The Raman spectra of the EVs of bone marrow and adipose tissue-derived MSCs were compared with human dermal fibroblast EVs in order to demonstrate the ability of the method to distinguish the vesicles of the three cytotypes automatically with an accuracy of 93.7%. Our data attribute a Raman fingerprint to EVs from undifferentiated and differentiated cells of diverse tissue origin, and provide insights into the biochemical characteristics of EVs from different sources and into the differential contribution of sphingomyelin, gangliosides and phosphatidilcholine to the Raman spectra themselves.Extracellular vesicles (EVs) from mesenchymal stromal cells (MSC) are emerging as valuable therapeutic agents for tissue regeneration and immunomodulation, but their clinical applications have so far been limited by the technical restraints of current isolation and characterisation procedures. This study shows for the first time the successful application of Raman spectroscopy as label-free, sensitive and reproducible means of carrying out the routine bulk characterisation of MSC-derived vesicles before their use  Exosomes (30–100 nm) and microvesicles (up to 1000 nm) differ in size and cellular origin, but both mediate intercellular communication within a tissue and among organs thanks to body fluid transportation1.Extracellular vesicles (EVs) are a heterogeneous group of membrane-bound vesicles that are constitutively released by cells of different tissue origins. Past controversies concerning nomenclature have now been resolved by the scientific community, which defines EVs as the group of particles made up of exosomes, microvesicles and apoptotic bodies2. It has been suggested that MSC-derived EVs may be sometimes even more therapeutically valuable than whole cells, because of their remarkable handling advantages, which can accelerate their clinical application in the so-called cell therapy without cells3. The possibility of overcoming the cell therapy drawbacks of having to administer living, replicating and difficult to control cells is currently one of the main challenges facing regenerative medicine, and EVs can be an effective means of stimulating the restoration of organ function through tissue regeneration and repair in the context of an integrated strategy of regenerative rehabilitation4.As is the case for most body cells, part of the secretome of mesenchymal stromal cells (MSCs) includes exosomes and microvesicles, which are currently being investigated because of their striking regenerative and immunomodulating potential. The bioactive molecules loaded onto/into EVs are involved in the paracrine effects of stem cells, and even the membrane constituents of vesicles seem to trigger intracellular protective/regenerative pathways in recipient cells6 and, in 2014, EVs ability to influence the activity of recipient cells and regulate immune responses was successfully exploited in a patient undergoing allogeneic hematopoietic stem cell transplantation who developed therapy-refractory graft-versus-host disease7. Their regenerative potential has also been assessed in in vitro and in vivo models of many diseases affecting heart10, kidney13, liver15, bone and cartilage17, muscle18, skin19, and central nervous system23.Over the last ten years, many studies have demonstrated the role that MSC-derived EVs can play in tissue repair and immunomodulation25 by preventing reproducible quality and safety assessments26.However, there are still concerns about the effect that the source of MSCs and cell culture conditions can have on EV production and characteristics as there is no standardised and optimised method for isolating and characterising EVs. Furthermore, the technical restraints of current techniques have limited their potential use in regenerative medicine34. In the field of regenerative medicine, it has been used to analyse undifferentiated and differentiated human and murine embryonic stem cells37 and to monitor MSCs stimulated towards osteogenic differentiation39. Efforts have also been made to develop Raman-based methods for the individual characterisation of human vesicles41, but although these have provided information at single-vesicle level, they are still far from being used diagnostically. What is required to allow the immediate transferability of EV research to clinical practice is a procedure that allows i) the rapid characterisation of a sample before its use in vitro or in vivo; ii) the identification of fingerprints of the EV populations used for regenerative purposes in order to determine the best experimental settings and compare results from different cell sources; and iii) the routine application of the analysis. The third point should be favoured by the current availability of portable Raman spectrometers that can automatically scan and analyse complex samples, which could bring Raman analysis easily in the reach of most laboratories. RS is much more suitable for achieving these goals than the widely used techniques of immunoblotting, cytofluorimetry and spectrometry because it can provide reproducible results quickly and in a label-free manner, and only requires tiny sample volumes in comparison with the large amounts needed by other methods, which cannot easily cope with the nanoscalar dimensions of exosomes.The aim of this study was to test Raman spectroscopy (RS) as a label-free, non-destructive, sensitive, rapid and automatable means of carrying out the bulk characterization of EVs. This technique provides a spectrum that qualitatively and quantitatively describes the chemical composition of a sample and thus avoids the need for specific protein biomarkers. It has been widely used in the pharmaceutical industry as a mean of verifying raw materials and quality controlling drug production, and we suggest it could help in purity and quality checking vesicle suspensions. It has already proved its value by characterising a wide range of cells and tissue samples for the purposes of basic research, and as an innovative alternative to classic, time-consuming and operator-dependent diagnostic methods42, we also evaluated the contribution of lipid membrane constituents to the Raman spectra. Our findings provide evidence supporting the use of RS for the routine characterisation of MSC-derived EVs before their in vitro/in vivo application.This study provides the first Raman-based characterisation of the EVs of human MSCs isolated from bone-marrow  and subcutaneous adipose tissue . The results were compared with those obtained using EVs released by dermal fibroblasts (DFs), in order to verify the ability of Raman analysis to distinguish vesicles from undifferentiated and differentiated cells, and gain insights into the biochemical features of EVs from different sources. Multivariate analysis was used to assess spectral differences and automatically distinguish the three groups. In addition, given the growing body of evidence concerning the pivotal role of lipids in mediating EV functions43 and characterised by immunoblotting and transmission electron microscopy (TEM) to verify their peculiar features as suggested by the International Society for Extracellular Vesicles (ISEV)44.EVs were isolated from BM-MSCs, ASCs and DFs following a multi-step ultracentrifugation protocol45. The vesicles in all of the samples were round , phenylalanine , lipid and protein markers such as CH and CH2 groups (bands respectively centred at 1450 cm−1 and 2940 cm−1)  and peaks of varying intensity corresponding to the C-C stretch (around 1100 cm−1) and CH, CH2, and CH3 bonds . In addition, the areas usually assigned to NA bases  and phosphate backbone  were variably prominent in the three average spectra. This is in line with many data demonstrating that EVs contain intact mRNA, long non-coding RNA, miRNA and other forms of RNA loaded into EVs42. The recurrent peaks attributable to proline/hydroxyproline  and tryptophan  may be related to differences in cell metabolism and responses to serum-deprived culture conditions. Proline is known to be a signalling molecule and a sensor of cellular energy status when responding to metabolic stress47, and the kynurenine pathway of tryptophan has been reported as being involved in the immunosuppressive effects of MSCs48.The spectra showed characteristic Raman bands of nucleic acids  was used to simplify the original data (n = 198) and all of the spectra were collectively represented by their principal components (PCs). Starting from PC1 , the subsequent PCs describe differences in the Raman fingerprint that were progressively less prominent , ceramide (Cer), sphingomyelin (SM), phosphatidylcholine (PCh), phosphatidylethanolamine (PE), phosphatidic acid (PA), and monosialotetrahexosylganglioside  were used for CLS fitting. Lipid reference molecules were preferred to protein markers because proteins spectra are dominated by backbone conformation signals, whereas lipids have more specific and defined peaks and can be more easily distinguished by RS. The resulting CLS fitting scores reported in Table Based on the simple premise that a spectrum from a mixture of chemical ingredients is a mixture of the spectra from the pure ingredients, the PC1 and PC2 loadings were least squares fitted  fitting) with specific reference spectra to investigate one possible cause of the observed spectral differences, following a previously reported procedureThe first 25 PCs were used for Linear Discriminant Analysis (LDA), which made possible to verify the ability of the method to identify between-group differences by maximising the variance among classes while minimising intra-class variability. The results showed that RS clearly distinguished the biochemical fingerprints of the three groups. After leave-one-out cross-validation, the PCA-LDA model showed that the overall accuracy of the model was 93.7% and that its accuracy in distinguishing DFs from MSCs was 92% Table  revealed5.The possibility of using regenerative medicine to treat diseased, damaged or aged tissues and restore organ function without side effects is one of the main challenges facing modern medical science, and so is no surprise that the discovery of the regenerative potential of EVs released by MSCs has aroused great interest. However, the main obstacle to the clinical use of vesicles is the lack of a robust and standardised method of characterising themIn this study, we investigated the biochemical fingerprints of MSC-derived vesicles originating from different tissues and compared them with those of terminally differentiated dermal fibroblasts. Our findings demonstrate the ability of RS to identify tissue-related fingerprints for vesicles released by MSCs and fibroblasts without the use of any label. This is the first time that Raman analysis has been used to provide a biochemical overview of MSC-derived EVs from a limited volume of EV suspensions.49, our data on MSC-derived EVs confirm the ability of RS to reveal the presence of the main EV constituents in a single repeatable spectrum. Although we cannot exclude the possible presence of a limited amount of soluble factors other than vesicles in our suspension, the reproducibility of the results and the main peak attributions suggest the purity of the samples.As previously reported in relation to other types of vesicles50. The ability of RS to highlight unique, tissue-specific features of vesicles should therefore assist scientists working with stem cells. Even if assessing the possible correlation between biochemistry and function goes beyond the scope of this study, the biochemical variations observed provide suggestions for further investigations into the functional differences of EVs from multiple MSC types and sources for which there is still not a definite marker52.The main finding of this study is that RS can clearly distinguish not only vesicles from MSCs and terminally differentiated fibroblasts, but also vesicles of MSCs from bone marrow and adipose tissue. Although there are protein markers that define a stem cell phenotype exist, a straightforward distinction between bone-marrow and adipose-tissue MSCs, based on biological and functional features, is still difficult to be obtained49. The prominence of membrane constituents in determining the fingerprints of vesicles is in line with the growing body of evidence demonstrating that lipids play a crucial role in the formation of EVs1 and the fulfilment of their signalling functions42. It is known that a number of specific lipids are typically associated with lipid rafts and enriched in vesicles that inherit the plasma membrane composition of their cell of origin. In particular, cholesterol and sphingolipids are preferentially included in EV membranes and may be involved in the formation of vesicles and in their stability in the extracellular environment42. There is also evidence that lipids are involved in BM-MSC responses to a strongly pro-inflammatory environment53. Furthermore, it is possible that direct membrane interactions between vesicles and recipient cells is one of the mechanisms of action of EVs, as has already been demonstrated in the case of whole cells2.Analysis of the spectra of MSC- and DF-derived EVs revealed that lipids made a substantial contribution to the Raman signals, as previously reported54, but there is still a lack of data concerning the membrane composition of ASC-derived exosomes. Our observation that GM1 also contributes to the recorded spectra of BM-MSC vesicles is in line with the reported functional role of gangliosides in regulating the proliferation and neuronal differentiation of MSCs56. Similarly, it is known that ceramides and ceramide-containing lipids are involved in many of the pathways mediating immune responses, and that they modulate the adipogenic differentiation of MSCs57. Despite the reported functional significance of PA in the biogenesis and release of exosomes, our data did not reveal any significant difference of PA content in the EVs derived from the three cell types, as has also been noticed by Haraszti et al.54 Further lipidomic studies are needed to verify the exact membrane composition of MSC-derived vesicles and to establish the role of lipid species in mediating vesicle function.On the basis of CLS fitting results, we hypothesised that gangliosides, phosphatidylcholine and sphingomyelin directly contributed to the main spectral differences between the considered EVs. Our data are in agreement with those of a recent proteomic and lipidomic study demonstrating how sphingomyelins, ceramides, cholesterol and phosphatidylcoline were enriched in the exosomes of BM-MSCs in comparison with other cell types58, but, to the best of our knowledge, no specific study has been published concerning sex-related variations in the function of EVs derived from cultured cells. Studies evaluating the efficacy of MSC-derived EVs in vitro and in vivo rarely refer to the sex of the donors, but this is very useful and should be always clearly indicated together with donor age59. Future in-depth analyses of larger donor cohorts should evaluate age and sex-related differences in EV function and chemical composition.It is important to mention one limitation of our study related to the sex mismatch of our MSC donors. It is known that MSC activity and recipient responses are influenced by the sex of both donor and recipient because of circulating hormonesin vitro or in vivo. As independent MSC-derived EV preparations can have different therapeutic potentials, the overall characterisation of vesicles offered by Raman spectroscopy might become a pivotal quality check for comparing data coming from different experiments or research labs, and thus hasten the clinical application of stem cell-derived products.In conclusion, our findings demonstrate that RS can determine the chemical content of EVs in a label- and sample processing- free manner. The proposed method can be immediately transferred into laboratory practice as it allows the bulk characterisation of vesicle suspensions before their use 60.All of the relevant experimental data have been submitted to the EV-TRACK knowledgebase (EV-TRACK ID: EV170012)Human BM-MSCs were isolated from the residual bone marrow cells of healthy bone marrow (BM) transplantation donors  after approval by the Institutional Review Board of San Raffaele Hospital. Human ASCs and DFs were isolated from waste materials of abdominoplasty and liposuction procedures performed at IRCCS Galeazzi Orthopaedic Institute . Tissues were collected following the procedure PQ 7.5.125 regarding waste materials to be used for research purposes, version 4 dated 22.01.2015, approved by the same institute. Written informed consent was obtained from all of the patients in accordance with the ethical principles of the Declaration of Helsinki. All of the samples were anonymised and no information or images that could lead to identification of a study participant might occur. All experiments were performed in accordance with the relevant guidelines and regulations of San Raffaele Hospital and IRCCS Galeazzi Orthopaedic Institute.63. Briefly, mononuclear cells from BM aspirates were isolated by means of density gradient centrifugation  and plated in non-coated 75–150 cm2 tissue culture flasks  at a density of 160,000/cm2 in complete culture medium: DMEM  supplemented with 10% ultracentrifuged foetal bovine serum , penicillin 50 U/ml, 50 µg/ml streptomycin and 2 mM L-glutamine . Cultures were maintained at 37 °C in a humidified atmosphere, containing 5% CO2. After 48-hour culture, non-adherent cells were removed. The ASCs were isolated from adipose tissue samples following digestion with 0.75 mg/ml type I Collagenase  and the filtering of the stromal vascular fraction. The DFs were obtained from de-epidermised dermis fragmented and digested with 0.1% collagenase type I. The ASCs and DFs (plating density: 105 cells/cm2) were cultured  in complete culture medium. The medium was replaced every other day and, at 70–80% confluence, the cells were detached with 0.5% trypsin/0.2% EDTA, plated  and expanded. Once at 80–90% confluence, cells at 3rd–4th passage were washed twice with DMEM, kept for one hour in serum-free DMEM  and then cultured for 72 hours in serum-free DMEM.Cells were isolated following previously described protocols43. Briefly, after 72 hours of starvation, the medium conditioned from approximately 6 × 106 cells was centrifuged at 800 g for 10 min to remove non-adherent cells and then at 2,500 g for 15 min to remove potential apoptotic bodies. CM was then ultracentrifuged for 70 min at 100,000 g  at 4 °C, and the pellet was re-suspended in sterile saline solution and ultracentrifuged again. The collected EV suspension (approximately 500 µl) was kept at 4 °C before making Raman and TEM analyses, and then frozen.In order to avoid the presence of RS-visible isolation reagent residues in the EV suspension, the vesicles were isolated from cell-conditioned medium (CM) by means of differential centrifugation, as previously described44. The EV pellets were re-suspended in SDS sample buffer with protease inhibitors64. Electrophoresis was performed under reducing conditions, and then proteins were transferred to nitrocellulose membrane. The antigens were probed with anti-flotillin-1 , anti-CD63 and anti-CD9 , and anti-calnexin . As secondary antibodies goat anti-mouse  and goat anti-rabbit (System Biosciences) conjugated with HRP were used. Cell lysates were considered as the control for the specificity and working conditions of the considered antibodies.Immunoblotting was performed to characterise the EVs as suggested by ISEV minimal experimental requirementsFor the TEM visualisation of EVs, 5 µl of purified exosomes were absorbed on Formvar carbon-coated grids for 10 min. The drops were then blotted with filter paper and negatively stained with 2% uranyl acetate (5 μl) in aqueous suspension for 10 min. Excess of uranyl was removed by touching the grid to a filter paper. The grids were dried at room temperature and examined with a transmission electron microscope  at 80 kV. The TEM images obtained in order to verify EV ultrastructure were used to assess vesicles’ size using the particle analysis tool of ImageJ software . At least 30 measurements per sample were done.s were performed with 50× objective , 1800 grooves/mm diffraction grating, 400 µm entrance slit, and confocal mode (600 µm pinhole) in the spectral ranges 500–1800 cm−1 and 2600–3200 cm−1. Accumulation times were 2 × 10 s per spectrum.Freshly isolated EVs were analysed by means of Raman microspectroscopy  equipped with a diode-pumped solid-state laser operating at 532 nm and a Peltier-cooled CCD detector. 5–10 µl drops of EV suspension were deposited on a calcium fluoride slide and allowed to air dry. All of the measurement2  were acquired in the centre and at the borders of the air-dried drops. Before analysing the data, a two-class hierarchical clustering analysis (HCA) of the Raman maps was made in order to distinguish the spectra relating to vesicles from those related to background. At least 10 independent replicates of the Raman spectra were obtained for every donor of the different cell types.The Raman shift was calibrated automatically using LabSpec 6 software (Horiba) using zero order line and Si line of a Si reference sample. In order to capture the spectra randomly, maps of about 150 µmBaseline correction was made using LabSpec 6 processing tool by fitting all spectra with a sixth order polynomial curve in order to remove autofluorescence and background before unit vector normalisation. Post-acquisition calibration was carried out on normalised spectra, in order to compensate for possible thermal drifts.Principal component analysis (PCA) of the normalised and aligned spectra was made in order to reduce the dimension of the data and describe their major trends. The provided principal components (PCs) represent differences in the spectra of vesicles from the three cytotypes and therefore in their chemical composition. The first 25 PC scores were used in a supervised classification model, linear discriminant analysis (LDA), in order to discriminate and classify the data by maximizing the variance between groups. Data reduction by PCA before LDA was essential because LDA requires that the number of variables is smaller than the number of observations. The smallest number of PC scores was selected for the LDA to prevent data overfitting. A decreased number of PCs reduced the accuracy of the method in distinguishing the EVs, whereas an increased number did not improve the classification, but progressively decreased accuracy. Leave-one-out cross-validation was used to test the classification sensitivity, specificity, and accuracy of the LDA model. One-way ANOVA was performed on PC scores to verify that the means of each group were significantly different, despite within-group variance.Data manipulations and statistical analysis were performed using Origin2017 .Reference molecules of some of the major known constituents of EV membrane were used to investigate the lipid content of vesicles. Cholesterol (Chol), ceramide , sphingomyelin (SM), phosphatidylcholine , L-α-phosphatidylethanolamine (PE), phosphatidic acid (PA), and monosialotetrahexosylganglioside (GM1) were purchased from Avanti Polar Lipids  and used to acquire reference spectra using the same acquisition settings as those used for the EV analysis.Labspec 6 was used for the Classical Least-Squares (CLS) fitting of the PC1 and PC2 spectra, which allows to calculate the contribution of the reference chemicals to the PC spectra by evaluating any similarities. The resulting coefficients described the relationships between the PC spectra and the reference molecules.The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.Supplementary Information


South Africa continues to witness an increase in illicit poly-substance use, although a precise measurement continues to be compounded by difficulties in accessing users. In a pilot attempt to use respondent-driven sampling (RDS)—a chain referral sampling method used to access populations of individuals who are ‘hard-to-reach’—this article documents the feasibility of the method as recorded in a simultaneously run, multisite, poly-substance study in Cape Town. Here we aim to a) document the piloting of RDS among poly-substance users in the three socio-economic disparate communities targeted; b) briefly document the results; and c) review the utility of RDS as a research tool.Three cross-sectional surveys using standard RDS procedures were used to recruit active poly-substance users and were concurrently deployed in three sites. Formative research was initially conducted to assess the feasibility of the survey. To determine whether RDS could be used to successfully recruit poly-substance users, social network characteristics, such as network size was determined.A 42.5 % coupon return rate was recorded in total from 12 initial seeds. There were vast differences in the recruitment chains of individual seeds—two generated more than 90 recruits, and 2 of the 10 recruitment chains showing a length of more than 10 waves. Findings include evidence of the use of 3 or more substances in all three sites, high levels of unemployment among users, with more than a third of participants in two sites reporting arrest for drug use in the past 12 months.Our results indicate that RDS was a feasible and acceptable sampling method for recruiting participants who may not otherwise be accessible. Future studies can use RDS to recruit such cohorts, and the method could form part of broader efforts to document vulnerable populations. Over the last two decades, South Africa has witnessed an increase in the use of illegal substances and number of users—many of whom use more than one substance, termed poly-substance abuse . While sWhile these data are obtained from specialist substance abuse treatment services and only provide information on those that are able to access treatment  rather tDespite this, recruiting representative samples of illicit poly-substance users who are not already registered as in-treatment patients remains an on-going challenge in substance abuse research . While tRDS relies on respondents themselves to recruit people of interest from the target population, founded on the convenience sampling of selected initial respondents (‘seeds’) drawn from the intended cohort. Once a seed is found eligible for participation—as a function of meeting the predetermined criteria and on completion of the study procedures—they are compensated for participation (the ‘primary incentive’) but also further provided with a set number of coupons  with which to recruit others who meet the eligibility criteria from their social networks . This prRDS has been utilised extensively among marginalised and hard to reach populations in both local and international HIV research , 13–15. As such, the purpose of this article is to, a) document the piloting of RDS among poly-substance users in three socio-economically and racially disparate communities in Cape Town; b) to briefly document the levels of poly-substance use found in the sample; and c) to review the primary demographic and other variations revealed by the use of the method.From 26 January to 24 February 2015 we conducted three cross-sectional surveys using respondent-driven sampling (RDS) among poly-substance users residing in three suburbs in Cape Town, South Africa. To our knowledge, the simultaneous deployment of RDS aimed at poly-substance users, across multiple sites, had not been attempted before in South Africa.Prior to the pilot survey, we conducted formative research to assess the appropriateness and feasibility of using RDS to collect survey data from the study population. The questions used in the formative Focus Group Discussions (FGDs) were based on those recommended beforehand by Johnston and colleagues . During As noted, the study was conducted in three communities in the Cape Town metropolitan area chosen for their economic, cultural and ethnographic diversity. Site 1 has an estimated population of 156 000 where the median monthly income is R1601 (approximately US$133). Site 2, on the outskirts of the Cape Town central business district, has an estimated population of 9300 people, and the monthly median income is R18801 (approximately US$1566). Site 3 is a ‘township’ approximately 15 kilometres east of Cape Town. It has an estimated population of 392 000. The median monthly income in this community is R1301 (approximately US$108). Reliable population data is difficult to obtain, however the last national census (conducted in 2011) reported that the three sites had different racial makeups, as tabulated below  – cocaine, cannabis, methamphetamine, amphetamine, opiates, and methaqualone.Eleven ‘seeds’ See Fig.  were iniIn Site 1 we reduced the number of recruitment coupons to zero during week two of operation because the site had been overwhelmed with respondents wishing to participate. At this time, 47 respondents had participated. The seed recruited later in this site received three coupons until the last two days of operation, when coupons were reduced to zero in anticipation of the closure of this site. In total 190 coupons were issued at this site, and at study closure, 137 recruits had participated (a return rate of 66.3 %). In Site 2 we reduced the number of recruitment coupons to two on the second last day of operation, and zero on the final day. In total 283 coupons were issued at this site, and at study closure, 111 recruits had participated (a return rate of 36.4 %). In Site 3, we reduced the number of coupons to two on day 12 of operation, to one on day 15, and to zero on final day. In total 320 coupons were issued at this site, and at study closure, 126 recruits had participated (a return rate of 37.5 %).We implemented RDS using standard procedures . Once itQuestion #1: How many people do you know, who know your name and you know theirs, and you have seen them in the past 30 days?Question #2: How many of the people from question #1 are 18 years or older?Question #3: How many of the people from question #2 have used drugs in the past 30 days?Question #4: How many of the people from question #3 have used more than one drug in the past 30 days?Analysis of data from RDS surveys requires an accurate social network size from each participant as the data is weighted using this information. We used four questions to maximize the accurate reporting of each participant’s personal social network size:The response to this final question was used as the number representative of each participant’s social network size.The CBSQ included a section providing information on age, gender, and employment and education level. Questions regarding participants’ socio-economic status (SES) using the Family Affluence Scale (FAS)  were alsThe use of illegal substances was assessed by means of urine analysis and alcohol use was self-reported in the past 7 days. Use of any of these substances  was scored as 1 and each participant was given a poly-substance use score by summing the scores. We dichotomized the poly substance score to identify those who had used two to three substances, and those who had used more than three substances during a maximum of the past 7 days.Question items related to trading sex for drugs, theft and arrests in the last 12 months were taken from a questionnaire used in the 3-metro study on drugs and crime in South Africa [A self-report four-item test with questions on Cutting down, Annoyance at criticism, Guilty feelings and use of an Eye-opener was also used to screen for symptoms of a substance-related disorder. On the CAGE  two or mTo determine whether participants had ever received substance abuse treatment, are currently undergoing treatment or intending to receive treatment, three single questions were inserted into the questionnaire. Participants were asked to give a 1 = ‘Yes’; 2 = ‘No’ and 3 = ‘Refuse to Answer’ response to the three questions.To assess the feasibility of using RDS, the current study, which was a pilot study sought to recruit a minimum of 100 poly-substance users in each community.www.respondentdrivensampling.org) for poly-substance use and demographics. Using information about each participant’s social network size (degree) and cross- and within-group recruitment patterns , the RDS I estimator was used to derive population estimates. Given that the network range was 1 –600 with extremely small and large outliers, we chose to pull in 5 % of outliers in the reported network sizes. RDS methodology recommends that a value of 5 % be pulled in the top and bottom of the network size values  [Sample proportions, estimates of population proportions and 95 % confidence intervals (CIs) were calculated using the freely-available Respondent-Driven Sampling Analysis Tool 7.1.38 (RDSAT)  . We usedThere were very few instances of missing data because the CBSQ did not allow non-responses to questions. All questions included a ‘refuse to answer’ response option. Ethical clearance was obtained from the research ethics committee in the Faculty of Law at the University of Cape Town.Over a period of 18 week days (i.e. Monday to Thursday from 10 am to ±4 pm) we issued a total of 793 recruitment coupons of which 374 were redeemed 42.5 %). Coupon return rates and sample sizes for each site are described above. In site 1, 11 people were ineligible as they did not test positive for two or more illicit substances; in site 2, eight people were ineligible —six did not test positive for two or more illicit drugs and two were younger than 18 years; and in site 3, 16 did not test positive for two or more illicit drugs and one was younger than 18 years. Twelve seeds were recruited during the 4½ weeks of operation . Two seeds proved inactive ; and four seeds generated 20 or more recruits in site 3 (Table .5 %. CouTwo of the 10 recruitment chains had a length of more than 10 waves: in site 2 one recruitment chain produced 84.7 % (94 of 111) of recruits, and in site 3, one recruitment chain produced 76.9 % of recruits (97 of 126). In all sites the majority of respondents (93 %) were recruited by ‘friends’ whom they had seen ‘many times’ during the past 30 days.Across all three sites crystal methamphetamine, cannabis and methaqualone were reported as the dominant substances of use Table . The majOur results show some variations across sites on key demographic variables Table . CompareTable With respect to engaging in risk behaviours, 11.6 % in site 1, 19.4 % in site 2 and 10.5 % of participants in site 3 had exchanged sex for drugs, while 34 % in site 1, 19.7 % in site 2, and 23.3 % in site 3 had stolen items and goods for drugs. Across two sites (Sites 2 and 3) more than a third of participants reported having been arrested for drugs in the past 12 months . While the overall majority of participants indicated no past treatment, more than half of the participants in all three sites reported that they felt they would gain from specialist substance abuse treatment  with large numbers of people in all three sites reporting the use of three or more substances within the few days prior to the urine analysis. Poly-substance use potentially increases the likelihood of harm since illicit drugs that are taken concurrently or in close succession interact negatively in terms of their pharmacokinetic and pharmacodynamic mechanisms and can lead to the formation of unique psychoactive metabolites that may produce different effects than the original drug used , 40. AddThe study also found that poly-substance users run the risk for arrest by virtue of either their use of illicit drugs or through selling or possession of illicit drugs. Conclusions drawn from a 3 metro study conducted in 2000 in three cities in South Africa however still hold true. Clear strategies to reduce drug use and drug related crime such as ensuring police are trained to facilitate diversions to treatment are needed [Furthermore, our results indicate that very few poly-substance users in the three sites had ever accessed treatment, yet the majority reported the desire for treatment. Findings from a study in poorer disadvantaged communities in Cape Town found that people experience multiple barriers to accessing treatment that impacted on treatment need and thus service utilization . Myers aWhile our findings provide some insight into the effectiveness of using RDS in three diverse areas in the Western Cape, findings should be interpreted in the light of some limitations. First, given that this was a pilot study to test whether RDS is a methodology that could be used to recruit poly-substance users in three economically and social disparate sites only 100 participants were recruited from each site. Consequently, while RDS methods were applied, the study may fall short of full RDS assumptions, for example since the study it has been documented that the RDS-1 and RDS-2 estimators can suffer from serious bias, since it was a pilot. Second, recruitment ceased when 100 participants had been recruited and we did not take into account the number of eligible subjects each potential recruiter could have recruited beyond the pilot study (or the 100 participants). Third, our sample was recruited from a population of poly- substance users residing in three communities in Cape Town and cannot be generalized to other populations of substance users in South Africa.Our findings suggest a number of implications for RDS in the future and among other poly-substance-using populations. First, future studies can use RDS successfully to recruit poly-substance users. Second, given this preliminary success, RDS has the potential for successful use among poly-substance users for surveillance and monitoring over time. Third, the sampling strategy could be extended to recruitment of poly-substance users into prevention and early-interventions and facilitate referrals to specialist treatment centres. Finally, future research or recruitment of poly substance users should take into account the possible disparities in recruitment of women, and people of different ages and take measures to mitigate these potential biases.


Bacillus thuringiensis (Bt) interact with non-target herbivores and other elements of the food web. Stacked Bt crops expose herbivores to multiple Cry proteins simultaneously. In this study, the direct interactions between SmartStax® Bt maize producing six different Cry proteins and two herbivores with different feeding modes were investigated. Feeding on leaves of Bt maize had no effects on development time, fecundity, or longevity of the aphid Rhopalosiphum padi (Hemiptera: Aphididae), and no effects on the egg hatching time, development time, sex ratio, fecundity, and survival of the spider mite Tetranychus urticae (Acari: Tetranychidae). The results thus confirm the lack of effects on those species reported previously for some of the individual Cry proteins. In the Bt maize leaves, herbivore infestation did not result in a consistent change of Cry protein concentrations. However, occasional statistical differences between infested and non-infested leaves were observed for some Cry proteins and experimental repetitions. Overall, the study provides evidence that the Cry proteins in stacked Bt maize do not interact with two common non-target herbivores.In the agroecosystem, genetically engineered plants producing insecticidal Cry proteins from  Bacillus thuringiensis (Bt) have been developed. Cry proteins are known to have a relatively specific range of biological activity. Cry1 and Cry2 proteins exhibit activity for the larvae of some species of butterflies and moths (Lepidoptera) while Cry3 and Cry34Ab1/Cry35Ab1 proteins are active against larvae of some leaf beetles (Coleoptera: Chrysomelidae). To broaden the target spectrum, to delay the evolution of resistance in the target pest(s), and to simplify crop management, multiple Cry proteins have been combined into modern GE plants. SmartStax® maize produces the most combined GE traits of any currently commercially cultivated maize product, six different cry genes and two genes for herbicide tolerance  crops producing insecticidal Cry proteins from the bacterium olerance .vice versa. This confirms earlier studies demonstrating no synergistic effects of the lepidopteran-active Cry1Ab and the coleopteran-active mCry3A  and the spider mite Tetranychus urticae (Acari: Tetranychidae). Both species have a global distribution and are frequent pests of maize. While spider mites suck out mesophyll cells destructively, aphids feed on plant phloem. In consequence, spider mites ingest high amounts of Cry proteins, while aphids ingest only trace amounts, even when multiple Cry proteins are present  effects due to the transformation process and the plant’s physiological responses  and the nearest conventional non-Bt hybrid  were used for all experiments. SmartStax® expresses genes for the Lepidoptera-active Bt proteins Cry1A.105, Cry1F, and Cry2Ab, the Coleoptera-active proteins Cry3Bb1, Cry34Ab1, and Cry35Ab1, and two herbicide tolerance genes. Plants were grown individually in 12 L plastic pots and were fertilized with 40 g of slow release fertilizer  before sowing and weekly with 0.2–0.8 L of 0.2% liquid NPK fertilizer .Stacked Bt maize  and used when 9–10 leaves were expanded (approximately 4–5 weeks old plants). For the Cry-protein experiments, Bt maize was grown in a glasshouse (approximately 25°C) and used in the 10 leaves stage (5 weeks old).Rhopalosiphum padi aphids and T. urticae spider mites were used from our own cultures on maize plants, which were started with individuals supplied by Syngenta Crop Protection Münchwilen AG . Both species were reared on Bt and non-Bt maize in the same climate chamber, but spatially separated to limit exchange between the two treatments.The experiment was conducted twice with 13 and 16 plants per maize treatment in the first and second run, respectively, resulting in a total of 29 plants per treatment. Bt and non-Bt plants were distributed alternately in blocks of 3–4 plants in the climate chamber. Two reproductive aphids from the culture were settled on the 5th, 6th, and 7th leaf and covered with one transparent plastic clip cage  per leaf, resulting in a total of 39 and 48 clip cages per treatment in the first and second experimental run, respectively. Aphids for Bt maize or non-Bt maize were collected from the respective maize plants in the culture. Clip cages had a hole sealed with fine-mesh netting to provide air-circulation and foam rubber rings to gently seal against the leaf. In the first run of the experiment, all aphids except one neonate nymph were removed after 24 h (defined as day 0). Nymphs that were not sitting on the leaf were replaced by neonate nymphs within the following 2 days (unsuccessful settlement). The number of nymphs that needed to be replaced in this way was relatively high, but treatment-independent (20 in the Bt and 20 in the non-Bt treatment). In the second run of the experiment, the protocol was changed and 2–3 neonate nymphs were left on the leaves on day 0. After 24 h, the surplus nymphs were removed so that only one nymph remained in each clip cage. In this way, no nymphs needed to be replaced. Every day, aphid survival and the number of offspring produced by each aphid after reaching adulthood were recorded and neonate nymphs were removed. Aphids were monitored until death.The nymphal development time was defined as the number of days from day 0 to the day when first offspring was observed. Adult longevity was calculated from the day when first nymphs were observed to the day the adult was found dead. Total fecundity was the sum of all offspring produced by an aphid during the experiment.The experiment was conducted twice with 15 plants per maize treatment in each run, resulting in a total of 30 plants per treatment. Bt and non-Bt plants were distributed alternately in blocks of 3–4 plants in the climate chamber. Spider mites were kept on leaf disks according to Egg hatching time was defined as the number of days from day 0 to the day when the larva hatched from the egg. Nymphal development time was the number of days from the day when the larva hatched to the day when the adult emerged. Female longevity was calculated from the day when the female emerged to the day it was found dead. Total fecundity included all eggs laid by a female during the experiment. Sex ratio was defined as the percentage of females.Bt maize plants were grouped in six blocks of 3–4 plants in a large glasshouse cabin. The experiment was conducted twice with 20 and 22 plants in the first and second run, respectively. Before the plants were infested with aphids, four leaf disks (0.5 cm diameter) were punched from the middle part of the 9th leaf (counted from the base) using a common office hole-punch, placed in a 2 ml microtube and frozen at -80°C. The plants in half of the blocks  were infested with aphids (approximately 100 aphids per plant), while the plants in the other half of the blocks remained non-infested. During the experiment, the non-infested plants were controlled daily and incidental aphids were removed. The experiment ended after 20 days, when infested plants were heavily populated with aphids. At the end of the experiment, four leaf disks (0.5 cm diameter) were sampled as described previously from the same leaf that had been sampled at the start of the experiment (9th leaf) and additionally from the first fully expanded leaf at the top (14th or 15th leaf).Spider mites are much smaller than aphids, move through the air by ballooning, and it is practically impossible to control plants for unintended spider mite infestation. Therefore, infested and non-infested plants had to be kept in separate glasshouse cabins. The experiment was conducted twice with 24 and 26 plants in the first and second run, respectively. The cabins were switched for the non-infested and infested treatments for the two runs. As described previously, four leaf disks were sampled from the 9th leaf before spider mite infestation. The plants in one cabin were infested with several 100 spider mites per plant, while the plants in the other cabin remained non-infested. The experiment ended after 20–22 days. Leaf disks were sampled once more from the 9th leaf and also from the first unfolded leaf at the top (14th or 15th leaf). At that time, leaves in the lower part of the plant were heavily damaged by spider mites, while the youngest leaves showed no (run 1) or little (run 2) damage. To document leaf damage, photos were taken with a Leica microscope/camera system  ELISA kits for Cry1Ab-1Ac (used to detect Cry1A.105), Cry1F, Cry2A, Cry3Bb1, and Cry34Ab1 . The concentrations of the different Cry proteins were estimated as described by Supplementary Data Sheet 1). Data from the herbivore performance assays were analyzed with linear or generalized linear models (GLMs) using R statistical software . For all analyzed factors, contrasts were set to orthogonal. Applied models were full factorial for fixed factors.All data used for statistical analysis, tables and figures for this publication can be found in the Supplementary Material online  with Poisson distribution from the lme4 package. Fixed effects were treatment (Bt or non-Bt), leaf , and run  and random effect was plant. Effects of factor and interactions were determined from an ANOVA table with Type III sum of squares (car package). Total fecundity of both species was analyzed similarly with linear mixed-effects models (LMM) from the lme4 package.While the influence of the factors plant and leaf on spider mite egg fitness (which might affect hatching time) and sex were most likely minimal , run and treatment might have had an effect because spider mites were reared for several generations on Bt or non-Bt plants and the experiment was conducted at different points in time. Therefore, egg hatching time and sex ratio were analyzed by GLMs with the fixed factors treatment and run, a Poisson distribution for egg hatching time, and a binomial distribution (Logit link function) for sex ratio.Total longevity of both species was analyzed among the Bt and non-Bt treatment with pooled data (no separation of other variables) . Censored data included all males of spider mites after emergence, and damaged and lost individuals of both species. The treatments were compared with a log-rank test in the survdiff function.hypo) was calculated from the mean and the SD of the non-Bt group based on the equation  based on the means and SEs of the non-Bt treatment (package pwr). For time and fecundity data, effect size d was calculated based on two-sided t-tests, the true sample size (N) of the non-Bt treatment, a power of 80%, alpha-level of 5%, and assuming equal sample sizes. Using d, a hypothetical second mean  based on the equation  was reported.The equation was once solved for p2 with p1 = pBecause ELISA estimates of Cry protein concentrations were highly variable and variances were inhomogeneous, they were analyzed visually based on 95% confidence intervals around the means. Significant differences between herbivore-infested and non-infested plants were concluded from non-overlapping confidence intervals.Table 1). Individual spider mites developing on leaf disks from Bt and non-Bt maize had a similar egg hatching time, nymphal development time, female longevity, sex ratio, total fecundity, and total longevity (Table 1). The experimental repetition (run) had a significant effect on all measured parameters in the aphid experiment and on total fecundity and female longevity in the spider mite experiment . Highest detectable differences of 20 and 34% were revealed for total fecundity of aphids and spider mites, respectively.The observed non-Bt means and standard deviations and the true sample sizes were used to calculate the detectable differences, i.e., the percentage difference of detectable treatment means relative to control means . The lowest detectable differences (<10%) were observed for egg hatching time of spider mites and nymphal development time of aphids and spider mites , while parameters for adults  were less sensitive (20–34%). This is not surprising because nymphal development was relatively uniform while adult survival and reproduction showed high variation. Similar detectable differences were reported for T. urticae feeding on maize by The performance assays with spider mites and aphids did not reveal any significant difference between the Bt and the non-Bt treatment for any of the measured life-table parameters. The power analysis  provide information on the size of effects that could be detected with the given sample size and variation in the data, although those calculations, mainly based on simple ® were found to contain Cry proteins in concentrations of the same order of magnitude as leaves  for T. urticae and R. padi feeding on Cry1Ab-producing maize compared to non-Bt maize. R. padi aphids, but opposite effects on the offspring of apterous mothers. The authors conclude that the observed differences in aphid development may be linked to changes in host-plant quality. Rhopalosiphum maidis aphids among six different pairs of Cry1Ab-producing Bt maize and their near-isolines. However, they observed more aphids on Bt maize in five of the six Bt/non-Bt maize pairs when large populations were left to develop for 5 weeks. Sitobion avenae, when developing on Cry1Ab-producing Bt maize and its near-isoline.Independent from the exposure to Cry proteins, unexpected differences due to the transformation process or differences among the Bt and non-Bt comparator not related to the produced Cry proteins could occur. Such effects, however, have not been observed in the current study. Our results confirm those of previous studies with Bt maize producing single Cry proteins. ng maize  or Cry1Ang maize . Dutton T. urticae and R. padi reared on Bt maize were fed to the predators Chrysoperla carnea (Neuroptera: Chrysopidae), Phylloneta impressa (Aranea: Theridiidae), and Harmonia axyridis (Coleoptera: Coccinellidae)  . This suellidae)  or Cry pellidae) .T. urticae, they are in contrast to Tetranychus cinnabarinus spider mites at a comparable growth stage. No significant changes or elevated concentrations were evident with Cry1A.105  and Cry3Bb1 after infestation with spider mites in the present study.Herbivore infestation had no consistent effect on Cry protein production by the plants. While in eight out of 40 comparisons, confidence intervals of Cry protein concentrations between infested and uninfested Bt maize plants did not overlap, these differences were only significant in one of two runs. One exception is Cry34Ab1 in the spider mite study, which was higher in the 9th leaf of infested plants in both runs (and in the top leaf in run 1). Higher Cry34Ab1 levels were also measured in top leaves of aphid infested plants, significant in the first run and almost in the second. While our results are in line with those reported by The highly variable and sometimes very large confidence intervals demonstrate that ELISA estimates have to be interpreted with caution and should be regarded as semi-quantitative only. The commercial ELISA kits that we used are designed for qualitative detection of Cry proteins in leaves and our protocol for quantitative detection has not been truly validated. To ensure highest comparability among the treatments of one experimental run, we measured all samples from one Cry protein and one experimental run on the same plate. When several values of one Cry protein were out of range, the whole measurement  was repeated with a more appropriate dilution. Therefore, we are confident that the statistical comparisons that we made are based on comparable Cry protein concentration estimates. Repeated measurements for Cry1A.105 and Cry2Ab2 were generally in the same range as the first measurement, indicating that the additional freezing/thawing cycle did not degrade the Cry proteins to a large extent. In contrast, however, Cry3Bb1 showed ca. 80% lower values for the repeated samples and Cry1F showed 50% lower values. This clearly shows that values of the different Cry proteins and between experimental runs cannot be compared directly. Even more variation is likely when different protocols or different ELISA kit lots are used, thus comparing ELISA estimates across studies is cautioned .® maize did not interact with the two non-target herbivores studied, i.e., R. padi and T. urticae. Similar to Bt maize expressing single Cry proteins, stacked Bt maize is unlikely to affect populations of these herbivores via direct exposure. The hypotheses that stacked Bt maize does not affect non-target herbivore performance compared to a genetically near non-Bt maize line and that herbivore infestation does not affect Cry protein concentrations in stacked Bt maize compared to non-infested Bt maize are supported.Overall, the present laboratory and glasshouse studies provide evidence that the six Cry proteins (and two herbicide tolerance proteins) produced in SmartStaxAll authors contributed to the planning and designing of the experiments and to the drafting of the manuscript. YS and MM performed the bioassays and ELISA analyses. MM analyzed the data statistically.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Nutrient availability and stresses impact a cell’s decision to enter a growth state or a quiescent state. Acetyl-CoA stimulates cell growth under nutrient-limiting conditions, but how cells generate acetyl-CoA under starvation stress is less understood. Here, we show that general stress response factors, Msn2 and Msn4, function as master transcriptional regulators of yeast glycolysis via directly binding and activating genes encoding glycolytic enzymes. Yeast cells lacking Msn2 and Msn4 exhibit prevalent repression of glycolytic genes and a significant delay of acetyl-CoA accumulation and reentry into growth from quiescence. Thus Msn2/4 exhibit a dual role in activating carbohydrate metabolism genes and stress response genes. These results suggest a possible mechanism by which starvation-induced stress response factors may prime quiescent cells to reenter growth through glycolysis when nutrients are limited. Cell growth and proliferation are actively coordinated with extrinsic nutrient availability and intrinsic metabolic states . When nuHere, we exploited the yeast metabolic cycle (YMC) to study how transcriptionally regulated metabolism impacts cell growth program . In the Following this rationale, we developed a computational algorithm, DynaMO, to systematically predict which of the 175 yeast TFs that are linked to specific phases of the YMC . Briefly2 consumption) in the candidate TFs  than in control randomly selected TFs .   . We conf−5)  and intr strains . The numains −7; . On the ains −7; . To furte mutant . Collecte mutant , suggeste mutant . Interes mammals .msn2Δmsn4Δ double mutant compared to the WT strain  state of YMC by comparing it with multiple previously defined quiescent states, including starvation and limitation of one of the three essential nutrients, glucose, nitrogen or phosphate . We examMSN2 and MSN4 mutants is consistent with a transcriptional program in the quiescent phase which could be critical for subsequent cell proliferation. Our analyses suggest that Msn2/4 modulate the cellular programs by changing the intracellular acetyl-CoA levels, probably by promoting glycolysis. Metabolism has strong effects on cell growth and proliferation. Metabolites such as acetyl-CoA have been shown to function not only as building blocks for the synthesis of fatty acids, amino acids and nucleotides, but also as signals for gene expression and enzymatic activities 2SO4, 2 g/L KH2PO4, 0.5 g/L MgSO4•7H2O, 0.1 g/L CaCl2•2H2O, 0.02 g/L FeSO4•7H2O, 0.01 g/L ZnSO4•7H2O, 0.005 g/L CuSO4•5H2O, 0.001 g/L MnCl2•4H2O, 1 g/L yeast extract, 10 g/L glucose, 0.5 mL/L 70% H2SO4, and 0.5 mL/L Antifoam 204 (Sigma)  .All strains were generated from the CEN.PK background and manipulated by standard budding yeast protocols:Yeast strains:kanMX6 or hygMX) and 40 bp sequences flanking the target gene. Tagged-protein strains were generated similarly by integrating a cassette containing a protein tag and a drug resistance cassette at C terminus . Fast SYBR Green Master Mix  was used for real-time PCR and experiments were done on the platform of StepOnePlus Real-Time PCR System . RNA-seq libraries were prepared in the New York University Genome Technology Center using Illumina Trueseq RNAseq v2 library kit . PolyA beads were used for mRNA selection. 500 ng of RNA per sample was used as input and 12 cycles of PCR were run for amplification. Libraries were pooled together and sequencing was performed on Hiseq platform.2 OD cycling cells were collected and flash frozen. RNA was extracted with the Qiagen RNeasy Mini kit . First strand cDNA was synthesized by reverse-transcription using oligo(dT)AB_672215), Msn4 . Validation is provided on the manufacturer’s website and the antibodies were further tested by western blots of WT, msn2Δ and msn4Δ lysates  and 5 OD cells were spun down and lysed in 200 µl of 10% perchloric acid by bead beating. The lysate was spun down and the supernatant was neutralized to pH 6–8 with 3 M K bicarbonate, with vortexing and cooling on ice for 5 min. K bicarbonate was spun down and supernatants were used. For the second method, 5 OD cells were resuspended in 4 mL Quenching Solution M  and incubated at −40°C for 5 min. The cells were spin at 1000 g for 3 min at −10°C and washed once with Quenching Solution M. Pellets were resuspended in 1 mL Extraction Buffer M  and incubated at 80°C for 3 min. Mix was cooled on ice for 5 min and spun down. Supernatants were dried down in speedvac and dissolved in 30 µl water. The concentration of acetyl-CoA was measured using Acetyl-Coenzyme A Assay Kit .Cells in log or stationary phase were counted microscopically in a hemocytometer. A fixed number of cells was plated on YPD plates and the colonies were counted. The survival rate was the number of colonies/number of cells plated. Three biological replicates were examined.Log or stationary phase cells were harvested and washed with PBS twice. Forward scatter (FSC) was measured by BD Accuri C6 Flow Cytometer  and used as the indicator of cell size. Two biological replicated were examined and plotted.The DynaMO algorithm is described in detail in the manuscript .SCR_004694) annotation file (http://www.yeastgenome.org/download-data/curation#.UQFqKhyLFXY) with Fisher exact test. P values were converted into FDR by p.adjust to adjust for multiple testing.To evaluate the overlap between Msn2 and Msn4 binding sites , Msn2 anRNA-seq reads were mapped against SacCer2 genome using bowtie . Read cohttp://hgdownload.cse.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeUwDnase. Then, the whole genome (chromosome Y excluded) was separated by 200 base pair bins (i.e. genomic loci). Bins with read counts larger than 10,000 in one or more cell types  and bins with read counts smaller than 10 in all cell types (noisy loci) were excluded. After filtering, 1,108,603 genomic loci with unambiguous DNase-seq signal in at least one cell type were retained.To examine the enrichment of Msn2 and Msn4 motif sites in different processes, yeast genes were extended by 1000 bp upstream from the start codon. Glycolysis, fatty acid oxidation and galactose metabolism genes were selected based on SGD annotation and previous study . We examhttp://hgdownload.cse.ucsc.edu/goldenPath/sacCer2/phastCons7way/. Six strains were used for alignment, Saccharomyces paradoxus, Saccharomyces mikatae, Saccharomyces kudriavzevii, Saccharomyces bayanus, Saccharomyces castelli, and Saccharomyces kluyveri. Promoter regions were calculated as 0–500 bp upstream of start codons and ORF regions were regions from start codons to stop codons. Scores were averaged across each base pair and further averaged across interesting groups of genes.PhastCons scores for multiple alignments of the yeast strains to the Saccaromyces cerevisiae genome are downloaded from Data of gene expression, nutrient-specific genes and genes required for specific nutrient starvation were downloaded from a previous study . Gene exhttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72263).RNA-seq and ChIP-seq data have been deposited in the Gene Expression Omnibus database under accession code GSE72263 . Based on the results, the authors conclude that the two factors regulate key transition in this cycle by regulating the accumulation of acetyl-CoA.As you will see below, all three reviewers appreciated your study in providing interesting new data concerning the regulation of the metabolic cycle. However, there was also a serious concern about your claim of direct relationship between Msn2/4 regulation and acetyl-CoA pool levels (and production of this metabolite). Two of the reviewers were more supportive of this conjecture, yet even these reviewers requested that you will further validate your claims by analyzing the consequences of MSN2,4 over-expression, either transiently or by using a constitutive systemThe third reviewer, who is a metabolic expert, maintained that you should focus your study on the role of Msn2/4 in the shift between fermentation and respiration, and remove the claim about direct relationship between Msn2/4 regulation and acetyl-CoA pools. I would like to emphasize that since this reviewer is an expert in metabolism, we will not be accept the paper unless he/she accepts the arguments and claims presented.In addition, all three reviewers requested that you better discuss previous studies, and in particular refer to previous work on carbohydrate metabolism during nutrient deprivation.Reviewer #1:In this manuscript, Kuang and co-authors showed that the general stress responsive factors Msn2 and Msn4 play an important role in driving yeast metabolic cycle (YMC). They further revealed that it is through controlling the accumulation of acetyl-CoA and the same mechanism might generally contribute to cell growth regulation under prolonged nutrient limiting conditions.msn2/4 deletion to the progression of YMC is striking! (2) the proposed mechanism is based on careful genome-wide analysis and is convincing. (3) the potential relevance to cell growth regulation in general is intriguing. Therefore, I recommend the acceptance of this manuscript for publication in eLife provided the authors are able to address the major points below.I find this paper interesting in that (1) the effect of msn2/4 deletion) and the correlation analysis between metabolic enzyme expression or Msn2/4 genomic occupancy with YMC phases. It would significantly strengthen the paper if the authors can show some gain-of-function effects. For example, how does Msn2/4 overexpression affect YMC and the growth transition in general? This is especially important because most people in the field believe that activated Msn2/4 slow down cell growth.1) The conclusions are based primarily on loss of function mutant phenotypes (2) Msn2 and Msn4 are activated by dephosphorylation and translocation from the cytoplasm to the nucleus, therefore nuclear localization can serve as a reporter for Msn2/4 activity. In the paper, the authors only monitored the expression changes during YMC. It would serve as the direct supportive evidence for Msn2/4 activation if the authors can observe nuclear localization of Msn2/4 specifically during the RC phase. And if so, what would be the authors' speculation on the mechanisms of Msn2/4 activation? This will provide some clues on why Msn2/4 are activated specifically during the RC phase.3) The authors claim that Msn2/4 have a dual role in regulating carbohydrate metabolism genes and stress resistance genes. Under the conditions that they are focusing on (prolonged nutrient-limited condition), do the stress resistance genes also contribute to cell growth or they are unrelated by-products? An analysis or at least a discussion on this would be helpful.Reviewer #2:This paper describes analysis of how the transcription factors Msn2 and Msn4 regulates expression of genes encoding enzymes of the central carbon metabolism in yeast, with focus on regulation during the metabolic cycle of yeast. These TFs are known to play a central role in stress regulation, and they regulate expression of a very large number of genes in yeast (>100). The authors claim that based on their findings it can be suggested that Msn2/4 control the transition from reductive charging phase to oxidative phase by regulating the accumulation of AcCoA. Regulation of shifts in yeasts metabolism is highly complex, and it tends to the naive to suggest such a statement, and it is by no means supported by the data. Thus, the authors focus their discussion on how Msn2/4 binds (and control expression) to genes of the glycolysis, but these genes are controlled by a large number of other TFs. Furthermore, Msn2/4 themselves control a large number of other genes, including several other TFs, which is not considered in their analysis. A main line of argument is measurements of AcCoA, but this metabolite is present in several cellular compartments – mitochondria, cytosol, peroxisomes and the nucleus (probably equilibrated with the cytosolic pool), and there is no free transport of AcCoA between these compartments. Measuring the total level and linking this to enzymes that are operating in different compartments is not correct . Moreover, the authors show that RC phase gene expression closely resembles that of nutrient deprivation performed by other labs. Thus, data observed in the YMC is largely applicable to nutrient stress environments in general.The findings presented in the manuscript are of high significance and broaden the knowledge of factors and mechanisms that influence cell survival. In particular, recent publications on acetyl-CoA demonstrate that this a key metabolite in promoting cell growth and proliferation. This data demonstrates that well-established transcription factors support the metabolism of this critical metabolite. The data in this manuscript is clear and the writing is concise. In general, the conclusions support the main findings of the paper.The following are specific comments that can improve the manuscript:1) The authors could do a better job in integrating current literature on the importance of carbohydrate stores, particularly trehalose and glycogen, in survival in nutrient stress conditions and re-entry into the cell cycle from quiescence.2) Why did the authors focus on H3K9 acetylation, rather than gene expression, in the DynaMO prediction transcription factors that drive the YMC?  Does the focus on H3K9ac provide a better assessment of gene expression that is responsive to acetyl-CoA?3) The Introduction states that TFs were deleted to validate their role in the YMC. This is a bit vague. Can the authors state which ones? Is Msn2 and 4 unique in terms of its YMC disruption?4) In the Introduction, the authors state that Msn2 and 4 are known to regulate stress genes. This statement should include relevant references, such as Gasch et al., 2000; Martínez-Pastor et al., 1996; Schmitt and McEntee, 1996).5) The authors may also want to discuss upstream factors that regulate Msn2 function. In particular Tor signaling, which is influence by nutrient availability.[Editors’ note: what now follows is the decision letter after the authors submitted for further consideration.]eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Nan Hao (Reviewer #1); Ashby J Morrison (Reviewer #2).Thank you for submitting your work entitled "Stress-response factors Msn2/4 control re-entry of quiescent cells into growth through glycolysis" for consideration by eLife.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in eLife, however, we would have expected more solid insight into how one goes from altered glycolysis to changes in the regulatory molecule acetyl-CoA. This link, unfortunately, is not yet convincing enough and the revision did not help in solidifying this result.We found the data on Msn2/4 regulating glycolysis convincing and believe it can be published as is. For Reviewer #1:In the revision, the authors did not examine the YMC progression or acetyl-CoA level (which are where the major debates centered on) in the overexpression strain, because "the YMC requires coordination of various parameters"(?). Instead, they just checked the regrowth rate and got a variety of inconsistent results. This is not surprising given that they transformed yeast with a 2 micro plasmid (pRS425) expressing Msn2 or Msn4. The variability of plasmid copy numbers is huge, and therefore the kind of colony variability is expected no matter which output phenotypes (growth rate or others) you will be looking at. A genomic integration (with pRS305) will work way better.At this point, it seems my attempts to save the debated conclusion did not work out. However, given their striking findings summarized in my first review, I still support the publication of this manuscript, but would suggest the authors to tone down their conclusion and avoid claiming of direct causal or driving relationship.Reviewer #2:The authors have addressed my previously stated concerns in the revised manuscript. I believe the manuscript is a significant advancement in our understanding of the Msn2/4 involvement in quiescence to growth transitions and energy metabolism.Reviewer #3:The manuscript by Kuang et al., 2014 addresses transcriptional regulation during the yeast metabolic cycle. They attribute a key role of the transcription factors Msn2 and Msn4, well known for their role in the yeast stress response, to regulate the mRNA expression of glycolytic enzymes, during the metabolic cycle in the laboratory yeast strain CenPK (a popular model to synchronize yeast cells in their metabolic cycle). The authors conclude that transcriptional control of glycolytic enzymes though Msn2/Msn4 is causally linked to acetyl-CoA accumulation, triggering re-entry into cell growth.In principle, the storyline of the manuscript has two parts. The first is the one in which the study authors identify Msn2 and Msn4 as transcriptional regulators of glycolysis during the recovery phase of the metabolic cycle. There may be some little bumps here and there as pointed out by the other reviewers, and these warrant to be addressed. But overall I find this part convincing, and in fact impressive.Then there is the second part, the authors conclude from these transcriptional changes on how cells regulate metabolism via acetyl-CoA to drive cell growth. This part is not more than a (perhaps reasonable) speculation. It is intrinsically difficult to predict a change in the metabolome from transcriptional data, certainly one cannot conclude from the mRNA level of ~15 glycolytic enzymes on the acetyl-CoA concentration (that is no product of glycolysis) and its role in the recovery phase of the metabolic cycle.eLife. I think it perhaps does; eLife did aim for very high in the past, but one needs to see this in the light of the recent and effectual situation. One might recommend to the authors however to tone down the manuscript to the part that they actually show.In essence, the title of the manuscript should read something like 'Msn2 / Msn 4 regulate the expression of glycolytic enzymes during the recovery phase of the metabolic cycle'. I'm looking forward to discussing with the other reviewers whether this discovery on its own meets the bar set by eLife standard.Thanks for asking to re-consider the decision and sorry for the delay in getting back to you. The decision to reject the paper was based primarily on the worry, expressed by one of the reviewers and at least partly agreed to by the others, that, given the inability to establish the functional connection of MSN-related regulation to Acetyl-Co production, the significance of the paper may not reach the desired In particular, please:1) Tone-down the claims concerning acetyl-coA regulation. All reviewers were of the opinion that your study does not establish a functional connection from transcription regulation of glycolysis to the mechanism leading to altered acetyl-co levels.2) Validate the antibodies used, as requested by the new reviewer.3) Please see the other minor comments of the two new reviewers and perhaps emphasize some more the points that they emphasize as being mostly significant about your findings. These revisions are not essential but may improve your presentation.Reviewer #1[…] I do believe the authors present some important and novel findings worthy of reconsideration/publication. The data overall look pretty solid to me. It is not well-known that the stress response transcription factors Msn2/Msn4 also regulate glycolytic enzymes, and the evidence looks good that such genes may be induced by Msn2/4 to "prime" cells for rapid sugar utilization and re-entry into growth upon return to favorable nutrient conditions. I believe using the YMC system has enabled them to see this unanticipated relationship between Msn2/4 and glycolytic/carbohydrate metabolic enzymes, which may be obscured in traditional batch culture experiments. I also believe the RNA-seq and ChIP-seq could be high-quality datasets and will be of interest to those investigating both the overlapping and distinct roles for Msn2 and Msn4 in the regulation of stress-responsive gene transcription.msn2∆, msn4∆ lysates).I would ask them to improve Reviewer #2I am sorry about the delay. I had to read this a couple times to appreciate what I think is the importance of this work, but let me emphasize that I have not been up to speed on Ben Tu's most recent work or that of Linda Breeden who has also worked in this area. Hence, there may be things that are more broadly known that were new to me here.The importance of this paper to my mind is to broaden the appreciation of the roles of Msn2 and Msn4 from general stress resistance transcription factors to regulators of the metabolic cycle that have special roles in metabolism of cells that are coming out of quiescence that are not evident in standard growth conditions. The work is quantitative, incorporating multiple dimensions of analysis (though the magic sauce in DYNAMO was not described) and hence gives us a more comprehensive appreciation of the relevant targets of Msn2 and Msn4. As with any paper that provides a list of genes/proteins, there are some puzzles that are left unanswered such as why GIS1 is a target of this regulation. But on balance, the authors do a good job of drawing together logic behind the interactions described.In searching for broader relevance, the authors speculate that Myc might be the human equivalent for the regulation of tumor growth since they assert that tumors are nutrient limited. That assertion may be true, but it certainly isn't widely known and raised my skepticism about this point, which is not that important to the results of the paper, but should be checked.So why did I have to read it a couple times to get it? I think the authors could do a better job of emphasizing how different previous conditions used for the study of Msn2 and Msn4 are from the conditions encountered in the metabolic cycle. I would also advocate losing that damn acronyms for the three phases of the cycle as they are not commonly used and are disruptive to readers who are trying to understand this under explored region of metabolism. [Editors’ note: the author responses to the first round of peer review follow.]Reviewer #1:1) The conclusions are based primarily on loss of function mutant phenotypes (msn2/4 deletion) and the correlation analysis between metabolic enzyme expression or Msn2/4 genomic occupancy with YMC phases. It would significantly strengthen the paper if the authors can show some gain-of-function effects. For example, how does Msn2/4 overexpression affect YMC and the growth transition in general? This is especially important because most people in the field believe that activated Msn2/4 slow down cell growth.Because the YMC requires coordination of various parameters, it is not ideal for overexpression tests. Instead, we used inoculation of a stationary culture (similar in 2) Msn2 and Msn4 are activated by dephosphorylation and translocation from the cytoplasm to the nucleus, therefore nuclear localization can serve as a reporter for Msn2/4 activity. In the paper, the authors only monitored the expression changes during YMC. It would serve as the direct supportive evidence for Msn2/4 activation if the authors can observe nuclear localization of Msn2/4 specifically during the RC phase. And if so, what would be the authors' speculation on the mechanisms of Msn2/4 activation? This will provide some clues on why Msn2/4 are activated specifically during the RC phase.Besides the expression changes during YMC, we also observed the increase of ChIP-seq signals in the RC phase . Because3) The authors claim that Msn2/4 have a dual role in regulating carbohydrate metabolism genes and stress resistance genes. Under the conditions that they are focusing on (prolonged nutrient-limited condition), do the stress resistance genes also contribute to cell growth or they are unrelated by-products? An analysis or at least a discussion on this would be helpful.We think the stress resistance genes are primarily activated to improve survivability. However, we think some of the genes may also contribute to cell growth. For example, TPS2 and GAC1 are involved in synthesis of trehalose and glycogen. They are storage carbohydrates and important for stress resistance. They have also been shown to be metabolized during the exit of quiescence to fuel growth . We add discussions of these points in subsection “Regulatory network for Msn2/4 in YMC” and subsection “Systematic comparison between YMC and distinct quiescent states”.Reviewer #2:This paper describes analysis of how the transcription factors Msn2 and Msn4 regulates expression of genes encoding enzymes of the central carbon metabolism in yeast, with focus on regulation during the metabolic cycle of yeast. These TFs are known to play a central role in stress regulation, and they regulate expression of a very large number of genes in yeast (>100). The authors claim that based on their findings it can be suggested that Msn2/4 control the transition from reductive charging phase to oxidative phase by regulating the accumulation of AcCoA. Regulation of shifts in yeasts metabolism is highly complex, and it tends to the naive to suggest such a statement, and it is by no means supported by the data. Thus, the authors focus their discussion on how Msn2/4 binds (and control expression) to genes of the glycolysis, but these genes are controlled by a large number of other TFs. Furthermore, Msn2/4 themselves control a large number of other genes, including several other TFs, which is not considered in their analysis. A main line of argument is measurements of AcCoA, but this metabolite is present in several cellular compartments – mitochondria, cytosol, peroxisomes and the nucleus (probably equilibrated with the cytosolic pool), and there is no free transport of AcCoA between these compartments. Measuring the total level and linking this to enzymes that are operating in different compartments is not correct  We agree with the reviewer that regulation of shifts in yeast metabolism is highly complex, and that is one reason that our study focuses on a single metabolite, acetyl-CoA. The concept that acetyl-CoA stimulates cell growth and proliferation has been reported in many systems such as yeast , embryonic stem cells  and cancer cells  and the role of acetyl-CoA is specifically in YMC to activate growth-promoting genes  and cell cycle genes . Based on these works, our study provides a mechanistic advance about the source of acetyl-CoA in the RC of YMC. We believe this is a critical extension to help understand the exact role of acetyl-CoA in YMC and general cell growth. We did not claim that acetyl-CoA accumulation is the only route by which Msn2/4 drives the transition. We discuss the role of Msn2/4 in activating stress-response genes to maintain survival and the storage carbohydrates  in stress resistance and regrowth (subsections “Regulatory network for Msn2/4 in YMC” and “Systematic comparison between YMC and distinct quiescent states” and in the Discussion section.).2) We agree with the reviewer that glycolysis genes are controlled by other TFs and Msn2/4 controls other TFs that in turn regulate glycolysis genes. We also added msn2msn4 mutant. (B) The expression level of ACS1 is significantly induced in the RC phase but not PDA1 (PDH complex), suggesting an enhancement of the cytosolic pathway. (C) Cai et al., (2011) have shown that adding different components of the cytocolic route, like acetate, ethanol, acetaldehyde, can immediately induce the transition from RC to OX. It is unlikely that these molecules can all be transported into the mitochondria and converted to acetyl-CoA there. (D) Cai et al., (2011) have shown that during the transition, histone acetylations are strongly increased and a gcn5 (histone acetyltransferase) mutant shows a similar YMC defect, suggesting the role of the nucleo-cytosolic pool of acetyl-CoA in driving regrowth. Our transcriptional analysis is consistent with the hypothesis of a key nucleocytosolic pool of acetyl-CoA. (E) Finally, we would like to clarify that our study focuses on the mechanism of acetyl-CoA generation and not the mechanism of how acetyl-CoA stimulates growth and we agree that it will be very important to elucidate the detailed mechanisms of how acetyl-CoA stimulates growth, including the roles of each pool of acetyl-CoA, but this is beyond the scope of this paper. And we would like to emphasize that the majority of the glycolytic pathway, from glucose transportation to pyruvate, occur in the cytosol and are all controlled by Msn2/4 (3) We agree with the reviewer that the total level of acetyl-CoA doesn’t reflect the levels in each compartment and it may change the interpretation. We updated y Msn2/4 .Reviewer #3:1) The authors could do a better job in integrating current literature on the importance of carbohydrate stores, particularly trehalose and glycogen, in survival in nutrient stress conditions and re-entry into the cell cycle from quiescence.Thank you for the good suggestion. We have expanded the discussion and added references to emphasize their important roles in regrowth (subsection “Regulatory network for Msn2/4 in YMC”).2) Why did the authors focus on H3K9 acetylation, rather than gene expression, in the DynaMO prediction transcription factors that drive the YMC?  Does the focus on H3K9ac provide a better assessment of gene expression that is responsive to acetyl-CoA?Sorry for the confusion. We added a description in the Results section to explain how the prediction works. H3K9ac not only marks the actively transcribed genes but also marks the transcription regulatory regions (promoters and enhancers). Based on the assumption that TFs bind to promoters and enhancers to function, a combination of spatial information of H3K9ac and TF motifs increases the prediction accuracy of TFBSs.We didn’t find a special advantage of H3K9ac in predicting genes that is responsive to acetyl-CoA. We used H3K9ac because it shows the highest dynamics across YMC. Other histone acetylations show similar trends, but are a lot less dynamic and thus the signal is not as strong.3) The Introduction states that TFs were deleted to validate their role in the YMC. This is a bit vague. Can the authors state which ones? Is Msn2 and 4 unique in terms of its YMC disruption?We added the names of the TFs we examined in subsection “DynaMO identifies TFs associated with specific phases of YMC”. Different defects were observed in different TF mutants. Among the examined TFs, the elongated RC phase was only observed in the Msn2 and Msn4 mutants.4) In the Introduction, the authors state that Msn2 and 4 are known to regulate stress genes. This statement should include relevant references, such as Gasch et al., 2000; Martínez-Pastor et al., 1996; Schmitt and McEntee, 1996).We have added the corresponding references.5) The authors may also want to discuss upstream factors that regulate Msn2 function. In particular Tor signaling, which is influence by nutrient availability.We added a discussion about TOR signaling as a potential upstream factor that regulate Msn2/4 (Discussion section). We think it might be an interesting future direction.[Editors’ note: the author responses to the second round of peer review follow.]Reviewer #1:In the revision, the authors did not examine the YMC progression or acetyl-CoA level (which are where the major debates centered on) in the overexpression strain, because "the YMC requires coordination of various parameters"(?). Instead, they just checked the regrowth rate and got a variety of inconsistent results. This is not surprising given that they transformed yeast with a 2 micro plasmid (pRS425) expressing Msn2 or Msn4. The variability of plasmid copy numbers is huge, and therefore the kind of colony variability is expected no matter which output phenotypes (growth rate or others) you will be looking at. A genomic integration (with pRS305) will work way better.At this point, it seems my attempts to save the debated conclusion did not work out. However, given their striking findings summarized in my first review, I still support the publication of this manuscript, but would suggest the authors to tone down their conclusion and avoid claiming of direct causal or driving relationship.We have revised the text including the title and text to avoid implying any direct causal relationship between glycolysis regulation and acetyl-CoA accumulation.Reviewer #3:[…] In essence, the title of the manuscript should read something like 'Msn2 / Msn 4 regulate the expression of glycolytic enzymes during the recovery phase of the metabolic cycle'. I'm looking forward to discussing with the other reviewers whether this discovery on its own meets the bar set by eLife. I think it perhaps does; eLife did aim for very high in the past, but one needs to see this in the light of the recent and effectual situation. One might recommend to the authors however to tone down the manuscript to the part that they actually show.We have revised the text including the title to avoid any direct causal relationship between glycolysis regulation and acetyl-CoA accumulation.In particular, please:1) Tone-down the claims concerning acetyl-coA regulation. All reviewers were of the opinion that your study does not establish a functional connection from transcription regulation of glycolysis to the mechanism leading to altered acetyl-co levels.We have revised the text including the title to remove any explicit functional connection between the transcriptional regulation of glycolysis and altered acetyl-CoA levels. In the updated presentation, we only project that the transcription regulation of glycolysis is a potential mechanism by which acetyl-CoA is accumulated.2) Validate the antibodies used, as requested by the new reviewer.We have added western blot results of the WT, msn2 and msn4 lysates against Msn2 and Msn4 antibodies in 3) Please see the other minor comments of the two new reviewers and perhaps emphasize some more the points that they emphasize as being mostly significant about your findings. These revisions are not essential but may improve your presentation.We have addressed each comment of all the reviewers. Please see our point-by-point response below.Reviewer #1[…] I would ask them to improve Saccharomyces strains than coding regions of other genes or promoter regions of glycolytic genes  and hence gives us a more comprehensive appreciation of the relevant targets of Msn2 and Msn4. As with any paper that provides a list of genes/proteins, there are some puzzles that are left unanswered such as why GIS1 is a target of this regulation. But on balance, the authors do a good job of drawing together logic behind the interactions described.We have a short description of DynaMO in the end of introduction and beginning of the result section since it is not the main focus of this manuscript. We believe it will be a valuable tool to study transcription regulation in dynamic processes and the method paper describing DynaMO is currently under revision and will likely publish around the same time.msn2Δmsn4Δ double mutant. Based on this negative result we didn’t include it as a core target of Msn2/4.We described GIS1 as another TF identified by DynaMO as a candidate TF regulating RC phase and it shows similar targets as Msn2/4 . WhetherIn searching for broader relevance, the authors speculate that Myc might be the human equivalent for the regulation of tumor growth since they assert that tumors are nutrient limited. That assertion may be true, but it certainly isn't widely known and raised my skepticism about this point, which is not that important to the results of the paper, but should be checked.The nutrient environment of cancer cells is complex and dynamic. We have revised the words to clarify the conditions under which cancer cells survive under nutrient limited conditions. We have also added a reference that specifically demonstrates that acetyl-CoA synthetase is important to fuel cancer cells under nutrient limited conditions. We also carefully described the connection between Msn2/4 and Myc. We only speculate that Myc might be a functional equivalent of Msn2/4.So why did I have to read it a couple times to get it? I think the authors could do a better job of emphasizing how different previous conditions used for the study of Msn2 and Msn4 are from the conditions encountered in the metabolic cycle. I would also advocate losing that damn acronyms for the three phases of the cycle as they are not commonly used and are disruptive to readers who are trying to understand this under explored region of metabolism.We have carefully compared the previous conditions and the YMC condition in the discussion and analyzed the reason that YMC can be a good platform for revealing the roles of Msn2/4 in transitions from starvation to growth. We apologize for the difficult to remember acronyms  but these are the abbreviations used in all the other papers. To help readers understand the three phases of the cycles, we have used metaphors in the introduction to liken OX, RB and RC as growth, proliferation and quiescence. We have also sprinkled these pseudo-synonyms throughout the text to remind readers about the meanings of the three phases.