WRKY gene from banana, MusaWRKY71, which was inducible in response to a wide array of abiotic or biotic stress stimuli. The present work details the effects of MusaWRKY71 overexpression in transgenic banana plants. Stable integration and overexpression of MusaWRKY71 in transgenic banana plants was proved by Southern blot analysis and quantitative real time PCR. Transgenic banana plants overexpressing MusaWRKY71 displayed enhanced tolerance towards oxidative and salt stress as indicated by better photosynthesis efficiency (Fv/Fm) and lower membrane damage of the assayed leaves. Further, differential regulation of putative downstream genes of MusaWRKY71 was investigated using real-time RT-PCR expression analysis. Out of a total of 122 genes belonging to WRKY, pathogenesis-related (PR) protein genes, non-expressor of pathogenesis-related genes 1 (NPR1) and chitinase families analyzed, 10 genes  showed significant differential regulation in MusaWRKY71 overexpressing lines. These results indicate that MusaWRKY71 is an important constituent in the transcriptional reprogramming involved in diverse stress responses in banana.WRKY transcription factors are specifically involved in the transcriptional reprogramming following incidence of abiotic or biotic stress on plants. We have previously documented a novel  MusaWRKY71 from banana focusing mainly on its inducibilty under different stress conditions. MusaWRKY71 transcripts in banana plants were found to be up-regulated by cold, dehydration, salt, ABA, H2O2, ethylene, salicylic acid (SA) and methyl jasmonate (MJ) WRKY gene led us to postulate that a positive correlation may exist between MusaWRKY71 expression and stress tolerance in banana. Further, several reports published recently indicate that overexpression of select specific WRKY genes in transgenic plants can lead to significantly improved stress tolerance in plants. VvWRKY1 overexpression in grapes activated the expression of jasmonic acid pathway-related genes and improved the tolerance to the downy mildew ZmWRKY33 in Arabidopsis induced RD29A known to be involved in stress-signaling and enhanced salt stress tolerance in the transgenic plants GhWRKY15 transcript was mainly induced in cotton seedlings in response to biotic stress modulators like salicylic acid and methyl jasmonate and could impart tolerance to fungal pathogens in transgenic tobacco plants TaWRKY2 and TaWRKY19 of wheat imparted abiotic stress tolerance in transgenic Arabidopsis plants CaWRKY40 overexpression in tobacco led to enhanced resistance to Ralstonia solanacearum and tolerance to heat shock whereas it’s silencing in pepper led to enhanced susceptibility to R. solanacearum and lowered thermotolerance Plant growth and development are adversely affected by different abiotic and biotic stress factors. In order to survive these stresses, plants have evolved the capacity to sense and react to these diverse external signals by means of specialized physiological and biochemical strategies. Upon stress perception, plants trigger a cascade of cellular events involving several parallel transduction pathways that eventually modulate the level of specific transcription factors resulting finally in the up- or down-regulation of genes coding for synthesis of effector proteins and/or metabolites which participate in stress tolerance Musa spp.) is one of the most important food and fruit crops in the world and is widely grown in many of the tropical countries. Although research in banana has been mostly concentrated on unraveling the ripening-related processes MusaWRKY71 in transgenic banana plants and shown its involvement in oxidative and salt stress tolerance. Further, differential expression of several putative MusaWRKY71 target genes involved in biotic stress response pathways has been studied in detail.Banana . Secondary embryos also developed from these primary embryos upon subculturing onto fresh medium of the same composition (Banana cv. a plants . Three tposition . These eposition . The cloposition . Rooted position .MusaWRKY71 overexpressing lines showed a single 788-bp amplified fragment derived from hygromycin phosphotransferase coding sequence whereas it was absent in untransformed control plants (BamHI was used to restrict the genomic DNAs as it is expected to cut the T-DNA region of the overexpression vector (p1301-MusaWRKY71) only once and therefore the number of chemiluminescent bands obtained can directly be taken as the number of T-DNA copies transferred to banana genome in these transgenic lines. T-DNA copy numbers in the range of 1 to 4 were found in these four lines  in W2, 2.395 times in W3, 2.789 times in W8 and 2.610 times in W22 line  over the maximum fluorescence value (Fm)] is a sensitive marker of overall plant wellbeing and exposure to any form of abiotic or biotic stress stimuli causes reduction in photochemical quenching of energy within Photosystem II leading to a lowered Fv/Fm. The stressed leaves derived from untransformed control plants displayed the lowest Fv/Fm in both the stress assays suggesting deficient photosynthetic functions in these leaves as compared to the MusaWRKY71 overexpressing transgenic leaves , PR protein genes, NPR1 genes and chitinase genes. We studied the differential regulation of these gene families in MusaWRKY71 overexpressing plants by using specific primers in quantitative RT-PCR . Further, all the genes wherein differential expression was noticed had WRKY protein binding W-box like sequences [(C/T)TGAC(T/C)] in the 1 kb proximal promoter regions.Although l plants . Thus, te RT-PCR . Among tArabidopsis and rice has established that members of several families of transcription factors like ethylene-responsive element binding proteins, myb-like proteins, bZIP proteins, and WRKY proteins are likely involved in the transcriptional regulation of plant stress response genes WRKY gene family have been shown to be involved in both biotic and abiotic stress responses of plants Arabidopsis, tomato, tobacco and potato whereas monocots as a group have lagged behind despite their economic importance. Valuable fruit crops like banana were not studied in this perspective and hence, investigation into functions of WRKY proteins in banana stress response and signaling assumes importance as banana is the most important crop in the world and because of vulnerability to biotic and abiotic stresses.Transcriptional modulation of various plant effector genes is crucial for activation of plant inducible stress responses. Hence, understanding the intricate regulation of plant stress response mechanisms by identification of different regulatory components and their respective roles in transcriptional regulatory pathways assumes significance. Research in model plant systems like WRKY gene based on comparative analysis of stressed and non-stressed tissue derived EST data sets WRKY gene (MusaWRKY71) was further extended towards the 3′ end to get the complete gene sequence. MusaWRKY71 was observed to be nuclear localized and its transcription was inducible in response to a variety of abiotic and biotic stress conditions. To further characterize its functions in banana stress response pathways, we overexpressed this gene in transgenic banana plants as the best method to examine the functions of a protein is its overexpression in the parent plant species wherein direct study of its interactions with other macromolecules present in the native plant is possible. The MusaWRKY71 overexpressing transgenic banana grew normally in vitro in the presence of hygromycin selection and no gross abnormalities or stunting effects were noticed. Leaves derived from these transgenic plants performed better in the abiotic stress assays as demonstrated by lower phenotypic damage and positive membrane damage and photosynthetic efficiency parameters as compared to controls. When these plants were subjected to Fusarium wilt assay, we did not observe any difference between the untransformed controls and the four transgenic lines indicating that overexpression of MusaWRKY71 was not sufficient to modulate the plant defenses to achieve Fusarium tolerance. Failure to observe any difference with respect to biotic stress exerted by Fusarium, and our previous observation regarding the SA, MJ and ethephon inducibility of MusaWRKY71 led us to look for differential expression of genes which are known to be involved in biotic stress response pathways. Among the genes which are known downstream targets of WRKY proteins, genes of WRKYPR proteinschitinasesNPR1 proteinsMusaWRKY71 by itself is unable to prevent a fatal biotic interaction, but it still controls the expression of other genes related to biotic stress. Together with the fact that MusaWRKY71 overexpressing plants were tolerant towards oxidative and salt stress, the differential expression of several biotic stress related genes pointed towards a role for this protein in the interface between the abiotic and biotic stress response pathways of banana plant.In an earlier study, we had identified a banana EST sequence corresponding to a WRKY genes have been conducted in different plant species. OsWRKY72 overexpressing plants were retarded and displayed early flowering as well as reduced apical dominance along with an enhanced gravitropism response Arabidopsis ABO3, a WRKY transcription factor was found to mediate plant responses to abscisic acid and drought tolerance, as an ABA overly sensitive mutant (abo3) was hypersensitive to ABA in seedling establishment and growth CaWRKY1- overexpressing transgenic plants showed heightened hypersensitive cell death in response to tobacco mosaic virus and Pseudomonas syringe pv. tabaciCaWRKY1 was in fact observed to act as a regulator to turn off systemic acquired resistance once the pathogen challenge has diminished. Overexpression of OsWRKY89 in transgenic rice increased lignification and reduced internode length VvWRKY2 in transgenic tobacco plants reduced the susceptibility towards different fungal pathogens indicating the role for WRKY genes in fungal tolerance in plants WRKY expression in transgenic plants showed the multiple roles of these proteins in plants. Further, in our study at least one gene from the three out of four groups of genes  was found to be differentially regulated. But among the nine NPR1 genes tested, none of the genes were found to have differential expression in the transgenic lines. In contrast, the rice plants overexpressing OsWRKY71 (the closest homologue of MusaWRKY71) showed enhanced expression of OsNPR1 and OsPR1b as well as improved resistance towards rice bacterial blight WRKY and NPR1 genes can be quite divergent.Over the last decade several studies detailing overexpression and downregulation of select specific MusaWRKY71 is proposed to be an important member of the WRKY gene family in banana which has the capacity to cross regulate other WRKY gene members as well as other genes involved in biotic stress signaling pathways. Further, since overexpression oof this gene provides the transgenic plants with an improved oxidative and salt stress tolerance, it may well be a significant link in the interface between abiotic and biotic stress pathways. Further studies involving microarray experiments based on the newly uncovered banana genome sequence MusaWRKY71 gene in banana stress responses.In conclusion, MusaWRKY71 binary vector described earlier Rasthali embryogeneic cell suspension cultures as essentially described earlier Agrobacterium tumefaciens EHA105 by electroporation and this Agrobacterium culture was then cocultivated with 0.5 ml packed cell volume of banana embryogenic cells. These cells were then aspirated onto glass fibre filters and cultured on to solidified M2 medium added with 100 µM ACS. After three days of incubation in dark at 25±1°C, the cells  were transferred to fresh M2 medium added with cefotaxime (400 mg l−1). Three days later the cells were removed from the filters and transferred onto banana embryo induction medium supplemented with cefotaxime (400 mg l−1) and hygromycin (5 mg l−1). Somatic embryos developed in 3 to 4 weeks were then cultured on the same medium for three subculture routines of three weeks duration on hygromycin selection medium. Developed embryos were then transferred to MS medium supplemented with BAP (0.5 mg l−1) for germination. Rapidly developing embryos were subsequently transferred to banana multiplication medium to multiply each putatively transformed line. Individual shoots isolated from the multiple shoot cultures were transferred to MS medium added with NAA (1 mg l−1) for rooting. These tissue culture generated plantlets were then hardened in the greenhouse and used for all the analysis.p1301-MusaWRKY71 binary vector. Genomic DNA isolated from untransformed banana plant served as negative control in these PCR. Four lines were then randomly chosen from the lines which showed bands specific for hygromycin phosphotransferase gene. These four lines  were then analyzed by southern blot analysis (performed using DIG-labeled probes specific for hygromycin phosphotransferase gene) for estimation of T-DNA copy numbers in these lines. Chemiluminescence was detected on the nylon filters using a chemiluminescence enabled gel-documentation system. The exact quantum of overexpression of MusaWRKY71 in these four selected transgenic lines was examined by real time quantitative RT-PCR analysis. Total RNA extraction from young leaves of transgenic plants and first strand cDNA synthesis were done as described before Musa acuminata EF 1α gene MusaWRKY71 gene to allow gene-expression normalization and subsequent quantification. All real-time quantitative RT-PCR were performed using Qiagen make Rotor-Gene Q platform. PCR Cycling parameters for real time RT-PCR were 94°C for 10 min initially followed by 40 PCR cycles, each comprising of 94°C for 30 seconds, 56°C for 30 seconds and 72°C for 30 seconds. Ct values obtained from Rotor-Gene Q software were analyzed using REST-MCS software Genomic DNA isolated from seven putatively transformed banana lines actively growing on hygromycin containing medium using GenElute Genomic DNA miniprep kit  was used as template in PCR with primers specific for hygromycin phosphotransferase gene present within the T-DNA borders of p1301-MusaWRKY71 overexpressing lines were used in assays for improved drought and salt tolerance. Leaves were excised along with their petioles and their cut ends were dipped in 1/10 MS medium added individually with methyl viologen (10µM) or NaCl (350 mM) for 7 days in 16 hour light/8 hour dark regime. Detached leaves so treated with methyl viologen or NaCl were then analysed for membrane damage by estimating their malondialdehyde (MDA) content Rasthali were used as controls in all the above assays. All the stress assays were conducted in triplicates and after their completion representative samples for each assay were photographed.Deatched leaves derived from 2–3 months old transgenic hardened MusaWRKY71 on expression of other WRKY gene family members in banana, cDNA isolated from leaves of MusaWRKY71 overexpressing banana lines was utilized in real time quantitative RT-PCR together with primers specific for 67 annotated WRKY genes Click here for additional data file.Table S1WRKY genes and primers used in this study.(DOC)Click here for additional data file.Table S2PR protein genes and primers used in this study.(DOC)Click here for additional data file.Table S3Chitinase genes and primers used in this study.(DOC)Click here for additional data file.Table S4NPR1 genes and primers used in this study.(DOC)Click here for additional data file.


Cancer stem cells (CSCs) have been shown to promote tumorigenesis of many tumor types, including breast, although their relevance to cancer metastasis remains unclear. While subpopulations of CSCs required for metastasis have been identified, to date there are no known molecular regulators of breast CSC (BCSC) metastasis. Here we identify RhoC GTPase as an important regulator of BCSC metastasis, and present evidence suggesting that RhoC also modulates the frequency of BCSCs within a population. Using an orthotopic xenograft model of spontaneous metastasis we discover that RhoC is both necessary and sufficient to promote SUM149 and MCF-10A BCSC metastasis–often independent from primary tumor formation–and can even induce metastasis of non-BCSCs within these cell lines. The relationship between RhoC and BCSCs persists in breast cancer patients, as expression of RhoC and the BCSC marker ALDH1 are highly correlated in clinical specimens. These results suggest new avenues to combating the deadliest cells driving the most lethal stage of breast cancer progression. In the majority of cancers it is not the primary tumor that is lethal to the patient; the actual lethality arises from cancer cell metastasis to vital organs. Recent work has uncovered emerging roles for cancer stem cells (CSCs) in cancer metastasis. Initial links between CSCs and metastasis were circumstantial, including an invasiveness gene signature in breast CSCs (BCSCs) that predicted shorter metastasis-free survival Recent studies suggested stronger causative links between BCSCs and metastasis. BCSCs have been found to be enriched in spontaneous breast cancer xenograft metastases RhoC is a member of the Rho family of GTPases and functions in coordinating cell motility and actomyosin contractility in vitroThe metastatic influence of RhoC is exemplified by inflammatory breast cancer (IBC). IBC is the most lethal form of breast cancer and is metastatic from its inception. RhoC is overexpressed in 90% of IBC cases Here we reveal that RhoC can function in BCSC metastasis. Inhibiting RhoC in the highly metastatic, IBC-derived SUM149 cell line revealed that RhoC is necessary for SUM149 BCSC metastasis. Conversely, overexpressing RhoC alone was sufficient to enable BCSC metastasis from the non-tumorigenic, non-metastatic MCF-10A cell line. Surprisingly, RhoC often promoted spontaneous metastasis independent from primary tumor formation even within the non-BCSC population, suggesting that RhoC can act independent of BCSC status. RhoC also influences BCSC population size in the cell lines studied, as the abundance of BCSCs varied concurrent with changes in RhoC expression. Clinically, expression of RhoC and the BCSC marker ALDH1 strongly correlate in patient breast cancer specimens. To the best of our knowledge RhoC is therefore the first putative molecular promoter of BCSC metastasis–one which holds therapeutic promise for the most lethal form of breast cancer.To address whether RhoC functions in BCSC pathogenesis, we first asked whether RhoC expression was associated with BCSCs. Using the highly aggressive, RhoC-overexpressing SUM149 cell line we discovered that, after sorting for ALDH activity using the ALDEFLUOR assay To determine whether RhoC functions in BCSC aggressiveness, we generated genetically modified cell lines with either inhibited RhoC in SUM149 cells (“SUM149 shRhoC”) or overexpressed constitutively active RhoC Interestingly, when we sorted the modified cell lines and observed RhoC expression as in Upon observing a strong association between RhoC expression and activity of the BCSC marker ALDH, we asked whether this relationship was functional in BCSC behavior. The acquisition of motility by otherwise stationary cells is an indicator of cancer progression and a process regulated across many cell lineages and cancers by RhoC Modulating RhoC expression significantly impacted cell velocity even within the ALDH (+) BCSC population in each cell line . Inhibitin vitro3D cell culture is frequently used to observe physiologically-relevant developmental, tumorigenic, and metastatic mammary epithelial/breast cancer cell behaviors in vitro metastatic phenotype of both unsorted cell lines. MCF-10A G14V acinar-like structures were more disorganized and slightly larger than the MCF-10A vec acinar-like, with cells invading out from the central mass, similar to SUM149 scrambled cells  cells (While ALDH-sorting we made a surprising observation: there appeared to be a RhoC-dependent change in the abundance of ALDH (+) cells in each cell line. To further investigate, we compared the relative abundance of ALDH (+) cells in each control cell line (SUM149 scrambled or MCF-10A vec) to the corresponding RhoC-modified cell line (SUM149 shRhoC or MCF-10A G14V). Surprisingly, we observed almost identical reciprocal changes: a two-fold decrease in the relative number of ALDH (+) cells in the SUM149 shRhoC cell line compared to scrambled, and a two-fold increase in the relative number of ALDH (+) cells in the MCF-10A G14V cell line compared to vector . Togethein vitro observations, we asked whether RhoC affects SUM149 and MCF-10A BCSC metastasis in vivo. To address this question, we orthotopically xenografted NOD/SCID mice with either ALDH-sorted SUM149 scrambled/shRhoC cells or MCF-10A vec/G14V cells  and observed the incidence of tumorigenesis and metastasis.Based on our Knocking down RhoC in SUM149 cells significantly decreased tumor incidence in the ALDH (+) population see . 5 of 9 Surprisingly, we discovered large metastatic tumors completely filling the pleural cavity in many of the mice injected with ALDH (+) SUM149 scrambled and MCF-10A G14V cells i. We alsEven more surprising was the propensity for metastasis in mice that did not form primary tumors . 35% of in vivo, such that RhoC-low, ALDH (−) SUM149 cells would not be able to generate RhoC-high, ALDH(+) cells, and thus ALDH (−) SUM149 tumors would retain low RhoC expression. Conversely, RhoC-high, ALDH(+) tumors would retain the high RhoC expression characteristic of the unsorted SUM149 cell line. We used the SUM149 scrambled cell line to assay RhoC expression in vivo, as it is the only cell line in this study that expresses high levels of endogenous RhoC  SUM149 scrambled tumors maintained RhoC expression during tumor growth, whereas ALDH (−) SUM149 scrambled tumors did not regain RhoC expression (in vivo. Along with our previous findings illustrating the influence of RhoC on SUM149 and MCF-10A BCSC abundance (Since none of the mice injected with 50 ALDH (−) SUM149 scrambled cells formed tumors we increased the injection to 5000 cells, at which point the ALDH (−) population also formed tumors. After allowing tumors to develop, we euthanized the mice, extracted protein from the tumors, and assayed RhoC expression. The inverse relationship between RhoC expression and ALDH activity persisted pression . These dbundance , these din vitro and in vivo data, these findings further support an association between RhoC and BCSCs.Expression of ALDH1 protein has been shown to be a reliable marker for BCSCs in paraffin-embedded tissue CSCs have been shown to promote tumorigenesis in numerous cancer types By approaching RhoC expression from two distinct angles–its necessity for metastasis of a breast cancer cell line (SUM149), and its sufficiency to induce metastasis of a non-metastatic mammary epithelial cell line (MCF-10A)–we were able to clearly elucidate a putative role of RhoC in BCSC metastasis. It is important to note that we used an orthotopic xenograft system rather than an intracardiac or tail vein injection assay to measure metastasis. As emphasized in a recent publication We have shown that RhoC expression and BCSC marker expression are intimately linked in multiple ways , 2C. TakWe were surprised to find that a significant number of mice developed pleural metastases independent of primary tumor formation . We initAs an alternative explanation, we propose that high RhoC expression–either as a consequence of inherent tumor cell biology (SUM149) or genetic modification (MCF-10A G14V), and amplified by ALDH (+) status–may, in some cases, cause a sublimation of cell behavior from non-tumorigenic directly to metastatic. Such a phenomenon is observed clinically and is defined as cancer of unknown primary site, or CUP Interestingly, several mice injected with non-CSC ALDH (−) MCF-10A G14V still developed lung metastases . That thIn agreement with this assertion, we also observed metastasis in one mouse injected with ALDH (+) SUM149 shRhoC cells. As we previously observed, RhoC is preferentially expressed by the ALDH (+) SUM149 population , and altThese observations raise important questions about the true meaning of CSC identity. Labeling a cell a CSC may indicate that it has a specific collection of features , but ultimately these features are a product of the genetics of the CSC. Extending from this assumption, one can reason that targeting the specific molecular cogs driving the CSC machinery–rather than focusing on incidental markers that delineate CSCs–may have therapeutic potential. Eliminating CSCs may ultimately be necessary to cure certain cancers, but disrupting the molecular CSC machinery may be able to manage the CSC population in the interim.in vitro and in vivo efficacy with no apparent toxicity (unpublished data). As therapies targeting CSCs emerge The work presented here provides strong rationale for therapeutically targeting RhoC. RhoC was previously shown to be essential for metastasis www.cdna.org). RhoC shRNA and the scrambled control plasmid came from Origene and were obtained through the University of Michigan shRNA core facility. The shRNA sequence used was 5′-CCGTCCCTACTGTCTTTGAGAA-3′. shRNA was expressed off of either the pSM2c  or pGIPZ  plasmids.The constitutively active RhoC expression plasmid (RhoC G14V in pcDNA3.1) was purchased from the Missouri S&T cDNA Resource Center . Nucleofected cells were selected for and maintained using the appropriate antibiotic  and grown as pooled populations of nucleofected cells.4 cells/cm2 on top of the Matrigel coating. Cell media containing 4% Matrigel was then added and the cells were cultured for 6–10 days before imaging.Cell lines were cultured as previously described by Lee et al. http://www.imagescience.org/meijering/software/mtrackj/) for ImageJ DIC time-lapse videos were captured at 37°C using a Deltavision RT Live Cell Imaging System and acquired using SoftWoRx 3.5.1 software. Cells were cultured in 2D on collagen-coated chamber slides, and images were taken every 10 minutes for 18 hours. The velocities of individual cells were tracked manually using the MTrackJ plugin . After sorting into ALDH (+) and (−) populations, cells were diluted 1∶1 with Matrigel (BD Biosciences). NOD/SCID mice were anesthetized, the mammary fat pad was exposed, and the mice were injected with either 50 (SUM149 and variants) or 5000 (MCF-10A and variants) cells directly into the fourth mammary gland. Tumors were monitored weekly and mice were euthanized once tumor volume approached 2 cmFresh and formalin-fixed, paraffin-embedded breast cancer tissue blocks were obtained from the Department of Pathology files at the University of Michigan Medical Center. Ethics approval was obtained from the Institutional Review Board at the University of Michigan. Written consent was obtained from all patients, and diagnoses were confirmed by morphology. After pathological review, a tissue microarray was constructed from the most representative area using the methodology of Nocito et al. Triple immunofluorescence staining was performed as previously described All p-values were calculated by Student’s two-tailed t-test unless otherwise noted. Expression levels of ALDH1 and RhoC in TMA samples were compared using Spearman’s rank coefficient.Methods S1Supplemental materials and methods.(DOC)Click here for additional data file.


The aim of this study was to assess whether arterial stiffness increases in the second decade of life.Pathological studies have shown that atherosclerotic disease begins in early childhood. Furthermore, associations between risk factors and early atherosclerotic disease are evident in this population and therefore may be linked to future symptomatic coronary events.While we have shown that ageing preferentially stiffens the proximal aorta in an adult population, it is uncertain whether this also occurs in an adolescent cohort.2) were undertaken at 3 locations: the ascending (AA), proximal descending (PDA) and distal descending aorta (DDA). Arterial stiffness was evaluated by aortic distensibility [/].The evaluation of arterial stiffness was performed by gated magnetic resonance imaging  TrueFISP (fast imaging with steady state free precession) cine sequence with off-line analysis . Cross-sectional measurements of aortic areas (mmTen paediatric (10.6±2.3 years) and ten young adults (20.3±0.5 years) underwent cardiovascular magnetic resonance assessment of arterial stiffness.This is the first study to demonstrate a similar pattern of heterogenous stiffening occurring as early as the 2nd decade. These results must be interpreted in the context of this study being cross sectional in nature and involving only a small number of subjects. Nonetheless these findings warrant further assessment through a prospective, longitudinal study to further evaluate this parameter as a potential unique early marker of future cardiovascular events.


In natural systems, selection acts on both protein sequence and expression level, but it is unclear how selection integrates over these two dimensions. We recently developed the EMPIRIC approach to systematically determine the fitness effects of all possible point mutants for important regions of essential genes in yeast. Here, we systematically investigated the fitness effects of point mutations in a putative substrate binding loop of yeast Hsp90 (Hsp82) over a broad range of expression strengths. Negative epistasis between reduced expression strength and amino acid substitutions was common, and the endogenous expression strength frequently obscured mutant defects. By analyzing fitness effects at varied expression strengths, we were able to uncover all mutant effects on function. The majority of mutants caused partial functional defects, consistent with this region of Hsp90 contributing to a mutation sensitive and critical process. These results demonstrate that important functional regions of proteins can tolerate mutational defects without experimentally observable impacts on fitness. Changes in protein sequence or expression strength can both lead to adaptation in natural systems. While many studies have focused individually on either expression strength or protein sequence, in principle the fitness effects of these two protein properties are interdependent. We systematically investigated the fitness effects of both expression strength and protein sequence for the yeast Hsp90 gene (Hsp82). We analyzed the fitness effects of all possible point mutations in a putative substrate binding loop under seven different expression strengths. The fitness effects of amino acid substitutions were strongly dependent on expression strength. Many point mutations exhibited fitness defects at reduced expression strength that were hidden at the natural expression strength. Revealing these hidden mutant defects suggested that this region of Hsp90 contributes to a rate-limiting step in function, consistent with its putative role in substrate binding. This study is important because it indicates that critical regions in proteins are more prevalent than would be estimated based on experimental fitness analyses performed at natural expression strengths. As hidden fitness effects are likely to occur in other systems, these findings have broad implications for the field of experimental evolution. Genetic changes that alter protein sequence or expression level can lead to adaptation, suggesting these protein properties are central to evolutionary processes. Many studies have individually investigated the effects of changes to either protein sequence or expression level. For example, protein sequences have been optimized under selective pressure using in vitro evolution We recently developed an approach termed EMPIRIC Bi-modal DFEs are consistent with a recently proposed model where the impacts of mutations on protein stability have a dominant impact on fitness −7 for yeast The effects of mutations on protein function can be investigated based on fitness effects; however, fitness effects need not correspond directly to functional effects. For example, many essential proteins can be dramatically reduced in net function (defined here as the product of expression level and function per molecule) without dramatic reductions of fitness We searched for hidden fitness effects in Hsp90 by examining the Hsp90 elasticity function. We varied the expression level of the native protein sequence and monitored effects on yeast growth rate. Determining theHsp90 elasticity function enabled us to estimate mutant effects on per molecule function from fitness measurements. The elasticity function was non-linear such that at the endogenous expression level, mutant defects up to 79% in per molecule function were hidden to experimental fitness analyses. To reveal potentially hidden functional defects of mutants, we repeated EMPIRIC analyses at reduced expression strengths, which systematically varied fitness sensitivity to amino acid substitutions in Hsp90. Using this approach, we were able to construct a full distribution of mutant effects on function for a region of Hsp90. Structural analyses suggest that the region we chose to analyze is a putative substrate binding loop While our initial EMPIRIC study 2 = 0.96) in a full experimental repeat  without major impacts on growth rate, which is consistent with previous reports The non-linear elasticity function for Hsp90 describes the coupling of mutant effects on function and fitness. For example, when expressed at endogenous levels, an Hsp90 amino acid substitution would need to reduce per molecule function by 79% in order to result in a readily measureable growth defect of 5%. Thus the bimodal DFE that we observe for Hsp90  does notTo reveal the latent function of Hsp90 mutants, we analyze fitness effects at reduced expression strengths . The popThe DFEs that we observed exhibited a consistent trend as expression strength was reduced. At high expression strength, the majority of mutants had WT-like growth rates, with very few mutants of intermediate effect. As expression strength was reduced, the WT-like peak decreased and the prevalence of mutants with intermediate effects increased. In terms of epistasis, the fitness effects of amino acid substitutions displayed pervasive negative epistasis with expression strength (Supplementary 2 = 0.75) , but once broken destroy activity The distribution of functional effects  for the Our fitness-based estimates of mutant effects on function integrate over all properties that contribute to cell growth including catalysis, binding affinity, as well as the thermodynamic stability of folding to the native state To further examine the effect of mutations on stability, we simulated the stability effects of each possible point mutation based on the structure of Hsp90 To further test our model and conclusions, we experimentally investigated the biochemical properties of five non-conservative amino acid substitutions. We chose mutations that dramatically change the hydrophobic binding surface and largely destroy function (F583D and W585D), mutations that disrupt intra-molecular interactions and severely impair function , and a charge reversal mutation (E590K) on the surface that causes a moderate functional defect. The growth rate of these mutants in monoculture closely matched the fitness effects observed in the bulk competitions  spectra of all five mutant proteins overlay closely with WT  indicatiE. coli for β-galactosidaseE. coli, DHFR point mutations were commonly observed to impact protein degradation rates leading to fitness effects that were strongly dependent on the level of protein quality control Our studies as well as those of others This study clearly demonstrates that functional defects of mutants can be hidden to experimental fitness measurements due to a non-linear elasticity function. Uncovering these latent effects revealed that the region of Hsp90 we analyzed contributes to a rate-limiting step in Hsp90 function. These findings indicate that critical functional regions in proteins are more prevalent than considered based on fitness analyses performed without consideration of the elasticity function. The elasticity function relating net function and fitness is critical for a thorough understanding of mutant fitness effects.GGHHHHHHGGH) at the N-terminus to facilitate detection by Western blotting. Point mutant libraries previously generated in p417 plasmids For expression analysis, the yeast Hsp90 gene was cloned into the pRS414 plasmid with different promoters and 3′ untranslated region (UTR). We used constitutive promoters previously demonstrated to generate a wide variation in expression level 600∼0.8). Cells were collected by centrifugation, washed with synthetic dextrose (SD) –W media, and then grown in SD-W medium at 30°C in an orbital shaker. Culture density was maintained in log phase (OD600 between 0.1 and 0.8) by periodic dilution. Culture growth was monitored based on increases in OD600 taking into account cumulative dilution. The log of OD600 versus time was fit to a linear equation to determine growth rate. Analyses were performed on time points in dextrose where control cells lacking a rescue Hsp90 had depleted Hsp90 by Western analyses  plates lacking tryptophan (-W). Single colonies were then grown in liquid SRGal-W on a rotator at 30°C to late-log phase , followed by addition of SDS to 2%. Lysed cells were centrifuged at 18,000 g for 1 minute to remove debris, and the protein concentration of the supernatants was determined using a BCA assay (Pierce Inc.). Lysates with 15 µg of cell protein were resolved by SDS-PAGE, transferred to a PVDF membrane, and Hsp90 probed using α-HisG antibody (Invitrogen Inc.). Importantly, we have previously shown that detection of this 6×His Hsp90 construct in yeast can be detected with a broad linear range using this antibody and Western blot approach To analyze expression levels of different promoter constructs, cells were grown for 19 hours in SD -W media, and 106 cells/ml). Each strain was then further diluted 1∶50 in SD-W media for 48 hours at 30°C with dilution every 12 hrs in order to maintain cells in log phase growth. Samples of cells were collected after 19, 36, and 48 hours in dextrose. Collected cells were washed twice in wash buffer , diluted to 107cells/ml in wash buffer and analyzed on a Becton-Dickinson FACSCalibur flow cytometer equipped with a 15 mW air cooled 488 nm argon-ion laser using a 530 nm high-pass filter. Greater than 100,000 cells were analyzed for each sample. Data were processed and analyzed using FlowJo software. Debris including clumped cells was excluded by gating on the forward and side scatter (excluded less than 5% of points). To compare with bulk Western measurements, mean fluorescence was calculated using cells without GFP in order to subtract out background due to autofluorescence.Flow cytometry was used as an alternative approach to measure the expression level of Hsp90 at the single cell level in yeast cells. A gene encoding EGFP was inserted into the unstructured tail of Hsp90 after amino acid position 684. This Hsp90-GFP fusion construct was cloned into the variable strength promoter constructs used with non-GFP tagged Hsp90. These plasmids were transformed into DBY288 yeast competent cells and grown on SRGal-W plates. A single colony of each strain was grown for two days at 30°C in SRGal-W media to near saturation. These cultures were diluted 1∶50 into SRGal-W media and grown to late log phase  as previously described 7 high quality reads  were obtained and analyzed. The relative abundance of each point mutant at each time point for each promoter was tabulated. Effective selection coefficients for yeast growth were determined by linear fits to the change in mutant abundance relative to wild type for each possible codon substitution. To account for the rapid depletion of null-like mutants to noise levels, only the first three timepoints in selection were used to determine effective selection coefficients for stop codons and all other mutants with effective selection coefficients within two standard deviations of stop codons . Because these null and near-null mutants rapidly deplete from the culture it is challenging to precisely measure their relative growth effects and they were binned as “null-like” (Supplementary s>−0.1) synonymous codons exhibit a narrow distribution , or estimated from the observed growth rate and the elasticity function for weak promoter constructs where experimental measures of expression were noisier . Where growth rates prohibited accurate estimation of fitness , bounds on relative per molecule function were calculated  and is appropriate for interpreting the main features of the distribution of mutant effects on function. Of note, the mutations that we observe to improve function at reduced Hsp90 expression levels  may be aThe elasticity function does not include a cost of expression and as such has a maximum fitness at infinite expression level. Thus, we assume that expression cost is negligible relative to expression benefit over the range of our analyses. As the expression cost of native proteins is below experimental detection in yeast We infer differences in cellular growth rates from measurements of DNA abundance. This inference is valid if DNA and cellular abundance are coupled. In previous work, we demonstrated that EMPIRIC measurements of fitness based on measures of plasmid abundance correlate strongly with cellular growth rates for a large set of mutants Figure S1Hsp90 shutoff strain. (A) The DBY288 strain grows robustly in dextrose when provided with a rescue plasmid that constitutively expresses Hsp90, but stalls in growth with a null-rescue plasmid. (B) Expression level of Hsp90 in DBY288 is near-endogenous in media with galactose , but below Western blot detection after 19 hours in dextrose media (SD).(EPS)Click here for additional data file.Figure S2Correlation between effective selection coefficients measured in a full experimental repeat at endogenous expression level. Strongly deleterious mutants rapidly deplete in bulk competition and are not monitored as precisely as more fit mutants. Relative selection coefficients are strongly reproduced in these full experimental repeats performed on different days. The slope of this correlation is 1.7, likely due to a linear influence from estimates of the WT growth rate in these separate experiments.(EPS)Click here for additional data file.Figure S3Hsp90-GFP fusions. (A) Growth rate supported by Hsp90-GFP fusion in shutoff yeast. (B) Comparison of growth rates observed with and without GFP fusion. (C) GFP levels observed by flow cytometry. To estimate bulk expression comparable to the Western analyses in (EPS)Click here for additional data file.Figure S4Population management during bulk competitions. (A) Outline of experiment from transformation through selection. For all steps, the smallest population bottleneck is indicated. These bottlenecks were managed so that they were always in gross excess to the diversity of engineered mutations (64 codons × 9 positions = 576). (B) Population management during selective growth competition (after 12 hours in dextrose). Dashed lines represent dilutions to maintain cells in logarithmic growth, and arrows at the top indicate when samples were harvested for sequencing.(EPS)Click here for additional data file.Figure S5Expression of Hsp90-GFP fusions as a function of time in shutoff conditions. (A) GFP levels observed by FACS after 19 hours (grey filled), 36 hours (green dashed line), or 48 hours (red dashed lines) in dextrose for each promoter strength construct. To estimate the bulk expression level, mean fluorescence was calculated and corrected for the autofluorescence of cells lacking GFP. While the observed distribution of expression level among populations of cells was complex and will be interesting to examine in future studies, straightforward analyses of the population mean provided useful estimates for this study. (B) The fold increase relative to the 19 hour time point for each construct was plotted and fit to a linear model.(EPS)Click here for additional data file.Figure S6Influence of time in dextrose on growth rates. (A) Shutoff yeast harboring rescue plasmids with the WT Hsp90 coding sequence under different promoter strengths were monitored after 26 or 50 hours in dextrose. The TEF and TEFΔter constructs exhibit less than a 5% change in observed growth rate, consistent with the robust growth observed for these constructs immediately after selection begins in dextrose. (B) Relative growth rate of strains exhibiting growth defects upon selection in dextrose. Growth estimates at 50 hours were estimated from the growth rate at 26 hours, the elasticity function, and the observed increase in expression of Hsp90-GFP fusions.(EPS)Click here for additional data file.Figure S70 = 0.016 - calculated from observed growth rate and elasticity function, Em = 0.014 - from the elasticity function, τ = 0.11 hr−1 - based on flow cytometry of Hsp90-GFP fusions over time, Gmax = 0.45 hr−1 - based on observed growth curves with the GPD construct). Simulations were performed for other constructs and exhibited less variation between CE and LIE models, indicating that the model is most sensitive to changes in E around Em. (C) Comparison of the slope of linear fits to the plots in panel B. (D) Representative data for competition trajectories of mutants in the CYCΔter constructs with linear fits.Models of time-dependent changes in expression level. (A) Mathematical descriptions of numerical integration models relating abundance to function per molecule and expression level. (B) Trajectories of theoretical competitions of mutants with different impacts on function and WT. Trajectories simulations use Dt of 0.1 hours and growth and expression parameters from the CYCΔter construct (E(EPS)Click here for additional data file.Figure S8Epistasis between expression strength and amino acid substitutions. Fitness effects of point mutants under reduced expression strength compared to endogenous expression strength. Results observed for Hsp90 mutants with varied promoters with and without terminater sequences in the 3′ UTR: (A) TEF promoter, (B) TEF promoter without a terminater, (C) CYC promoter, (D) CYC promoter without a terminater, (E) ADH promoter, and (F) ADH promoter without a terminater.(EPS)Click here for additional data file.Figure S9Mutant effects on function. (A) Cross-correlation between function estimates generated at different expression-strengths. Comparisons were made between non bounded estimates of function in constructs that neighbored in expression strength  (B&C) Functional estimates for positions oriented towards the protein interior or exterior. Distribution of mutant effects on function for positions 582, 583, and 585 that are oriented towards solvent (B), compared to positions  that are oriented towards the protein core (C).(EPS)Click here for additional data file.Figure S10Monoculture growth of individual mutants. Individual mutants were generated in the GPD construct, introduced into DBY288 yeast, and growth observed after switching to shutoff conditions. We chose to analyze mutants with large differences in effective selection coefficients from the bulk competitions. Both mutants (W585D and A587D) with null-like EMPIRIC selection coefficients exhibited dramatic growth defects, while both mutants (E590K and F583D) with WT-like effective selection coefficients exhibited robust growth in monoculture. The S586H mutant that had an intermediate fitness defect in the bulk competitions also exhibited an intermediate growth defect in monoculture. For this data, the Spearman's rank correlation was −0.99 using identical EMPIRIC rankings for WT-like variants  as well as for null-like variants (W585D and A587D). The negative sign in the correlation indicates that selection coefficients and growth are inversely related by definition.(EPS)Click here for additional data file.Figure S11Effects of synonymous substitutions. For all mutations that persisted in the bulk competitions (s>−0.1), we calculated synonym variance as the difference between the effective selection coefficient for each codon and the average of all synonyms encoding the same amino acid.(EPS)Click here for additional data file.Table S1Expression level measurements.(DOCX)Click here for additional data file.Table S2Apparent selection coefficients for Hsp90 amino acid substitutions under different promoter strengths.(XLSX)Click here for additional data file.Table S3Effects of Hsp90 mutants on function.(XLSX)Click here for additional data file.Table S4Sequencing counts.(XLSX)Click here for additional data file.


Propionibacterium). In one NICU, we also detected fecal coliform bacteria  in a high proportion of the surface samples. Comparison of these NICU-derived sequences to previously published high-throughput 16S rRNA amplicon studies of other indoor environments , as well as human- and soil-associated environments, found the majority of the NICU samples to be similar to typical building surface and air samples, with the notable exception of the IHEs which were dominated by Enterobacteriaceae. Our findings provide evidence that NICU IHEs harbor a high diversity of human-associated bacteria and demonstrate the potential utility of molecular methods for identifying and tracking bacterial diversity in NICUs.Infants in Neonatal Intensive Care Units (NICUs) are particularly susceptible to opportunistic infection. Infected infants have high mortality rates, and survivors often suffer life-long neurological disorders. The causes of many NICU infections go undiagnosed, and there is debate as to the importance of inanimate hospital environments (IHEs) in the spread of infections. We used culture-independent next-generation sequencing to survey bacterial diversity in two San Diego NICUs and to track the sources of microbes in these environments. Thirty IHE samples were collected from two Level-Three NICU facilities. We extracted DNA from these samples and amplified the bacterial small subunit (16S) ribosomal RNA gene sequence using ‘universal’ barcoded primers. The purified PCR products were pooled into a single reaction for pyrosequencing, and the data were analyzed using QIIME. On average, we detected 93+/−39 (mean +/− standard deviation) bacterial genera per sample in NICU IHEs. Many of the bacterial genera included known opportunistic pathogens, and many were skin-associated (e.g.,  This study found high infection rates, high mortality rates, and significantly increased risks of developing severe neuro-developmental disorders among infection survivors et al. found that 65% (3932/6093) of ELBW infants contracted at least one infection during hospitalization; 38% had sepsis; 27% died from a hospital infection; surviving infected ELBW infants were significantly more likely than surviving uninfected infants to have severe neuro-developmental disorders; and 25% of ELBW infants had clinical manifestations of bacterial infection but were negative for culture growth.Low Birth Weight and Extremely Low Birth Weight (ELBW) infants in Neonatal Intensive Care Units (NICUs) are typically immunocompromised, and therefore susceptible to hospital-acquired infections Mycobacteria), or samples were poorly handled.One of the biggest difficulties in preventing NICU and Hospital-Acquired Infections (HAIs) is understanding the sources of the infectious agents and the routes of transmission. While culture-based methods are used to identify many infectious agents post-infection, it is not feasible to detect these a priori using culture-based techniques. The infectious bacteria may come from many different sources , and even if it were possible to culture samples from all of these sources, culturing may still fail if growth conditions are not known, because a particular microbe grows very slowly  and one collection time in February 2009 (NICU2).  −1 lysozyme, and 0.2 µm filtered sterile water . Samples were incubated in a 37°C water bath for thirty minutes. Next, Proteinase K  and AL Buffer  were added to the tubes and gently mixed. Samples were incubated in a 70°C water bath for 10 min. All samples were subjected to purification using the DNeasy Tissue Kit. Following extraction, the DNA was quantified using a NanoDrop ND-1000 Spectrophotometer .Prior to DNA extraction, the cotton from the swab was removed using a flame-sterilized razor blade and the cotton threads were deposited into a lysozyme reaction mixture. The DNA extractions for all 30 swabs from surfaces in both NICUs and two negative controls were conducted at the same time. The reaction mixture had a total volume of 200 µl and included the following final concentrations: 20 M Tris, 2 mM EDTA (pH 8.0), 1.2% P40 detergent, 20 mg ml−1) of sample DNA as template, each deoxynucleoside triphosphate at 400 µM, 1.65 mM MgCl2, 5 µl 10× buffer , each primer at 1 µM, and 1 µl of REDTAQ™ DNA polymerase . Thirty cycles of PCR amplification were performed for the environmental swab samples. We used the lowest number of cycles that yielded a visible band on an agarose gel in order to minimize differential amplification of sequences and production of chimeric sequences. All PCR cycles included an initial denaturation step at 94°C for 1 min, an annealing step at 55°C for 45 sec and an extension step at 72°C for 1.5 min. The amplification cycles were preceded by a one-time denaturing step at 94°C for 5 min prior to the first cycle and included a final 72°C extension for 10 min to ensure complete extension.PCR reactions were performed in small lots (six plus positive and negative extraction and PCR controls) to reduce the possibility of laboratory contamination. Barcoded PCR amplification was performed with the widely used 27F and 338R 16S rRNA “universal” bacterial primers. The primers flank the highly variable V2 region of the 16S rRNA gene sequence that is taxonomically informative across most of the bacteria. The barcodes allowed us to pool the PCR products from all samples into one 454 sequencing run. The forward primer constructs consist of a short adapter sequence necessary for the pyrosequencing reaction, the unique 12-base DNA “barcode” encoded with Golay codes −5 moles/µL (50 µL volume) and were then pooled with a total combined concentration of 2×10−5 moles/µL . PCR purification, dilutions and pyrosequencing on a 454 Life Sciences FLX Genome Sequencer were all conducted by the core facility at the University of South Carolina .Individual barcoded PCR products were purified using the AMPure purification kit  following the manufacturer’s protocol. After Ampure purification every sample was quantified on an Agilent 2100 Bioanalyzer. All samples were diluted down to 2×10www.microbio.me/qiime), QIIME The barcoded pyrosequencing data was analyzed using the QIIME database  using a closed-reference OTU picking protocol at the 97% sequencing identity level using UCLUST As part of the QIIME database processing, a metaanalysis was performed to compare our two NICUs to data obtained in five previously published studies: office workspaces in three major cities All sample metadata is providing in Supplementary DNA extractions from all surface samples contained measurable quantities of bacterial DNA (4–10 ng/µl): negative extraction controls had no quantifiable DNA. All samples produced visible PCR products, and the negative PCR and DNA extraction controls were blank. The pyrosequencing of 30 surface swab samples yielded a total of 321,000 sequences with an average of 245bp (75.9 Mb of data). There were approximately 193,546 sequences after removal of the low quality reads and closed-reference OTU picking. Approximately 37,384 sequences were obtained from NICU1, averaging 2200 reads per surface, while 156,162 sequences were obtained from NICU2, averaging 12,012 reads per surface.Enterobacter, Neisseria, Pseudomonas, Streptococcus, and Staphylococcus were found abundantly in both NICUs  of the pairwise weighted UniFrac distances between samples in the NICUs found that nine of the NICU1 surfaces were easily separable from the rest of the surface samples from both NICU1 and NICU2  commonly inhabit the digestive tract and can be found abundantly in feces. E. coli, Klebsiella and Enterobacter in particular are very well known hospital ICU pathogens that appear to spread and proliferate quite easily in hospitals Propionibacterium, which was one of the most common in both NICUs. We also found considerable proportions of Corynebacterium, Lactobacillus, Staphylococcus, and Streptococcus, all of which are very common on hand surfaces A closer inspection of the microbial diversity in the divergent NICU1 samples indicated that an excess of Enterobacteriaceae sequences was responsible for the divergence of these samples . MembersAccording to Flores et al. (2011), some restroom samples were either dominated by gut- , vagina- and soil-associated bacteria, while others were dominated by skin-associated bacteria. The majority of the office surfaces contained both skin and soil-associated bacteria Our data provide evidence that NICUs harbor pools of diverse bacteria, that NICU diversity is similar to other indoor surface environments, and that human skin is a primary contributor indicating that hand transfer (touch) can move organisms through such settings. Future work in hospitals should attempt to integrate these molecular methods with long-term assessment of surface bacterial diversity and infection rates over time and record the cleaning schedule to investigate the rate at which skin microbes colonize IHEs.One clear limitation to our study is that we cannot determine which of the microbes we identified were viable. Non-viable organisms cannot directly cause infection, although they may still contribute antibiotic resistance genes to the wider bacterial community. Numerous previous studies have shown the potential ability of many of the microbes detected by molecular methods to be viable, and it is generally easy to grow microbes from any given indoor surface. Setting aside the futility of trying to cultivate dozens or hundreds of different microbes from even a single sample, future work in this area would benefit from the combination of molecular and cultivation assays, increasingly rapid sequencing technologies, and perhaps the addition of molecular assays that simultaneously determine diversity and viability.A drawback of this approach is the lack of taxonomic resolution at the strain level, which can be problematic for differentiating pathogens from their non-pathogenic close relatives. Short reads of the 16S rRNA obtained (as obtained with recent sequencing technologies) are effective at providing broad, genus-level characterization of microbial communities. Longer reads or different marker genes that provide strain-level resolution for taxonomic groups of interest  will likely be necessary to accurately detect the presence of specific pathogenic bacterial species and strains.As these high-throughput methods become cheaper and easier, and as the associated bioinformatics becomes more accessible, techniques such as those described here could be routinely applied in detecting or monitoring the spread of bacteria in NICUs. By detecting departures from “typical” NICU bacterial diversity, an early warning system for infectious agents could be developed. To achieve these goals, more data (including NICU surface time-series data) will need to be gathered to understand what normal bacterial diversity and temporal variability looks like on NICU surfaces. This information is essential to accurately identifying deviations from normality.Table S1Raw sequence counts of bacterial genera found on NICU1 surfaces. Identifications were made using the Ribosomal Database Project Classifier . Genera with less than a combined total of 50 sequence matches were excluded from the table. Genera containing known opportunistic pathogens are highlighted in boldface.(DOC)Click here for additional data file.Table S2Raw sequence counts of bacterial genera found on NICU 2 surfaces. Genera containing known opportunistic pathogens are highlighted in boldface.(DOC)Click here for additional data file.Table S3QIIME-compatible sample metadata mapping file.(TXT)Click here for additional data file.Table S4Supplementary Methods.(DOCX)Click here for additional data file.Table S5QIIME-compatible text-formatted OTU table.(GZ)Click here for additional data file.Table S6QIIME-compatible biom–formatted OTU table.(GZ)Click here for additional data file.


Solenopsis invicta, newly-mated queens may start new colonies either individually (haplometrosis) or in groups (pleometrosis). However, only one queen (the “winner”) in pleometrotic associations survives and takes the lead of the young colony while the others (the “losers”) are executed. Thus, colony founding in fire ants provides an excellent system in which to examine the genes underpinning cooperative behavior and how the social environment shapes the expression of these genes. We developed a new whole genome microarray platform for S. invicta to characterize the gene expression patterns associated with colony founding behavior. First, we compared haplometrotic queens, pleometrotic winners and pleometrotic losers. Second, we manipulated pleometrotic couples in order to switch or maintain the social ranks of the two cofoundresses. Haplometrotic and pleometrotic queens differed in the expression of genes involved in stress response, aging, immunity, reproduction and lipid biosynthesis. Smaller sets of genes were differentially expressed between winners and losers. In the second experiment, switching social rank had a much greater impact on gene expression patterns than the initial/final rank. Expression differences for several candidate genes involved in key biological processes were confirmed using qRT-PCR. Our findings indicate that, in S. invicta, social environment plays a major role in the determination of the patterns of gene expression, while the queen's physiological state is secondary. These results highlight the powerful influence of social environment on regulation of the genomic state, physiology and ultimately, social behavior of animals.One of the fundamental questions in biology is how cooperative and altruistic behaviors evolved. The majority of studies seeking to identify the genes regulating these behaviors have been performed in systems where behavioral and physiological differences are relatively fixed, such as in the honey bee. During colony founding in the monogyne (one queen per colony) social form of the fire ant  Solenopsis invicta is a complex process that serves as a useful model for investigating how the interplay between genes and social environment shapes social behavior. Unrelated, newly mated S. invicta queens may start a new colony as a group, but ultimately only one queen will survive and gain full reproductive dominance. By uncovering the genetic basis for founding behavior in fire ants we therefore provide useful insights into how cooperative behavior evolved in a context that might be considered primitively eusocial, because newly mated queens in a founding association are morphologically, physiologically and genetically very similar and display no evident division of labor. Our results suggest that social environment  is a much greater driver of gene expression changes than social rank itself, suggesting that social environment, and not reproductive state, is a key regulator of gene expression, physiology and ultimately, behavior.The characterization of the genomic basis for complex behaviors is one of the major goals of biological research. The genomic state of an individual results from the interplay between its internal condition (the “nature”) and the external environment (the “nurture”), which may include the social environment. Colony founding in the fire ant  Behavior is a complex phenotypic trait, which results from the interactions of multiple intrinsic and extrinsic factors that associate in a nonlinear, often unpredictable fashion Acromyrmex versicolorPogonomyrmex californicusVariation in colony founding among ant queens is an ideal model to examine the interplay between genes and social environment that has shaped the evolution of cooperative behavior in primitively social systems. Colony founding can occur in two modalities: haplometrosis, where a single queen independently starts a new colony, and pleometrosis, where multiple queens associate and cooperate to start a new colony Solenopsis invicta is an excellent system for studying the genes associated with haplometrotic and pleometrotic behaviors, because queens from the monogyne social form (characterized by a single egg-laying queen per nest once established) can adopt either approach for colony founding depending on multiple factors, e.g. the density of newly mated queens in nesting sites The red imported fire ant We performed two separate experiments to characterize the genomic basis for haplometrotic and pleometrotic founding behavior in fire ants. We developed a microarray platform using the official gene set of the fire ant genome Haplometrotic queens (haplo) and paired pleometrotic winners (win) and losers (los) were collected shortly after emergence of the first workers . Microarray analysis of gene expression patterns  in whole bodies of these queens revealed that 4080 of the 9388 transcripts included in the analysis were differentially regulated at FDR<0.001 . A princDrosophila orthologs with FlyBase annotations using DAVID We performed Gene Ontology analysis on the 3003 differentially regulated transcripts  that have Drosophila orthologs with FlyBase annotations) that were differentially regulated between both haplo vs. win and haplo vs. los , we examined the overlapping set of 3192 transcripts , which is involved in the Toll signaling pathway during the response against Gram positive bacteria endosulfine (endos), which functions in the insulin-signaling pathway during oogenesis Pheromone-binding protein-related protein 3 (Pbprp3), a member of the odorant binding proteins responsible for chemoreception bubblegum (bgm), which is involved in the metabolism of very long-chain fatty acids and prevents neurodegeneration I'm not dead yet (Indy), associated with aging pale (ple), which plays a role in the response to wounding desat1, a major regulator of cuticular hydrocarbon biosynthesis involved in pheromone emission and detection juvenile hormone acid methyltransferase (jhamt), a key enzyme in the biosynthesis of JH, the major endocrine regulator in insects Interestingly, only 43 transcripts were differentially regulated between win and los queens. A GO analysis performed on this small set of transcripts revealed that fatty acid and hormone metabolic processes were significantly enriched GO terms  and target of rapamycin (TOR), two major players in the insulin-signaling pathway which is associated to caste determination in honey bees Peroxiredoxin 5037 (Prx3), which is associated with enhanced learning ability when expressed at higher levels in honey bee workers GO categories related to aging and longevity were significantly enriched in sets of transcripts that were differentially regulated in haplo vs. pleo . When we compared downregulated GO terms, we found that 11 were shared between haplo and old flies while 67 were shared between old flies and pleo: the difference was statistically significant (Fisher's Exact Test: P = 0.0029). Most of these 67 overlapping GO terms . Thereafter, we compared the relative proportions of the parent/ancestor immune categories between the two groups , TGF-beta activated kinase 1 (Tak1), immune deficiency (imd), bendless (ben) and Death related ced-3/Nedd2-like protein (Dredd). Furthermore, several members of the Immunoglobulin (IG) Superfamily were differentially regulated, including bent (bt), turtle (tutl), sidekick (sdk) and Down syndrome cell adhesion molecule (Dscam).Next, we examined the expression of the fire ant orthologs of the 177 canonical immune-related genes annotated in honey bees Indy and Sod2 for determination of adult life span. In the arrays, Indy was downregulated in haplo and Sod2 was upregulated in haplo: qRT-PCR analysis confirmed these trends and the difference between groups was statistically significant for both genes (P<0.001). For immune response, we validated Dredd and kay, which were both upregulated in haplo in the arrays: qRT-PCR analysis confirmed this trend and the difference between the two groups of queens was statistically significant for kay (P<0.05) but not for Dredd (P = 0.29). Desat1, ifc and putative fatty acyl-CoA reductase CG5065 were analyzed for their involvement in the synthesis and metabolism of fatty acids. In the arrays, desat1 and putative fatty acyl-CoA reductase CG5065 were downregulated in haplo while ifc was upregulated in haplo: these trends were confirmed by qRT-PCR analysis and the difference in the expression levels was statistically significant for putative fatty acyl-CoA reductase CG5065 (P<0.001) and ifc (P<0.05) but not for desat1 (P = 0.11). Reproductive genes included br and Btk29A, both upregulated in haplo in the arrays: this was confirmed by qRT-PCR (P<0.001). We validated Sema-5c and Mer because they play a role in olfactory behavior: these genes were downregulated in haplo in the arrays and in the qRT-PCR analysis . Finally, we analyzed fru for aggressive behavior and woc for neurogenesis: these genes were upregulated in haplo in the arrays and in the qRT-PCR analysis .We examined gene expression levels of candidate genes that were included in one or more GO terms that were significantly enriched in our GO analyses . ExpressWe further examined the role of social rank on gene expression patterns by manipulating social rank of individuals in pleometrotic pairs. We swapped winners and losers between groups to generate four groups of queens: winners switched to losers (win/los), losers switched to winners (los/win), continuing winners (win/win) and continuing losers (los/los). Very few transcripts were differentially regulated among these groups, with a total of 616 transcripts at a relatively low significant threshold  scully (scu) putative fatty acyl-CoA reductases. Additional genes with significant differences in expression included Coenzyme Q biosynthesis protein 2 (Coq2), which plays a role in the response to pathogens, aging and in the insulin-signaling pathway juvenile hormone acid methyltransferase (jhamt), which was also significantly differentially regulated between win and los in experiment 1 (see above), and radish (rad), which is involved in learning and memory immune response deficient 5 (ird5) Ras-related protein Rac1 (Rac1) Hemolectin (Hml) Argonaute 2 (AGO2) caspar (casp) We performed GO analysis with Functional Annotation Clustering on the 527 differentially regulated transcripts that have tion see . Among t−13). These transcripts corresponded to 80 Drosophila orthologs, which were used to perform a GO analysis using Functional Annotation Clustering: 6 GO terms appeared to be significantly enriched, including lipid metabolic process (P<0.001) and regulation of hormone levels  and in experiment 2 : this is significantly more than expected by chance  strongly influenced expression of thousands of genes, and the difference between pleometrotic winners and losers was much smaller. However, in terms of gene expression differences, pleometrotic winners were more similar to haplometrotic queens, suggesting that reproductive and social rank still does impact, albeit more subtly, gene expression patterns. In the second experiment, we manipulated both the social environment and social rank of queens in pleometrotic pairs. Switching social rank significantly affected gene expression patterns more than the initial or final social rank of the individual. Several categories of genes were differentially regulated among these groups of queens, including genes involved in core processes such as metabolism, aging and immunity.This study demonstrates that social environment greatly influences gene expression in founding queens of the fire ant Polistes wasp. These authors found that 75% of the 2,442 genes differentially expressed between queen and worker phenotypes were novel, having no significant similarity with described sequences. This result supports the hypothesis that novel genes are likely important for eusocial evolution, as previously suggested by other studies Polistes may be associated with novel genes, plasticity in founding behavior in fire ants seems to rely predominantly on more conserved genes.In a recent study Ferreira et al. Differences in gene expression between haplometrotic and pleometrotic queens were likely due to differences in the physiological demands placed on singly- vs. multiply-founding queens and differences in the costs associated with social environment, where pleometrotic queens are more likely to incur in higher levels of stress due to the establishment of social ranks. We found that genes involved in core physiological processes, including metabolism, cellular processes, development, morphogenesis and biosynthesis were significantly differentially regulated between these groups of queens. Haplometrotic queens produce more eggs and lose more weight than pleometrotic queens during the claustral period of colony founding Sod2 and CCS), two Peroxiredoxins (6005 and 5037), Glutathione S transferase S1 and PTEN-induced putative kinase 1 (Pink1), which plays an essential role in maintaining neuronal survival by preventing neurons from undergoing oxidative stress SOD was upregulated in pleo. Interestingly, overexpression of SOD has been correlated to increased organismal longevity in DrosophilaLasius niger, where long-lived queens expressed lower levels of this gene than short-lived males and workers SOD on longevity is highly dependent upon the sex and genetic background Genes associated with stress response were differentially regulated between haplo and pleo queens. Stress tolerance may be achieved by reducing the production of reactive oxidant species  and/or by increasing the antioxidant activity juvenile hormone acid methyltransferase (jhamt), an enzyme that converts inactive precursors of JHs to active JHs juvenile hormone epoxide hydrolase 2 (Jheh2), involved in juvenile hormone catabolic process, were upregulated.The overrepresentation of GO terms associated to biosynthesis and metabolism (in particular those related to lipids) prompted us to look closer at the nutritional state of founding queens. Nutrition is closely linked to fertility and longevity Drosophila melanogaster, identified sets of genes in which upregulated expression either accelerates or decelerates the aging process D. melanogaster cannot be transferred directly to our study system.There is no clear prediction about which group of queens should have a longer life-span. Our analyses show that a large set of aging-related GO terms was upregulated in haplometrotic queens, while a smaller set was upregulated in pleometrotic queens. This result is not sufficient to establish which group of queens is expected to have longer life-span, since ageing is a quantitative trait determined by both environmental and genetic components. Previous studies of the genetics of longevity in kayak, hemipterous, misshapen, anterior open and Cdc42. Hemopoiesis is the process that is responsible for production and differentiation of immune cells serrate and serpent, were upregulated in haplo. Haplo queens may have better constitutive immune responses perhaps because they experience less social stress than pleo queens do: in fact, once initial cooperation transitions into open competition, pleo queens frequently engage in reciprocal aggressions which can lead to serious injuries or death. It is hypothesized that there is a trade-off between reproduction, nutrition and immunity Immune-related genes were overexpressed in haplometrotic vs. pleometrotic queens. Most of the overrepresented immune-related GO terms were associated to cellular immunity: endocytosis, phagocytosis, cell adhesion, apoptosis, cytokinesis, the cascade regulating mitogen-activated protein kinase (MAPKKK) and the c-Jun amino-terminal protein kinase (JNK) cascade. In particular, the JNK pathway controls the rapid up-regulation of cytoskeletal genes in response to infection and plays a major role in wound healing Only 43 transcripts were significantly differentially regulated between winners and losers in couples of pleometrotic queens from experiment 1. Although surprising, this result might be explained by the small phenotypic differences between the two types of queens. Previous studies showed that some phenotypic traits such as head width are weakly correlated with the reproductive investment and survival (hence the rank) of pleometrotic cofoundresses bubblegum, desat1, Dmel_CG17374 and Dmel_CG31522, which function in fatty acid metabolism, were differentially regulated. Long-chain fatty acids are the precursors of cuticular hydrocarbons in insects, which can function as nestmate recognition cues and social pheromones in many insect species  in honey bees desat1 appears to play a role in autophagic responses The two GO terms that were differentially regulated between winners and losers were related to metabolism of lipids and metabolism of hormones. Four transcripts, reviewed . InteresBehavioral manipulation of the social rank in pairs of pleometrotic queens demonstrated that manipulation of social environment  had a much larger effect on gene expression than the initial or final social rank of the individual. Note, however, that all individuals in the study switched social partners, which may have elicited additional (undetected) changes in gene expression. Studies in vertebrates have demonstrated that social interactions and changes in the social environment can be one of the most potent stressors In both experiments, genes involved in lipid biosynthesis and metabolism were differentially regulated, suggesting that these processes play a key role in mediating fire ant founding behavior and foundress associations. Lipids such as cuticular hydrocarbons play a role in advertising the fertility state in many ant species: these compounds are usually more abundant in reproductive queens and egg-laying workers  is more important than the social rank or internal condition of the individual in regulating gene expression patterns, and presumably downstream physiological and behavioral traits. Furthermore, because the process of pleometrotic colony founding in fire ants has all the features of a primitively social system in which morphologically, physiologically, and genetically similar individuals perform cooperative behavior to form social groups of unrelated individuals, this is an excellent model to examine the genes that underlie these social behaviors. We found that several core processes were significantly differentially regulated, including metabolism, stress response, aging, reproductive processes, and immunity. Interestingly, lipid metabolic processes were regulated across experiments; these may play a role in both nutrient storage/mobilization and chemical communication. In the future, it will be interesting to investigate whether the molecular pathways characterized in this study also are operating at earlier stages of the co-founding process . Such studies will help elucidate the mechanisms responsible for the transition from cooperation to conflict in pleometrotic founding queens. Finally, fire ants also display genetically distinct monogyne (colonies headed by a single queen) and polygyne (colonies headed by multiple queens) social forms. It will be of great interest to determine if the same genes that regulate haplometrosis and pleometrosis also are involved in regulating queen number in mature colonies.th, 2010 in a large parking lot  and weighed. Since the area of collection has a high prevalence of monogyne colonies, we expected these queens to belong to the monogyne social form; this was subsequently confirmed by screening 108 queens for social form using Gp-9 as a marker following the protocol as described in A total of 787 fire ant queens were collected immediately after a nuptial mating flight on May 5After eclosion of the first batch of workers (minims), incipient colonies were provided with water, sugar water and frozen crickets. Glass tubes were set open in pencil boxes coated with Fluon to prevent escape. Queens were subsequently monitored daily until it was possible to identify the social rank of the two cofoundresses in pleometrotic couples. Previous studies have found that the initial cooperation between the two cofoundresses turns into conflict after the emergence of minims, resulting in the execution of one queen This assay was performed with 34 couples of pleometrotic queens from the same pool of newly mated queens as experiment 1. The queens were paired and placed in nesting chambers as before. After emergence of minims, queens' behavior was monitored as before. Once the behavioral observation revealed the social rank of the two cofoundresses, queens were weighed again and re-paired with a different partner. We created the following three groups of queens: a) winner+winner (similar weight), b) loser+loser (similar weight), and c) winner+loser (different weights). Again, we monitored the behavior until the social rank of the newly coupled specimens was evident and we collected 4 new behavioral phenotypes in the same way as above: a) winners switched into losers , b) losers switched into winners , c) continuing winners  and d) continuing losers .Individual fire ant queens were thawed and dissected under cold RNAlater  to confirm the mating status: unmated queens were not included in the analysis. Total RNA was extracted using the RNeasy Plus kit (Qiagen) combined with a RNase-Free DNase step (Qiagen) to remove any possible contamination by genomic DNA. Subsequent steps in the microarray analysis were performed at the Penn State Genomic Core Facility. RNA concentration and purity were assessed using NanoDrop and Qubit and RNA quality was assessed using RNA Nano Chips on the Agilent Bioanalyzer. 1 µg of each sample was amplified using the Ambion (Life Technologies) Amino Allyl MessageAmp II aRNA Amplification Kit (AM1753). 15 µg of aRNA were dyed with Cy3 or Cy5  and subsequently purified according to the Ambion Kit instructions. 1.5 µg of a Cy3 labeled sample were combined with 1.5 µg of a Cy5 labeled sample and fragmented using RNA Fragmentation Reagents (Ambion AM8740) according to the manufacturer's instructions. Samples were hybridized with mixing in a MAUI hybridization instrument overnight at 42°C. Arrays were scanned using Axon GenePix 4000B.For the first microarray developed to validate the efficiency of probe sequences, we pooled RNA samples  from different castes, developmental instars and social forms as follows: 3 female alates, 15 workers, 5 larvae and 5 pupae from both monogyne and polygyne social forms and 5 males from monogyne colonies only.ab initio, EST-based, and sequence similarity-based methods The fire ant genome includes an official gene set of 16,569 protein-coding genes that were generated by a combination of We developed and used a first microarray  to validate the probe design and test multiple probes per transcript. On average, we designed 7 probes per transcript for a total of 355,930 probes. Each probe was tested for both the red (Cy5) and the green (Cy3) dyes. For transcripts with only one probe (N = 296), we verified that the probe had acceptable intensities for both dyes. For the other transcripts we examined the performance of the probes with the green dye only, because these showed consistently higher intensity compared to the red dye. Probes were ranked in the follow manner: a) if there were only 2 probes per transcript (N = 230), we selected the one with higher intensity; b) if there were 3 to 6 probes (N = 744), we calculated the ratio “probe intensity/median intensity of all probes for that transcript” and selected the probe with highest ratio if the value was <3, otherwise we selected the probe with the second highest ratio; c) for transcripts with 7 probes , we followed the procedure as in “b” but, in case the probe with the highest ratio was >3, we removed that probe, calculated new ratios and selected a new probe with highest ratio. This procedure allowed us to select the probes with highest intensity that were not outliers.Selected probes were printed in pairs on two 12-plex microarrays . We used a loop design with dye swaps incorporated, allowing us to hybridize 24 RNA samples to each array. For experiment 1 we hybridized 8 haplometrotic queens, 8 pleometrotic winners and 8 pleometrotic losers  and for Any spots with an intensity of less than 300 (the background level on the arrays) were removed from the analyses, as were spots present on less than 20 out of 24 arrays. Expression data were log-transformed and normalized using mixed-model normalization  with the following model:Drosophila orthologs in FlyBase (http://flybase.org/). CateGOrizer Hierarchical clustering, using the Ward method, and principal component analysis (PCA) for global patterns of gene expression were performed in JMP 9.0.2 . We used Genesis 1.7.6  to cluster differentially regulated genes based on average linkage and to perform k-means clustering in experiment 1. Gene Ontology analysis was performed using functional annotation chart/clustering in DAVID version 6 We compared the results from experiment 1 to the following studies:experiment 2 from this study: we overlapped 548 transcripts that were significantly differentially regulated at FDR<0.1 between win and los in experiment 1 to all the significantly differentially regulated transcripts from experiment 2 .aging in Drosophila: we compared directional expressions for all the significantly enriched GO terms obtained from Functional Annotation Chart for experiment 1 and the significantly enriched GO terms that were associated to aging in the fruit fly based on Zhan et al. Drosophila orthologues (FlyBase numbers) to compare fire ant transcripts to the genes of the fruit fly. Statistical significance of the overlap was calculated using a hypergeometric test (http://nemates.org/MA/progs/overlap_stats.html). Selected GO analyses based on study overlap were performed in DAVID as above. In the second study, to test for the significant agreement in the patterns of expression between two studies we performed Fisher's Exact Tests in JMP.We performed overlaps between list of transcripts and GO terms with Venny Indy and Sod2 (determination of adult life span); Dredd and kay (immune response); desat1, ifc and Putative fatty acyl-CoA reductase CG5065 (synthesis and metabolism of fatty acids); br and Btk29A (reproductive functions); Sema-5c and Mer (olfactory behavior); fru (aggressive behavior) and woc (neurogenesis). We used the total RNA extracted from fire ant queens for the microarray analysis and compared gene expression between haplo and los on an ABI Prism 7900 sequence detector . cDNA was made using SuperScript III First-Strand Synthesis System for RT-PCR  and Random Hexamers according to the manufacturer's protocol. The cDNA was then diluted 2(x) with ultra-pure water. Amplification was performed in a 10 µl reaction mixture containing 5 µl of 2× SYBR Green Master Mix , 1 µl of each primer (10 µM) and 2 µl of cDNA at the following conditions: 50°C for 2 min, 95°C for 10 min, 40 cycles of 95°C for 15 sec and 60°C for 1 min, a dissociation step of 95°C for 15 sec and 60°C for 15 sec. We used 8 queens per group: triplicate reactions were performed for each of the samples and averaged for use in statistical analysis. Expression levels of candidate genes were normalized to the geometric mean of two housekeeping genes, Rp-9 and Rp-37http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi) and primer efficiency was first validated using standard curves. Statistical analysis was performed with nonparametric Kruskall-Wallis rank sums in JMP 10 . The data were shown normalized to the haplo group.We examined gene expression levels of the following candidate genes : Indy anFigure S1Transcripts upregulated in haplometrotic queens. Expression patterns of 2280 significantly differentially regulated transcripts grouped in cluster 1 by k-means clustering in Genesis Click here for additional data file.Figure S2Transcripts downregulated in haplometrotic queens. Expression patterns of 912 significantly differentially regulated transcripts grouped in cluster 2 by k-means clustering in Genesis Click here for additional data file.Figure S3Indy and Sod2 (determination of adult life span); Dredd and kay (immune response); desat1, ifc and Putative fatty acyl-CoA reductase CG5065 (synthesis and metabolism of fatty acids); br and Btk29A (reproductive functions); Sema-5c and Mer (olfactory behavior); fru (aggressive behavior) and woc (neurogenesis). Mean expression levels in losers were normalized to levels of expression in haplometrotic queens. Each sample group consisted of 8 individuals; these 8 individuals were also used in the microarray analysis. Statistical analysis was performed with Kruskall-Wallis rank sums: * = P<0.05; ** = P<0.01; *** = P<0.001. #For a better visualization of the results, the bar associated to the gene Putative fatty acyl-CoA reductase CG5065 is not represented in full length in losers: average relative expression for this gene was 14.06 with S.E. ±0.35.Quantitative real-time PCR validation of expression levels of genes of interest. Expression levels of the following genes associated with GO terms of interest were analyzed using quantitative real-time PCR (see (EPS)Click here for additional data file.Figure S4Behavioral observation of pleometrotic couples. Just before the emergence of the first workers, haplometrotic queens and pleometrotic couples were placed in pencil boxes where it was easier to observe queen-queen and queen-workers interactions. As shown in the figure, in pleometrotic couples the winner queen was usually found inside the nest chamber  while the loser was find outside the nest chamber, frequently hiding in order to avoid any contact with the winner or the workers.(TIF)Click here for additional data file.Figure S5Microarray hybridization scheme for experiment 1. For each group of queens, 8 individuals where hybridized in a loop design: 4 individuals were labeled with the Cy3 dye and other 4 with the Cy5 dye. 12-plex array slides with 135,000 probe capacity where designed by Roche NimbleGen, Inc. (Madison WI). haplo = haplometrotic queens; los = pleometrotic losers; win = pleometrotic winners.(EPS)Click here for additional data file.Figure S6Microarray hybridization scheme for experiment 2. For each group of queens, 6 individuals where hybridized in a loop design: 3 individuals were labeled with the Cy3 dye and other 3 with the Cy5 dye. 12-plex array slides with 135,000 probe capacity where designed by Roche NimbleGen, Inc. (Madison WI). win/los = winners switched to losers; win/win = continuing winners; los/win = losers switched to winners; los/los = continuing losers.(EPS)Click here for additional data file.Movie S1Movie clip showing the occurrence of aggressive interactions among cofoundresses after the emergence of workers. Note the difference between pair #9, where pleometrotic queens clearly perform fighting behavior and the two other pairs of cofounding queens, where the interactions are still cooperative.(WMV)Click here for additional data file.Table S1Experiment 1: significantly differentially regulated transcripts (P<0.001).(XLSX)Click here for additional data file.Table S2Experiment 1: significantly enriched GO terms and KEGG pathways .(XLSX)Click here for additional data file.Table S3Experiment 1, output of the cateGOrizer analysis: representation of GO_slim2 ancestor terms.(XLSX)Click here for additional data file.Table S4Experiment 1, k-means clustering, cluster 1 (genes upregulated in haplo): significantly enriched GO terms .(XLSX)Click here for additional data file.Table S5Experiment 1, k-means clustering, cluster 2 (genes downregulated in haplo): significantly enriched GO terms .(XLSX)Click here for additional data file.Table S6Experiment 1: GO terms significantly enriched  and significantly differentially regulated transcripts (P<0.001) between winners and losers.(XLSX)Click here for additional data file.Table S7Drosophila in Zhan et al. Comparative studies: GO terms encompassing significantly downregulated genes that were in common between experiment 1 and old (XLSX)Click here for additional data file.Table S8Experiment 1, cluster 1: significantly enriched KEGG pathways and GO terms that were used in the analyses with the cateGOrizer “Immune system gene classes” .(XLSX)Click here for additional data file.Table S9Experiment 1, cluster 2: significantly enriched KEGG pathways and GO terms that were used in the analyses with the cateGOrizer “Immune system gene classes” .(XLSX)Click here for additional data file.Table S10Quantitative real-time PCR validation of expression levels of genes of interest from experiment 1: gene lists and primers' sequences.(XLSX)Click here for additional data file.Table S11Experiment 2: significantly differentially regulated transcripts (P<0.1).(XLSX)Click here for additional data file.Table S12Experiment 2: significantly enriched GO terms .(XLSX)Click here for additional data file.Table S13Comparative studies: GO analysis of overlapping transcripts that were significantly differentially regulated (P<0.1) between win and los in experiment 1 and experiment 2.(XLSX)Click here for additional data file.