Moving beyond pure transcription networks, more and more networks are being implemented at the level of RNA, taking advantage of mechanisms of translational control and aptamer–small molecule complex formation. Unlike gene expression systems, metabolic components are generally not as interconnectable in any combination, and so engineering of metabolic circuits is a particularly challenging field. Nonetheless, metabolic engineering has immense potential to provide useful biosynthesis tools for biotechnology applications. Finally, although prokaryotes are mostly studied as single cell systems, cell–cell communication networks are now being developed that result in spatial pattern formation in multicellular prokaryote colonies. This represents a crossover with multicellular organisms, showing that prokaryotic systems have the potential to tackle questions traditionally associated with developmental biology. Overall, the current advances in synthetic gene synthesis, ultra-high-throughput DNA sequencing and computation are synergizing to drive synthetic gene network design at an unprecedented pace.Engineering of synthetic gene circuits is a rapidly growing discipline, currently dominated by prokaryotic transcription networks, which can be easily rearranged or rewired to give different output behaviours. In this review, we examine both a rational and a combinatorial design of such networks and discuss progress on using  In 2000, four independent studies hallmarked the beginning of the synthetic biology era, by introducing the first synthetic gene circuits. Three of them were purely transcriptional regulation circuits, based on repression and accompanied by rigorous mathematical modelling . Most ofIn prokaryotes, transcription repressors are self-contained units: single proteins with a specific DNA-binding function that leads to transcription downregulation by blocking the binding of the RNA polymerase to a target promoter. By contrast, transcriptional activation is more complicated as there are at least three distinct mechanisms for it in bacteria . This isincrease because discrete, stochastic expression events can occur, causing output variability. Thus, an important caveat for all synthetic gene network studies is to consider extensive measurement and testing of parameters and variables; unexpected behaviours can arise even in very simple networks.Out of all the possible gene circuits based on repression, the negative feedback loop is the simplest, consisting of a single gene component that downregulates its own production by binding to its promoter . Even thRepressors were also the building blocks of choice in two other pioneering gene circuits: increasing in network constituents, the genetic toggle switch consists of two repressors mutually repressing each other . The resEscherichia coli that exhibited damped oscillations. In contrast to the repressilator, these oscillations were synchronous because, using an isopropyl β-d-1-thiogalactopyranoside (IPTG)-inducible lac repressor, whole bacterial cultures could be reset to different network states. It is worth noting that the system included as part of its architecture the first synthetic prokaryotic-positive feedback loop with a positive regulator: this was based on the Nitrogen Regulator I (NRI) response regulator and an engineered glnAp2 promoter  and developed a model to describe it. The success of the model was double, making it one of the best examples of where the interplay of modelling and experiments takes the researcher much further than either approach on its own. On the one hand, it predicted accurately  the behaviour of the network when it was expanded to include positive feedback, suggesting that characterization of smaller systems can be useful for predicting the behaviour of larger, more complex networks. On the other hand, it revealed unknown aspects of the network, such as the increase of noise in protein expression levels from high copy number plasmids under arrest of cell growth and division.As in the case of the negative feedback loop by Becskei and Serrano, Pseudomonas aeruginosa  had to be coupled to signal amplifiers with a suitable dynamic operating range. A simplified analogy would be to think of it as a buffer or a pH indicator that can be used only for a range of pH values and not for the entire pH scale. Adjusting the strength of the CI-λPRO12, through mutation of the operator sites, resulted in circuits with different dynamic operational ranges. This work neatly exemplifies the crossover between systems biology and synthetic biology, where the quantitative measurements required by the former are enabled by the tinkering approaches of the latter.While following the output of the circuit is rather straightforward in all the aforementioned cases, sometimes the transcriptional differences in gene expression are too small to be monitored directly, even though they are biologically important. A signal-amplifying circuit can solve this problem and has been applied to reveal previously undetectable responses of Rhl quorum-sensing promoters in ruginosa . A commoRM . In fact, this approach led to the development of a new tool for synthetic biology, the tunable intergenic regions . However, for an increasing number of components, there will be an increasing difficulty to find and encompass several separate transcription units. The use of multicistronic operons is an approach that has already been successfully used by the Keasling group , lac operators and RBS of varying strength, but instead of testing each construct separately, they used genetic selection to isolate engineered circuits that confer regulatory and phenotypic behaviour similar to wild-type λ phage.A hybrid combinatorial-directed evolution strategy was followed by lac/LacI/IPTG interactions and equivalent to ‘NOT LacI OR IPTG’) to an inverter (defined by CI/λPRO12). Specifically, LacI was constitutively expressed at high levels from PlacIq, and so in the absence of IPTG, CI would be repressed and EYFP would be expressed. Addition of IPTG would relieve repression by LacI, leading to expression of CI and repression of EYFP. The circuit, however, was not functional due to a mismatch of the two gates. It was initially ‘debugged’ through a rational design with mutations in the RBS of cI and the OR1 operator in λPRO12, which were based on simulations of the circuit. However, as this is a rather ‘labour-intensive’ process, as described by the authors, they decided to attempt to restore the function of the circuit through directed evolution: they used error-prone PCR to amplify the cI gene, generating a library of mutant circuits in which the only variable is the sequence of the cI gene and screened for the desired property. In this way, they isolated mutants that restore circuit function and the mutations actually affect the dimerization of CI more rather than the DNA binding. Thus, directed evolution is a very powerful method with which components of synthetic networks can be fine-tuned to give functional circuits with the desired properties even in more diverse ways than a rational design.Yokobayashi and colleagues introduced a different approach, a method for ‘debugging’ a nonfunctional synthetic circuit, which can also be used in engineering synthetic circuits. The engineered circuit, depicted in Early synthetic gene circuits were limited mainly to the use of transcription factor-responsive promoter combinations. However, as RNA-based bacterial gene expression regulation is gaining more ground , synthetin vivo with small molecules that bind to RNA was first demonstrated for eukaryotes a decade ago  was used for selection of both the ON and the OFF states , minimizing the possibility of false positives. More importantly, the take-home message is that a single aptamer can be used for both positive and negative regulation.An interesting alternative genetic selection procedure is the dual selection used by E. coli to respond to a new stimulus, theophylline taxis to theophylline gradients. It is noteworthy that the specificity of the system is remarkable: caffeine, which is structurally very close to theophylline (caffeine can be considered as 7-methyl-theophylline), fails to activate it.With the RNA aptamer–small molecule strategy, new inputs can be easily implemented in synthetic gene circuits while avoiding some of the obstacles in engineering protein–small molecule interactions. A beautiful network engineering exercise based on this principle is the engineering of the chemotaxis pathway in phylline . AlthougB. subtilis to actually shut down expression of the enzyme glutamine-fructose-6-phosphate amidotransferase in response to its product, glucosamine-6-phophate (Another approach is the use of RNA aptamers that can cleave part of the RNA transcript (aptazymes). Although such a system is used in nature by phophate .A different RNA-based strategy was developed where the 5′-untranslated region of the mRNA is designed so that the nascent transcript forms a stem–loop structure sequestering the RBS and thus inhibiting translation  and thus it is one of the most modular synthetic gene circuits yet described.In contrast to all the aforementioned methods, which were based on a regulatory RNA component, other RNAs can also be used as a component of synthetic gene circuits. An AND gate was recently constructed in E. coli so that the engineered ribosomes translate only their cognate mRNAs without any cross-talk with the natural components; neither can the engineered ribosome translate endogenous mRNA nor can the wild-type ribosome translate engineered mRNA. They further constructed logic AND and OR gates in E. coli using different combinations of orthogonal ribosome–orthogonal mRNA pairs in the same cell  it adds the extra dimensions, both spatial and temporal, for separating circuit components from each other. It is likely that cross-talk between such components will result in some very interesting synthetic circuits in the years to come.E. coli (lac promoter (Plac) and only the two genes encoding the rate-controlling enzymes under the regulation of a circuit engineered to be responsive to the metabolic state. The circuit used the response regulator NRI, which, upon activation by phosphorylation, induces expression of the two genes from a minimal glnAp2 promoter. Coupling the system to metabolism required only deleting the sensor kinase NRII (NtrB), because in its absence NRI becomes responsive (phosphorylated-activated) to acetyl-phosphate (AcP), a small molecule that has been suggested to be an indicator of glucose availability but also able to phosphorylate many response regulators. With this set-up, production was improved threefold not just because there was an increase in the carbon flux to lycopene but because the rechannelling of carbon flux was carried out in a way that did not compromise cell growth .As mentioned in the Introduction, the fourth of the pioneering synthetic prokaryotic circuits coupled gene expression to the metabolic state and was used to improve lycopene production in E. coli . Five enpta) gene; in the absence of pta acetate and AcP, production occurs only through the amino acid biosynthesis pathway and thus becomes proportional to cell growth. Acetate, in the form of acetic acid, diffuses freely across the bacterial membrane into neighbouring cells, where it is phosphorylated to AcP, which in turn phosphorylates NRI and induces GFP expression.The Farmer–Liao circuit was also the basis for the development of a synthetic quorum-sensing circuit  under glnAp2 and as an output plasmid-encoded GFP under Ptac  and CILVA (destabilized CI); the latter represses expression of another allele of LacI. When the AHL concentration is high, induction is strong and LacIM1 levels are enough to shut down expression of the FP. When the AHL concentration is too low, neither LacIM1 nor CILVA is expressed but LacI is, again ensuring there is no FP production. At intermediate concentrations of AHL, the levels of LacIM1 produced are too low to repress the expression of the FP tightly, but the very strong repressor CILVA can repress expression of LacI tightly. Thus, FP can be produced only under these conditions. The dynamic operating range, i.e. the range of AHL concentrations that trigger the system, can be set by tuning the LuxR–AHL interaction; using a mixture of cells that contain a BD circuit with a GFP reporter and cells containing a BD circuit with LuxR expressed from a lower copy number plasmid and a dsRed-Express reporter, a bullseye pattern was generated around a group of sender cells, with a red fluorescent outer rim and a green inner rim. The dynamics of pattern formation and its final form depend on the parameters of the circuit, such as the degradation rate of LacI, but also on the spatial arrangement of the sender cells. Even when only one type of BD circuit is used, different patterns can be generated by placing groups of sender cells appropriately .The Weiss group has been developing the components for just such an approach, step by step, beginning with a bacterial multicellular system for programmed pattern formation , promoters with multiple operators deviated in behaviour from what was expected (Even with a state-of-the art model and considerable biochemical data, biological circuits can be context dependent in an unpredictable way. For instance, even using well-characterized components in expected  of a hybThe discovery and analysis of network motifs in natural gene networks  can greaNonetheless, the designed system will probably need fine-tuning and this can be greatly facilitated by directed evolution. Directed evolution can be applied in parts of the system and, actually, for complex networks, it is better to characterize and fine-tune a target component using a simpler construct and selection procedure, and then transfer it to the desired network and check for functionality (The toolbox of synthetic biology is constantly enriched in new types of components, expanding the possibilities for circuit design. Unravelling the principles behind naturally occurring networks has already been successfully applied for recruiting different types of RNAs and there are still more candidates with a great potential for synthetic biology such as robust oscillators based on the cyanobacterial KaiC phosphorylation rhythm (As a final note, it is important to consider the latest advances in technology that are likely to have a direct impact on prokaryotic gene circuit engineering and on synthetic biology as a whole. We already mentioned the use of Biobricks – standard parts that use common restriction enzymes and protocols to link combinations together (


A novel mouse model reveals that the dynamic behavior of transcription factors can vary considerably between different cells of an organism. Studies based on cell-free systems and on in vitro–cultured living cells support the concept that many cellular processes, such as transcription initiation, are highly dynamic: individual proteins stochastically bind to their substrates and disassemble after reaction completion. This dynamic nature allows quick adaptation of transcription to changing conditions. However, it is unknown to what extent this dynamic transcription organization holds for postmitotic cells embedded in mammalian tissue. To allow analysis of transcription initiation dynamics directly into living mammalian tissues, we created a knock-in mouse model expressing fluorescently tagged TFIIH. Surprisingly and in contrast to what has been observed in cultured and proliferating cells, postmitotic murine cells embedded in their tissue exhibit a strong and long-lasting transcription-dependent immobilization of TFIIH. This immobilization is both differentiation driven and development dependent. Furthermore, although very statically bound, TFIIH can be remobilized to respond to new transcriptional needs. This divergent spatiotemporal transcriptional organization in different cells of the soma revisits the generally accepted highly dynamic concept of the kinetic framework of transcription and shows how basic processes, such as transcription, can be organized in a fundamentally different fashion in intact organisms as previously deduced from in vitro studies. The accepted model of eukaryotic mRNA production is that transcription factors spend most of their time diffusing throughout the cell nucleus, encountering gene promoters (their substrate) in a random fashion and binding to them for a very short time. A similar modus operandi has been accepted as a paradigm for interactions within most of the chromatin-associated enzymatic processes . However, it is not known whether such behavior is indeed a common characteristic for all cells in the organism. To answer this question, we generated a knock-in mouse that expresses in all cells a fluorescently tagged transcription factor (TFIIH) that functions in both transcription initiation and DNA repair. This new tool, when combined with quantitative imaging techniques, allowed us to monitor the mobility of this transcription factor in virtually all living tissues. In this study, we show that, in contrast to the aforementioned paradigm, in highly differentiated postmitotic cells such as neurons, hepatocytes, and cardiac myocytes, TFIIH is effectively immobilized on the chromatin during transcription, whereas in proliferative cells, TFIIH has the same dynamic behavior as in cultured cells. Our study also points out that results obtained from in vitro or cultured cell systems cannot always be directly extrapolated to the whole organism. More importantly, this raises a question for researchers in the transcription field: why do some cells opt for a dynamic framework for transcription, whereas others exhibit a static one? Basal transcription/repair factor IIH (TFIIH) is a ten-subunit complex Most transcription factors, including basal factors and transcription activators, are only very transiently bound, on the order of a few seconds, to their substrate y/y) that expresses homozygously a YFP -tagged TFIIH subunit under control of the endogenous transcriptional regulatory elements. Using this tool, we explored TFIIH binding kinetics directly in different cells and tissues of the organism.To study the consequences of different transcriptional programs on the kinetic behavior of TFIIH and further understand the complex phenotypic expression of mutated TFIIH, we created a knock-in mouse model  of the Xpb gene intact. The XPB-YFP fusion protein further contains additional convenient C-terminal His6- and HA-tags. Embryonic stem (ES) cells transfected with the targeting fusion construct were selected for proper targeting (by homologous recombination) by Southern blotting  tagged at its C-terminus with the yellow GFP variant (YFP). Gene-targeting constructs and strategy are schematically depicted in blotting . Immunobblotting . Selecteblotting .y−Neo/+Xpb mice were crossed with a ubiquitous Cre-Recombinase–expressing mouse model y/+Xpb) were intercrossed to generate homozygous knock-in mice (y/yXpb)  progeny were obtained in a Mendelian ratio (32 homozygous knock-in mice out of 125 offspring), indicating that homozygosity for the knock-in fusion gene does not impair embryonic development. A small cohort of homozygous  and heterozygous (six of each) littermates was allowed to age, until natural death, which occurred around 2 years for both y/yXpb and y/+Xpb mice. No obvious features of premature aging or spontaneous carcinogenesis of the y/yXpb other than those occurring in +/+Xpb mice were observed. Knock-in y/yXpb mice appeared healthy and fertile, indicating that the presence of the fluorescent tag does not significantly interfere with the vital functions (transcription initiation) of the Xpb gene.To avoid any possible interference with expression of the fusion gene by the presence of the dominant selectable NeoMarker in the 3′ UTR, heterozygous (Xpby/y) . Homozygy/yXpb dermal fibroblasts were similar to wild-type (+/+Xpb) cells assayed in parallel . We also measured the mobility of nonfused GFP in neurons within organotypic cerebellar slices derived from a mouse that expressed GFP under the control of actin promoter To study the spatiotemporal distribution of TFIIH and to determine its kinetic engagements in different cell types within intact tissues, we established organotypic cultures of several organs and tissues. Within organotypic slices of cerebral cortex, isolated and maintained according to established procedures y/y mice , indicaty/y mice  stably by/y mice  suggestiy/y mice . Moreovey/y mice with Xpc−/−, to generate Xpby/y•Xpc−/− mice. In the absence of XPC, TFIIH does not bind damaged DNA y/y•Xpc−/− mice appeared identical as in neurons derived from NER-proficient mice  promoter sequences versus adjacent untranscribed areas by semiquantitative PCR  on adult cortex tissues slices under normal conditions and after cold shock, and measured the proportion of active housekeeping genes , and TFIIH mobility was measured in cortex neurons and hepatocytes. Remarkably, we observed a progressive TFIIH immobilization during development , the dynamic organization of the basal transcription machinery is radically changed, whereas in other cell types , the dynamic framework of TFIIH activity is maintained.To further investigate differentiation-dependent mobility of TFIIH, we used an in vivo keratinocyte differentiation model. Within the hair shaft, highly differentiated nonproliferative keratinocytes, known as trichocytes Previous live-cell studies on complex multifactorial chromatin-associated processes that take place in mammalian cell nuclei, such as transcription, replication, and various DNA repair processes, have disclosed a general model in which highly mobile proteins (process factors) interact with sites of activity  on the basis of stochastic collisions to form transient local machineries in an ordered but highly versatile manner In an attempt to study the essential basal transcription initiation factor TFIIH within cells embedded in their natural environment (tissue), we designed a mouse model that allows quantitative determination of TFIIH dynamics. Using carefully designed targeted integration of the live-cell marker YFP at the Xpb locus (expressing the TFIIH subunit XPB), we obtained expression of functional YFP-tagged XPB protein under control of the endogenous promoter ; we now find evidence for a fundamentally different scenario for the organization of basic transcription initiation in some cell types in the organism. Postmitotic cells  appear to apply a largely static organization of transcription initiation with components being stably bound to chromatin that otherwise in other cell types  exchange constantly in a highly dynamic manner. In postmitotic cells, TFIIH is bound to promoters with a much longer residence time than in proliferative cells. A possible explanation for this static behavior is that in these postmitotic terminally differentiated cells, a large part of the transcription program is dedicated to a specific subset of genes defining cellular specialty and housekeeping functions, without the need to continuously switch to transcribed genes that are involved in proliferation . It is possible that regular replication of the genome in proliferating (cultured) cells and tissues causes a continuous resetting of the transcription regulation machinery after each round of cell division and, in parallel, would involve a more open and accessible chromatin conformation.It is generally accepted that differentiation requires and causes a resetting of the transcriptional program by activating and down-regulating specific genes in response to internal and external stimuli, thereby utilizing lineage-specific transcription activators and/or repressors. However, here, we have identified a novel concept of differentiation-dependent spatio-temporal organization of transcription initiation. This concept implies that transcription initiation factors, such as TFIIH, will be bound to promoters much longer in certain cell types than in others . PreviouThe fast TFIIH remobilization observed in neurons after a cold shock or the induction of local DNA damage demonstrates that, within neurons, TFIIH is still able to respond promptly to a “stress situation” that requires a rapid adaptation of the transcriptional program (in the case of the cold-shock response) or to be implicated in DNA repair. Thus remarkably, the static involvement of TFIIH in transcription initiation does not interfere with the flexibility of cells to change the nuclear organization in response to changing conditions. It is of interest to know how multifunctional factors such as TFIIH are still able to switch from one functional role to another and to relocate to other activity sites despite their virtually immobile nature. Further analysis is required to identify which subroute of NER  ethical requirements and according to the respect of the 3R animal welfare rules.Xpb locus. The locus was modified by site-directed mutagenesis (SDM) to transform the stop codon into a coding amino acid (tryptophan) and two unique restriction sites were inserted for cloning purposes . Between the SacII and NotI, a modified YFP was cloned, the YFP start was modified into a valine to avoid undesired translation of nonfused YFP. The YFP gene was further tagged at the C-terminus with a stretch of six histidines and an HA epitope sequence. A unique ClaI site was created 10 bp downstream of the stop codon of the modified YFP to introduce a neomycin gene-expression cassette, flanked by two LoxP sites Xpb gene.The knock-in targeting vector (backbone pGEM5ZF) consisted of an approximately 7 Kb  mouse genomic DNA (isogenic to 129 OLA), which contains the 3′ part of the 7 ES cells in 500 µl. Selection with 0.2 µg/ml G418 was started 24 h after electroporation. After 8–10 d, G418 resistant clones were isolated. Screening for homologous recombinants was performed using DNA blot analyses of NcoI-digested DNA with a 400-bp 5′ external probe  were cultured in BRL-conditioned medium supplemented with 1,000 U/ml leukemia inhibitory factor. A total of 20 µg of the PmeI linearized targeting vector was electroporated into approximately 10robe see . Out of yXpb  allele was maintained in both FVB and C57BL/6 backgrounds. We thank P. Vassalli for pCAGGSCre plasmid used to generate the transgenic CAG-Cre recombinase–expressing mice. EGFP-expressing mice were kindly provided by Dr. Okabe The knock-in +/+Xpb, y/yXpb, and y/yXpb•−/−Xpc mice, following established procedures 2, and 5% CO2. To induce in vitro differentiation of XPB-YFP–expressing ES cells, we applied the hanging drop method 5/ml in DMEM [Lonza] with 20% fetal calf serum [Lonza], 50 U penicillin/ml, and 50 µg streptomycin/ml [Lonza], 1% nonessential amino acids [Lonza], 0.1 mM B-mercaptoethanol [Sigma]) were placed on the lids of Petri dishes filled with PBS. After culturing for 3 d, the aggregates were transferred into bacteriological Petri dishes. Two days later, embryoid bodies were placed in Costar six-well plates with gelatin-coated coverslips for further development into different cell tissues. From day 3 on, retinoic acid (10−8 M) was added to induce skeletal muscle differentiation. Treatment with ultraviolet (UV) light at 254 nm (UV-C) was performed using a Philips germicidal lamp. For UV-survival experiments, cells were exposed to different UV-C doses, 2 d after plating. Survival was determined 3 d after UV irradiation by incubation at 37°C with 3H-thymidine, as described previously 2 of UV-C and processed as described previously Murine dermal fibroblasts (MDF) were extracted from 2, and 5% CO2. FRAP analysis on cells within organotypic slices, maintained in culture for 1 wk, gave the same results as when performed on freshly extracted slices. Organotypic slices of heart, liver, and intestine were produced by cutting 300 µm of the organ with a Tissue Chopper (McIllwain). Slices were analyzed within 2 h following preparation, unless differently specified in the text. Skin tissues (epidermis and dermis) were prepared as described Organotypic explants of cerebral cortex and cerebellum were produced as previously described +/+Xpb and y/+Xpb ES cells were prepared by isolating cells from a semiconfluent Petri dish (10 cm). Cells were washed with phosphate-buffered saline (PBS) and homogenized by sonication. Western blot analysis was performed as previously described Whole-cell extracts (WCE) of +/+ and Xpby/y cells were grown on glass coverslips and fixed with 2% paraformaldehyde at 37°C for 15 min. Immunofluorescence analysis was carried out as previously described A mixture of XpbFRAP experiments were performed as described before Fmin) as an approximation of the baseline fluorescence recovery (BF = 1), i.e., the fluorescence level in the absence of recovery, when all proteins are considered immobile. We calculated the time-average fluorescence signal taken between 2 and 3.9 s prior to the photobleaching step to obtain the average prebleach fluorescence (Fpre), and then between 10 and 15 s to estimate the final fluorescence recovery level (Fmax). The bound fraction is then given by:To estimate the relative TFIIH bound fractions (BF) from the FRAP measurements, we used the first data point after photobleaching (Fmin) was estimated as the average fluorescence intensity loss between the prebleached image (Fnucleus (pre)) and the last image of the nucleus (Fnucleus (last)):We corrected for the photobleached fraction, i.e., the incomplete recovery of fluorescence due to irreversible YFP bleaching during the FRAP procedure, as follows: whole nuclei of cultured keratinocytes were first imaged, subsequently strip-bleached (60-ms photobleach at maximum laser intensity), then imaged immediately after the bleach pulse and again 1 min later when no traces of the bleached strip were observed. The photobleached fraction (PBF) relative to the baseline fluorescence recovery  for 30 min at 37°C and by proteinase K (200 µg/µl) 3 h at 55°C in 50 mM Tris-HCl (pH 8), 1 mM EDTA, 100 mM NaCl, 0.5% SDS. Formaldehyde cross-links were reverted by heating the samples at 65°C for 6 h. The cross-linked DNA was extracted with phenol:chloroform and precipitated with ethanol in the presence of carrier glycogen. Pellets were resuspended in 30 µl of distillated water. Chromatin-immunoprecipitated DNA was subjected to PCR amplification. PCR was performed using 200 ng (for promoter XPB gene amplification) and 100 ng (for untranscribed sequences) of the chromatin immunoprecipitate and 400 nM concentration of both sense and antisense primers (primer sequence available upon request) in a final volume of 25 µl using the PureTaq Ready-to-go PCR beads . PCR products were analyzed on 2% agarose7 gels by SYBR Green reagent. Data were analyzed and quantified using Quantity One program (Bio-Rad). For adjustment of amplification efficiency of each primer set, PCR signal intensities from chromatin-immunoprecipitated DNA were normalized to those from the input genomic DNA and expressed as a percentage of the input (gDNA).Brain slices and cultured cells were incubated at 37°C and 4°C. Additionally, cultured cells were incubated with 0.1 µg/ml of actinomycin D for 2 h. Samples were homogenized in Trizol using the Tissuelyser (Qiagen) for 90 s. RNA isolation was performed using the RNeasy Mini Kit (Qiagen). cDNA was produced using 2 µg of RNA with a Reverse Transcription Kit (Invitrogen), random primers (Invitrogen), 1 µg of cDNA, and 400 nM concentration of both sense and antisense primers (primer sequence and PCR cycle available upon request) in a final volume of 25 µl using the PureTaq Ready-to-go PCR beads . PCR products were analyzed on 2.5% agarose gels.Figure S1Xpb gene targeting.Analysis of the  (A) Southern blot analysis of genomic DNA (NcoI digest) from IB10 ES cells (OLA 129) electroporated with the targeting construct (Xpb) as an external probe (not included in the targeting construct), indicated as a yellow bar. The genomic organization, including relevant restriction sites of both the wild-type (wt) allele and the homologous targeted allele were indicated: exons in orange, YFP coding sequence in green, LoxP_Neo_LoxP cassette in purple, and sizes of NcoI restriction fragments in red. Untargeted genomic DNA from IB10 ES cells (lane 1) and chromosomal DNA of tails from 129 mouse strain (lane 2) were loaded as control. Twelve homologous recombinant ES cell lines  out of 128 analyzed G418-resistant transformants were obtained (9% targeting frequency). (B) Immunoblot of total cellular protein extracts from three targeted ES clones (lanes 2–4) and a nontargeted ES clone (lane 1) as negative control was performed using an antibody against the HA tag . (C) Scheme of the amplification strategy to identify wt, heterozygous, and homozygous animals. Two sets of primers: set A (Iforward [fI] + Ireverse [rI]) and set B (Iforward [fI] + IIreverse [IIr]) amplify the untargeted allele (set A) or the targeted allele (set A and B). The size of the fragment amplified by primer set A discriminates the untargeted allele  from the targeted allele containing the neomycin-resistant marker , and from the targeted allele without the NeoMarker . (D) Amplification of genomic DNA from untargeted +/+Xpb (lane 1), heterozygous y/+Xpb (lane 2), and homozygous y/yXpb mice (lane 3) after recombining out the LoxP_Neo_LoxP cassette. Lane M: marker. (E) Amplification of genomic DNA from +/+Xpb (lane 1and 2), y−Neo/+Xpb (lane 3 and 4), and y/+Xpb mice (lane 5 and 6). Lane M: marker.onstruct  to ident(0.53 MB TIF)Click here for additional data file.Figure S2Characterization of cell expressing fluorescently tagged TFIIH (XPB-YFP). (A) Comparative immunofluorescence of a mixed population of dermal fibroblasts isolated from an untargeted mouse  and a targeted mouse , stained with anti-p62  or anti-HA , which recognizes the XPB-YFP-His6HA protein. Bar: 10 µm. (B) DNA repair or unscheduled DNA synthesis (UDS); expressed as percentage of wild-type (wt) UDS  of dermal fibroblasts isolated from +/+Xpb mice , y/yXpb mice (red), and y/yXpb mice crossed with −/−Xpc (NER-deficient) mice (blue). UDS in +/+Xpb was set at 100%. (C) Cultured cells derived from the Xpby/y mouse model. The YFP signal and transmission image were merged from a primary cultures of dermal fibroblasts (left panel) and keratinocytes (right panel). Note the uniform expression of TFIIH in both cell types. Bar: 20 µm.(1.55 MB DOC)Click here for additional data file.Figure S3TFIIH expression and mobility in neurons. (A) Image of an organotypic tissue slice of cortex brain from Xpby/y. Bar: 50 µm. (B) Prolonged time-lapse imaging of a cortex neuron after strip-FRAP. Time scale is expressed in minutes.(3.17 MB DOC)Click here for additional data file.Figure S4Strip-FRAP graphs used to calculate TFIIH bound fractions indicated in . The dotted square indicates the approximate time frame used to calculate the immobile fractions. Mean SEM is the average standard error of the mean calculated over the entire time range of each FRAP curve.(0.25 MB TIF)Click here for additional data file.Figure S5Mobility of TFIIH and free-GFP in neurons from the cerebellum. (A) TFIIH mobility in Purkinje cells and granular neurons in cerebellar organotypic slices. Left upper panel: immunohistochemistry of a cerebellum paraffin-embedded section stained with an HA antibody (brown). Left bottom panel: image of DAPI-stained Purkinje cell and a granular neuron, bar: 10 µm. Right panel: FRAP curve of Purkinje cells (red) and granular neurons (blue), showing the same TFIIH in these two different neuron types having different TFIIH concentrations and chromatin make-ups. (B) Mobility of free GFP in granular neurons (GN) of the cerebellum from mice that ubiquitously express nontagged GFP. Left panel: image of GFP-expressing GN, the dotted circle indicates the nuclear contour, bar: 5 µm. Right panel: FRAP curve of free GFP (red), indicating that nonfused GFP is freely mobile in GN.(1.01 MB TIF)Click here for additional data file.Figure S6Strip-FRAP graphs used to calculate TFIIH bound fractions indicated in , section DNA repair. In blue is TFIIH mobility from cortex neurons of XPBYFP mice and in red, TFIIH mobility from cortex neurons of XPBYFP mice crossed into the XPC background. The dotted square indicates the approximate time frame used to calculate the immobile fractions. Mean SEM is the average standard error of the mean calculated over the entire time range of each FRAP curve.(0.19 MB TIF)Click here for additional data file.Figure S7Strip-FRAP graphs used to calculate TFIIH bound fractions indicated in , section Pol 2 transcription. The dotted square indicates the approximate time frame used to calculate the immobile fractions. Mean SEM is the average standard error of the mean calculated over the entire time range of each FRAP curve.(0.26 MB TIF)Click here for additional data file.Figure S8α-Amanitin transcription inhibition. (A) Confocal imaging of chondrocytes isolated from y/yXpb mice, untreated (left panel) and (B) treated (right panel) with α-amanitin. Transcription activity has been measured by incorporating BrU into nascent m-RNA. Anti-BrU (red), YFP (green), and Dapi (Blue).(1.60 MB DOC)Click here for additional data file.Figure S9RNAP1 and RNAP2 transcription in neurons. (A) Image of a cortex neuron within organotypic slice in transmitted light (TL) (left panel) and fluorescent light (middle panel). Bar: 10 µm. Left panel: image shows a cortex neuron within an organotypic brain slice 10 min after bleaching part of the nucleolus. (B) Semiquantitative reverse transcriptase (RT)-PCR of pre-rRNA extracted from organotypic cortex slices and cultured chondrocytes (left panel) and Hela cells (right panel), incubated at different temperatures and treated with actinomycin D. (C) Nuclear extract from the brain was immunoprecipitated with HA-antibodies and subjected to Western blot analysis with the indicated antibodies. The input lanes represent Immunoprecipitation fraction (IP), cleared chromatin (CC) cross-linked extract before IP, supernatant (S) of cross-linked extract after IP. (D) Bar graphs representing the quantification of a semiquantitative PCR on 200 ng of genomic DNA (gDNA) and 200 ng of HA-precipitated chromatin from cortex slice incubated at 37°C and 4°C. Promoter sequence from Pol1 gene (−119/+7) and untranscribed region adjacent to the Pol1 gene (Chr 6: 71937600–71938000). The y-axis represents the percentage of amplified sequences from the HA-precipitated chromatin versus amplified sequences from gDNA, set to 100%. Error bars have been calculated for three biological replicates.(0.94 MB DOC)Click here for additional data file.Figure S10Strip-FRAP graphs used to calculate TFIIH bound fractions indicated in , section Pol 1 transcription. The dotted square indicates the approximate time frame used to calculate the immobile fractions. Mean SEM is the average standard error of the mean calculated over the entire time range of each FRAP curve.(0.23 MB TIF)Click here for additional data file.Figure S11Strip-FRAP graphs used to calculate TFIIH bound fractions indicated in . The dotted square indicates the approximate time frame used to calculate the immobile fractions. Mean SEM is the average standard error of the mean calculated over the entire time range of each FRAP curve.(0.29 MB TIF)Click here for additional data file.Figure S12TFIIH mobility in chondrocytes. (A) Confocal imaging of chondrocytes isolated from y/yXpb mice proliferating (left panels) and confluent (right panels). Ki67 immunostaining (in red) was used to confirm the proliferative state of the cells. (B) TFIIH mobility in proliferating (blue) and confluent (red) chondrocytes.(1.34 MB TIF)Click here for additional data file.Figure S13Strip-FRAP graphs used to calculate TFIIH bound fractions indicated in . The dotted square indicates the approximate time frame used to calculate the immobile fractions. Mean SEM is the average standard error of the mean calculated over the entire time range of each FRAP curve.(0.27 MB TIF)Click here for additional data file.Figure S14Strip-FRAP graphs used to calculate TFIIH bound fractions indicated in . The dotted square indicates the approximate time frame used to calculate the immobile fractions. Mean SEM is the average standard error of the mean calculated over the entire time range of each FRAP curve.(0.29 MB TIF)Click here for additional data file.Figure S15Strip-FRAP graphs used to calculate TFIIH bound fractions indicated in . The dotted square indicates the approximate time frame used to calculate the immobile fractions. Mean SEM is the average standard error of the mean calculated over the entire time range of each FRAP curve.(0.24 MB TIF)Click here for additional data file.Figure S16Strip-FRAP graphs used to calculate TFIIH bound fractions indicated in . The dotted square indicates the approximate time frame used to calculate the immobile fractions. Mean SEM is the average standard error of the mean calculated over the entire time range of each FRAP curve.(0.29 MB TIF)Click here for additional data file.Figure S17Differentiation markers in ES clones. (A) Transmission light image of a differentiated ES clone stained with Alizarin Red. Red-stained cells and inclusion are characteristically highly mineralized (Ca2+-containing) cells. (B) Transmission light image of a differentiated ES clone stained with alkaline phosphatase. Violet-stained cells are osteocytes producing the enzyme.(5.88 MB TIF)Click here for additional data file.


Mathematical models allow to verify biological hypotheses and to predict new possible dynamical behaviours. Here we use the tools of non-linear analysis to understand how to change the dynamics of the genes composing a novel synthetic network recently constructed in the yeast Saccharomyces cerevisiae for In-vivo Reverse-engineering and Modelling Assessment (IRMA). Guided by previous theoretical results that make the dynamics of a biological network depend on its topological properties, through the use of simulation and continuation techniques, we found that the network can be easily turned into a robust and tunable synthetic oscillator or a bistable switch. Our results provide guidelines to properly re-engineering in vivo the network in order to tune its dynamics.Systems and Synthetic Biology use computational models of biological pathways in order to study  Saccharomyces cerevisiae, IRMA, built for In vivo Reverse-engineering and Modelling techniques Assessment. We measured time series and steady-state expression data after multiple perturbations and used them to assess the state of the art of both modelling and reverse-engineering techniques. The yeast synthetic network is composed of five genes that directly regulate the transcription of each other  and includes one protein-protein interaction . The topology , while the second is present only in glucose growing condition, when there is a protein-protein interaction between Gal4 and Gal80. Such negative loop can be switched off by culturing cells in galactose. We derived and identified a non-linear Delay Differential Equations model containing The emerging field of Synthetic Biology aims at constructing novel biological circuits in the cell topology  consistsin vivo experiments and post hoc tweaking to be performed The main aim of this paper is to show how to use novel tools from numerical bifurcation theory . Hysteretic examples have been observed in several natural examples, including the control of lactose utilization in CBF1 both in the switch-on and in the switch-off time-series. Regarding the transient dynamics of the switch-on, we observed seemingly damped oscillatory behavior in SWI5 and CBF1 concentrations and monotonic dynamics for all the other genes. In the switch-off, as expected, the transcription of the network genes is rapidly and monotonically turned off.In With the aim of tuning the dynamics of IRMA and turning it into an autonomous biochemical oscillator, we shall seek to achieve the desired dynamic behaviour by appropriately varying the model parameters. In so doing it is obviously fundamental both to remain inside the physically feasible range and to minimize the number of changes to the existing network topology and nominal parameter values, in order to speed up the experimental implementation.In our specific case, the number of physical parameters is quite high (33), thus an exhaustive exploration of the parameter space would be excessively complicated and time consuming. On the other hand, from the analytical view point it is cumbersome to get any results about the structural stability of equilibria under parameters variations since the system is time-delayed and highly non-linear, due to the large value that the Hill coefficients can assume. For the case of our multi-parametric delayed gene network, it is then crucial to restrict the number of parameters to be changed to induce sustained oscillations. For the selection of the parameter subset to be used to carry out the bifurcation analysis, we use as guidelines the links between the topology and the occurrence of autonomous oscillations presented in the recent literature In the analytical studies of simple two-components networks modelled by differential equations CBF1, GAL4, SWI5 with a delayed reaction due to the presence of the HO promoter  and the Michelis-Menten coefficient Swi5 on ASH1 gene. In particular, the parameter By looking at the values of the kinetic parameters estimated from CBF1.Then, we evaluate the effect of the non-linearity of the reaction kinetics generated by the Hill functions on the network behavior. Since the stiffness of such sigmoidal function is determined by the Hill coefficients, which describe the cooperativity of the promoters, we perform our numerical investigation increasing the Hill coefficients  and  . AccordiOnce oscillations are obtained, a fundamental step in the theoretical analysis is the investigation of the robustness and the tunability of the oscillator. To this aim we use numerical continuation techniques ASH1 strong enough) is fundamental to guarantee persistent oscillations. The range of MET16 promoter increases, remarking that the reaction in the loop must be balanced in terms of strength. In ASH1 is strong enough, the cooperativity coefficient can be decreased without losing persistent oscillations.The limit cycle can be continued on each of the ases see : it meanCBF1 oscillations. By simulating the dynamics of the network using the parameters of Scenario 1 B  fixing the parameters to the values in in vivo according with our theoretical results.At this point, it is crucial to address the feasibility of re-engineering IRMA MET16 promoter, the idea is to decrease the level of methionine in the yeast. Methionine modulates the expression of the MET genes by affecting the formation of the Cbf1-Met4-Met28 transcriptional complex MET16 promoter weak for the methionine concentrations used in our medium  representing the expression levels of the MET genes, including MET16, when yeast cells are grown in the presence of low , its transcription appears to be strongly decreased.In GAL4 expression, due to MET16 activation after the removal of methionine, turns on all the network genes, while addition of methionine inhibits them, independently from galactose. From such experimental results, we can conclude that increasing the the maximal transcriptional rate MET16 promoter and allows to tune the amplitude of the oscillations, can be achieved by simply decreasing the level of methionine in the medium.In ASH1 promoter, needed to vary EGT2 promoter Regarding the changes to the HO promoter are kept equal to their nominal values, but for in vivo by increasing the number of binding sites for Ash1 on the HO promoter, although it has not been previously demonstrated that experimental re-engineering would affect only the Hill coefficient and not other parameters, e.g. the Michaelis-Menten constant of the promoter. Furthermore, such promoter is also activated by Swi5 and the regulatory mechanisms are quite complex HO promoter could be troublesome.The last parameter to be tuned is the Hill coefficient The positive loop in Scenario 1 seems difficult to implement in vivo. Therefore, we consider a second scenario, in CBF1 by reducing the value of the Michaelis-Menten coefficient CBF1.Again, we tune both the strength of the negative loop .in vivo are ASH1 promoter with the EGT2 promoter. Moreover, it is possible to tune also the MET16 promoter with low methionine concentrations should become switch like, thus leading to an increase of the stiffness of the sigmoidal Hill function modelled by the The critical parameters which have to be tuned to implement scenario 2 HO promoter with a promoter which is not activated by Swi5. Secondly, we need to replace ASH1 gene with a gene whose expression is driven by the EGT2 promoter and that is able to inhibit strongly the new promoter. A good candidate inhibitor-promoter couple is given by ROX1 repressor and ANB1 promoter The tuning of parameters in vivo implementation and the oscillations appear robust to varying parameters and initial conditions. For the sake of completeness, we consider also the possibility of including in the network a positive feedback loop, in order to check if the robustness and the tunability of the oscillations increase, according to what shown in a number of works The topology proposed in Scenario 2 appears feasible for SWI5  in For the MET16 promoter by Cbf1 by tuning the parameters Again, we can increase the strength of the activation of the In this work, using numerical and continuation techniques, we showed how IRMA can be turned into a robust and tunable oscillator, or a bistable genetic switch. The deterministic mathematical model, previously formulated and identified to allow data interpretation and experiment planning, is here analysed to guide the re-engineering of the network with predictable functions.in vivo. The robustness to parameters changes and the tunability of the oscillator were assessed via continuations performed using the software DDE-BIFTOOL, the first package for bifurcation analysis of systems with delays that, up to now, has not been commonly used in the Synthetic Biology community.IRMA showed great flexibility. Its topology can be re-engineered in a number of ways in order to achieve the desired dynamical behaviour. Of note, all the proposed changes are viable The major conclusion we can draw from our results is that, aiming at constructing a robust and tunable oscillator, the best option is to include in the topology both a delayed negative feedback loop and a fast positive one. This is the case explicitly analyzed in Scenario 3 that results to be most robust and tunable as compared to Scenario 2, in which the topology of the network is reduced to a single negative feedback loop.In the context of Synthetic Biology, our model guided re-engineering framework can be applied to existing topologies with the aim of turning them into oscillators or switches. We analyzed three topologies for the oscillator case and one for the switch case. A crucial point was to minimize the number of experiments needed to modify the synthetic network. Surely, other possible ways to re-engineering IRMA can give rise to other oscillatory, switch-like and maybe more complex dynamical behaviours. Of note, once the best performing scenario has been chosen from our deterministic approach, it will be crucial to resort to stochastic simulations in order to estimate the impact of noise on the network dynamics HO promoter by Swi5, is fixed (100 minutes). In GAL4 and GAL80 that describes the starvation effect due to the washing of the cells before the medium shift The mathematical model we used is made up of of five nonlinear Delay Differential Equations that describe the production rates of the five mRNA concentrations, assuming Hill kinetics and proportionality between protein and mRNA levels. The time delay, describing the delayed activation of the SWI5 are fixed to their values in galactose. Moreover, since the transient dynamics are neglected, the starvation effect induced by the medium shift is removed.For this work, the medium-dependent parameters in the equation of HO promoter. Since the real-time qPCR data showed in ACT1 gene, concentrations are reported in arbitrary units  and its period are computed as solutions of the corresponding periodic boundary value problem by using a piecewise polynomial collocation. The local asymptotic stability of a periodic solution is determined by the spectrum of the linear so-called monodromy operator All the numerical continuation experiments were performed using DDE-BIFTOOL package Figure S11  and k6 . (B) Two parameters continuation of the Hopf bifurcation on parameters v2  and k6 . (C) Two parameters continuation of the Hopf bifurcation on parameters k6  and h2 (Hill coefficient of the HO promoter). (D) Two parameters continuation of the Hopf bifurcation on parameters h2 (Hill coefficient of the HO promoter) and h6 (Hill coefficient of the ASH1 promoter). (E) Tunability of the oscillations in terms of amplitude. Amplitude of x1 (level of the CBF1 gene) continuing the periodic solution on v2 . (F) Tunability of the oscillations in terms of period. Period of x1 (CBF1 gene) continuing the periodic solution on h2 (Hill coefficient of the HO promoter).Continuation results for Scenario 1 A using DDE-BIFTOOL software. (A) Two parameters continuation of the Hopf bifurcation on parameters k(0.26 MB TIF)Click here for additional data file.Figure S2MET genes in wild type yeast cells. MET genes regulated by Cbf1 are transcriptionally activated in the presence of low levels of methionine (10 µm) while they are repressed at high methionine concentrations (1000 µm). Semi-quantitative (A) and quantitative (B) RT-PCR of MET genes were performed on total RNA extracted from yeast cells grown in the standard complete medium YPD (140 µm of methionine)and at two different methionine concentrations.Expression of (0.47 MB TIF)Click here for additional data file.Figure S32  and h3 (Hill coefficient of the MET16 promoter). (B) Two parameters continuation of the Hopf bifurcation on parameters v2  and k6 . (C) Two parameters continuation of the Hopf bifurcation on parameters k6  and h3 (Hill coefficient of the MET16 promoter). (D) Two parameters continuation of the Hopf bifurcation on parameters h3 (Hill coefficient of the MET16 promoter) and h6 (Hill coefficient of the ASH1 promoter). (E) Tunability of the oscillations in terms of amplitude. Amplitude of x1 (level of the CBF1 gene) continuing the periodic solution on h3 (Hill coefficient of the MET16 promoter). (F) Tunability of the oscillations in terms of period. Period of x1 (CBF1 gene) continuing the periodic solution on h3 (Hill coefficient of the MET16 promoter).Continuation results for Scenario 2. Continuation results for Scenario 2 using DDE-BIFTOOL software. (A) Two parameters continuation of the Hopf bifurcation on parameters k(0.30 MB TIF)Click here for additional data file.Figure S42  and h3 (Hill coefficient of the MET16 promoter). (B) Two parameters continuation of the Hopf bifurcation on parameters v2  and k6 . (C) Two parameters continuation of the Hopf bifurcation on parameters k6  and h3 (Hill coefficient of the MET16 promoter). (D) Two parameters continuation of the Hopf bifurcation on parameters h3 (Hill coefficient of the MET16 promoter) and h6 (Hill coefficient of the ASH1 promoter). (E) Tunability of the oscillations in terms of amplitude. Amplitude of x1 (level of the CBF1 gene) continuing the periodic solution on h3 (Hill coefficient of the MET16 promoter). (F) Tunability of the oscillations in terms of period. Period of x1 (CBF1 gene) continuing the periodic solution on h6 (Hill coefficient of the ASH1 promoter).Continuation results for Scenario 3. Continuation results for Scenario 3 using DDE-BIFTOOL software. (A) Two parameters continuation of the Hopf bifurcation on parameters k(0.26 MB TIF)Click here for additional data file.Figure S51 . Two saddle-node bifurcation points  = (0.02 0.007) and  = (0.14 0.01)) delimitate the bistability region. (B) Scenario 4 A. One parameter continuation of the steady state on h1 (Hill coefficient of the HO promoter). Two saddle-node bifurcations  = (2 0.008) and  = (40 0.019)) delimitate the bistability region. (C) Scenario 4 A. Two parameters continuation of one saddle-node bifurcation point on v2  and h3 (Hill coefficient of the MET16 promoter). The cusp bifurcation occurs at  = (0.0005 0.39). (D) Scenario 4 B. One parameter continuation of the steady state on k1 . Two saddle-node bifurcation points  = (0.03 0.002) and  = (0.08 0.05)) delimitate the bistability region.Continuation results for Scenario 4. (A) Scenario 4 A. One parameter continuation of the steady state on k(0.27 MB TIF)Click here for additional data file.


Distant linkages, which usually correspond to polymorphisms influencing trans-acting factors, are more condition-dependent, and often show effects in different directions in the two conditions. We characterized a locus that influences expression of many growth-related transcripts, and showed that the majority of the variation is explained by polymorphism in the gene IRA2. The RM allele of IRA2 appears to inhibit Ras/PKA signaling more strongly than the BY allele, and has undergone a change in selective pressure. Our results provide a broad overview of the genetic architecture of gene–environment interactions, as well as a detailed molecular example, and lead to key insights into how the effects of different classes of regulatory variants are modulated by the environment. These observations will guide the design of studies aimed at understanding the genetic basis of complex traits.The effects of genetic variants on phenotypic traits often depend on environmental and physiological conditions, but such gene–environment interactions are poorly understood. Recently developed approaches that treat transcript abundances of thousands of genes as quantitative traits offer the opportunity to broadly characterize the architecture of gene–environment interactions. We examined the genetic and molecular basis of variation in gene expression between two yeast strains (BY and RM) grown in two different conditions (glucose and ethanol as carbon sources). We observed that most transcripts vary by strain and condition, with 2,996, 3,448, and 2,037 transcripts showing significant strain, condition, and strain–condition interaction effects, respectively. We expression profiled over 100 segregants derived from a cross between BY and RM in both growth conditions, and identified 1,555 linkages for 1,382 transcripts that show significant gene–environment interaction. At the locus level, local linkages, which usually correspond to polymorphisms in  Plasmodium falciparum parasite are susceptible to malaria, but not if they carry the sickle-cell allele of hemoglobin. The general properties of gene–environment interaction are poorly understood, and a better understanding is essential if individuals are to make informed health choices guided by their genomic information. We have investigated gene–environment interaction on a genomic level, characterizing its role in over 4,000 traits at once by investigating natural variation in yeast gene expression. We compared lab and vineyard strains of yeast growing in two conditions (glucose and ethanol as carbon sources) in which they adopt two different metabolic states: fermentation and aerobic respiration, respectively. We show that gene–environment interaction is a common phenomenon, describe how different classes of genetic variants affect the nature of the interactions, and provide detailed molecular examples of interactions.Individuals frequently encounter different environmental conditions, and the physiological and behavioral responses to these conditions can depend on an individual's genetic makeup. This phenomenon is known as gene–environment interaction. For example, individuals who are infected with the  We show that gene-environment interaction is a common phenomenon in the regulation of gene expression, we describe how different classes of genetic variants affect the nature of the interactions, and we provide detailed molecular examples of interactions. We are rapidly approaching an age when genomic sequence will be used to inform life decisions. The extent to which lifestyle choices will change how our genetic blueprint is expressed, however, depends on the presence of gene–environment interactions: the phenomenon where the effect of a genetic variant differs in multiple environments. In humans, gene–environment interactions have been reported for many diseases, including heart disease (reviewed in ), depresArabidopsis [Recently, gene transcript abundance has been used as a model to study the genetics of thousands of quantitative traits at a time ,8. The sbidopsis . Becausebidopsis ,20 and gbidopsis .Caenorhabditis elegans [Gene–environment interaction recently has been studied using transcript levels in yeast  and in w elegans . They idWe have used a large family of yeast segregants to characterize gene–environment interaction on a global scale. Two parental strains . These genetic correlations are lower than those observed for outbred livestock populations [We characterized the extent of gene–environment interaction influencing transcriptional phenotypes in the parental strains BY and RM by growing six independent cultures of each strain in glucose and in ethanol, and measuring gene expression with microarrays . We deteulations ,27. Thusulations  and condulations  effects gxeQTL) by performing linkage analysis on the difference between conditions , but there was not a one-to-one correspondence. For some transcripts, effects were revealed in the segregants that were not observed in the parents. For others, we were unable to map specific loci responsible for differences observed in the parents. Some of this may due to differences in power, but because of genetic complexity, we do not expect to map a locus for all transcripts that show significant genetic or gene–environment interaction effects in the parent strains. Previous work has also shown that effects that are revealed only in the segregants (transgressive segregation) are common in the genetic regulation of gene expression within a condition [To understand the genetic basis of gene–environment interaction, we measured transcript expression in 109 segregants derived from a cross between BY and RM . Each ofasticity  and is sasticity   of all gxeQTL being local, as compared to linkages within either condition, where 22%–25% of all linkages were local (cis-regulatory sites are less condition dependent than those influencing trans-acting factors.Since transcripts derive from open reading frames (ORFs) with physical locations in the genome, we can make a distinction between linkages that are physically near the ORF  and those that are far away from the ORF (distant). Local linkages are often due to variation at ng sites –32, wherre local . This regxeQTL that fell within each of the bins .As has been previously reported in mouse –16 and ythe bins . The majectively . Some ofly . Within this region lies DIG1, which codes for an inhibitor of the pheromone response/invasive growth transcription factor Ste12 [DIG1 in both backgrounds show that variants in DIG1 make a major contribution to the effects at this locus , but not for sites that have been shown to experimentally bind MSN2 or MSN4. Experimental binding of MSN2 and MSN4 was tested in a variety of conditions [CIN5 binding sites that are both conserved and show experimental binding in the conditions tested . CIN5 is located in this region, shows local linkage with gene–environment interaction, and contains 17 nucleotide changes in the 500 bp upstream of start . A total of 386 transcripts show distant linkage to this locus in ethanol, whereas in glucose, only ten show distant linkage. In contrast to most other peaks, the transcripts linking here do not show functional enrichment for gene ontology (GO) terms. Targets of similar signaling pathways should share transcription factor binding sites, even if the functional implications of the pathway are unknown. To determine whether we could find enrichment for specific transcription factor binding sites, we split this group of transcripts into four groups, depending on whether they were up or down-regulated by condition and genotype. We used transcription factor binding site data derived from experimental binding as well as species conservation  to deternditions , but theof 0.005 ), in addgxeQTL with linkage results for transcript levels in glucose or ethanol showed that only 965/1,555 (62%) gxeQTL also reached genome-wide significance levels in at least one condition  showing an effect only in glucose, 327/1,555 (21%) showing an effect only in ethanol, and 963/1,555 (62%) showing an effect in both conditions. The fact that all gxeQTL showed linkage to the expression phenotype in at least one condition is not surprising, as a difference in how the genotypes differ between two conditions mathematically requires a change in the mean phenotypic value in at least one condition. The QVALUE method also provides an estimate of the proportion of loci found within a condition that show gene–environment interaction. We inspected the distribution of all p-values for linkage between the most significant marker in one condition and the difference in expression between the conditions, and found that 77% and 76% of loci in glucose and ethanol, respectively, also influence the difference between conditions.After examining large differences in distant peaks, we sought to more generally and rigorously describe how the effect of a locus changes across conditions. Comparisons of h QVALUE . All 1,5cis-acting regions would be more likely to be condition specific. We did not observe this relationship between local and distant linkages, however. Distant linkages were significantly more likely to be condition specific than local linkages, as 546/1,383 (40%) of distant linkages were significant in only one condition, as compared to 46/172 (27%) of local linkages , gxe5, gxe6 (GPA1), and gxe12 (IRA2) were enriched for glucose-specific linkages, whereas peak gxe13 was enriched for ethanol-specific linkages. These results agree with the observation that distant peaks change between conditions, but provide a more quantitative result that indicates that some distant loci show differential effects between conditions. This may be due to changes in the activity states of the proteins and pathways involved as conditions change, or it could also be due to epistasis that masks the effects of the polymorphisms in the alternate condition.We investigated whether individual hot spots showed condition specificity by comparing the proportion of ric test . Five hogxeQTL that had an effect in both conditions , the effects were in opposite directions 72% of the time. This phenomenon was dependent on the type of locus, as local gxeQTL were more likely to act in the same direction in both conditions (66%), whereas distant gxeQL were more likely to act in opposite directions (78%)  . The majns (78%) . This pap = 8 × 10−13), whereas those that are activated in ethanol are enriched for generation of precursor metabolites and energy (p = 1 × 10−6). IRA2 is a strong candidate for the regulation of these transcripts. Ira2 is an inhibitor (GTPase activating protein) of the Ras proteins, which mediate the cellular response to glucose via the Ras/PKA pathway [IRA2 coding region is highly polymorphic, with 87 SNPs and a single 3-bp indel differing between the strains, resulting in 61 synonymous changes, 26 nonsynonymous changes, and one RM insertion. Of the 26 nonsynonymous changes, BY carries the ancestral amino acid (Saccharomyces paradoxus) in 20 cases, and RM carries the ancestral amino acid in four cases, with the remaining two different from S. paradoxus in both strains.We further investigated peak gxe12, located at chromosome 15 50–80 cM, which we observed to be enriched for transcripts that show glucose-specific effects. A total of 372 transcripts link here distantly, and many are generally involved in energy metabolism and growth A. Most o pathway . Compone pathway . The IRAIRA2 is responsible for the observed linkages, we generated allele replacement strains that carried the coding region of each parent strain in the background of the other parent. We felt that targeting the coding region was appropriate because the expression of IRA2 has not shown local linkage [IRA2 polymorphisms also contribute to the variation in transcript levels within these conditions , with S. paradoxus as an outgroup. The S288c sequence is more similar to the ancestral S. paradoxus than either RM or YJM, indicating that RM IRA2 is the more diverged allele. BY is more similar to the ancestral sequence, suggesting that RM has gained the ability to inhibit signaling more. However, since we do not know the specific causal variant(s), we are unable to rule out the possibility that a small number of polymorphisms that are not characteristic of the overall trend are responsible for a decrease in the BY allele's ability to inhibit.The BY allele of IRA2 by comparing rates of nonsynonymous and synonymous substitutions at polymorphic versus fixed sites within the three sequenced S. cerevisiae strains, as compared to the outgroup S. paradoxus. Of the sites that were fixed in S. cerevisiae, 133/882 (15%) were nonsynonymous, whereas within polymorphic sites, this rate was 2-fold higher . This difference is significant (Fisher exact p = 0.0001), suggesting that positive selection, or a relaxation of negative selection, has occurred in the S. cerevisiae strains. The RM strain appears to be contributing the most to the nonsynonymous rates, as the ratio of nonsynonymous to synonymous changes along the RM branch is the highest within the strains. Thus, the RM allele appears to have experienced a change in selection pressure in the past. Further analyses of a variety of yeast isolates should help elucidate the evolutionary history of this locus.If the RM allele has gained the ability to more strongly inhibit Ras/PKA signaling, then it might be expected to show signs of selection. We used the McDonald-Kreitman test to look for evidence of selection in gxeQTL. The high number of gxeQTL that we detected has allowed us to make some general observations. We have shown that local and distant linkages differ dramatically in how they act across multiple conditions. Local linkages appear to be more stable: they are less likely to be dependent on the environment, and even when they are, they are more likely to have an effect in both conditions, with the direction of effect often being the same. Distant linkages, on the other hand, are more volatile: they are more likely to be dependent on condition and to show an effect in only one condition. Entire distant peaks can change across conditions, and when they do have an effect in multiple conditions, distant loci are more likely to act in different directions. Finally, we characterized the gene responsible for influencing the largest gene–environment interaction distant peak, IRA2. We showed that the RM allele of IRA2 is a stronger inhibitor of Ras/PKA signaling than the BY allele in the conditions that we tested, and that this locus has experienced a change in selective pressure in RM.Here, we have used yeast gene expression as a model system to describe gene–environment interaction at strain, locus, and gene levels. We showed that 2,037 transcripts were jointly dependent on strain and condition in two parental strains. Then, we performed linkage analysis on the difference in transcript levels across conditions with 109 segregants, and identified 1,555 cis-regulatory sites, whereas distant linkages typically influence levels via a protein intermediate (trans factors). In a cis-regulatory site that interacts with a binding protein to directly increase or decrease transcript levels, mutations can either disrupt or enhance binding, but are unlikely to be able to do both in a condition-specific manner. If the binding protein is an activator, a loss of binding mutation will result in lower transcript levels in all conditions where the activator is present, and will show no change when the activator is absent, resulting in either a condition-specific pattern or in a pattern in which the locus has the same effect or the same direction of effect in both conditions  is at least partially responsible for the gxeQTL in this region  that experience different unmeasured environments are studied. This is because when conditions are not controlled, effects of opposite direction will cancel each other out, resulting in no overall association between the trait and the locus. This also has implications for selection, as these loci may be hidden from selection in organisms that experience fluctuating environmental conditions. The detection of loci that act in opposite direction is additionally complicated by our observation that the majority of these loci did not reach genome-wide significance in either condition alone, emphasizing the importance of using methods to directly test for gene–environment interaction without prior reliance on linkage in a single environment.One general implication of our results for studies in other species, including humans, is that many genetic effects on most traits are likely to be detected without testing for gene–environment interactions, provided that the relevant environmental factors are known and controlled either experimentally or statistically. However, analyses that ignore gene–environment interactions introduce strong biases with regard to the types of loci that are detected. Moreover, gene–environment interactions play a dominant role for a minority of traits. We have studied the prevalence and importance of gene–environment interactions in a single-cell organism grown in two very different and precisely controlled environmental conditions. Our focus on transcript levels as quantitative traits allowed us to study a very large number of traits simultaneously and to delineate general patterns, as well as to provide detailed molecular examples of loci that show gene–environment interactions. The quantitative details would undoubtedly differ if different species, environments, and phenotypes were studied. It is possible that many environmental differences experienced by humans may be less drastic than those between growth on glucose and ethanol are for yeast. However, some environmental differences  can have a dramatic effect on health. Our detailed studies in a model organism provide examples of the types of effects that may be expected in humans, and thereby inform practical study design. Understanding the subset of human genetic variants whose phenotypic effects are modifiable by the environment will be key in making full use of personal genomic variation.5 (glucose) or 107 (ethanol) cells were transferred to 15 ml of fresh medium in a 125-ml Erlenmeyer flask and grown overnight on a rotary shaker at 200 rpm. Cells were harvested at an optical density at 600 nm (OD600) between 0.36 and 0.40. Cells were collected (5 ml) at 30 °C by vacuum filtration, and the filters were immediately frozen in liquid nitrogen. Replicates of parental strains and parental strains used to generate the reference sample were grown interspersed with segregant strains such that the parental variation would reflect experimental variation throughout the experiment.The segregants used in this study have previously been expression profiled in condition similar to the glucose condition used here . HoweverRNA was extracted from the filters using a standard hot acid phenol method, followed by RNeasy cleanup (Qiagen). Samples were quality controlled with RNA 6000 Nano kit of the Bioanalyzer 2100 (Agilent). Samples were labeled with either Cy3 or Cy5 dye, using the Low RNA Input Linear Amp Kit (Agilent) with the modification that half reactions were used with a quarter of the recommended dye.All samples were hybridized against the same common reference consisting of equal amounts of RNA from both parents (BY and RM) grown in both conditions (glucose and ethanol). Six independent replicates of each strain–condition combination were grown and harvested, for a total of 24 samples. RNA was isolated for each sample individually and then pooled, with an equal amount of RNA contributed by each sample. Multiple labeling reactions (24 for each dye) were pooled, and the same samples were used for all segregant and parental arrays. Experiments for knockout and allele replacement arrays used the same reference RNA sample, but a new labeling reaction.http://puma.princeton.edu/). Spots were considered good data if intensity was well above background and the feature was not a nonuniformity outlier. Additionally, transcripts were retained if data were present in all parental strains in the parental analysis or in at least 80% of the segregants in both conditions for the segregant analysis. We ultimately used 4,342 transcripts for the parental analysis and 4,482 transcripts for the segregant analysis.Samples were hybridized to Agilent 11k yeast arrays, which are two-color, 60-mer oligo arrays with two arrays per slide, each containing spots for 6,256 transcripts, with some duplicated spots. In order to minimize experimentally induced bias, we randomized the order in which samples were processed, hybridizing glucose and ethanol samples at the same time and on the same slides. After hybridizing and washing per Agilent instructions, the arrays were scanned using an Agilent scanner and analyzed with Agilent's Feature Extraction software (versions 8.0–9.5). The arrays were uploaded into PUMAdb for processing  in each condition (glucose and ethanol) were grown, expression profiled, and labeled . There were 4,342 transcripts with high-quality data: there were no polymorphisms within the probe sequence, and good data was present for each of the replicates. For each transcript, an ANOVA of the formGenetic correlation was calculated from the variance results of the ANOVA study using the following equation .Segregants were grown and expression profiled in both conditions, resulting in a total of 218 segregant arrays: 109 segregants each in glucose and ethanol. Samples were randomized with respect to dye, and all arrays were linearly adjusted for dye. Three phenotypes were used in linkage analysis: expression in glucose, expression in ethanol, and the difference in expression between glucose and ethanol (ethanol minus glucose for each segregant). Mapping a significant locus for the change across conditions is interpreted as gene–environment interaction. It can also be conceptualized as a paired test where the segregant's ethanol phenotype is effectively corrected for its phenotype in glucose.Genotypes have been previously characterized , but we Saccharomyces Genome Database, defined the ORF region. A distant linkage is any linkage that fell outside of the ORF region.A linkage was classified as a local linkage if the confidence interval of the linkage overlapped with the region containing the open reading frame of the gene of interest (ORF region). The two markers flanking the region from 500 bp upstream of the start of the ORF to 500 bp downstream of the stop of the ORF, as defined by the p < 9 × 10−5). These cutoffs were greater than 15 for glucose, 14 for ethanol, and nine for interaction. Significant bins that were located immediately next to each other were merged into a single peak. Peaks in different conditions were considered overlapping if any bin contained in one peak was contained in the other.The genome was broken into 10-cM bins, and the peak of each linkage in each condition  was assigned to a bin. A bin was considered to have an excess of linkages if the number exceeded the number expected by chance by Poisson distribution, given the total number of distant linkages and Bonferroni correction for 563 tests . To estimate the proportion of linkages within a condition that also showed gene–environment interaction, we calculated p-values for the linkage between peak markers found within a condition and the difference in expression between conditions. The proportion of significant tests is For each software , we dete2 and p-values were calculated in Excel with expected values derived from row and column expectations. The Mann-Whitney test was performed in R using the wilcox.test command of the stats package.RR estimates and 95% CIs were calculated in R using the twoby2 command of the Epi package. Values of χThe RM genome was downloaded from the Broad Web site and aligned to the S288c genome (SGD). The sequence of each probe was found in the S288c sequence, and the corresponding sequence determined for RM. We were able to find 6,217 probes in the alignment, and of these, 5,029 have no polymorphisms at all, 747 have a single polymorphism or gap, and 438 have two or more strain differences. Missing probes were either in the mitochondrial genome or differed from the most recent reference sequence. The presence of even a single polymorphism was associated with an increased probability of apparent local linkage, as would be expected if there were differences in binding efficiencies of the alleles. We thus excluded them from all further analysis.IRA2 and DIG1 replacement strains were generated by a two-step allele replacement method [IRA2 was replaced with URA3 in BY4724 [MATa ura3 lys2) and RM11-1a [MATa leu2 ura3 ho::KAN), generating ira2Δ::URA3 knockout strains. New IRA2 alleles were amplified by PCR with approximately 500-bp overlapping sequence and introduced into the appropriate background to replace URA3. See MKT1 D30G replacement strain in the BY4742 background [t method . For exan BY4724  (MATa ur RM11-1a  (MATa leckground .IRA2 sequence was obtained from the whole-genome sequencing project at the Broad Institute (http://www.broad.mit.edu/annotation/genome/saccharomyces_cerevisiae/Home.html), with the exception of a small gap, which we sequenced using standard dideoxy methods. Replacement strains were also sequenced using standard dideoxy sequencing methods.The RM IRA2 replacement strains recapitulated the effect due to the locus in the segregants by comparing the locus effect to the replacement effect for all linking transcripts. For a given transcript, the locus effect is the difference between the BY change across condition  and the RM change across condition . For the replacement effect, the calculation is similar, except instead of segregant strains, we use the parental and replacement strains. Thus, for a single transcript, the replacement effect would be:We quantified how well the IRA2 and repeating the analysis 1,000 times. The p-value is the number of times that we got a correlation as high or higher than what was observed. A significant correlation indicates that gene that was replaced functionally influences the traits. We also analyzed this relationship by linear regression using the lm command in R. If the regression slope is close to 1, then it indicates that the gene is a major contributor to the phenotype.We then compared the locus effect to the replacement effect across all transcripts by calculating a Pearson correlation. We estimated significance of this correlation by randomizing the genotype at the marker nearest to p-values were Bonferroni corrected for multiple testing.Of the 4,482 transcripts that were analyzed in the segregants, 4,339 had GO terms associated with them. This set was used as a background for GO term enrichment. Hypergeometric tests were performed using GOLEM , and p-vhttp://rd.plos.org/pbio.0060083) were used to look for enrichment of binding sites. These lists are constructed by creating lists of genes that contain a predicted TF site at three levels of conservation and three levels of experimental binding [p-Values were Bonferroni corrected.Of the 4,482 transcripts that were analyzed in the segregants, 4,465 were used to look for enrichment of transcription factor (TF) binding sites. All nine “ORFs_by_factor” files that are provided by MacIsaac et al. on their Web site  values for all parental replicates. Data have been processed according to (1.7 MB XLS)Click here for additional data file.Dataset S2Log2 ratios  of segregant glucose and ethanol expression values. Data are processed according to (6.6 MB XLS)Click here for additional data file.Figure S1The linkage map was calculated with 109 segregants using the Haldane mapping function in R/qtl, with the chromosome and order of the markers determined by their physical position. Chromosomes are indicated by vertical lines, and markers are indicated by horizontal lines. The map covers a total of 5,545 cM.(663 KB PDF)Click here for additional data file.Figure S2(A) The number of transcripts that show significant strain–condition interaction, strain, and condition effects in the parental strains.(B) The number of linkages showing genome-wide level significance for gene–environment interaction, glucose, and ethanol. For each linkage, the marker closest to the peak of linkage was investigated in each condition. If the LOD score exceeded the genome-wide–level threshold, then the marker was considered significant in that condition. Since we are pooling results from different analyses, there are multiple linkages per chromosome. We randomly picked one of these linkages for the diagram and removed the rest. The totals within each condition do not add up to the total number of significant linkages reported because although a linkage can occur on the same chromosome in glucose and ethanol, they are not necessarily close enough that both markers are significant in both conditions, particularly when the scores are near the significance thresholds.(116 KB PDF)Click here for additional data file.Figure S3DIG1. We chose to use the marker closest to DIG1 to limit the proportion of transcripts that were being influenced by nearby peaks (gxe15). Each point represents a transcript. Solid lines indicate the best fit by linear regression . The grey dotted line indicates y = x. Both lines are significantly different from 0, as indicated by the slopes.The relationship between the segregant effect and the replacement effect is shown for all transcripts that have a peak of linkage at the marker closest to (150 KB PDF)Click here for additional data file.Figure S4IRA2 are shown. Associations are shown for linkages in (A) glucose and (B) ethanol. Each point represents a transcript. Solid lines indicate the best fit by linear regression . The grey dotted line indicates y = x. Linear regression slopes, confidence intervals, and R2 values are shown for each line.The relationship between the segregant effect and the replacement effect is shown for all transcripts that have confidence that overlap with (283 KB PDF)Click here for additional data file.Figure S5(A) A scenario involving the loss of a transcription factor binding site across conditions that differ in the concentration (or activity) of the transcription factor. Depicted in the graph are two alleles, one with a mutation upstream of a start site (asterisk) and one without. Dotted lines indicate time points when measurements are taken. The activity at the locus is depicted below the graph for these two time points where the transcription factor (double circle), when present, is binding to the functional site and causing an increase in expression or no change. Expression levels are shown increasing from green to red, relative to the average expression.(B) A scenario involving a transcription factor that is able to bind an activator or an inhibitor. An open circle indicates a protein capable of binding both factors, while the three-quarter circle indicates a protein unable to bind to either. The activator and the inhibitor vary in concentration depending on condition.(C) A scenario in which one mutation (blue) changes the ancestral state (black) such that the average phenotype changes. A second mutation on the same haplotype (brown) changes expression such that the average phenotype is the same as the ancestral state. Thus the final phenotype averaged across the conditions is the same for both haplotypes, and the effects of the haplotype in the two conditions are in opposite directions.(126 KB PDF)Click here for additional data file.Figure S6gxeQTL with confidence intervals that overlap MKT1 are shown. Each point represents a transcript. Solid lines indicate the best fit by linear regression . The grey dotted line indicates y = x. Linear regression slopes, confidence intervals, and R2 values are shown for each line. The slope is significantly different from 0, indicating that the variant contributes to phenotypic variation at this locus.Segregant and replacement effects for distant (187 KB PDF)Click here for additional data file.Table S1Each distant peak is described in further detail, with the location, number of linkages, verified or potential candidates, top significant GO term, and significant enriched transcription factor binding sites.(29 KB XLS)Click here for additional data file.Table S2A list of the strains used in this study.(20 KB XLS)Click here for additional data file.Table S3p-values for each factor, percent variance explained for each factor, whether the effect was significant, and average effect size are listed.A list of the results from the ANOVA for all transcripts tested. For each transcript, the presence of a polymorphism in the probe, (1.7 MB XLS)Click here for additional data file.Table S4A list of all significant linkages. For each linkage, the presence of a polymorphism in the probe; the linkage peak marker, location, LOD score, confidence interval, and distant peak name; whether the peak is local or distant, and average effect sizes are listed.(10.5 MB XLS)Click here for additional data file.Text S1(44 KB DOC)Click here for additional data file.Saccharomyces Genome Database (http://www.yeastgenome.org): AMN1 (S000000362), CIN5 , HAP1 (S000004246), HXT6 (S000002751) , HXT7 (S000002750), IDP2 (S000004164), IRA2 , MSN2 (S000004640),and MSN4 (S000001545).Gene accession numbers reference the http://www.ncbi.nlm.nih.gov/projects/geo/) accession number GSE9376 and through PUMAdb (http://puma.princeton.edu/).Expression raw data can be obtained via Gene Expression Omnibus (GEO;