, subjecting them to population biases that limit both genetic and exposure diversity associated with the limited subsample. This increases the possibility of selecting candidate markers that are not sufficiently robust when validating in a broader population, as any confounders relating to geographic or community relationships may no longer be controlled for.A small number of miRNAs are significantly associated to the regional centre of collection. Although all samples were transported by the next day, the transport time from venipuncture to receipt in central laboratory varied as the centres were located across England, Wales and Northern Ireland. Outside of any difference in blood collection methods, these miRNAs may then potentially relate to other exposures  that are distinct to the different geographic areas. Exposure to dietary components such as isoflavonesWe also found a substantial number of microRNAs that are influenced by time to centrifugation, representing a third of our detected miRNAs. This highlights a need to consider sample collection procedures more carefully in the design of serum miRNA biomarker studies, as these can exert a substantial influence on the microRNA profile. In the top 5 miRNAs affected, time to centrifugation accounts for 10–16% of explained variance, which may be enough to mask the disease discriminant capacity of markers at early stages of disease. However, when we limited the analysis to samples centrifuged 17–27 hours after collection (excluding samples at either extreme), no miRNAs were significantly associated with time to centrifugation. This indicates that serum microRNAs may be robust to impacts of deviations in time to centrifugation during sample processing within that 10-hour period between 17–27 hours, but large variations may be observed with a more extensive range of deviations in sample processing time. This highlights the importance of protocol standardization in these studies, as controlling for these variables experimentally makes a substantial impact on the robustness of the data.We found no large differences in the contribution of the different confounders examined and in the ICCs when data was TMM-normalized. This indicates that the normalization procedure does not introduce large changes in the variance structure that we would have ascribed to biological or experimental factors in this study.45.Our analysis of the long-term stability of serum miRNA profiles from small RNA sequencing identified a small number (25) of miRNAs with good to excellent reliability (ICC ≥0.75). Another 119 miRNAs had moderate reliability (0.50 ≤ICC ≤0.75), with the remainder of consistently detected miRNAs having poor reliability . The high predictive accuracy of models built on the most longitudinally-stable miRNAs indicate that there is a subset of miRNAs that are characteristic of individuals and do not exhibit large fluctuations over time. While the capacity to uniquely identify individuals on the basis of 25 miRNAs will likely be significantly reduced when expanded to populations in the thousands, it is remarkable that we find such high accuracies of identifying individuals on the basis of two earlier measurements that may be several years apart from the test sample. These findings have positive implications for the likelihood of identifying miRNA biomarkers in serum that report on chronic effects and long-term risk of disease. Such miRNAs may open the possibility for personalized longitudinal screening, where deviations from earlier healthy baseline profiles may indicate the presence of disease. Efforts to achieve this will require small RNA profiling of prospectively collected samples stored for many decades but again the signs are positive; our study involves legacy samples up to 16 years old, while others have reported that concurrent DNA sequencing and sRNAseq is viable in archived serum samples over 40 years oldOur study is limited by the fact that our cohort is composed entirely of women. This precludes the examination of gender-related effects on circulating miRNA levels. We are also unable to examine circulating miRNA changes that occur dynamically with weight change in individuals, as measurements of BMI at sampling are not available. In addition, while our study is currently one of the larger sample sets measuring longitudinal miRNA profiles in healthy individuals, we are still limited by the number of individuals included (n = 66). It is possible that more significant associations to factors like age and regional centre could be identified with a larger cohort, affording greater statistical power and sample diversity. However, the identification of significant regional centre-related effects in our sample indicates a sufficiently strong role for regional centre of collection, despite our limited sample.et al. study and our own data, which may be effects of differences between miRNA analysis platforms  that have been extensively demonstrated in other studies46. However, we cannot rule out that these differences in ICCs are in part due to the differences in the study populations. The Keller et al. study covers a younger age range (31–62 compared to 53–81) and has, on average, a wider gap between sampling, which may contribute towards increasing the within-individual variability in this sample set. However, the inclusion of both males and females in their study, as well as the smaller sample size, might have been expected to contribute towards increased between-individual variability in their sample.We observed a marked difference in the ICC distributions between the Keller C14MC 14q32.2–14q32.31 miRNA cluster is a major source of circulating miRNAs that are stable and discriminating between individuals over extended periods of time, opening the door to monitoring the progression of many important normal and patho-physiological states. Germline mutations and epigenetic alterations in the 14q32.2 imprinted region are associated with developmental defects such as Kagami-Ogata syndrome47, but we do not currently know their impact on circulating miRNAs. The C14MC microRNA cluster is reported to be actively and selectively exported via exosomes, which may contribute to its ready detection in serum48. While detected in a number of tissues including epithelial tissues and brain, C14MC miRNAs are frequently reported as highly expressed specifically in embryonic & placental tissue. These miRNAs have therefore been presented as microRNAs associated with, and with potential for monitoring, pregnancy49. C14MC expression is regulated by differentially methylated regions (DMR) that appear to be sites for maternal imprinting; selective DNA hypermethylation at the DMR prevents expression of the paternal allele24. Thus, it may be possible to assess the significance of gene imprinting in the action of in utero or post-natal exposures that impact upon health in later life by monitoring the serum expression of miRNAs from this locus. Altered methylation of C14MC miRNA coding sequences in lymphocytes has been associated with obesity in childhood50, but the relationship of this to serum expression of C14MC is currently unknown. Serum levels of cluster miRNAs have been associated with progression of steatosis to non-alcoholic steatohepatitis in mice and humans51. The C14MC cluster has also been associated with the presence and progression of several malignancies55, supporting the case for further investigation of the utility of C14MC miRNAs in serum for cancer diagnosis and prognostication.Our data suggest that the C14MC miRNAs in our cohort. Given the association of the cluster with pregnancy, and that all our data are from females, it is possible that pregnancy history has an influence on our specific dataset. Also, no genetic data was available for our cohort to assess the impact of germline variation on the C14MC expression profile. The role of imprinting in this genome region suggests that epigenetics and other drivers of phenotypic variation might be more influential. Future work could confirm this through concurrent genome sequencing and epigenetic analysis. In addition, miRNA isoform analysis would inform on any differences relating to nucleotide substitutions within the mature miRNA sequence that are not currently examined in canonical miRNA analyses such this.In the current work, it was not possible to determine the major drivers for differential expression of C14MC cluster that is known to be subject to maternal imprinting. This assessment was not significantly affected by common confounders such as age, BMI or time to centrifugation nor alternative methods to data normalisation. Our analysis provides important benchmark data supporting the development of miRNA biomarkers for the impact of life-course exposures as well as the diagnosis and prognostication of chronic disease.In summary, we demonstrated that a subset of miRNAs that are longitudinally stable in serum could be defined from small RNA sequencing data. We found that profiles of these longitudinally-stable miRNAs were able to individually discriminate between healthy women over a period of up to 5 years. These miRNAs were dominated by those originating from the 56. UKCTOCS participants were post-menopausal women aged 50–74, with no active malignancy and no previous history of ovarian cancer at recruitment between 2001 and 2005. In all, 202,638 post-menopausal women were randomly assigned (2:1:1 ratio) to routine care , annual screening using serum cancer antigen 125 (CA125)  or transvaginal ultrasound . All participants were linked using their National Health Service number to national cancer and death registry electronic health records as well as hospital episode statistics (for those resident in England). In addition, women were sent two follow-up questionnaires, the most recent in 2014.We examined a subset of serum samples collected during the course of the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), a 13 centre, randomised controlled trial investigating the impact of ovarian cancer screening on disease mortality. Details of the trial design have been outlined in previous publications57. Blood samples were collected in gel tubes  at the trial centres and transported at room temperature to a central laboratory58. Samples were centrifuged at 1500 g for 10 min and the separated serum aliquoted into 10 × 500 μL straws (IMV) using a semi-automated MAPI platform (IMV). The straws were stored in liquid nitrogen tanks at the central laboratory which when full were transported to a HTA licensed, ISO-accredited commercial cryofacility .All women provided a blood sample at recruitment with women randomised to the multimodal screening group  continuing to donate serum annually for up to 11 years from randomisation. Sample collection stopped at the end of screening in December 2011The current study uses a subset of 66 UKCTOCS participants with samples collected at three time points over a period of up to 5 years. All individuals included in the current study had no past or present record of any cancer apart from non-melanoma skin neoplasms (ICD-10 C44*) and other benign conditions based on Health and Social Care Information Centre cancer and death registration data obtained on June 17, 2014 , and July 2, 2014 (Northern Ireland) and Hospital Episode Statistics (HES) data 2000–2010.UKCTOCS was approved by the UK North West Multicentre Research Ethics Committee (North West MREC 00/8/34) with site specific approval from the local regional ethics committees and the Caldicott guardians (data controllers) of the primary care trusts. All women gave informed written consent for use of samples and data in ethically approved secondary studies undertaken in collaboration with academia and/or industry. Research was performed in accordance with the relevant guidelines and regulations. Analysis of the subset of samples used for the present study undertaken in collaboration with CRUK has been approved by the South Central-Hampshire B NRES Committee (REC Ref 14/SC/1323).Aliquots of selected UKCTOCS Biobank human serum samples were sent to Asuragen, Inc. , for RNA extraction, NGS-based microRNA profiling, and data analysis for biomarker discovery. All serum samples were visually inspected for evidence of hemolysis before sample processing. Total RNA including small RNAs was isolated by Asuragen from 0.55 mL Process Controls and UKCTOCS Biobank human serum samples according to a proprietary procedure developed by Asuragen for downstream small RNA sequencing. The isolation procedure was based on the miRVana™ PARIS™ Kit published protocol for extraction of total RNA enriched for small RNAs , with modifications for maximal RNA recovery and yield. Eluted RNA samples were precipitated and resuspended in a volume of 7.5 microliters nuclease-free water each. One microliter of each RNA sample was removed for quality control (QC), and the remaining RNA was stored frozen at −80 °C. Relative miRNA recovery was assessed for QC on the RNA input equivalent of (8) microliters of serum per sample, using single-target RT-qPCR analysis for an endogenous miRNA demonstrating abundant expression in a broad range of samples types as determined by Asuragen. Of note, this method of QC has been validated by Asuragen for prediction of RNA performance in miRNA RTqPCR and microarray applications, but evaluation for miRNA NGS applications was prospective at the time of this study. Inputs for RT-qPCR or library prep are expressed as serum equivalents (SE), reflecting the serum equivalent volume (mL) of isolated and precipitated RNA.A volume of isolated and precipitated RNA equivalent to 0.44 mL of serum were used for the generation of small RNA libraries. Library preparations were assessed for purity using an Agilent 2100 BioAnalyzer or Calper LabChip GX before and after size selection for small RNAs. Size selection for miRNAs was performed using a Pippin Prep instrument (Sage Science) with 3% agarose gels. Libraries were quantified using KapaQuant qPCR (Kapa Biosystems). Small RNA libraries were then sequenced with 50 bp single-end reads on an Illumina HiSeq2000 instrument.FASTQ files for each small RNA library were stripped of adapter sequence using cutadapt 1.2.1 and aligned to the human reference genome (Ensembl hg19) using Bowtie 0.12.9. miRNA quantification was performed using htseq-count 0.6.1 using mature miRNA annotations in miRBase v20. miRNA expression data was normalized by the total number of reads per million mapped (RPM) to mature miRNA sequences for a given library.All statistical analyses were carried out in the R statistical environment (R 3.3.1).e scale. Linear mixed effects models were fit using the lme4 package. Variance explained (R2) by each factor was calculated using the method described by Nakagawa and Schielzeth59 using the package r2glmm. The statistical significance of fixed effects were assessed by F-tests based on Kenward-Roger’s approximations for denominator degrees of freedoms using the package lmerTest, while random effects were assessed by a restricted likelihood ratio test based on 100,000 simulations from the finite sample distribution using the exactRLRT function from the package RLRsim.The contribution of the confounding factors age, body mass index (BMI) at recruitment, regional centre of collection, and time to centrifugation were evaluated using a linear mixed effects model including the aforementioned factors as fixed effects and individual IDs as a random effect, with microRNA levels on a loget al.23 study were downloaded from the Gene Expression Omnibus (GEO) under reference number GSE100768.Processed microarray data from the Keller, e scale. Intra-class correlation coefficients (ICCs) were calculated by dividing the between-subject variance by the total variance (the sum of between-subject and within-subject variances). We interpreted ICCs according to the guidelines suggested by Koo and Li60: ICC >0.9 – excellent reliability, ICC <0.9 and ICC >0.75 – good reliability, ICC <0.75 and ICC >0.5 – moderate reliability, and ICC <0.5 – poor reliability. 95% CIs of ICCs were calculated from a bootstrapped distribution with 1000 iterations.The between- and within-subject CVs were calculated as the square root of the between- and within-subject variance components from a mixed effects model with the timepoint order as a fixed effect and individual IDs as a random effect, with microRNA levels on a logSupplementary Tables and FiguresSupplementary Dataset 1Supplementary Dataset 2Supplementary Dataset 3


Ustilago maydis, sexual pheromones elicit mating resulting in an infective filament able to infect corn plants. Along this process a G2 cell cycle arrest is mandatory. Such as cell cycle arrest is initiated upon the pheromone recognition in each mating partner, and sustained once cell fusion occurred until the fungus enter the plant tissue. We describe that the initial cell cycle arrest resulted from inhibition of the nuclear transport of the mitotic inducer Cdc25 by targeting its importin, Kap123. Near cell fusion to take place, the increase on pheromone signaling promotes Cdc25 degradation, which seems to be important to ensure the maintenance of the G2 cell cycle arrest to lead the formation of the infective filament. This way, premating cell cycle arrest is linked to the subsequent steps required for establishment of the infection. Disabling this connection resulted in the inability of fungal cells to infect plants.In the fungus  Ustilago maydis, the cause of a disease known as corn smut.Many fungi that cause diseases in plants need specialized structures to penetrate the plant’s tissues. To form these structures, the fungus must carefully control when and where its cells divide. As in other organisms, the sequence of events that lead to a fungal cell dividing in two are known as the cell cycle. Progress through the distinct steps in the cell cycle is regulated by enzymes including many that add or remove phosphate groups on other proteins. It remains unclear which regulatory enzymes allow any plant-infecting fungus to control its cell cycle when it forms an infection structure, but one fungus that could help answer this question is The corn smut fungus forms infection structures after two different mating strains meet on the surface of the plant, stop dividing and then fuse. This implies that the cell cycles of both strains need to be coordinated to allow the fungus to infect the plant. The two strains recognize each other via chemical signals known as pheromones, and Bardetti et al. now show that pheromone recognition in the corn smut fungus results in an enzyme called Cdc25 being disabled, which in turn causes cell division to stop. Cdc25 is a phosphatase, meaning it removes phosphate groups from cell cycle regulators that are found in the nucleus of the cell. Specifically, Cdc25 targets phosphate groups that would otherwise inhibit the activity of these proteins. Further experiments showed that, following pheromone recognition, Cdc25 is disabled via a two-step process: first it is prevented from entering the nucleus which keeps it away from its targets, and then it is degraded. Bardetti et al. went on to show that this last step was required for the fungus to infect corn plants, since interfering with the breakdown of Cdc25 impairs its ability to stop the cell cycle and form an infection structure.Entry into plant tissue is a critical step for any parasites looking to invade a plant. Since it is difficult to reach the interior of plants with pesticides, most antimicrobial treatments in plants aim at prevention rather than cure. This means that increasing the delay between a fungus recognizing the surface of a plant and penetrating its tissues could give more time to prevent infections. These new findings represent a step towards achieving that goal, though more research is needed to better understand the molecular mechanisms required for the formation of infection structures in plant-infecting fungi. Saccharomyces cerevisiae. In this fungus, pheromone recognition is mediated by plasma membrane-located receptors, which transmit the signal toward the cell cycle machinery using a widely conserved MAP kinase cascade . On the plant surface, infection is initiated upon the recognition of mating pheromone secreted by haploid cells of the opposite mating type , provided by each mating partner. The mechanisms involved in this sustained cell cycle arrest have been described only partially. While the manner by which the presence of b-factor arrests the cell cycle is comprehended in detail, the molecular intricacies associated with the cell cycle arrest induced in response to pheromone are still unknown.During the differentiation process resulting in plant penetration, the presence of a sustained G2 cell cycle arrest is mandatory and the impairment of this cell cycle arrest resulted in the inability of the fungus to infect plants . This ceU. maydis is regulated by the presence of two distinct cyclin-dependent kinase (CDK) complexes: Cdk1-Clb1 and Cdk1-Clb2  allowing us to focus on the connections between the pheromone response MAPK cascade and cell cycle regulators.We sought to address the molecular mechanisms behind the G2 cell cycle arrest observed upon pheromone response in  cascade . However  , it mimi. maydis : cells aDDfuz7 allele was correlated with an increase in the level of inhibitory phosphorylation of Cdk1, which has been reported to be associated with G2 cell cycle arrest in U. maydis  seemed to be shared by b- and pheromone cascade-induced cell cycle arrest. However, other elements, like the downregulation of biz1 and the Chk1-mediated retention of Cdc25 at cytoplasm seemed to be unique for b-dependent cell cycle arrest.In summary, these previous results indicated that some elements, like the downregulation of pcl12, which has been reported to be strongly induced by the pheromone MAPK (crg1 promoter) was sufficient to induce the formation of a cell structure resembling a conjugation tube, which was also arrested at G2 phase  or by Kpp2 (AAAcrk1)   (Garridocrk1AAA) . Much toD allele . Moreoverylation .These results indicated that the described activation of Crk1 via the pheromone cascade during filamentation played no role in the cell cycle arrest induced by the same MAPK cascade. To explain this apparent paradox, we propose that Crk1, which is a kinase that have features of both CDK-like and MAPK-like kinases (see discussion section for a detailed description of this hypothesis), can be activated by two distinct manners: either as a MAPK, by T-loop phosphorylation through the pheromone MAPK cascade, to support polar growth; or as a CDK, by interaction with Pcl12 cyclin, to support cell cycle arrest. Distinct activation mechanisms probably also involve distinct targets explaining the distinct outcomes.pcl12 or crk1, the effects of each gene mutation on the morphology of the resulting filament upon expression of DDfuz7 allele were distinct, and the double mutant was more affected than single mutants  belongs to the family of β importins of class 3 and 4 . Since Ps fungus . Therefot it was . This ree arrest . In suppe arrest .DDfuz7 expression, the mobility of a Kap123-HA allele in SDS-acrylamide gels was reduced  resulted in abrogation of the electrophoretic mobility shift of Kap123 upon DDfuz7 expression  carrying the T867Akap123 allele, and infected corn plants with compatible mixtures of control and mutant strains. We found that strains unable to undergo pre-mating cell cycle arrest were as virulent as wild-type strains   . We obseenotype) . Therefopromoter . In thispromoter . We did T867Akap123 allele did not affect the ability of b-factor to arrest the cell cycle  will result, most likely, in the fusion of mating partners at different stages of the cell cycle. However, once the two cytoplasms fuse, the establishment of a barrier at G2 by the b-factor would resynchronize the respective nuclear content in the resulting filament. In the case of mating partners fusing their cytoplasms during G1 to G2 phases (i. e. providing a single nucleus each), the result will be a dikaryotic filament with nuclei arrested at G2. Only for those partners that fuse during or immediately after mitosis (and before cytokinesis), the result will be a filament carrying three nuclei (only one partner carried two nuclei before fusion) or four nuclei (the two partners carried two nuclei each), with these nuclei arrested at G2. In any case, the final result will be a cell cycle arrested filament (by virtue of b-factor) and therefore it will be proficient for infection, explaining the ability of mutant cells to infect corn plants. Interestingly, this ability to synchronize the cell cycle status of distinct nuclei in a common cytoplasm by homeodomain proteins of the b-factor family is the basis for accurate cell division in other basidiomycete dikaryotic fungi such as Coprinopsis cinerea . To uncover the involvement of the MAPK cascade in the downregulation of Cdc25, we constructed a strain simultaneously expressing the DDfuz7 allele and pcl12 under the crg1 promoter. In this way, we can induce cell cycle arrest, as well as the retention of Cdc25 in the cytoplasm (as a consequence of the ectopic expression of pcl12), even in conditions that potentially disable the MAPK cascade, such as the disruption of the kpp2 gene. In agreement with our hypothesis, we found that under conditions of cell cycle arrest and the retention of Cdc25 in the cytoplasm, Kpp2 was required for the decrease in Cdc25 levels upon DDfuz7 expression (We hypothesized that Cdc25 could be a target (direct or indirect) of the pheromone MAPK cascade, which induces its degradation only when Cdc25 is retained at the cytoplasm and the activation of the pheromone cascade reaches some threshold level   and the presence of a wild-type Kap123 allele   , as welltoplasm) .Taken together, our results were consistent with the idea that activation of the pheromone cascade resulted in the negative regulation of Cdc25 levels by a two-step mechanism: First, Cdc25 is retained in the cytoplasm as a result of the formation of the Pcl12-Crk1 complex, and then Cdc25 is potentially phosphorylated by the Kpp2 MAPK, which probably promoted its degradation.U. maydis Cdc25 carries an unusually long N-terminal extension of approximately 270 amino acids with no similarity to orthologues in the database . Encouragingly, we found that the triple mutant was resistant to the downregulation observed in the wild-type allele upon expression of the DDfuz7 allele  carrying the AAAcdc25 allele to infect plants. Strikingly, we found that the presence of this mutant allele strongly affected the ability of fungal cells to produce disease to corn plants  b-induced cell cycle arrest. However, the absence of downregulation of Cdc25 levels seems to affect the cell cycle arrest in b-dependent filaments originating from crosses . These unexpected results can be explained keeping in mind that, in of Cdc25 . We beliU. maydis, it has been described strong connections between the regulation of the cell cycle and the ability to infect plants: During the steps previous to plant infection, it is required a sustained G2 cell cycle arrest  disappears and mating was not successful. A control mediated by the phosphorylation of importin could probably be easily reversed by some phosphatase (specific or not) once the signaling through the pheromone cascade is abrogated. The formation of the dikaryotic filament, however, is a more terminal decision in the sense that once the mating partners fuse their respective cytoplasms, if they are compatible at the AAAcdc25 allele to infect plants.Our results also reveal the importance of the transition from pheromone-dependent to b-dependent cell cycle arrest once the respective partners fuse. Although the downregulation of Cdc25 activity consists of the retention of Cdc25 in the cytoplasm in both regulatory networks, there is one relevant difference: while the inhibition of nuclear translocation (pheromone-dependent) cannot be bypassed by high concentrations of Cdc25, the retention of Cdc25 by 14-3-3 (b-dependent) is sensitive to the amount of Cdc25 (probably because of saturation of the available retention proteins). This difference gains importance because Cdc25 most likely accumulates in the cytoplasm of conjugation tubes during G2 cell cycle arrest. Once the respective cytoplasms fuse, if the amount of Cdc25 has not been previously tuned, the 14-3-3 retention system will probably be overwhelmed, and the time window required for the establishment of permanent G2 cell cycle arrest will not be provided. For that reason, it seems logical that once the pheromone cascade reaches some signaling threshold (probably reflecting a close encounter between compatible conjugation tubes), it triggers the downregulation of Cdc25 to levels that are most likely easily assimilable by the 14-3-3 proteins. The importance of adjusting the levels of Cdc25 is reflected by the impaired ability of cells carrying the U. maydis was the complex formed by the kinase Crk1 and the cyclin Pcl12. Both proteins were previously reported to be required for the proper morphogenesis of the infective filament in this fungus. Interestingly, in this function, they were part of distinct complexes or regulatory networks: Pcl12 complexed with the cyclin-dependent kinase Cdk5  and pJET1.2 (Thermo Fisher) was used for subcloning and sequencing of genomic fragments generated by PCR.clb2, cdc25, wee1, and kap123 with 3 HA or GFP as well as conditional alleles of kap123 and srp1 were performed using Golden Gate assembly, following published procedures  to be inserted by homologous recombination into the endogenous locus. The cassette also allowed the insertion at the C-terminus of a 3 HA epitope as well as an antibiotic resistance marker. For ocedures . Cdc25 tombinase .Further details of the constructions explained above are available on request.Total RNA was extracted with acidic phenol solution. After extraction, the RNA was cleaned using the High Pure RNA Isolation Kit (Roche Diagnostics GmbH). For RT- PCR, cDNA was synthesized using the High Capacity cDNA Reverse Transcription Kit (Applied Biosystems) employing 1 μg total RNA per sample. qRT-PCR was performed using the SsoAdvanced Universal SYBR Green Supermix (BioRad) in a CFX96 Real-Time PCR system (BioRad). Reaction conditions were as follows: 3 min 95°C followed by 40 cycles of 10 s 95°C/10 s 60°C/30 s 72°C.g for 3 min, and the pellet was thoroughly resuspended in 100 μl of 2 × Laemmli buffer and 50 μl of 2 M Tris base. After boiling for 5 min, 10–20 μl were loaded in the gels.Protein extracts were performed using an adapted chloroacetic acid (TCA) method. Briefly, cells from 5- to 10 ml aliquots of cultures were harvested, and 1 ml of 20% TCA was added. The supernatant was removed after centrifugation, and the pellet was resuspended in 100 μl of 20% TCA and stored at −80°C for 1 hr. Samples were thawed on ice, glass beads were added, and cells were broken using a FastPrep FP120 cell disrupter . The lysate was recovered by punching a hole on the bottom of the tube, and the glass beads were further washed with 200 μl of 5% TCA. Lysates were centrifuged at 1000 × For general purposes, TGX (4–20%) gels from BioRad were used, at constant 100 v running conditions. To detect the phosphorylated forms of Kap123, 8% acrylamide/0.1% bisacrylamide, pH 9.2 custom-made gels were used at constant 5mA running conditions.Western blots were repeated from at least three independent experiments in each case.2, 15 mM EGTA, 1% Triton X-100, PhosSTOP [1 tablet per 10 mL], Roche protease inhibitor cocktail [1 tablet per 10 mL]) and cells were broken using a FastPrep FP120 cell disrupter. The lysate was recovered by punching a hole on the bottom of the tube, and the glass beads were further washed with ice-cold HB buffer, and the lysated was cleared by centrifugation (10 min/13000xg).To perform immunoprecipitations, crude protein extracts were prepared. Briefly, cells were harvested by centrifugation at 4°C and washed twice with ice-cold water. The cell pellet was resuspended in ice-cold HB buffer  were incubated with 1 μg of the monoclonal antibody for 2 hr at 4°C and then prewashed G-protein coupled magnetic beads (50 μl) were added and incubated for 30 min at 4°C with agitation. For GFP trap, 50 μl GFP trap beads were mixed with 3.5 mg of total protein extracts (1 ml) for 2 hr at 4°C, with agitation. Immunoprecipitates were washed six times with 1 ml of HB buffer.Crude extracts were obtained and incubated with GFP-trap beads as explained above. After separation of the protein samples by SDS-PAGE and Coomassie Brilliant Blue R250  staining and distaining of the SDS-Gel was performed and each lane was cut into 10 pieces. A digest with trypsin NB sequencing grade  was performed with each gel piece over night at 37°C. The eluates of the five upper and the five lower gel pieces were combined, suspended in sample buffer  and analyzed by LC-MS.Peptides generated with trypsin on-bead digestion were subjected to LC-MS analysis: 1.5 µL of each peptide sample were separated with nano-flow LC using an RSLCnano Ultimate 3000 system (Thermo Fisher Scientific). Peptides were loaded for 5 min with 0.07% trifluoroacetic acid on an Acclaim PepMap 100 pre-column  with a flow rate of 20 µL/min. Separation of peptides was done with reverse phase chromatography on an Acclaim PepMap RSLC column  at a flow rate of 300 nL/min. The solvent composition was gradually changed within a time period of 94 min from 96% solvent A (0.1% formic acid) and 4% solvent B  to 10% solvent B within 2 min, to 30% solvent B within the following 58 min, to 45% solvent B within the next 22 min, and to 90% solvent B within the following 12 min. All solvents and acids had Optima LC/MS quality and were purchased from Thermo Fisher Scientific. Eluting peptides were on-line ionized with nano-electrospray (nESI) using the Nanospray Flex Ion Source (Thermo Scientific) at 1.5 kV (liquid junction) and on-line transferred into an Orbitrap VelosPro mass spectrometer (Thermo Fisher Scientific). Full scans were recorded in a mass range of 300 to 1650 m/z at a resolution of 30,000 followed by data-dependent top 15 CID fragmentation (dynamic exclusion enabled). LC-MS method programming and data acquisition was performed with the XCalibur 4.0 software (Thermo Fisher Scientific).Zea mays) variety Early Golden Bantam (Olds seeds) as described before . Images were further processed with ImageJ software.To determine the presence of Cdc25 at nucleus, merged (RGB) images from cells carrying a cut11-cherry allele (marking the nuclear membrane) and a GFP-Cdc25 allele were used. In each nucleus, a line was traced covering all nucleus diameter and also surrounding cytoplasm using the ImageJ software and the plot intensity for each channel was obtained (using the RGB line profile plugging from ImageJ). Those cells in which the signal inside the nucleus was above the cytoplasmic background were considered as positive for nuclear GFP. In the few cases of strong cytoplasmic background, the sorting or not of that particular nucleus was decided case by case, by looking the intensity of nuclear signal.t-test was used. P-Values were calculated with the GraphPad Prism 5.0 software.To determine the statistical significance of differences a two-tailed Student  In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.eLife. Your article has been reviewed by Christian Hardtke as the Senior Editor, a Reviewing Editor, and two reviewers. The reviewers have opted to remain anonymous.Thank you for submitting your article "Cytoplasmic retention and degradation of a mitotic inducer enable the steps for plant infection by a pathogenic fungus" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Both reviewers agree that your work is of interest. However, they are also of the opinion that your data do not yet unequivocally support your interpretation, especially the novelty aspect of temporarily separate G2 blocks that are achieved by diverse mechanisms. It is essential that you address these issues comprehensively in a revised version of your manuscript.We also all agreed that your manuscript requires extensive re-writing to make your study more easily accessible and comprehensible for the average reader. In its current form, we all found the story difficult to follow.Finally, please do your best to address, wherever possible within a reasonable time frame, the other comments in the reviews pasted below.Reviewer #1:In this manuscript, the authors provide a large amount of data addressing the mechanisms by which the corn smut Ustilago maydis arrests the cell cycle in G2 in response to pheromone during and after mating, which precedes plant colonization. Their main findings are (1) that activation of the pheromone-MAPK cascade leads to retention of the Cdc25 phosphatase in the cytosol due to phosphorylation of the Cdc25 importin likely by a complex formed between the cyclin Pcl12 and the CDK-like kinase Crk1, and (2) high-level activation of the pheromone-MAPK kinase leads to downregulation of Cdc25 protein levels due to phosphorylation of the N-terminal tail of Cdc25 likely by the MAPK Kpp2. Interestingly, preventing the cell cycle block by blocking importin phosphorylation does not prevent mating, nor plant pathogenesis. By contrast, preventing Cdc25 downregulation has no effect on pre-fusion cell cycle arrest, but impairs post-fusion cell cycle block and pathogenesis. The data is interpreted in terms of importance of this dual regulation during a cell fate change.This is an interesting story, for which a lot of the data is solid, with genetics and biochemistry supporting the claims. There are, however, a few areas that need to be looked at to make it a strong paper for publication.Experiments/data analysis:The figures showing the localization of GFP-Cdc25 are not convincing . Providing both single channels and merge would help, maybe of single examples at higher magnification. This should be coupled with quantification of nuclear and cytosolic levels.One important message from the paper is that Cdc25 is regulated by two distinct mechanisms: a cytosolic retention mechanism keeping pre-mating cells in G2, and a degradation mechanism important to keep post-mating cell in G2. From the data using mating cells, I am not fully convinced that this distinction in temporality can be made. First, the authors claim that mating appears to proceed normally in the kap123-T867A mutant, which fails to block the cell cycle in G2 when fuz7-DD or pcl12 are overexpressed. However, Figure 4A, showing Kap123 migration pattern in response to pheromone stimulation, is not convincingly showing the claimed mobility shift. The gel appears to be slightly smiley, but I don't see bands migrating slower. This lowers the confidence that Kap123 is in fact phosphorylated in these conditions. In addition, though kap123-T867A mutants form filaments that often have >2 nuclei in mating conditions , it is not clear whether this results from a pre-mating failure in G2 block as claimed by the authors, or a post-mating block failure. In fact, when assessing the cdc25-AAA mutant, the authors perform exactly the same experiment  and interpret the multi-nucleated defect as a post-fusion cycling issue. Imaging the filaments pre- and post-fusion in these conditions would address the issue.Similarly, because Cdc25 levels decrease with increasing dosage of pheromone, the authors hypothesize that Cdc25 is a low-affinity substrate for the MAPK (subsection “The MAPK Kpp2 directs Cdc25 downregulation”), which is phosphorylated and destabilized only upon high pheromone signal. However, the authors do not test whether the cytosolic retention of Cdc25 is also pheromone-level dependent. It could equally be that Kap123 importin phosphorylation scales with pheromone concentration and that Cdc25 is retained more in the cytosol in high pheromone levels, which will lead to more Cdc25 phosphorylation and degradation given this only happens in this compartment. These issues need to be clarified.It is unfortunate that the paper just falls short of demonstrating a direct substrate-kinase relationship between the importin and Crk1 and between Cdc25 and Kpp2, though it shows existence of complexes between the importin and Ltc12, and between Cdc25 and Kpp2.Text changes:The writing needs to be looked at. From the abstract it is not clear whether there are two distinct cell cycle arrests during distinct cycles, or two distinct mechanisms of arrest during the same cell cycle. It is only after reading the paper that I understood that the two arrests refer to two distinct mechanisms that occur during mating of haploid cells pre-fusion and in dikaryotic cells post-fusion, which together maintain the cell in the same G2 phase. The same goes in other parts of the manuscript. For instance, "the mutant filaments" (subsection “The pheromone-dependent decrease in the Cdc25 level enables b dependent cell cycle arrest.”) could be more clearly spelled out as dikaryotic post-fusion cells. There are also a number of grammatical mistakes.In the Discussion section, I do not understand in what way repression of clb1 transcription by Biz1 would be irreversible. The discussion spends much time discussing these regulatory mechanisms, which are not the object of this paper, but does not discuss some pertinent questions raised by the data, for instance why Cdc25 levels do not drop upon Pcl12 over-expression but Pcl12 is required for the Cdc25 levels drop upon fuz7-DD expression. My guess is that Cdc25 downregulation can only happen in the cytosol and that Pcl12 is required for Cdc25 cytosolic retention, but this is not explicitly formulated.Reviewer #2:Cytoplasmic Retention and degradation of a mitotic inducer enable the steps for plant infection by a pathogenic fungus. Bardetti et al.This study seeks to determine the mechanism of cell cycle control that leads to plant infection by the corn smut fungus Ustilago maydis. During infection, the fungus forms a dikaryon, which is the infective state. This requires synchrony between the cell cycles of the fusing haploid sporidia, so that subsequent development is co-ordinated. There are two discrete cell cycle arrests. The first is initiated upon pheromone recognition by each mating partner, while a second cell cycle arrest occurs after cell fusion leading to establishment of the infective dikaryotic filament. The significance of this study is that the authors propose that the cell cycle arrests occur by two distinct mechanisms. Furthermore, the first pheromone-induced cell cycle arrest results from inhibition of the nuclear import of the mitotic inducer Cdc25, based on inhibiting the function of the importin, Kap123. This is a novel mechanism, distinct from what is known in the model yeasts. Interestingly, the absence of the first G2 cell cycle arrest is dispensable for virulence, while the second cell cycle control point is essential. Linking the two control mechanisms is therefore necessary.The paper is interesting because it reveals a new mechanistic insight into the control of virulence by a dikaryotic basidiomycete pathogen. This builds upon the pioneering work of the Kahmann lab, in particular  in working out how pheromone signalling and self/non-self recognition via bE/bW mating type locus functions. The link to cell cycle control is, however, vital, but often overlooked, so this paper is potentially significant, building on some excellent previous work by lead author Perez-Martin. Furthermore, the work is carefully executed in the main. What really lets it down (but which I hope can be remedied) is the way the paper is written. The manuscript is quite impenetrable in parts and needs to be re-drafted to include a set of intermediate conclusions at each stage in which the work is presented. This is absolutely vital, because currently I think the number of people who will understand this work, let alone appreciate its significance, is currently very small.The Abstract and Introduction section describe the conclusions in a relatively straightforward way, including the key finding that the pheromone-induced G2 arrest is a consequence of inhibition of nuclear transport of Cdc25 via Kap123. I think the key point that is missed in the Results section is the comparison with established mechanism by which b-factor induces G2 cell cycle arrest. I think the opening of the Results section should be re-written in a way that structures more tightly to the three effects by which b-factor mediates the sustained cell cycle arrest? This will help to explain the rationale for the study and the way in which the experiments were carried out. It will, for example, help to point out the differences in relation to the results of cdc25 transcriptional levels found in Figure 1—figure supplement 3B with protein levels reported in Figure 1E and Figure 1F, that are important to understand if the rest of the paper is to be understood.Essential revisions:1) In each section, the authors need to make a firm conclusion to help orient the reader. This is almost entirely missing, which makes the manuscript really difficult to follow. In the subsection” Activation of the pheromone cascade promotes G2 cell cycle arrest via inhibitory phosphorylation of Cdk1”, the rationale for using the fuz7DD mutant, for instance, is not fully explained and there is no conclusion provided to subsection “Activation of the pheromone cascade promotes G2 cell cycle arrest via inhibitory phosphorylation of Cdk1” at all.2) The introduction of the subsection “Pheromone cascade-induced cell cycle arrest depends on an alternative cyclin interacting with a Cdk-like kinase” has no introduction and does not follow logically from the previous section. The rationale for this part of the paper is not introduced until later in this subsection. This needs to come sooner, and with a better explanation.3) In Figure 1, I would prefer to see discrete data points rather than the bar charts shown in 1C and D.4) The Cdc25 overexpression experiment (subsection “Activation of the pheromone cascade promotes G2 cell cycle arrest via inhibitory phosphorylation of Cdk1”) needs more emphasis, as it is critical for understanding why the nuclear localisation idea was followed. I think some of this data should be in Figure 1 and not in Figure 1—figure supplement 4.5) Figure 2A- please add a micrograph of pcl12.6) Figure 2B showing Cdk1 phosphorylation in FuzDD Δpcl12. The blot in 2B seems to show less total Cdk1 and Cdk5. Instead of having Figure 3—figure supplement 1, it might be better to more clearly to show CDK1 phosphorylation in Figure 2B. Could maybe authors clarify why they show Figure 3—figure supplement 1 vs. a blot of CDK1P?7) Please clarify sentences subsection “Pheromone cascade-induced cell cycle arrest depends on an alternative cyclin interacting with a Cdk-like kinase” on the role of CDK and link with MAPK and Prf1 signaling. It is not clear what authors are trying to point out here and it is not clear that this work is already published.8) In subsection “Pheromone cascade-induced cell cycle arrest depends on an alternative cyclin interacting with a Cdk-like kinase”, the authors suggest that Pcl12 and Crk1 interact to cause the cell cycle arrest. Can a physical interaction be demonstrated either by co-localization of these proteins in arrested vs. non-arrested cells or in vitro by Y2H to show if this is direct or indirect interaction. This statement really needs much better support as no evidence is provided.9) In Figure 2E, a GFP-cdc25 control is needed to show difference between arrested vs. non-arrested cells.10) Figure 2 we understand that Cdc25 degradation depends on Pcl12 and independent on crk1. But this conclusion reads diluted within the text. For example, the conclusion comes in the end of subsection “Pheromone cascade-induced cell cycle arrest is mediated by the phosphorylation of Cdc25 importin, Kap123” all together but maybe authors could slowly build up during the progression of the text this conclusion.11) Subsection “Pheromone cascade-induced cell cycle arrest depends on an alternative cyclin interacting with a Cdk-like kinase”. Again, there is no conclusion made to this section at all, which means that the next  part of the paper on the Kap123 protein is introduced without any real reasoning.12) What happen to nuclear localization of Cdc25AAA? Has this been checked?13) Have the authors expressed Cdc25 with an NLS to force Cdc25 into the nucleus? This would be valuable to test the model presented.14) It would be valuable to carry out Y2H to define the physical interaction of Kpp2 with Cdc25 and also between Pcl12, CRK1, Kap123 and Cdc25. Have these experiments been carried out?15) Subsection “Pheromone cascade-induced cell cycle arrest is mediated by the phosphorylation of Cdc25 importin, Kap123” is the first time a definitive conclusion is made in the manuscript. This is problematic for me. The paper needs to be more simply presented and broken into conclusions that build the story logically. I am not convinced by the way, or order in which the paper is currently presented. The nuclear localisation status of cdc25 needs to be introduced as a concept much earlier, following what is already known about the b-factor cell cycle arrest mechanism. This would make much more sense than how the paper is currently written.U. maydis to infect plants”. This section doesn't logically follow the one that precedes it.16) Subsection “The absence of G2 cell cycle arrest during pheromone response has little impact on the ability of 17) In Figure 1—figure supplement 2A, why do the authors not include a loading control to show that amounts of protein loaded are the same for every lane? Differences in loading of samples are corrected by dividing each phosphopeptide-specific antibody signal by the Cdk1 (anti-PSTAIRE) antibody signal. However, this only gives a relative abundance of P/Cdk1. I would prefer to see loading controls too.S. cerevisiae models). What about cell cycle-dependent infection by other pathogenic fungi? What happens in other smuts? Are they also G2 arrested for infection? What about Colletotrichum and Magnaporthe, where cell cycle controls have been implicated in appressorium development? Are there parallels or not? Are the mechanisms distinct in basidiomycete pathogens? Some wider context is important too. Otherwise the paper's potential main conclusions are lost in impenetrable detail.In summary, I think the paper has definite merit and potential significance in explaining the link between the two distinct cell cycle arrests and the onset of virulence by U. maydis. However, it is illogically presented in my view and often the detail obscures the truly significant findings. For me, a key finding is that the pheromone-induced cell cycle arrest is on its own dispensable for virulence. However, it enables the b-dependent cell cycle arrest, which is absolutely essential. Secondly, although mechanistically the b-dependent G2 arrest and pheromone arrest differ, they actually have a conserved underlying mechanism which requires retaining Cdc25 in the cytoplasm. This needs much greater emphasis too. Finally, the paper is also very narrowly framed. Only studies in Ustilago maydis are described , a Reviewing Editor, and two reviewers.Thank you for resubmitting your work entitled "Cytoplasmic retention and degradation of a mitotic inducer enable plant infection by a pathogenic fungus" for further consideration at Before we can accept the manuscript, please detail how the nuclear GFP-Cdc25 was quantified, as requested by reviewer 1.Reviewer #1:This revised version of the manuscript is much improved: the authors have performed some of the suggested experiments and addressed most of my experimental concerns, or provided convincing arguments. The only small experimental point I would like to raise concerns the quantification of nuclear GFP-Cdc25. The images provided a more convincing, and there is some quantification given, though the quantification is not described in the methods and appears rather subjective: the authors appear to visually classify cells to have nuclear GFP-Cdc25 or not. What's the cut-off used to decide for or against nuclear localization? An objective quantification requires to measure the fluorescence intensity in nuclear and cytosolic compartments in individual cells, for instance derive a ratio, and then present the mean and deviation on a graph.The text has also been much improved: the logical flow is better apparent, and the text is overall easier to read. However, the Abstract is still difficult to understand and suffers from poor English language.Reviewer #2:I think the authors have done a very commendable revision of the paper, addressing both my concerns and those of the other expert reviewer. I think the paper is much clearer and accessible now, which is important, and I think the response to questions about additional potential experiments are thoughtful and valid. These have been carried out or described where feasible, and credible explanations provided where they are not. I am satisfied with this. Importantly, the authors are careful to explain the limitations of their conclusion too. I am happy that this version is published. It is a valuable contribution. Both reviewers agree that your work is of interest. However, they are also of the opinion that your data do not yet unequivocally support your interpretation, especially the novelty aspect of temporarily separate G2 blocks that are achieved by diverse mechanisms. It is essential that you address these issues comprehensively in a revised version of your manuscript.We also all agreed that your manuscript requires extensive re-writing to make your study more easily accessible and comprehensible for the average reader. In its current form, we all found the story difficult to follow.Finally, please do your best to address, wherever possible within a reasonable time frame, the other comments in the reviews pasted below.Reviewer #1:In this manuscript, the authors provide a large amount of data addressing the mechanisms by which the corn smut Ustilago maydis arrests the cell cycle in G2 in response to pheromone during and after mating, which precedes plant colonization. Their main findings are (1) that activation of the pheromone-MAPK cascade leads to retention of the Cdc25 phosphatase in the cytosol due to phosphorylation of the Cdc25 importin likely by a complex formed between the cyclin Pcl12 and the CDK-like kinase Crk1, and (2) high-level activation of the pheromone-MAPK kinase leads to downregulation of Cdc25 protein levels due to phosphorylation of the N-terminal tail of Cdc25 likely by the MAPK Kpp2. Interestingly, preventing the cell cycle block by blocking importin phosphorylation does not prevent mating, nor plant pathogenesis. By contrast, preventing Cdc25 downregulation has no effect on pre-fusion cell cycle arrest, but impairs post-fusion cell cycle block and pathogenesis. The data is interpreted in terms of importance of this dual regulation during a cell fate change.This is an interesting story, for which a lot of the data is solid, with genetics and biochemistry supporting the claims. There are, however, a few areas that need to be looked at to make it a strong paper for publication.Experiments/data analysis:The figures showing the localization of GFP-Cdc25 are not convincing . Providing both single channels and merge would help, maybe of single examples at higher magnification. This should be coupled with quantification of nuclear and cytosolic levels.We have included single channels in each figure  as well as quantification of filaments showing nuclear GFP signal. Also, as reviewer suggested, we included, in some of the images, higher magnification of selected merged nuclei . We thank the reviewer for the suggestion.One important message from the paper is that Cdc25 is regulated by two distinct mechanisms: a cytosolic retention mechanism keeping pre-mating cells in G2, and a degradation mechanism important to keep post-mating cell in G2. From the data using mating cells, I am not fully convinced that this distinction in temporality can be made. First, the authors claim that mating appears to proceed normally in the kap123-T867A mutant, which fails to block the cell cycle in G2 when fuz7-DD or pcl12 are overexpressed. However, Figure 4A, showing Kap123 migration pattern in response to pheromone stimulation, is not convincingly showing the claimed mobility shift. The gel appears to be slightly smiley, but I don't see bands migrating slower. This lowers the confidence that Kap123 is in fact phosphorylated in these conditions.We agree the reviewer that the shift on electrophoretic mobility is not impressive.The electrophoretic shift is not present in regular SDS gels (we use BioRad TGX 4-20%). When we initiated this study, we tried PhosTag gels, but after a considerable effort with no success, we tried empirical manipulation of the ratio of acrylamide to bisacrylamide, which has been reported by different groups to improve resolution of differently phosphorylated forms of a protein. The best condition we found was 8% acrylamide/0.1% bisacrylamide. Regarding the claimed gel in old Figure 4A, we repeated this gel but alternating the lanes for control and mutant samples, conditions that help to detect more clearly the mobility shift. This new gel is now in Figure 5B. We hope this new gel helps to convince the reviewer about this mobility shift.In addition, though kap123-T867A mutants form filaments that often have >2 nuclei in mating conditions , it is not clear whether this results from a pre-mating failure in G2 block as claimed by the authors, or a post-mating block failure. In fact, when assessing the cdc25-AAA mutant, the authors perform exactly the same experiment  and interpret the multi-nucleated defect as a post-fusion cycling issue. Imaging the filaments pre- and post-fusion in these conditions would address the issue.The reviewer is right in the sense that in both experiments the result seems to be the same (multinucleated filaments), but we consider the defect that cause this phenotype to be different in each case. We base our conclusion in two main differences between these two results. The first one is that while the cdc25AAA mutant does not affected the cell cycle arrest induced by the activation of pheromone MAPK cascade , the kap123-T867A mutant disables the pheromone-dependent cell cycle arrest, so we believe that in this last case the observed defects most-likely are pre-fusion. The second difference is the total number of nuclei that we found in the respective mutant filaments. While in kap123-T867A mutant crosses we never found filaments with more than 4 nuclei each , in the case of cdc25AAA crosses, we found filaments with more than 4 nuclei, suggesting that the b-dependent cell cycle arrest was not functional. We detailed the number of nuclei per filaments in both cases to made more evident these differences . In addition, we tried to clarify these differences along the text in the revised version.Similarly, because Cdc25 levels decrease with increasing dosage of pheromone, the authors hypothesize that Cdc25 is a low-affinity substrate for the MAPK (subsection “The MAPK Kpp2 directs Cdc25 downregulation”), which is phosphorylated and destabilized only upon high pheromone signal. However, the authors do not test whether the cytosolic retention of Cdc25 is also pheromone-level dependent.We found that the lowest pheromone concentration was still able to arrest the cell cycle , to induce retarded electrophoretic mobility of Kap123  and to avoid the nuclear localization of Cdc25 . However, since the same amount of pheromone did not result into decrease of Cdc25 levels  we believe that retention of Cdc25 (and thereby cell cycle arrest) occurs even a low pheromone concentration but that the proposed degradation of Cdc25 needs higher signaling through the MAPK cascade. The reviewer is right in the sense that these results does not directly implies Cdc25 to be a low-affinity substrate and therefore we removed this sentence. At least, these results suggest that some threshold of MAPK signaling is required to induce the decrease of Cdc25 levels.It could equally be that Kap123 importin phosphorylation scales with pheromone concentration and that Cdc25 is retained more in the cytosol in high pheromone levels, which will lead to more Cdc25 phosphorylation and degradation given this only happens in this compartment. These issues need to be clarified.The reviewer is right and it seems that the proposed degradation of Cdc25 needs the protein to be retained at the cytoplasm , but this is not the only requisite, since it also needs signaling through the MAPK cascade . In the revised version, we explained this in a clearer way.It is unfortunate that the paper just falls short of demonstrating a direct substrate-kinase relationship between the importin and Crk1 and between Cdc25 and Kpp2, though it shows existence of complexes between the importin and Ltc12, and between Cdc25 and Kpp2.We believe that physical interactions are supported by the genetics results, but reviewer is correct, and we are aware of this weakness. Therefore, we are cautious with our conclusions about the complex. We tried along the time invested in this study other approaches to show direct substrate-kinase relationships, such as 2-yeast hybrid or in vitro assays, but they were not successful, mainly because technical problems (see answers to reviewer 2).Text changes:The writing needs to be looked at. From the abstract it is not clear whether there are two distinct cell cycle arrests during distinct cycles, or two distinct mechanisms of arrest during the same cell cycle. It is only after reading the paper that I understood that the two arrests refer to two distinct mechanisms that occur during mating of haploid cells pre-fusion and in dikaryotic cells post-fusion, which together maintain the cell in the same G2 phase. The same goes in other parts of the manuscript. For instance, "the mutant filaments" (subsection “The pheromone-dependent decrease in the Cdc25 level enables b dependent cell cycle arrest.”) could be more clearly spelled out as dikaryotic post-fusion cells. There are also a number of grammatical mistakes.The reviewer is correct, and we changed substantially the text to be more clear.We also asked a professional editing system to edit the text for English.In the Discussion section, I do not understand in what way repression of clb1 transcription by Biz1 would be irreversible. The discussion spends much time discussing these regulatory mechanisms, which are not the object of this paper, but does not discuss some pertinent questions raised by the data, for instance why Cdc25 levels do not drop upon Pcl12 over-expression but Pcl12 is required for the Cdc25 levels drop upon fuz7-DD expression. My guess is that Cdc25 downregulation can only happen in the cytosol and that Pcl12 is required for Cdc25 cytosolic retention, but this is not explicitly formulated.The reviewer is again correct and in this new version we followed her/his advice.Thanks for it.Reviewer #2:Cytoplasmic Retention and degradation of a mitotic inducer enable the steps for plant infection by a pathogenic fungus. Bardetti et al.This study seeks to determine the mechanism of cell cycle control that leads to plant infection by the corn smut fungus Ustilago maydis. During infection, the fungus forms a dikaryon, which is the infective state. This requires synchrony between the cell cycles of the fusing haploid sporidia, so that subsequent development is co-ordinated. There are two discrete cell cycle arrests. The first is initiated upon pheromone recognition by each mating partner, while a second cell cycle arrest occurs after cell fusion leading to establishment of the infective dikaryotic filament. The significance of this study is that the authors propose that the cell cycle arrests occur by two distinct mechanisms. Furthermore, the first pheromone-induced cell cycle arrest results from inhibition of the nuclear import of the mitotic inducer Cdc25, based on inhibiting the function of the importin, Kap123. This is a novel mechanism, distinct from what is known in the model yeasts. Interestingly, the absence of the first G2 cell cycle arrest is dispensable for virulence, while the second cell cycle control point is essential. Linking the two control mechanisms is therefore necessary.The paper is interesting because it reveals a new mechanistic insight into the control of virulence by a dikaryotic basidiomycete pathogen. This builds upon the pioneering work of the Kahmann lab, in particular  in working out how pheromone signalling and self/non-self recognition via bE/bW mating type locus functions. The link to cell cycle control is, however, vital, but often overlooked, so this paper is potentially significant, building on some excellent previous work by lead author Perez-Martin. Furthermore, the work is carefully executed in the main. What really lets it down (but which I hope can be remedied) is the way the paper is written. The manuscript is quite impenetrable in parts and needs to be re-drafted to include a set of intermediate conclusions at each stage in which the work is presented. This is absolutely vital, because currently I think the number of people who will understand this work, let alone appreciate its significance, is currently very small.The Abstract and Introduction section describe the conclusions in a relatively straightforward way, including the key finding that the pheromone-induced G2 arrest is a consequence of inhibition of nuclear transport of Cdc25 via Kap123. I think the key point that is missed in the Results section is the comparison with established mechanism by which b-factor induces G2 cell cycle arrest. I think the opening of the Results section should be re-written in a way that structures more tightly to the three effects by which b-factor mediates the sustained cell cycle arrest? This will help to explain the rationale for the study and the way in which the experiments were carried out. It will, for example, help to point out the differences in relation to the results of cdc25 transcriptional levels found in Figure S3B with protein levels reported in Figure 1E and Figure 1F, that are important to understand if the rest of the paper is to be understood.The reviewer is right, and we followed her/his advices and tried to make the manuscript more readable. We thank the reviewer for thoughtful comments on the manuscript and supportive remarks.Essential revisions:1) In each section, the authors need to make a firm conclusion to help orient the reader. This is almost entirely missing, which makes the manuscript really difficult to follow. In the subsection” Activation of the pheromone cascade promotes G2 cell cycle arrest via inhibitory phosphorylation of Cdk1”, the rationale for using the fuz7DD mutant, for instance, is not fully explained and there is no conclusion provided to subsection “Activation of the pheromone cascade promotes G2 cell cycle arrest via inhibitory phosphorylation of Cdk1” at all.DD allele (subsection “Activation of the pheromone cascade promotes G2 cell cycle arrest via inhibitory phosphorylation of Cdk1”).We added a phrase with the conclusion at the end of each paragraph. Also, we tried to explain more carefully the rationale to use the fuz72) The introduction of the subsection “Pheromone cascade-induced cell cycle arrest depends on an alternative cyclin interacting with a Cdk-like kinase” has no introduction and does not follow logically from the previous section. The rationale for this part of the paper is not introduced until later in this subsection. This needs to come sooner, and with a better explanation.We tried to explain the reason for study the Pcl12 protein in this context.3) In Figure 1, I would prefer to see discrete data points rather than the bar charts shown in 1C and D.We added the source data for all the graphs in Figures.4) The Cdc25 overexpression experiment (subsection “Activation of the pheromone cascade promotes G2 cell cycle arrest via inhibitory phosphorylation of Cdk1”) needs more emphasis, as it is critical for understanding why the nuclear localisation idea was followed. I think some of this data should be in Figure 1 and not in Figure 1—figure supplement 4.We agree the reviewer that this result needs more emphasis. In the new revised version, we tried our best (subsection “The molecular mechanisms for pheromone cascade-mediated cell cycle arrest are likely to be different from those described for b-dependent cell cycle arrest”).5) Figure 2A- please add a micrograph of pcl12.We added a micrograph of pcl12D mutant.6. Figure 2B showing Cdk1 phosphorylation in FuzDD Δpcl12. The blot in 2B seems to show less total Cdk1 and Cdk5. Instead of having Figure 3—figure supplement 1, it might be better to more clearly to show CDK1 phosphorylation in Figure 2B. Could maybe authors clarify why they show Figure 3—figure supplement 1 vs. a blot of CDK1P?Honestly, we do not understand this question. Old figure 2B does not show Cdk1 phosphorylation but Cdc25 levels.DD expression probably use the same mechanism to arrest cell cycle, which is relevant because in one case we observed a decrease in Cdc25 levels, while in the other not.Figure 3—figure supplement 1 showed that interfering with the ability to produce Cdk1 inhibitory phosphorylation  disables the cell cycle arrest which results from ectopic expression of pcl12. This result indicated that both pcl12 and fuz77. Please clarify sentences subsection “Pheromone cascade-induced cell cycle arrest depends on an alternative cyclin interacting with a Cdk-like kinase” on the role of CDK and link with MAPK and Prf1 signaling. It is not clear what authors are trying to point out here and it is not clear that this work is already published.We changed all this part of text and now we believe it is more clear 8) In subsection “Pheromone cascade-induced cell cycle arrest depends on an alternative cyclin interacting with a Cdk-like kinase”, the authors suggest that Pcl12 and Crk1 interact to to cause the cell cycle arrest. Can a physical interaction be demonstrated either by co-localization of these proteins in arrested vs. non-arrested cells or in vitro by Y2H to show if this is direct or indirect interaction. This statement really needs much better support as no evidence is provided.We agree the reviewer that a much better support will be desirable. During the course of this work we tried to express these proteins in bacteria to analyze the ability of these proteins to make a complex as well as to study the kinase activity. Unfortunately, in spite of a considerable effort we were not successful obtaining soluble protein in both cases.S. cerevisiae was toxic and cells were not able to grow.We also tried Y2H. Actually, before to try the Mass Spec approach, we tried a Y2H search expressing Pcl12 as a bait and a 2H library from U.maydis  as prey. The problem was that expression of pcl12 in 9) In Figure 2E, a GFP-cdc25 control is needed to show difference between arrested vs. non-arrested cells.We do not included images of non-induced cells, but an image of GFP-Cdc25 in control cells growing in non-inducing conditions can be observed in Figure 3—figure supplement 2C.10) Figure 2 we understand that Cdc25 degradation depends on Pcl12 and independent on crk1. But this conclusion reads diluted within the text. For example, the conclusion comes in the end of subsection “Pheromone cascade-induced cell cycle arrest is mediated by the phosphorylation of Cdc25 importin, Kap123” all together but maybe authors could slowly build up during the progression of the text this conclusion.Decrease of Cdc25 levels is dependent on Pcl12, Crk1 and an active MAPK. We agree the author that in the former version probably this conclusion reads diluted.We make more clear statement on this in the revised version.11) Subsection “Pheromone cascade-induced cell cycle arrest depends on an alternative cyclin interacting with a Cdk-like kinase”. Again, there is no conclusion made to this section at all, which means that the next  part of the paper on the Kap123 protein is introduced without any real reasoning.The reviewer is right, and we changed substantially the text.12) What happen to nuclear localization of Cdc25AAA? Has this been checked?The retention of Cdc25 in response to pcl12 expression is not affected in the cdc25AAA allele. We added these data in Figure 7I.13) Have the authors expressed Cdc25 with an NLS to force Cdc25 into the nucleus? This would be valuable to test the model presented.It is a great idea. Unfortunately, from previous studies done in the laboratory many years ago  we know that adding a canonical NLS to Cdc25 is lethal in U. maydis. We believe that the reason for that is the loss of the subcellular regulation, which seems to be really important to control premature entry into mitosis in this fungus.14) It would be valuable to carry out Y2H to define the physical interaction of Kpp2 with Cdc25 and also between Pcl12, CRK1, Kap123 and Cdc25. Have these experiments been carried out?S. cerevisiae. As explained above, expression of pcl12 was toxic in S. cerevisae. crk1 (which is the homologue to Ime2) can be expressed only using the kinase dead version and kap123 expression resulted in cell cycle arrest in S. cerevisiae (probably because interferes with appropriated nuclear transport).For some reason, several of the proteins we were working with were toxicor produced undesirable effects when expressed in 15) Subsection “Pheromone cascade-induced cell cycle arrest is mediated by the phosphorylation of Cdc25 importin, Kap123” is the first time a definitive conclusion is made in the manuscript. This is problematic for me. The paper needs to be more simply presented and broken into conclusions that build the story logically. I am not convinced by the way, or order in which the paper is currently presented. The nuclear localisation status of cdc25 needs to be introduced as a concept much earlier, following what is already known about the b-factor cell cycle arrest mechanism. This would make much more sense than how the paper is currently written.We followed the reviewer advice and tried to present the results in a clearer way.16) Subsection “The absence of G2 cell cycle arrest during pheromone response has little impact on the ability of U. maydis to infect plants”. This section doesn't logically follow the one that precedes it.We tried to explain the reasoning for these experiments.17) In Figure 1—figure supplement 2A, why do the authors not include a loading control to show that amounts of protein loaded are the same for every lane? Differences in loading of samples are corrected by dividing each phosphopeptide-specific antibody signal by the Cdk1 (anti-PSTAIRE) antibody signal. However, this only gives a relative abundance of P/Cdk1. I would prefer to see loading controls too.In this experiment, what we are actually trying to determine is the level of inhibitory phosphorylation in each lane (corresponding to each specific condition and time). We are not comparing the signal from one to the other lane. For that, we have to divide each phosphopeptide-specific antibody signal by the corresponding Cdk1 (anti-PSTAIRE) antibody signal for each lane. Immunoblots were incubated successively first with anti-Cdc2-Y15P and after quantification and stripping with anti-PSTAIRE.In summary, I think the paper has definite merit and potential significance in explaining the link between the two distinct cell cycle arrests and the onset of virulence by U. maydis. However, it is illogically presented in my view and often the detail obscures the truly significant findings. For me, a key finding is that the pheromone-induced cell cycle arrest is on its own dispensable for virulence. However, it enables the b-dependent cell cycle arrest, which is absolutely essential. Secondly, although mechanistically the b-dependent G2 arrest and pheromone arrest differ, they actually have a conserved underlying mechanism which requires retaining Cdc25 in the cytoplasm. This needs much greater emphasis too. Finally, the paper is also very narrowly framed. Only studies in Ustilago maydis are described . What about cell cycle-dependent infection by other pathogenic fungi? What happens in other smuts? Are they also G2 arrested for infection? What about Colletotrichum and Magnaporthe, where cell cycle controls have been implicated in appressorium development? Are there parallels or not? Are the mechanisms distinct in basidiomycete pathogens? Some wider context is important too. Otherwise the paper's potential main conclusions are lost in impenetrable detail.In the revised version we tried to broad our discussion to other pathogenic fungi as the reviewer suggested. Thanks so much for all suggestions and comments.Before we can accept the manuscript, please detail how the nuclear GFP-Cdc25 was quantified, as requested by reviewer 1.Reviewer #1:This revised version of the manuscript is much improved: the authors have performed some of the suggested experiments and addressed most of my experimental concerns, or provided convincing arguments. The only small experimental point I would like to raise concerns the quantification of nuclear GFP-Cdc25. The images provided a more convincing, and there is some quantification given, though the quantification is not described in the methods and appears rather subjective: the authors appear to visually classify cells to have nuclear GFP-Cdc25 or not. What's the cut-off used to decide for or against nuclear localization? An objective quantification requires to measure the fluorescence intensity in nuclear and cytosolic compartments in individual cells, for instance derive a ratio, and then present the mean and deviation on a graph.The reviewer is right in the sense that quantification should derive a ratio between the fluorescent signal at nuclear and cytosolic compartments and then present that numbers in a graph. In fact, we did that approach using Image J. However, we found that in some filaments the signal in cytoplasm was as strong as the signal at nucleus, and then just looking at the ratio between both signals, they were sorted as no nuclear GFP, when actually they were positive for GFP at nucleus. For that reason, we decided to plot fluorescent profile for each channel and in each nucleus and to decide as positive or not for GFP at the nucleus just looking that profile. We are aware that it could be more subjective that numbers, but the true is that this way it was more accurate that just looking at the ratio of fluorescence. We are including We have also included in Materials and methods section a description of the method to determine positive and negative cells for Cdc25 at nucleus, as reviewer was also correct in that it was not well described in the previous version. Thanks for the comment.The text has also been much improved: the logical flow is better apparent, and the text is overall easier to read. However, the Abstract is still difficult to understand and suffers from poor English language.We sent the previous version to a professional editor since English is not our native language. We tried our best with the Abstract.


Nitro group reduction is a reaction of a considerable importance for the preparation of bulk chemicals and in organic synthesis. There are reports in the literature showing that incorporation of metal nanoparticles (MNPs) inside metal–organic frameworks (MOFs) is a suitable strategy to develop catalysts for these reactions. Some of the examples reported in the literature have shown activity data confirming the superior performance of MNPs inside MOFs. In the present review, the existing literature reports have been grouped depending on whether these MNPs correspond to a single metal or they are alloys. The final section of this review summarizes the state of the art and forecasts future developments in the field. Metal–organic frameworks (MOFs) are solid porous crystalline materials constituted by metallic nodes coordinated to rigid organic linkers, typically bi- or multipodal aromatic carboxylic acids or nitrogenated heterocycles ,6,7,8,9.For the last application, MOFs are especially suited materials because they exhibit high surface area, large porosity and the pore dimension can be tuned from micro to mesoporosity ,35,36,37The field of catalysis by MNPs@MOFs has been extensively reviewed ,29,30,31In the present review, we will focus on these types of solid catalysts for the specific reduction of nitro groups, particularly nitro aromatics. This reaction has considerable importance both from the academic and industrial points of view for the preparation of anilines as well as in organic synthesis combined with nitration of aromatic rings for the preparation of aniline derivatives ,42,43.The reduction of nitro groups to amino derivatives can be catalyzed by different metals depending on the nature of the reducing agent. As a general rule, the activity of these metals increases when they are prepared as MNPs. In this context, the activity and selectivity of MNPs for the specific case of nitro reduction also benefit from the incorporation of these MNPs inside MOFs.4+-based MOFs . Ui. Ui60 haMNPs . Po. Po3BH3 e metals ,84,85,863BH3 under mild conditions  catalyst. Among the various catalysts  compared for the decomposition of NH3BH3 to hydrogen, experimental data showed that Cu@Co@Ni exhibits the highest activity, releasing the full theoretical amount of hydrogen in 14 min. In contrast, an analogous catalyst with identical metal loading and NP size, namely CuCoNi/ZIF-8 required 30 min for the decomposition of NH3BH3 under similar experimental conditions, which is much longer than the time measured for Cu@Co@Ni/MIL-101(Cr). This superior activity of Cu@Co@Ni/MIL-101(Cr) is due to the high population of active sites without the drastic diffusion limitations occurring in ZIF-8. Interestingly, the Cu@Co@Ni/MIL-101(Cr) completed the reaction within 10 min at 25 °C and 5.5 min at 30 °C with TOF values of 31 and 56 molH2 molcat−1 min−1 which are comparatively higher than the TOF values reached by other catalysts including those based on noble metal catalysts . TE. TE3BH3 atalysts ,88,89,90The present review has shown that the incorporation of MNPs inside structurally robust MOFs is a general methodology to obtain highly efficient and frequently stable heterogeneous catalysts to promote the reduction of nitro groups. Depending on the reducing agent, different transition metals should be used as catalysts for this reaction as described in the review. Besides the transformation of nitro to amino group, it has also been shown that MNPs inside MOFs can behave as multifunctional catalysts that can promote tandem reactions in where more than one elementary step is combined in a single process promoted by same catalyst. Tandem reactions represent a clear advantage of process intensification avoiding intermediate workup and separation processes.It has also been shown that there are possibilities to prepare nano alloys of more than one metal inside MOF cavities. Bimetallic and trimetallic catalysts provide sites with unique catalytic activity different from that of analogous catalysts having a single metal. The electronic density and the generation of partial charges can play a remarkable role in catalysis by MNPs and these parameters can be tuned by forming alloys with the appropriate composition and morphology. In this regard, it can be expected that the number of studies dealing with encapsulated alloys will grow in the near future, including the influence of morphology either as core–shell or uniform atomic distribution.It is also a current tendency to increase the number of studies in which the active sites of the MNPs cooperate the activity of the MOFs. It is therefore expected that the field will continue growing with further examples of different MOFs including two-dimensional and mixed-metal MOFs in the near future.