Different materials can be used to reconstruct the core foundation in all-ceramic restorations.Bond strength of the core material to zirconia is an important factor in long-term restoration success.  The aim of this study was to assess shear bond strength (SBS) of zirconia to four different core materials.in vitro study, 40 zirconia ceramic disks (10×3 mm) were prepared and divided to four groups based on core material.Cylinder shaped core specimens (3×4 mm) of non-precious gold alloy (NPG), zirconia ceramic, natural dentin, and compositeresin were prepared and bonded perpendicularly to the zirconia disks using Gillmore Needle Apparatus and dual cure resin cement.All samples were thermocycled for 2000 cycles. To evaluate SBS, the specimens were tested by universal testing machine.Data were analyzed using Kruskal-Wallis test followed by Dunn's test with Bonferroni correction.Statistical significance was set at p< 0.05. In this experimental p< 0.05). The highest values for SBS were achieved in composite resin group (11.58±1.74 MPa) followed by NPG (10.32±0.94 MPa),zirconia (7.3±1.11 MPa) and dentin group (6.53±0.56 MPa). SBS in composite resin and NPG core materials were significantly higherthan other core materials ( Composite resin and NPG cores showed significant higher bond strength to zirconia in comparison to dentine and zirconia core materials. There are several methods mentioned in the literature for restoring damaged anterior teeth  .IncreasSeveral materials can be used to compensate the missing tooth structure as a core foundation for extra-coronal restorations.The choice of proper material depends on the amount of remaining tooth structure, esthetics, finances, and treatment duration .The nonIn translucent ceramic crown systems, there is concern about the impact of abutment shade beyond the restoration . For higThe bond strength of core foundation and crown plays essential role in success rate of full ceramic restorations .PreviouThe use of airborne particle abrasion technique results in improvement of the bond between resin cementand yttria-stabilized tetragonal zirconia polycrystalline ceramics by producing roughness at the zirconia surface.This surface roughness increases the surface wettability and micromechanical retention with luting agents .Several methods including use of phosphoric acid ester monomers like 10-methacryloyloxy-decyl-dihydrogen- phosphate, zirconiacoupling agent, and organic silane have been suggested to improve zirconia crowns bond strengths - 19.TheBond strength of zirconia crowns to different core materials has been investigated in limited studies . So, the40 disk samples of multilayer zirconia  were made with10mm in diameter and 3mm in thickness) using computer-aided design/computer-aided manufacturing technique(CAD/CAM) . All samples were polished by silicon carbide paper and then were mounted in acrylic molds(1×2×4.5 cm) by locating them in the same level of acrylic resin surface. Specimens were sandblasted with 50 µm aluminumoxide particles under 3 bar pressure for 15 seconds from a 10mm distance. All samples were cleaned in ultrasonic bath with96% propanol for 3 min. Subsequently they were divided into four groups according to the core material tested (N=10).2 with light-emitting diode light-curing unit.In the group 2, NPG alloy cylinders (4mm height ×3mm diameter) were fabricated by cylindrical patternresin LS molds and casted with NPG alloy by lost wax technique.In the group 3( dentin cylinders), 10 freshly non-carious extracted human third molars were cleaned and disinfectedin 0.5% chloramine T solution for 7 days. The roots were cut below the cementoenamel junction usinga double-sided diamond disk and cylindrical specimens were obtained using trephine with 3mm inner diameter and 4mm height.In the group 4, zirconia cylinders (4mm height ×3mm diameter) were prepared from Katana zirconia blocks usingcomputer-aided design/computer-aided manufacturing technique.All materials and appliances are described in detail in In the group 1, composite resin cylinders were built up using plastic cylinder (4mm height ×3mm diameter) and werecovered with glass slide in order to reach the smooth surface. All the samples were light cured for 40 seconds at powerdensity of 600 MW/cmAll cylindrical specimens were etched with phosphoric acid 37% for 5 seconds, rinsed, and air dried before bonding.Clearfil ceramic primer plus was used to prepare the surface of zirconia disks according to the manufacturer's instructions.After 30 seconds, surface of zirconia disks were gently air dried with oil-free compressed air for 5 sec.A drop of each bottle A and B of ED Primer II was mixed and applied on the surface of core material specimensby micro brush according to manufacturer’s instruction. After 30 seconds, surface of core specimens were gently airdried with oil-free compressed air for 5sec.Dual resin cement (Panavia F2.0) was used for bonding procedure. Equal amount of each tube A and B of PanaviaF2.0 resin cement was mixed for 20 seconds and applied on zirconia surface by spatula.The core material samples were put on the cement surface subsequently .The cemResidual amount of cement was removed by a small brush after primary curing for 10 sec. Inhibitorgel (Oxyguard Kurary) was applied for 3 min on the margins of the samples afterward.Finally, all of the samples were cured in four directions for 20 .After bonding procedure, all samples were stored in distilled water at 37°C for 24 hours and then thermocycled for 2000 cyclesin water bath between 5°C and 55°C with a dwell time of 20 seconds for each temperature with transfer time of 10 seconds.SBS test was performed using universal testing machine  with the cross head speed of 0.5 mm/min until bond failure occurred.The maximal force (MPa) for debonding was recorded. The data was subjected to Kruskal-Wallis test followed byDunn's test with Bonferroni correction. The significance level was 0.05%.Mean ± standard deviation values of SBS for all groups are presented in p< 0.05) .To investigate the normality of the quantitative variable distribution of data, Kolmogorov Smirnov test was used.With no confirmation of normality assumption, nonparametric analysis of Kruskal-Wallis test and Bonferroni correctionwere used for comparing the groups. Based on the Kolmogorov-Smirnov test results, the normality of the data distributionin composite resin, NPG, and zirconia except dentin were confirmed. The result of Kruskal-Wallis test showed significantdifferences between groups (p< 0.05).Differences between Dentin- Zirconia and NPG-Composite SBS values were not statistically significant (p> 0.05).Selection of an appropriate material in restoring the core of a damaged tooth depends on the remaining dentalstructure and esthetic factors  . Proper et al.[ et al.[ Bonding of zirconia to the core materials is challenging. Chemical structure contains polycrystalline and lackof amorphous glass phase is believed to be the main cause of weak bond of yttria-stabilized tetragonal zirconiapolycrystalline ceramics  . Severalet al.  mentioneet al. .Our res et al. , which e et al. .The high organic content and moisture of natural dentin could be the main reasons of weak bond betweenzirconia and dentin core, these findings are in agreement with other studies - 35.Altet al. [ et al.[ Frattes et al.   reported et al.  study wa et al. .Althouget al. [ Similar to our findings, Al-Harbi et al.  showed tet al.  . Due to Prior to bonding, Clearfil ceramic primer was used to prepare zirconia disk surfaces according tothe manufacturer's instructions. This primer contains 3-methacryloxy propyl trimethoxy silane mixedwith 10-methacryloyloxy-decyl-dihydrogen-phosphate monomer.By using this primer, chemical bond is formed between zirconia and aluminum oxide substrate and the phosphateester group of this monomer  - 38.Thein vitro study. Confounding factors like masticatory forces, salivary flow, andfatigue should also be considered to stimulate oral cavity condition and obtain more valid and reliable results for clinical practice.There are few studies about the effect of different types of core material on the SBS values of zirconia crowns.Therefore, more studies with different core materials, bonding systems, aging process and debonding forces is recommended.This study was an Regarding the limitation of this study, the effect of different core materials on the SBS of zirconia was significant.Considering the importance of bonding between crown and core, composite and NPG could be recommended as foundation core material.


P ≤ 0.05) on the cloud stability, most of the physicochemical properties and microbial count of pineapple juice as compared to the control. The turbidity and viscosity of the juice samples were increased significantly (P ≤ 0.05) by the starch addition; in contrast, pulp sedimentation and microbial counts were decreased. As storage duration increased, turbidity, viscosity, TSS, pH, and vitamin C content of juice decreased, whereas sedimentation, TA, and microbial count increased. The results revealed that the total bacterial and fungal counts of pineapple juice samples were rising as storage durations increased. The maximum cloud retention was observed in juice added with 5% anchote starch. Finally, it is confirmed that starch (anchote and potato) addition positively affected the colloidal stability of pineapple juice and also possessed high potential to extend the shelf life.Starch is one of the most important value-added food ingredients used as a thickener in many foods and industrial applications. This research investigated the effect of different concentrations of starch (anchote and potato) addition on the colloidal stability of pineapple juice. The experiment was carried out on a two-factor factorial design arranged in CRD. The first factor (starch type with two levels (anchote and potato)) and the second factor ) were considered. The starch-added juice samples were preserved for 15 days at room temperature. The physicochemical properties, colloidal stability, microbial counts, and sensory analysis were conducted in a 7-day interval including the first day. The results revealed that different starch concentrations showed a significant effect ( Ananas comosus (L.) Merr.) is the most versatile, commercially important, nonclimacteric tropical fruit that belongs to the Bromeliaceae family [Pineapple  , 3. HencA small percentage of insoluble particles  remain in the cloudy juice processing. Achieving the bright, natural color is not possible in cloudy fruit juices –14. In aHydrocolloids are used widely in fruits, vegetables, and protein-based juices to improve color or cloud stability for prolonged periods due to their thickening (raising the viscosity) and suspension properties , 14–17. Hence, using starch as a stabilizer in fruit juice proves to be vigorous due to its effectiveness, availability, and low in cost. On the other hand, starch does not alter the organoleptic and sensory properties of the juice compared to other hydrocolloids. Researchers reported the use of carboxymethyl cellulose (CMC), low-methoxyl pectin, guar, xanthan, and gellan gum, , 19, sodCoccinia abyssinica) is an indigenous root tuber crop widely produced in southern and southwestern parts of Ethiopia. Anchote is a drought tolerant crop providing food security; it is a highly productive and nutritionally ample crop [Anchote  tuber was collected from the local market of Nekemte Town, Oromia Region, Ethiopia. Commercial potato starch was purchased from the local market of Bahir Dar City, Ethiopia.Fully ripened pineapple (Cayenne cultivar) was purchased from the local market of the Bahir Dar City. Anchote (w/v) crushed tuber mass and water suspension were prepared. The suspension was filtered through 250 μm sieves, and the filtrate was allowed to settle for 12 h. The supernatant was decanted, and the sediment was washed until the pure white in color is obtained. The resulting starch was finally dried at room temperature, milled to fine powder in a mortar and pestle, sieved through 224 μm mesh, and stored in an airtight plastic container under dry conditions for further use [As the anchote starch was not readily available, isolation and purification were done by Sit et al.  method. The experiment was set as 2 × 3 factorial design arranged in CRD. The first factor considered starch type with two levels (potato and anchote). The second factor is starch concentration in three levels , and each treatment was conducted in triplicate and total experimental runs conducted in this experiment were 18 and pineapple juice without any starch addition was considered as control.μm aperture stainless steel sieve screen into a clean transparent plastic bowl [w/v) as a designed experiment. The juice was mixed by a magnetic stirrer until complete dissolution of the starch. After incorporation of the starch, pineapple juices were bottled and allowed for storage studies at room temperature (28°C ± 2°C) for fifteen (15) days. The stability of color and cloud was measured at every 7-day interval for 15 days.The fully ripened pineapple fruits were washed thoroughly with potable water to remove dirt. The cleaned pineapples were peeled off and cut into pieces with sterile stainless steel kitchen knife. Then, the pineapple pieces were homogenized in a clean electric laboratory disperser . The pineapple juice was filtered through 500 tic bowl , 25. The3) was calculated by dividing the weight of sample on its volume after tapping.Bulk density of starch samples was determined according to the method of Ohizua et al. . Starch C) for 30 min, and then centrifuged  for 30 min at 3000 rpm. The clear supernatant was decanted. Water absorption was expressed as percent water bound per gram flour.The WAC of the starch samples was determined by the method of Bello-Pérez et al. . One graSwelling power and WSI were determined according to the method described by Bello-Pérez et al. . 0.5 g oThe moisture content, ash, crude protein, and fat contents were determined according to the AOAC (2006) method numbers 950.46, 920.153, 992.15, and 989.05, respectively. The total starch content was determined by the AOAC (2007) method number 996.11 .Amylose and amylopectin contents were determined by using the method of Hassan and Hassan  as citedTotal soluble solid (TSS) content of pineapple juice was measured using a hand refractometer  (AOAC 2000). 1 ml of a well-homogenized pineapple juice was placed on the prism of a calibrated hand refractometer. The readings were taken, and results were expressed in °Brix.pH of starch and pineapple juice was determined by using a calibrated pH meter with standard solution of pH 4 and 7. Each sample (10 ml) was taken into a beaker, and then, electrodes of the pH meter  were immersed into the sample and the readings were recorded directly .The titratable acidity (TA) was determined according to AOAC (2004) procedure. 10 ml of sample was taken into a conical flask, and three drops of phenolphthalein indicator were added. The mixture was titrated against 0.1 N NaOH solution. The TA was calculated by the standard formula by using a citric acid titer value and expressed as the citric acid .Dynamic viscosity of starch and pineapple juice was determined in an Ostwald viscometer  at 20°C and expressed in mPa s , 31.The stability of pineapple juice was assessed by serum cloudiness (turbidity). Cloud stability of the centrifuged samples (4000 rpm for 15 min) (expressed in terms of % light transmission) was determined by measuring absorbance at 660 nm using a UV-Vis spectrophotometer  calibrated with distilled water. The absorbance at 660 nm was directly related to the turbidity of pineapple juice and expressed in nephelometric turbidity units , 15, 16.Vinf is the sediment volume (ml) and Vtotal is the total volume of the sample (ml).The pulp sedimentation of pineapple juice was determined according to the method of Silva et al. . The pinVitamin C content was determined by an iodometric titration method as described by Nweze et al. .Sensory acceptability of pineapple juice was conducted by untrained 20 volunteer panelists  in a hedonic test. The coded pineapple juice samples were presented to panelists randomly for likeness scores on sensory evaluation  by using a seven-point hedonic rating scale where 7 represents like extremely and 1 represents dislike extremely .The pineapple juices with different starch levels were stored at ambient temperature for fifteen (15) days. The shelf life of the juice was determined by checking the pH weekly and by determining the microbial growth .Enumerations of aerobic plate count (APC) and total fungal counts were performed according to the ISO standard method (ISO-4833:2003(E)). Enumeration of APC was done by the serial dilution technique followed by a pour plate method.th day, and 15th day, serial dilution was carried out by taking pineapple juice from each bottle. The serially diluted juice sample (0.1 ml) with dilution factor 103 for fungal enumeration and 104 for bacterial enumeration was plated and incubated at 37°C for 24 h for bacteria and for fungi at room temperature for 72 h. The colonies from bacteria were counted using the colony counter. The mean value of the triplicate was taken, and the number of colonies was multiplied by the dilution factor and calculated as 1 ml of original sample. It was then expressed as colony-forming unit per ml (cfu/ml) of the sample [At the initial day, 7e sample .P ≤ 0.05. The results were presented as the mean ± standard deviation.A triplicate data was subjected to analysis of variance (ANOVA) using Minitab version 19.2. Analysis of variance was performed with the general linear model. The mean separation was done by the Tukey method and considered a significant difference at The results on physicochemical and functional properties of the potato and anchote starches are given in P ≤ 0.05) was observed in the functional properties of potato and anchote starch except in bulk density and pH. The bulk densities of potato and anchote starches were observed as 1.5 g/ml and 1.4 g/ml, respectively. The bulk density is affected by the particle size and the density of the starch flour. This functional property is very important in determining the packaging requirements, material handling, and the compatibility in wet processing in food industry [A significant difference  and fiber. The fibers have good ability to associate with water under limited water presence (high hydration properties) . The WACThe SP showed 4.9% for potato starch, whereas 6.2% for anchote starch. The capacity to hydrate and swelling allows changes in starch viscosity. The higher amylopectin content is responsible for higher SP and viscosity at low temperatures. The swelling capacity of starch granules allows increasing their viscosity and gelling properties . The difThe WSI values of the starches were 1.2 g/g and 0.6 g/g for potato and anchote, respectively. WSI measures the number of free molecules leached out from the starch granules in addition to excess water and thus reflects the extent of starch degradation .The mean viscosity values were observed as 235.3 mPa s and 241.7 mPa s for potato and anchote starches, respectively. The higher viscosity was observed for the anchote starch. The results of pH for starch samples were reported as 6.0 and 6.1 for potato and anchote starch, respectively. The pH values of the starch in water suspension are important because some functional properties like solubility and emulsion properties are highly affected by change in pH . The minP ≤ 0.05) among potato and anchote starches in the proximate composition  stored for 15 days, whereas no (0%) sedimentation was observed on the initial day of storage. In terms of starch type, the highest sedimentation (31.17%) was observed in 5% potato starch-added samples while the lowest sedimentation (6.50%) was observed in 5% anchote starch-added pineapple juice samples stored for 15 and 7 days, respectively.The results on the physicochemical properties of pineapple juice as a function of storage time are given in Pineapple juices with potato starch showed higher sedimentation than juice with anchote starch. The control samples showed highest degree of sedimentation as compared to starch-added samples during the storage time. The sedimentation and phase separation were observed in the first 24 h of stored control samples; it is a common behavior of some fruit juices . After tAs the storage time increased, the sedimentation also increased; in contrast, as the starch concentration increased, the sedimentation decreased in the first 7 days of storage while increased after 7 days of storage. This may be due to the increase in concentration of starch that leads to the settlement of cloud. It has been reported that incorporation of starch with higher concentrations leads to a higher electrolyte concentration, resulting in salting out of the starch . PineappP ≤ 0.05) with increasing levels of starch incorporation into the pineapple juice as compared with the control samples. This may be attributed to the rise in the juice viscosity due to the addition of the starch. In general, the sedimentation of juices varied significantly (P ≤ 0.05) with increasing levels of starch addition and storage durations. As given by the Stokes law, particle sedimentation velocity is inversely proportional to the dispersed medium viscosity [The sedimentation decreased significantly  that was observed due to the effect of starch type and concentration on pH of pineapple juice as a function of storage durations. As the results have showed, the highest pH (3.49) value was observed in 1% anchote starch-added juice at the initial day, while the lowest (2.0) was determined in 5% potato starch-added pineapple juice after 15 days of storage. Besides, depending on the starch type, the highest pH was shown in 1% anchote starch-added samples, while the lowest was observed in 5% potato starch-incorporated juice  in TA of pineapple juice as illustrated in P ≤ 0.05) due to starch concentration on TA of pineapple juice except on the 7th day of storage in both the starch type additions. The highest TA (0.52%) was observed in 3% potato starch-added pineapple juice samples after 15th days of storage. In contrast, the lowest TA (0.25%) is in 5% anchote starch-added pineapple juice samples at the initial day. In terms of starch type, the highest TA was observed in 3% potato starch, whereas the lowest is in 5% anchote starch-added pineapple juice. The juices with potato starch showed slightly higher TA than anchote starch-added juices. However, it remained almost unchanged with the increasing concentration of starch.The effect of interactions and main effects was significant (P ≤ 0.05). The TA results of this study were in agreement with reports of Ghafari and Ansari [The ANOVA showed that pineapple juices exhibited a slight increase in the TA over storage time and were significantly varied  on TSS due to starch type except on the 15th day of pineapple juice has stored. There was a significant effect (P ≤ 0.05) on TSS of pineapple juice during storage by starch concentration except the 15th day of storage. The results showed that the highest TSS was observed in 5% anchote starch (15.13%) at the initial day, while the lowest TSS (13.03%) was observed in 1% potato starch-added pineapple juice after the 15th day of storage. In addition, in terms of starch type, the highest TSS was observed in the samples with 5% anchote starch addition, while the lowest was observed in 1% potato starch-incorporated juice. Pineapple juices with anchote starch showed a slightly higher TSS than juice with potato starch addition. However, there was no statistically significant difference; a slight increase in TSS was observed as starch concentration increased.The ANOVA showed that the effect of interactions was not significant on TSS of pineapple juice at P ≤ 0.05) between the treatments. At lower pH, juices are comparatively effluent in organic acids and result in low TSS content [P ≤ 0.05) were not observed on TSS by starch type and starch concentration. The results of TSS are similar to the values reported by Lu et al. [The analysis of variance revealed that TSS of pineapple juices slightly decreased over storage durations and were not significantly varied  difference observed due to the interactions effects on vitamin C content of pineapple juice, except at the initial day of storage. A significant difference (P ≤ 0.05) was not observed in vitamin C content of pineapple juice by the effect of starch type and starch concentration, except a significant effect by starch concentration was observed at the 7th day of storage. The highest vitamin C concentration was observed in 5% potato starch-added juice sample (21.64 mg/100g) at the initial day, whereas the lowest vitamin C content (17.30 mg/100g) was determined in 1% potato starch-added pineapple juice after 15 days of storage. Similar results were observed due to the starch type with respect to the highest and lowest vitamin C content of pineapple juice. The vitamin C content of pineapple juice with potato and anchote starches was almost unchanged. The vitamin C content in starch-added and control pineapple juice samples was observed higher on initial day. Similarly, there was no significant statistical difference among the vitamin C composition of different samples on initial day. However, as the storage duration increases, the vitamin C content decreased in all the samples. This result was in agreement with reports of Rashima et al. [P ≤ 0.05) on vitamin C content. Besides, there was no difference (P ≤ 0.05) observed in vitamin C content of pineapple juice due to the starch concentration and type of starch.There was no significant  depends on interactions and main effects. The results showed that the highest viscosity (70 mPa s) was observed in 5% anchote starch-added pineapple juice at the initial day, while the lowest viscosity (10 mPa s) was observed in control juice after 15 days of storage. Moreover, in terms of starch type, the highest viscosity (70 mPa s) was shown in 5% anchote starch, whereas the lowest viscosity (19 mPa s) was observed in 1% potato starch-incorporated juice. Juices with anchote starch were observed for higher viscosity than juice with potato starch addition. As the concentration of the starch increased in the juice, the viscosity also increased. Besides, as the storage time increased, the viscosity of pineapple juice decreased significantly (P ≤ 0.05).The ANOVA showed that the viscosity of pineapple juice significantly (P ≤ 0.05).This tread may be attributed to the degree of starch polymerization. With a high degree of polymerization, the solution viscosity will be high. The results of the present study are in agreement with the findings of Shamsudin et al. . CompareP ≤ 0.05. The results revealed that the highest turbidity (696.33 FTU) was observed in 5% anchote starch-added pineapple juice at the initial day, while the lowest turbidity (102 FTU) was observed in control juice after 15 days of storage. Besides, in terms of starch type, the highest turbidity (696.33 FTU) was observed in 5% anchote starch, whereas the lowest turbidity (152.33 FTU) was in 1% potato starch-incorporated juice. Pineapple juice with anchote starch showed higher turbidity than juice with potato starch-added samples. As the concentration of the starch increased in the juice, the turbidity increased significantly (P ≤ 0.05). Moreover, as the storage duration increased, the turbidity of pineapple juice decreased significantly (P ≤ 0.05). The major changes in the turbidity of the pineapple juice were taken place in the first 7 days of the storage. As storage time increased, cloudiness decreased with rise in the starch concentration. This can be attributed to the decrease in viscosity and increase in particle sedimentation which allows more light to transfer through the juice. The results of this study revealed that as the increase in starch concentration is from 1% to 5%, the pineapple juice cloudiness also increased due to the loss in viscosity and movement of particles in the system. The control juice showed a lesser degree of cloudiness as compared to the starch-added juice samples during the storage period.The statistical analysis showed that the effect of interactions and main effects was significant on the turbidity of pineapple juice at P ≤ 0.05) with increase of storage duration as compared to the control samples. In addition, the turbidity of juices varied significantly (P ≤ 0.05) with increase of starch concentration and starch type. Moreover, all the juice samples showed cloudiness variation along the storage durations, reflecting the particle aggregation due to attractive forces. The juices with higher concentration of starch showed lower variation in turbidity than the other samples.The turbidity of the pineapple juice decreased significantly  between the juices containing potato and anchote starch. Therefore, the addition of starch did not affect these sensory parameters of the pineapple juice. However, pineapple juice with starch addition showed better acceptability than pineapple juice with no starch, in terms of appearance, texture, and mouth feel. As the starch concentration increased in the juice, the panelists are given sensory score from like very much to like moderately for all evaluated sensory attributes. The type and amount of starch had no significant effect on sensory acceptability of pineapple juice. Similar studies have been done by Akkarachaneeyakorn and Tinrat [The results of sensory acceptability of pineapple juices as a function of storage time are presented in d Tinrat  and Lv ed Tinrat  on the eSubsequently, aroma, flavor, and mouth feel of juice are the most important factors that affect the pineapple juice acceptability; all consumers like pineapple juice with good flavor, mouth feel, and aroma. In terms of the texture, it is also an important factor; generally, soft texture is easy to accept by humans. Overall, acceptance of the juices with starch is important because consumers are not interested in consuming pineapple juice which is not in good appearance.P ≤ 0.05 at the initial day of storage. However, a significant effect (P ≤ 0.05) was observed due to the interactions and main effects after the initial storage day on microbial counts of pineapple juice. The results revealed that the highest bacterial count was observed in control juice (1.14 × 105 cfu/ml) after 15 days of storage, whereas the lowest bacterial count (7.04 × 103 cfu/ml) was observed in 5% anchote starch-added pineapple juice at the initial day. Besides, the highest fungal count was observed in control juice (1.00 × 105 cfu/ml) samples at the initial day, while the lowest fungal count (8.77 × 103 cfu/ml) was observed in 3% and 5% anchote starch-added pineapple juice after 15 days of storage. In terms of starch type, the highest bacterial count was shown in 1% anchote starch, whereas the lowest is in 5% anchote starch-incorporated juice. Similarly, the highest and lowest fungal counts were observed in 1% and 5% anchote starch-added samples, respectively. Juices with anchote starch were observed for higher bacterial and fungal counts than juice with potato starch added. As the starch concentration increased in the juice, the bacterial and fungal counts decreased significantly (P ≤ 0.05) except between 1% and 3% starch concentrations. Higher bacterial and fungal counts were observed in control samples than the starch-added pineapple juice during the storage. This can be ascribed to strong water binding characteristics of the starch, which combine with water to form viscous solutions which restrict access of water for microbial growth [The effect of storage time, type, and amount of starch on the total viable bacterial and fungal counts of pineapple juice during storage is illustrated in l growth . The pinThe results revealed that the total bacterial and fungal count in all pineapple juices increased throughout the storage time. However, increased starch concentration had a significant decreasing effect on the microbial content compared with the control juice during storage. In general, control sample showed remarkably high microbial loads during the period of storage and this may be possibly a major cause of spoilage commonly experienced by the producers of this product . It is eThe results of this study revealed that addition of potato and anchote starch significantly improved the cloud stability of the pineapple juice as compared to the control. The findings showed that starch type and starch concentration levels had significantly influenced some of the physicochemical parameters  and microbial content of the pineapple juice in comparison to the control. Moreover, storage time had significant influenced on turbidity, viscosity, sedimentation, and microbial counts of the stored pineapple juice. As starch concentration in the juice increased, minimum influence on vitamin C content, TSS, TA, and pH of juice was observed. With increasing in storage time, turbidity, viscosity, TSS, pH, and vitamin C content of juice decreased, whereas sedimentation, TA, and microbial counts were increased. Adding starch with different concentrations had not significantly influenced the sensory acceptability of pineapple juice, even though there were some changes observed for some sensory attributes. In general, the findings showed that addition of starch with different concentrations had significantly influenced some physicochemical and shelf life of pineapple juice as a function of time.The result showed that pineapple juice with 5% anchote starch had better cloud stability during storage period. This is because of product exhibited less pulp sedimentation and high viscosity and turbidity. Generally, addition of 5% starch helped in maintaining the cloud stability, reduction in microbial load, and no influence on the sensory acceptability of the pineapple juice over a period of 15 days.


At the request of the Journal Editor(s) the following article has been retracted.International Journal of Immunopathology and Pharmacology 33: 1-11. DOI: 10.1177/2058738418824521.Li X, Wei W, Zhao Z, Lv S (2019) Tripterine up-regulates miR-223 to alleviate lipopolysaccharide-induced damage in murine chondrogenic ATDC5 cells. Significant similarities between this paper and a number of other papers have been identified, as well as indication of image and data manipulation suggestive of the use of a “paper mill”. These papers share common structures and wording yet do not share any common authors.The authors did not provide a sufficient response or evidence to refute these claims. As a result, the scientific accuracy of this paper is not reliable.Adhering to the international guidelines established by the Committee on Publication Ethics, the Journal has determined these are grounds for retraction.


The naturally occurring Δ40p53 isoform heterotetramerizes with wild-type p53 (WTp53) to regulate development, aging, and stress responses. How Δ40p53 alters WTp53 function remains enigmatic because their co-expression causes tetramer heterogeneity. We circumvented this issue with a well-tested strategy that expressed Δ40p53:WTp53 as a single transcript, ensuring a 2:2 tetramer stoichiometry. Human MCF10A cell lines expressing Δ40p53:WTp53, WTp53, or WTp53:WTp53 (as controls) from the native TP53 locus were examined with transcriptomics (precision nuclear run-on sequencing [PRO-seq] and RNA sequencing [RNA-seq]), metabolomics, and other methods. Δ40p53:WTp53 was transcriptionally active, and, although phenotypically similar to WTp53 under normal conditions, it failed to induce growth arrest upon Nutlin-induced p53 activation. This occurred via Δ40p53:WTp53-dependent inhibition of enhancer RNA (eRNA) transcription and subsequent failure to induce mRNA biogenesis, despite similar genomic occupancy to WTp53. A different stimulus (5-fluorouracil [5FU]) also showed Δ40p53:WTp53-specific changes in mRNA induction; however, other transcription factors  could then drive the response, yielding similar outcomes vs. WTp53. Our results establish that Δ40p53 tempers WTp53 function to enable compensatory responses by other stimulus-specific TFs. Such modulation of WTp53 activity may be an essential physiological function for Δ40p53. Moreover, Δ40p53:WTp53 functional distinctions uncovered herein suggest an eRNA requirement for mRNA biogenesis and that human p53 evolved as a tetramer to support eRNA transcription. How does Δ40p53, a naturally occurring isoform of p53 that is linked to accelerated aging, alter WTp53 function? Using an innovative approach, this study reveals that Δ40p53 suppresses enhancer RNA transcription and allows other stimulus-specific transcription factors to modulate the p53 transcriptional response. Transcription factors (TFs) are the primary drivers of cell state and cell physiology . As a teIsoform Δ40p53 has many aliases such as p44, p53/p47, and ΔNp53; Δ40p53 lacks only the N-terminal 39 amino acids, which encompass the first p53 activation domain (AD1). All other p53 domains, including the oligomerization and DNA-binding domains, are retained. AD1 is a key p53 domain that drives most p53 transcriptional responses in vivo ,12 and iIn the absence of wild-type p53 (WTp53), the Δ40p53 isoform is transcriptionally inactive in vitro, despite forming stable tetramers . MoreoveWhereas the molecular and cellular mechanisms by which Δ40p53 acts to alter WTp53 function remain unclear, it involves formation of heterodimers with WTp53. The Δ40p53 isoform forms mixed Δ40p53:WTp53 tetramers when expressed together with WTp53 ,21. GeneFig 1A). As a proof of principle, we tested the function of tethered Δ40p53:WTp53 tetramers (versus standard WTp53 tetramers) in a series of biochemical and cell-based experiments [A major barrier to understanding how Δ40p53 affects WTp53 function is that co-expression  will confound analyses due to tetramer heterogeneity, including formation of “contaminating” WTp53 tetramers. Under such circumstances, the activity of Δ40p53:WTp53 tetramers cannot be decoupled from WTp53 function. The cryo-electron microscopy (cryo-EM) structure of the WTp53 tetramer  revealederiments . As expeeriments . In facteriments . These rUsing precision nuclear run-on sequencing (PRO-seq), we measured rapid transcriptional responses following p53 activation, whereas RNA sequencing (RNA-seq) probed subsequent changes in steady-state mRNA levels, and these transcriptional responses were linked to p53 occupancy using chromatin immunoprecipitation sequencing (ChIP-seq). Combined with metabolomics and phenotypic assays, we have better defined how Δ40p53 alters WTp53 function in human cells. Notably, the Δ40p53 isoform tempers WTp53 activity, which allows other TFs to drive stimulus-specific responses. We also uncovered unexpected aspects of Δ40p53 function that link enhancer RNA (eRNA) transcription and mRNA biogenesis and that suggest that 4 complete p53 activation domains  must occupy a p53 binding site to induce eRNA transcription.Fig 1A). In each case, the p53 cDNA sequence was inserted at the first translational start site in exon 2 of the TP53 gene, to ensure expression would be controlled through the native p53 promoter . We chose MCF10A cells because they endogenously express WTp53 and are derived from non-tumorigenic mammary tissue. MCF10A cells therefore have a stable genome that is not prone to mutations or polyploidy. Edited cells were sorted based on mCherry selection, and single cell clones were expanded and verified homozygous using PCR , western blot , and sequencing .Three distinct genome-edited (CRISPR/Cas-9) MCF10A cell lines were generated: a WTp53 control, in which WTp53 was simply inserted back into the native TP53 locus; a WTp53:WTp53 control, to probe for potential tether-specific effects; and Δ40p53:WTp53 . Furthermore, the growth rate of each cell line was similar ; whereas growth was slightly enhanced in Δ40p53:WTp53 cells, the increase was not statistically significant. As a tumor suppressor, p53 significantly impacts cell metabolism [S1 Table). Metabolites relevant to cell cycle progression are shown in S4B Fig.In non-stressed conditions, cellular p53 activity is typically very low. All 3 cell lines  were derived from the same parental MCF10A line and were therefore isogenic except at the TP53 locus. Under normal growth conditions, no significant change in cell cycle was observed between WTp53, WTp53:WTp53, or Δ40p53:WTp53 cells . By contrast, cell cycle data for Nutlin-treated Δ40p53:WTp53 cells resembled control-treated (DMSO) cells, with only a modest Nutlin-dependent shift in G1 that did not reach statistical confidence of p ≤ 0.01 .We next examined how each cell line  would respond to p53 activation by Nutlin-3a. Notably, Nutlin-3a is non-genotoxic and highly specific for p53 ; thus, uFig 1E; WTp53:WTp53 in S5B Fig). Although p21 protein levels were greater in WTp53 cells, it was evident that Nutlin-3a activated Δ40p53:WTp53 tetramers, consistent with increased Δ40p53:WTp53 protein levels upon Nutlin-3a treatment . Parallel experiments in p53 null MCF10A cells confirmed that the Nutlin-dependent increase in p21 protein was p53 dependent . Also, as expected, cell cycle changes were not observed in p53 null cells in response to Nutlin-3a treatment, nor was there a change in p21 mRNA levels .A p53 target gene that drives cell cycle arrest is CDKN1A (aka p21), and p21 can be induced by mixed Δ40p53:WTp53 tetramers, as shown by us  and otheS5D Fig, WTp53:WTp53 cells responded similarly to WTp53, as expected. By contrast, a stark difference was observed between WTp53 and Δ40p53:WTp53 cells. Growth was arrested in WTp53 cells, but proliferation continued in Δ40p53:WTp53 cells, even after multiple rounds of Nutlin treatment . This result contrasts with untreated (DMSO) WTp53 versus Δ40p53:WTp53 cells, which showed no significant difference in growth rate .Others have shown that cell stress transiently increases Δ40p53 protein levels ,24–26,32S2 Table). As expected, Nutlin-treated WTp53 cells showed metabolic changes that reflected their reduced proliferation . By contrast, metabolic differences evident in Δ40p53:WTp53 cells were consistent with their maintenance of proliferation and were observed even prior to Nutlin stimulation (S1 Table). For instance, decreased levels of sphingosine or increased levels of sphingomyelin metabolites in Δ40p53:WTp53 cells  is each independently consistent with maintenance of cell cycle and proliferation [Fig 1B (Nutlin-dependent increase in Δ40p53:WTp53 levels) or Fig 1E (p21 protein induction). To further probe the underlying mechanisms, we next assessed how Nutlin-dependent p53 activation affected the transcriptomes of Δ40p53:WTp53 versus WTp53 cells.Because the metabolic needs for proliferating versus arrested cells will be distinct, we also compared the metabolomes of WTp53 versus Δ40p53:WTp53 cells following Nutlin-3a treatment , with reference to Nutlin-treated HCT116 cells that we evaluated previously [To compare and contrast transcriptional changes in Δ40p53:WTp53 cells (versus WTp53), we used PRO-seq, which measures nascent transcription genome-wide . PRO-seqeviously . This eap-value ≤ 0.01) after Nutlin-3a treatment , with similar transcriptional changes in WTp53:WTp53 cells . By contrast, only 315 annotated regions were differentially transcribed  in Δ40p53:WTp53 cells after Nutlin-3a treatment . Of these 315 Nutlin-induced transcripts in Δ40p53:WTp53 cells, 268 were also observed in WTp53 cells, and 47 annotated genes were differentially transcribed in Δ40p53:WTp53 versus WTp53 cells , with most linked to cell growth. We confirmed that a significant p53 response was occurring in Δ40p53:WTp53 cells by completing parallel PRO-seq experiments in p53 null MCF10A cells . Thus, the magnitude of Nutlin-3a induction was simply greater in WTp53 cells versus Δ40p53:WTp53, with examples shown in S9E Fig.In WTp53 cells, 4,338 annotated regions were differentially transcribed . Enrichment of the E2F pathway in Nutlin-treated Δ40p53:WTp53 cells (versus WTp53) was notable because the E2F TF family has overlapping function with p53 [Fig 2B) was consistent with dampened response in Δ40p53:WTp53 cells compared with WTp53. An integrated pathway analysis of the Nutlin-induced genes showed results consistent with GSEA, with growth and cell cycle pathways enhanced in Δ40p53:WTp53 cells . In particular, mammalian target of rapamycin (mTOR) signaling and insulin-like growth factor 1 (IGF-1) signaling were increased in Δ40p53:WTp53 cells compared to WTp53; each of these pathways has been linked to Δ40p53 expression in human cells [Gene set enrichment analysis (GSEA) of the Nutlin-induced genes revealed substantial differences, with pathways associated with growth and cell cycle progression increased in Δ40p53:WTp53 cells  after Nutlin-3a treatment , we emphasize that the PRO-seq data were obtained after only 3-hour treatment; changes in steady-state mRNA levels  after a longer period of Nutlin-3a treatment are described later.Although the transcriptional changes summarized in p-value ≤ 0.01) eRNAs after 3-hour Nutlin-3a treatment, only 118 eRNAs changed in Δ40p53:WTp53 cells. Among the 510 WTp53 eRNAs , 109 mapped to p53 binding sites identified by ChIP-seq . These 109 p53-associated eRNAs are indicative of direct eRNA activation by p53 [Fig 2D and S11 Fig further highlight the contrast in eRNA induction between WTp53 and Δ40p53:WTp53 tetramers. We note that Nutlin-induced eRNAs that mapped to sites not bound by p53 likely represent weak p53 binding  or secondary effects from p53 activation.In addition to gene expression changes in annotated regions, the PRO-seq data revealed stark differences in transcription of eRNAs. Whereas WTp53 cells had 510 differentially transcribed , as expected. In Δ40p53:WTp53 cells, however, no evidence of p53 activation was apparent ; moreover, a TFEA comparison of WTp53 versus Δ40p53:WTp53 cells indicated a defect in p53 activation by Δ40p53:WTp53 tetramers . Metagene analyses further illustrated the contrast between eRNA transcription in Nutlin-treated WTp53 versus Δ40p53:WTp53 cells . In agreement with the TFEA results, ChIP-seq analyses confirmed that Δ40p53:WTp53 binding fails to induce eRNA transcription, in direct contrast to WTp53 (see below). Collectively, these results establish that Δ40p53:WTp53 tetramers fail to induce eRNA transcription upon p53 activation.Whereas the biological roles for eRNA transcription remain unclear, they display cell type–specific expression patterns  and rapiFig 2F and S13A and S13B Fig, ChIP-seq data revealed that Δ40p53:WTp53 bound the same sites as WTp53, genome-wide, as expected given their identical DNA-binding domains. The corresponding eRNAs are shown in Fig 2D. Consistent with TFEA results  these data show that Δ40p53:WTp53 binding fails to induce eRNA transcription, in contrast to WTp53. Metagene analyses showed that occupancy of both WTp53 and Δ40p53:WTp53 increased after Nutlin-3a treatment, as expected . No Δ40p53:WTp53-specific binding sites were detected.Because eRNA induction is dependent upon TF binding ,41,42, iS13B Fig). Because ChIP-seq is qualitative, we cannot draw conclusions about the relative levels of p53 binding across the cell lines. Moreover, proteins with increased molecular weight  are more susceptible to degradation during sonication [Note that the antibody used for ChIP-seq recognizes the p53 N-terminus, which is absent in Δ40p53; thus, Δ40p53:WTp53 possesses 50% fewer epitopes per tetramer, and this will reduce its overall signal. Nevertheless, occupancies of WTp53:WTp53 and Δ40p53:WTp53 appeared nearly identical under basal or Nutlin-treated conditions  in WTp53 cells, with similar results in WTp53:WTp53 cells . In stark contrast, Δ40p53:WTp53 cells showed essentially no Nutlin response at the mRNA level; CDKN1A/p21 was the only gene that was induced . The lack of p53 activation in Δ40p53:WTp53 cells is further highlighted by the heat map of p53 pathway genes, shown in S14B Fig. As with the PRO-seq data , GSEA identified the same pathways upon comparison of RNA-seq data from Nutlin-treated Δ40p53:WTp53 versus WTp53 cells . For instance, the inhibited (p53 pathway) and activated  pathways were consistent, again supporting the enhanced proliferation of Δ40p53:WTp53 cells during Nutlin treatment .We next completed biological replicate RNA-seq experiments, to compare cellular mRNA levels with the nascent RNA changes identified by PRO-seq. We treated each cell line  with Nutlin-3a for 20 hours. This time point was chosen to allow time for Nutlin-induced changes to manifest in the steady-state mRNA transcriptome. As shown in Fig 3C, approximately 10% of genes  were identified as differentially expressed  in both the PRO-seq  and RNA-seq  experiments in Nutlin-treated WTp53 cells . This percentage is roughly consistent with previous studies that compared global run-on sequencing (GRO-seq) and RNA-seq data in Nutlin-treated cells [n = 315) was reduced in Nutlin-treated Δ40p53:WTp53 cells at 3 hours , over 30 genes would be expected to be induced at the mRNA level, but this was not observed. These results suggest that mRNA biogenesis is defective for transcripts induced by Δ40p53:WTp53. Moreover, the mRNA data show striking parallels with bidirectional eRNA transcription. Like the mRNA transcriptome, changes in p53-dependent eRNA transcription were largely absent in Δ40p53:WTp53 cells.As shown in ed cells  and provS15 Fig). Stable knockdown of p63  in each cell line  did not impact proliferation or the cell cycle compared with controls ; moreover, p63 was not induced by Nutlin-3a in any cell line . Finally, reverse transcription quantitative PCR (RT-qPCR) experiments showed that loss of p63 did not significantly impact CDKN1A/p21 or PUMA gene induction in Nutlin-treated WTp53, WTp53:WTp53, or Δ40p53:WTp53 cells . Collectively, these results suggest that p63 and p73 do not contribute to the differential phenotypic or transcriptional responses in Δ40p53:WTp53 cells.Analysis of PRO-seq and RNA-seq data revealed that whereas p63 was expressed in MCF10A cells, p73 was not , with a significant increase in S phase and a significant decrease in G2 phase. As expected, WTp53 and WTp53:WTp53 cells also showed similar results upon treatment with 5FU . These results were not entirely surprising, since each cell line  was isogenic apart from the TP53 locus; thus, all auxiliary pathways were expected to respond similarly.Finally, we asked whether a different p53 stimulus would cause the same transcriptional defects in Δ40p53:WTp53 cells, i.e., a lack of response at the mRNA level. Because Nutlin-3a is highly selective for p53 , it provided an efficient means to interrogate p53-specific transcriptional effects. However, Nutlin-3a is not physiologically relevant, and typical stress responses activate multiple pathways and their respective signal-specific TFs. We therefore selected 5-fluorouracil (5FU), a well-studied, clinically relevant chemotherapeutic that not only activates the p53 pathway but also other signaling cascades associated with DNA damage . WTp53, Fig 4B, 5FU caused differential expression of 926 genes in WTp53 cells and 823 genes in Δ40p53:WTp53 cells . Of these, 415 were shared among WTp53 and Δ40p53:WTp53, and many of these genes were p53 targets , as revealed by IPA. About half (408 out of 823) of the differentially expressed genes in Δ40p53:WTp53 cells were regulated by other TFs such as E2F1 . This result indicated that, in contrast to the targeted, p53-specific Nutlin response, the complex stress response induced by 5FU enabled other signal-specific TFs to augment the attenuated p53 response in Δ40p53:WTp53 cells.To further compare and contrast the cellular responses to 5FU, we conducted biological replicate RNA-seq experiments. As shown in Fig 4C, mRNA levels of the TF E2F2 were selectively up-regulated in Δ40p53:WTp53 cells, suggesting that E2F2 helps compensate for the reduced p53 response in cells expressing Δ40p53:WTp53. In WTp53 cells, E2F2 mRNA levels decreased by 57%  upon 5FU treatment. By contrast, E2F2 mRNA levels increased by 398% in Δ40p53:WTp53 cells (fold change of 14.4), and E2F2 pathway genes were selectively up-regulated in Δ40p53:WTp53 cells . Interestingly, the E2F family of TFs have overlapping functions with p53, but p53 activation typically down-regulates E2F gene expression [S18B Fig), the upstream regulators identified by IPA were similar . Consistent with these results, 5FU treatment caused similar cell cycle changes in WTp53 versus Δ40p53:WTp53 cells .As shown in pression . E2F2 inS18D Fig), suggesting that, despite strong p53 activation by 5FU in WT cells, p53 is not required for cell cycle changes in this context. We next analyzed published RNA-seq data that compared 5FU-treated WTp53 and p53 null HCT116 cells [S18E Fig), compared with Δ40p53:WTp53 cells , providing further support for a network of TFs that can compensate for p53 if its activity is reduced  or even lost (as in p53 null cells). Taken together, the results from 5FU-treated cells revealed that other signal-specific TFs can drive transcriptional programs under conditions with tempered p53 activity, such as in Δ40p53:WTp53 cells .To further probe p53-dependent effects on the 5FU response, and whether E2F and other TFs compensate for reduced p53 activity in Δ40p53:WTp53 cells, we completed cell cycle analyses in p53 null MCF10A cells following 20-hour treatment with 375 μM 5FU . The results showed that cellular responses were similar to WTp53 , unlike many cancer-derived cell lines. MCF10A cells also endogenously express WTp53, and we confirmed that WTp53 or WTp53:WTp53 cells were phenotypically similar to unedited MCF10A cells under normal or p53-stimulated conditions . We also tested additional genome-edited cell clones to rule out potential effects from clonal expansion . Whereas more cell lines could be tested with identical sets of experiments, others have shown that basic p53 transcriptional responses are similar across cell types [The Δ40p53 isoform is naturally occurring, and its levels appear to increase during cell stress ,24–27 orll types ,48, and Fig 4F). Expression of Δ40p53 (1) modulates the transcriptional activity of WTp53, such that Δ40p53:WTp53 tetramers suppress typical p53 transcriptional responses. Despite this, (2) Δ40p53:WTp53 tetramers retain the ability to activate p53 target genes, as seen most clearly in 5FU-treated cells  or upon comparison with p53 null cells . That is, Δ40p53:WTp53 tetramers do not functionally mimic p53 null conditions. This ability of Δ40p53 to dampen the p53 response 3) allows other sequence-specific, DNA-binding TFs to tune cellular stress responses, because they are not dominated by p53. For example, the gene expression signatures for the signal-specific TF E2F2 substantially contributed to the 5FU response in Δ40p53:WTp53 cells . In this way, Δ40p53 allows augmentation of the p53 response by other stress- or signal-responsive TFs. This functional versatility may be especially important in specific contexts, cell types, or developmental stages that require a modified p53 response [Based upon the transcriptomics data, several themes have emerged about how Δ40p53 alters WTp53 function , and each pathway is broadly implicated in aging [Furthermore, (4) the continual, enforced expression of Δ40p53 in the context of WTp53  causes accelerated aging in mice . Whereasin aging ,55. Concin aging ; notablyin aging ,57. Senein aging , and, coin aging .Related to items (3) and (4) above, the contrast between Nutlin-3a and 5FU is especially informative. Because Nutlin-3a is exquisitely specific for p53 ,31, it wFig 4B). These results suggest that functional coordination among other signal-specific TFs helps drive mRNA biogenesis in Δ40p53:WTp53 cells. Whereas 5FU-treated Δ40p53:WTp53 cells showed diminished p53 activation (versus WTp53 cells), E2F2 expression levels  increased significantly, revealing that other signal-responsive TFs augment cellular responses in the presence of Δ40p53:WTp53. Thus, the dampened p53 response in Δ40p53:WTp53 cells allows other TFs to drive the cellular response. These results are consistent with the concept of collaborative TF networks that enable compensatory responses if the function of typical “driver” TFs is compromised [A clear distinction in mRNA biogenesis was observed in cells treated with 5FU (versus Nutlin). Hundreds of mRNAs were induced in Δ40p53:WTp53 cells after 20 hours treatment with 5FU, including many canonical p53 target genes —despite similar genomic occupancy versus WTp53—and this tracked with mRNA levels. For instance, PRO-seq data showed that several hundred transcripts increased after 3-hour Nutlin treatment in Δ40p53:WTp53 cells , but only 1 gene (0.3%), CDKN1A/p21 increased at the mRNA level . By contrast, PRO-seq data from Nutlin-induced WTp53 cells showed an increase in 4,607 transcripts, with 439 increased at the mRNA level . These results establish a direct connection between eRNA transcription and mRNA biogenesis . Indeed, the experiments described herein were (i) completed in virtually isogenic cell lines, with (ii) virtually the same TF (only lacking 2 of 4 AD1 domains for Δ40p53:WTp53), with (iii) the same genomic occupancy, and (iv) under the same stimulus. The mechanistic basis remains to be determined, but eRNA-dependent regulation of RNA processing  or nuclear export could contribute.Here, we inadvertently established a system that, for the first time, allowed analysis of the transcriptional response to p53 activation in the presence or absence of p53-dependent eRNA induction, in largely isogenic cell lines. Our results implicate eRNA transcription as a prerequisite for mRNA biogenesis. In support of this conclusion, eRNA transcription at p53 binding sites was blocked in Δ40p53:WTp53 cells , at least in part, to induce eRNA transcription. Although additional experiments are needed to better define the molecular mechanisms that underlie this unexpected result, it is notable that p53 binding sites are more isolated in the human genome , whereasTo date, no structural data exist for the entire p53 tetramer at atomic resolution, and it is evident that the p53 tetramer is structurally dynamic ,67. ThisS8 Fig), and ChIP-seq data showed similar genomic occupancy for WTp53 versus WTp53:WTp53 . Likewise, MCF10A cells expressing WTp53 or WTp53:WTp53 were phenotypically indistinguishable under normal growth conditions or in response to Nutlin-3a or the genotoxic agent 5FU. These results best support the structural model of Orlova and colleagues [Previous proof-of-concept biochemical and transcriptomics experiments demonstrated that the flexible tethers linking p53 monomers did not disrupt normal p53 tetramer function. For instance, WTp53:WTp53 tetramers matched gene expression changes induced by WTp53 in H1299 cells . Similarlleagues . Neverthlleagues ,69. BecaFig 1D). This phenotypic difference (p53 activation without cell cycle arrest) suggests Δ40p53 expression may be oncogenic; however, this was not observed in mouse models [Figs 3A). This low-level p21 expression is likely an important aspect of Δ40p53:WTp53 function in biology. Loss of p21 results in polyploidy, due to defects in DNA damage response and cycling in the absence of mitosis [Despite the inability of Δ40p53:WTp53 tetramers to induce eRNA transcription or mRNA production in Nutlin-treated cells, an exception was CDKN1A (aka p21), which is a well-established p53 target gene. CDKN1A inhibits the cyclin-dependent kinases (CDKs) CDK2 and CDK4/6, which phosphorylate RB-related proteins to promote G1/S cell cycle arrest. Whereas Nutlin-dependent activation of p53 triggered cell cycle arrest in WTp53 cells, this was not observed with Δ40p53:WTp53 (e models . Notably mitosis . Low-levCas-9 purification was completed as described .Single-guide RNA (sgRNA) was formed by adding tracrRNA  cat# 1072533) and crRNA  in a 1:1 ratio and heated to 95°C then slowly cooled to room temperature over 1 hour. Purified Cas-9 was added to sgRNA at a ratio of 1:1.2 and incubated at 37°C for 15 minutes, forming Cas-9 RNP. Cas-9 RNP was used at 10 μM concentration within an hour.S1A Fig) was flanked by 1.5-kb homology arms, and mCherry was inserted as a selection marker. Insertion sizes were as follows: WTp53: 2820bp, WTp53:WTp53: 4041bp, and Δ40p53:WTp53: 3924bp.VectorBuilder was used to construct the plasmid. The insert  #MPK5000). Cells were resuspended in resuspension Buffer R  with 10 μM Cas-9 RNP and 1 μg donor plasmid . Furthermore, 10 μL Neon pipet tip  electroporated using the Neon Transfection Kit . Transfected cells were transferred to 2-mL antibiotic free media. Cut location: hg38 chr17:7,676,510. Insertion location: hg38 chr17:7,676,591; after the first ATG for TP53 in exon 2. Cells were single cell sorted into 96-well plates based on mCherry expression. Clones were then verified with sequencing, PCR, and western blot.MCF10A cells were split 24 hours prior to each experiment and grown to approximately 70% confluence on a 15-cm plate. Cells were washed with PBS, followed by trypsinization (4 ml per plate) and resuspended in resuspension media (8 mL DMEM/F12 containing 20% horse serum and 1x pen/strep). Moreover, 5 × 10MCF10a cells cultured in DMEM/F12 (Invitrogen #11330–032) media containing 5% horse serum  #16050–122), 20 ng/mL epidermal growth factor  #AF-100-15), 0.5 ug/mL Hydrocortisone  #H0888-1g), 100 ng/mL Cholera toxin (Sigma #C8052-2mg), 10 ug/mL insulin (Sigma #I1882-200mg), and 1x Gibco 100x Antibiotic-Antimycotic  15240062) penicillin-streptomycin.NEB Phusion polymerase  #M0530S) was used to the manufacturer’s specifications. Seq_TP53_exon2_Forward and Seq_TP53_exon2_Reverse primers were used at 65 to 68°C; homozygous knock-in clones had no 500-bp product. Product was sequenced using TP53exon2-Forward primer. Using DBD_Forward and Seq_TP53_exon2_Reverse primers at 67.8°C, a 2,542-bp band indicated knock-in for at least 1 allele (either homozygous or heterozygous clone). A homozygous clone was then verified if Seq_TP53_exon2_Forward and Seq_TP53_exon2_Reverse primers at 65 to 68°C had no 500-bp product.S3 Table.Because mutations in p53 can yield a survival advantage , we periNutlin-3a  #S8059) stock concentration 10 mM, and treatment concentration 10μM. 5FU  stock concentration 100 mM, and treatment concentration 375 μM. Equivalent volumes of DMSO vehicle were used for controls.n scans. The scan range varied slightly between methods but generally covered 70 to 1,000 m/z.Cells were harvested after 20-hour treatment with Nutlin-3a (10 μM) or 0.1% DMSO controls, with 6 biological replicates for each cell line and each condition. Sample preparation was carried out at Metabolon , in a manner similar to a previous study . BrieflyS4 Table.Metabolites were identified by automated comparison of the ion features in the experimental samples to a reference library of chemical standard entries that included retention time, molecular weight (m/z), preferred adducts, and in-source fragments, as well as associated MS spectra, and curated by visual inspection for quality control using software developed at Metabolon. Identification of known chemical entities was based on comparison to metabolomic library entries of purified standards . A summahttp://cran.r-project.org/) was used. Following log transformation and imputation of missing values, if any, with the minimum observed value for each compound, Welch 2-sample t test was used as a significance test to identify biochemicals that differed significantly (p < 0.05) between experimental groups. An estimate of the false discovery rate  was calculated to take into account the multiple comparisons that normally occur in metabolomic-based studies. Classification analyses included principal component analysis (PCA), hierarchical clustering, and random forest. For the scaled intensity graphics, each biochemical in original scale (raw area count) was rescaled to set the median across all samples and time points equal to 1.Two types of statistical analyses were performed: (1) significance tests and (2) classification analysis. Standard statistical analyses were performed in Array Studio on log-transformed data. For analyses not standard in Array Studio, the R program  Flow Cytometry Kit  ab139418) was used as specified by the manufacturer. Samples were then placed on ice and analyzed with a BS FACSCelesta cell analyzer. FLOWJO was used to analyze fluorescence-activated cell sorting (FACS) data. All experiments were performed in biological triplicate.WTp53, WTp53:WTp53, and Δ40p53:WTp53 were treated with 10 μM Nutlin-3a or 0.1% DMSO for 20 hours, collected, and fixed in 66% ethanol for at least 24 hours. Cells were then incubated with a blocking/permeabilization buffer  for 1 hour at room temperature. Primary antibody, anti-p21  2947) at 1:250 dilution, staining was carried out overnight at 4°C in the blocking buffer and visualized using secondary antibodies conjugated to Alexa Fluor 488.S5D Fig). Growth rate was calculated by dividing the total cell growth by time of growth (68 hours). Total cell growth was calculated by counting with Nexcelom Bioscience Cellometter Auto T4 Bright Field Cell Counter . All experiments were performed in biological triplicate.To examine how growth rate change over time, cells were treated with 0.1% DMSO or 10 μM Nutlin-3a for 20 hours, passaged 1:10, and given 48 hours to recover  under a constitutive hU6 promoter with puromycin resistance. Knockdown cells were selected with 48-hour treatment of 2 μg/mL puromycin followed by recovery in standard growth media.S3 Table.WTp53, WTp53:WTp53, Δ40p53:WTp53 cells ± p63 knockdown or alternate p53 cell clones were treated with 10 μM Nutlin-3a or DMSO control for 20 hours before RNA isolation using TRizol  as specified by the manufacturer. Total RNA was converted to cDNA using High-Capacity cDNA Reverse Transcription Kit  4368813) as specified by the manufacturer, followed by a cleanup performed using 0.8X AMPure XP beads  #A63881). Sybr Select Master Mix  4472908) was added to cDNA at 0.1 ng/μL and amplified per manufacturer instructions. ΔΔCT values were background normalized using a gene desert. PCR primers are listed in Total RNA was isolated from WTp53, WTp53:WTp53, and Δ40p53:WTp53 cells treated with 10 μM Nutlin-3a, 375μM 5FU, or DMSO controls  using TRizol  as specified by the manufacturer. Moreover, 1 μg of total RNA with a RIN number of ≥8 was used for RNA-seq library prep. Sample was enriched for mRNA using NEBNext Poly(A) mRNA Magnetic Isolation Module (E7490), and library was prepared using NEBNext Ultra II Directional RNA Library Prep Kit from Illumina (E7765).6 cells per experiment) were treated with 10 μM Nutlin-3a or 0.1% DMSO for 6 hours then cross-linked with 1% formaldehyde for 10 minutes at 25°C followed by glycine (0.125 M) quenching for 5 minutes. Nuclei were isolated by resuspending cells in NRO buffer , 1 mM Sodium Metabisulfite , 0.25 mM Phenylmethylsulfonyl Fluoride  #AB01620) 0.012 TIU/mL aprotinin , followed by a 5 minutes incubation on ice, a low speed spin, and then a final wash with NRO buffer. The isolated nuclei were prepared for shearing based on the Covaris truChIP Chromatin Shearing Kit  PN 520237) and sheared for 11 minutes with Covaris M220 Focused-ultrasonicator. A total of 25 μL of Protein G Dynabeads beads  was used per 100 μg chromatin (approximately 15 million nuclei). Beads were incubated in blocking solution  then 6 μg of DO1 p53 antibody  BD554293) was conjugated to beads in blocking solution, nutating at 4°C for 4 hours. Conjugated beads were washed 1x block solution and 1x IP buffer . Sheared chromatin was added (100 μg chromatin (approximately 15 million nuclei)) to conjugated beads in IP buffer and then nutating at 4°C for at least 12 hours. Bound chromatin was washed 3x with IP buffer, 3x with RIPA buffer , 2x with LiCl buffer  and 2x with TE Salt buffer . Sample was eluted from beads with PK buffer  containing 100 μg of Proteinase K (NEB: P81075) incubated at 50°C, with shaking, for 1 hour then at 65°C for 1 hour. Eluted DNA was transferred to a new tube and incubated at 65°C for 12 hours to reverse cross-linking. DNA was purified by phenol chloroform extraction using Light-5PRIME Phase Lock Tubes : 2302820) based on the manufacturer’s instructions. Libraries were prepared using the KAPA Hyper Prep Kit  and sequenced on Illumina NextSeq V2 high output 75-cycle.MCF10A cell lines  and FastQ Screen . Trimming and filtering of low-quality reads was performed using BBDUK from BBTools (v37.99) (https://www.osti.gov/servlets/purl/1241166) and FASTQ-MCF from EAUtils (v1.05) [Sequencing of RNA-seq, ChIP-seq, and PRO-Seq libraries was performed at the BioFrontiers Sequencing Facility (UC Boulder). Single-end fragment libraries (75 bp) were sequenced on the Illumina NextSeq 500 platform , demultiplexed and converted BCL to fastq format using bcl2fastq (bcl2fastq v2.20.0.422); sequencing data quality was assessed using FASTQC (v0.11.5) ( (v1.05) . Alignme (v1.05)  in unpai (v1.05) .https://github.com/Dowell-Lab/RNAseq-Flow). A full pipeline report of the run as well as a quality control report generated by MultiQC (v. 1.7), including trimming, mapping, coverage, splicing, and complexity metrics are included in S5 Table. Gene counts were generated using featureCounts [The RNA-seq data were processed using a Nextflow pipeline v1.1 (reCounts  and diffreCounts . DuplicareCounts  were usehttps://github.com/Dowell-Lab/ChIP-Flow). A full pipeline report of the run as well as a quality control report generated by MultiQC (v. 1.7), including trimming, mapping, coverage, and complexity metrics are included in S6 Table. Peak calls were generated using MACS2 narrowPeak. The q-value default cutoff was also decreased from the default of 0.05 to 1e-5. Blacklisted regions  were removed using BEDTools intersect [All ChIP-seq data were processed  using a Nextflow pipeline, ChIP-Flow v1.0 (ntersect . PyGenomntersect  was usedhttps://doi.org/10.5281/zenodo.2641755). A full pipeline report of the run as well as a quality control report generated by MultiQC (v. 1.7), including trimming, mapping, coverage, and complexity metrics, is included in S7 Table. PyGenomeTracks [p-value ≤ 0.01; after removal of all repetitive regions, 25 eRNAs remained. TFEA was used to identify changes in bidirectional transcription and map to underlying TF sequence motifs to infer changes in TF activity [Two biological replicates for each treatment (DMSO/Nutlin-3a) were generated for each of the 3 cell lines were processed using a Nextflow pipeline for nascent data , counts were calculated for each sorted BAM file using multiBamCov in the BEDTools suite (v. 2.25.0). Genes (NM accession type) and lncRNAs (NR accession type) were then filtered such that only the isoform with the highest number of reads per annotated length was kept in order to minimize duplicate samples being included in differential transcription analysis. DESeq2  was then used to determine differentially transcribed genes between the different treatments both within and between time points. A prerank file was generated using the results from the differential analysis from each pairwise comparison and used in differential pathway analysis using GSEA 4.0.3, using hallmark pathways gene sets. Qiagen IPA (v7.2) was used for identification of activated and inhibited pathways based on transcriptional changes. For bidirectional/enhancer comparisons, all bidirectional prediction Tfit calls were merged using mumerge software (merge component of TFEA) to generate an annotation file. Counts were then calculated for each sample using multicov from the BEDTools suite (v. 2.28.0) , and DESCell lines were internally validated by mapping PRO-seq reads to the p53 construct as a mini “genome” using Hisat2 (v2.1.0) , and aliPRO-seq and RNA-seq experiments were completed in biological replicate. ChIP-seq experiments were completed with biological triplicates. Metabolomics experiments were completed with 6 biological replicates. Statistical analysis of sequencing data and metabolomics data is provided in Method details.S1 Fig(A) Overview of donor repair plasmids co-transfected with Cas-9 RNPs. (B) Scheme for CRISPR/Cas-9 insertions at the native TP53 locus: WTp53 (2820bp), WTp53:WTp53 (4041bp), and Δ40p53:WTp53 (3924bp). (C) PCR validation. Blue primer sets span the insertion and red primer sets have forward primer within the insertion and the reverse primer outside the insertion. Lack of 500-bp band with blue primers indicated homozygous insertion. PCR experiments were completed on genomic DNA isolated from each of the indicated cell lines. Non-edited MCF10A cells were tested as an additional control. (D) Western blot validation of each genome-edited cell line, using a p53 antibody (DO-1). As designed, each tethered construct (Δ40p53:WTp53 or WTp53:WTp53) migrated at around 100kDa, indicative of dimer expression as a single transcript. The flexible tether for Δ40p53:WTp53 or WTp53:WTp53 contained a TEV cleavage site, and treatment with TEV protease  cleaved the dimers to p53 monomers, as expected. Note that the Δ40p53 monomer lacks the epitope detected by the p53 DO-1 antibody. See  for uncropped gels. CTR, carboxyl-terminal region; IRES, internal ribosomal entry site; PolyA, SV40 polyA sequence with terminator to prevent downstream transcription; TA1, transactivation domain 1; TA2, transactivation domain 2; WTp53, wild-type p53.(EPS)Click here for additional data file.S2 Fig(A) Sequencing results from the p53 DNA-binding domain show no mutations at common hotspots in any of the cell lines. Because p53 mutations can yield proliferative advantages in culture [(B) Internal validation of the edited p53 cell lines, using PRO-seq data. The PRO-seq data were mapped to the inserted p53 sequences at the native TP53 locus. RNA sequence corresponding to the first 39 amino acids of p53 was reduced by half in the Δ40p53:WTp53 cell line vs. WTp53, as expected. This method also allowed verification of p53 copy number at 2 per cell line. PRO-seq, precision nuclear run-on sequencing; WTp53, wild-type p53. culture , DNA-bin(EPS)Click here for additional data file.S3 Fig(A) Cell cycle analysis (PI) and bar plots (B) represent the average of 6 biological replicates (bars = standard error of mean). Note that MCF10A cells endogenously express WTp53. Underlying FACS data in the BioStudies database under accession number S-BSST672. Raw data for panel B in  FACS, fluorescence-activated cell sorting; PI, propidium iodide; WTp53, wild-type p53.Unedited “off-the-shelf” MCF10A cells, edited WTp53, and WTp53:WTp53 cells each display the same cell cycle phenotypes. (EPS)Click here for additional data file.S4 Fig(A) Cell cycle analysis (PI) of each genome-edited cell line. Chart (inset) represents the average of 6 experiments (bars = standard error of mean). (B) Sample of metabolomics data comparing IPA identified cell cycle metabolites under normal growth conditions in each cell line . Log2 (fold change) was normalized to WTp53. Underlying FACS data in the BioStudies database under accession number S-BSST672. Raw data for panels A and B in S6 Data. FACS, fluorescence-activated cell sorting; IPA, ingenuity pathway analysis; PI, propidium iodide; WTp53, wild-type p53.(EPS)Click here for additional data file.S5 Fig(A) Cell cycle analysis (PI) in WTp53:WTp53 cells; chart (inset) represents the average of 6 biological replicate experiments (bars = standard error of mean). Arrow highlights the loss of S phase cells, similar to WTp53 cells (and in contrast with Δ40p53:WTp53 cells). (B) Measurement of p21 protein levels by FACS; similar to WTp53, p21 protein levels increase in WTp53:WTp53 cells after Nutlin-3a treatment. (C) Cell cycle analysis (PI) in p53 null MCF10a cells; chart (inset) represents the average of 3 biological replicate experiments (bars = standard error of mean). Bar plot shows time correlated p21 RT-qPCR . (D) Growth rate measured over 5 treatment cycles; each cycle encompassed Nutlin treatment for 20 hours, followed by splitting cells 1:10, then growth under normal conditions for 48 hours . Note the similar growth characteristics of WTp53 vs. WTp53:WTp53 cells. Underlying FACS data in the BioStudies database under accession number S-BSST672. Raw data for panels A, C, and D in  FACS, fluorescence-activated cell sorting; PI, propidium iodide; RT-qPCR, reverse transcription quantitative PCR; WTp53, wild-type p53.(EPS)Click here for additional data file.S6 FigMetabolic changes in Δ40p53:WTp53 cells are similar vs. WTp53:WTp53 cells and vs. WTp53 cells after Nutlin-3a treatment for 20 hours. WTp53, wild-type p53.(EPS)Click here for additional data file.S7 FigS8 Data. GRO-seq, global run-on sequencing; PRO-seq, precision nuclear run-on sequencing; RT-qPCR, reverse transcription quantitative PCR.RT-qPCR of nuclear RNA levels for CDKN1A/p21 in either HCT116 or MCF10A cells at 1 hour and 3 hours. These data show a delayed p53 response in MCF10A cells vs. HCT116, with the 3-hour induction roughly matching the 1-hour p21 nuclear RNA levels from HCT116 cells (arrows). Past GRO-seq results in 1 hour Nutlin-treated HCT116 cells  were use(EPS)Click here for additional data file.S8 Figp-value 0.01. Venn diagram shows overlap between WTp53 and WTp53:WTp53 cells. Raw data in S9 Data. PRO-seq, precision nuclear run-on sequencing; WTp53, wild-type p53.Summary of PRO-seq data for annotated genes differentially transcribed in WTp53 (y-axis) vs. WTp53:WTp53 cells (x-axis). Dashed line represents (EPS)Click here for additional data file.S9 Fig(A) List of genes that are selectively transcribed in Δ40p53:WTp53 vs. WTp53 cells .  Volcano plots that show differentially expressed transcripts (PRO-seq) at gene bodies after 3-hour Nutlin-3a treatment (vs. DMSO controls) in (B) p53 null or (C) Δ40p53:WTp53 MCF10A cells. Green dots represent down-regulated and red dots up-regulated transcripts . (D) Ward cluster maps of enrichment of canonical p53 target genes in either Δ40p53:WTp53 (left) or p53 null MCF10A cells (right) after 3-hour Nutlin-3a treatment (PRO-seq). (E) Representative PRO-seq data from Nutlin-treated WTp53 or Δ40p53:WTp53 cells, and DMSO controls. Although Nutlin induction is observed in each cell line, activation is reduced in Δ40p53:WTp53 cells. (F) IPA based upon PRO-seq data (3-hour Nutlin treatment). Pathways highlighted in bold font have been linked to progeroid phenotypes in mice expressing Δ40p53 + WTp53 [S10 Data. IPA, ingenuity pathway analysis; PRO-seq, precision nuclear run-on sequencing; WTp53, wild-type p53. + WTp53 ,17. Raw (EPS)Click here for additional data file.S10 Figp-value 0.01. Venn diagram shows overlap between WTp53 and WTp53:WTp53 cells. Raw data for panel C in S11 Data. eRNA, enhancer RNA; PRO-seq, precision nuclear run-on sequencing; WTp53, wild-type p53.Summary of PRO-seq data of eRNA transcription for WTp53 (y-axis) vs. WTp53:WTp53 (x-axis). Dashed line represents (EPS)Click here for additional data file.S11 Figp-value ≤ 1 × 10−5) is shown with a dashed line, and the peaks correspond directly with ChIP-seq peaks shown in S13B Fig. ChIP-seq, chromatin immunoprecipitation sequencing; eRNA, enhancer RNA; PRO-seq, precision nuclear run-on sequencing; WTp53, wild-type p53.Note the similar responses in WTp53 and WTp53:WTp53 cells, whereas Δ40p53:WTp53 cells lack the ability to induce bidirectional eRNA transcription. The p53 binding motif ((EPS)Click here for additional data file.S12 Fig(A–C) TFEA [p-val ≤ 1 × 10−6). This reflects a lack of eRNA transcription at p53 binding sites in Δ40p53:WTp53 cells. Note that TP53 and TP63 have almost identical binding motifs. (D) TFEA [p-val ≤ 1 × 10−6). This reflects a lack of eRNA transcription at p53 binding sites in Δ40p53:WTp53 cells. Note that TP53 and TP63 have almost identical binding motifs. (E) Metagene analysis showing average eRNA peak height, genome-wide, at p53 responsive eRNAs  in WTp53:WTp53 cells. Raw data for panels A–E in S12 Data. eRNA, enhancer RNA; PRO-seq, precision nuclear run-on sequencing; TFEA, transcription factor enrichment analysis; WTp53, wild-type p53.–C) TFEA  from PRO(EPS)Click here for additional data file.S13 Fig(A) A series of ChIP-seq experiments were completed 1 hour, 3 hours, or 6 hours after Nutlin-3a treatment to assess the time dependence of p53 occupancy changes. Metagene analyses are shown  at p53 binding sites in Nutlin-3a-treated WTp53, WTp53:WTp53 or Δ40p53:WTp53 cells at 1 hour, 3 hours and 6 hours. To our knowledge, following Nutlin treatment, all published p53 ChIP-seq data sets used 6 hours or longer time points. (B) Examples of ChIP-seq data in Nutlin-treated (6 hours) cells, compared with DMSO controls. ChIP-seq peaks correspond directly with PRO-seq eRNA peaks shown in S11 Fig. (C) Metagene analyses (filtered for peaks containing p53 motif) showing average ChIP-seq signal, genome-wide, at p53 binding sites in DMSO control vs. Nutlin-treated WTp53:WTp53 cells. Raw data for panels A and C in S13 Data. ChIP-seq, chromatin immunoprecipitation sequencing; eRNA, enhancer RNA; PRO-seq, precision nuclear run-on sequencing; WTp53, wild-type p53.(EPS)Click here for additional data file.S14 Fig(A) Volcano plot showing differentially expressed mRNAs after 20 hours Nutlin treatment (vs. DMSO controls) in WTp53:WTp53 cells. Green dots represent down-regulated and red dots up-regulated transcripts . (B) Venn diagrams showing overlap among significantly induced transcripts from PRO-seq data (3 hours Nutlin) and RNA-seq data (20 hours Nutlin) in WTp53:WTp53 cells. (C) IPA of canonical pathways from RNA-seq data after 20 hours Nutlin-3a treatment in Δ40p53:WTp53 vs. WTp53 cells flanked by heat maps of log2 (fold change) of genes from p53 on the left, and cell cycle chromosome replication pathway on the right. P53 and cell cycle chromosome replication represent the most significantly down-regulated or up-regulated pathways, respectively, in Δ40p53:WTp53 cells (vs. WTp53). Raw data for panels A and B in S14 Data. IPA, ingenuity pathway analysis; PRO-seq, precision nuclear run-on sequencing; RNA-seq, RNA sequencing; WTp53, wild-type p53.(EPS)Click here for additional data file.S15 FigGenome browser views of p63 and p73, showing that p63 is transcribed (PRO-seq) and expressed (RNA-seq) in the MCF10A cell lines listed at right. The p73 locus, by contrast, shows transcription of p73-AS1 (p73 antisense 1), a mature antisense transcript. Neither p63 nor p73 was induced by Nutlin-3a treatment. Because p73 was not transcribed in MCF10A cells, we focused on p63, but we note some transcription is observed from the adjacent TP73-AS1 locus. Top: DMSO and Nutlin-3a tracks for PRO-seq in WTp53, WTp53:WTp53, and Δ40p53:WTp53 cells; bottom: DMSO and Nutlin-3a tracks for RNA-seq in WTp53, WTp53:WTp53, and Δ40p53:WTp53 cells. PRO-seq, precision nuclear run-on sequencing; RNA-seq, RNA sequencing; WTp53, wild-type p53.(EPS)Click here for additional data file.S16 Fig(A) Western blot to probe p63 levels before or after lentiviral p63 knockdown. (B) Growth rate measured over 5 Nutlin treatment cycles; each cycle encompassed 20 hours under basal (0.1% DMSO) vs. Nutlin-treated conditions, splitting cells 1:10, then growth for another 48 hours. (C) Cell cycle analysis (PI) of each genome-edited cell line in either control or p63 knockdown. Left shows the cell cycle after 20 hours of 0.1% DMSO treatment (control), and right shows the cell cycle after 20 hours of Nutlin3a treatment. (D) Knockdown of p63 shows no impact on p53 target gene induction in WTp53, WTp53:WTp53, or Δ40p53:WTp53 cell lines. RT-qPCR data are shown for p21 and PUMA . N.S. designates non-significant comparisons. Underlying FACS data in the BioStudies database under accession number S-BSST672. Raw data for panels B and D in S15 Data. FACS, fluorescence-activated cell sorting; PI, propidium iodide; RT-qPCR, reverse transcription quantitative PCR; WTp53, wild-type p53.(EPS)Click here for additional data file.S17 Fig(A) Cell cycle analysis (PI); chart (inset) represents the average of 3 experiments (bars = standard error of mean). Arrow highlights increased S phase in 5FU-treated WTp53:WTp53 cells, similar to both WTp53 and Δ40p53:WTp53 cells . (B) Volcano plot showing differentially expressed mRNAs after 20 hours 5FU treatment (vs. DMSO controls) in WTp53:WTp53 cells. Green dots represent down-regulated and red dots up-regulated transcripts . Underlying FACS data in the BioStudies database under accession number S-BSST672. Raw data for panels A and B in S16 Data. 5FU, 5-fluorouracil; FACS, fluorescence-activated cell sorting; PI, propidium iodide; WTp53, wild-type p53.(EPS)Click here for additional data file.S18 Fig(A) RNA-seq data (GSEA) shows that differential E2F2 TF activity characterizes the Δ40p53:WTp53 transcriptional response to 5FU in comparison to WTp53 and WTp53:WTp53. (B) IPA showing upstream regulatory TFs, inferred from RNA-seq data in 5FU-treated Δ40p53:WTp53 vs. WTp53 cells. (C) The general upstream regulators (IPA) controlling the cellular response to 5FU are similar  between Δ40p53:WTp53 and WTp53 cells, with a few exceptions (p < 0.01). (D) Cell cycle analysis (PI); chart (inset) represents the average of 3 experiments (bars = standard error of mean). Arrow highlights increased S phase in 5FU-treated p53 null MCF10a cells. (E) GSEA based upon RNA-seq data from Yang and colleagues comparing p53 null HCT116 cells vs. WTp53 HCT116 cells after a 24-hour 5FU treatment. Pathways with FDR q-val < 0.1 are colored dots. Red represents increased in p53 null cells compared to WTp53 cells, and green is decreased. X-axis is NES. Top significant pathways are shown in the ranked list. Underlying FACS data in the BioStudies database under accession number S-BSST672. Raw data for panels A–E in S17 Data. 5FU, 5-fluorouracil; FACS, fluorescence-activated cell sorting; FDR, false discovery rate; GSEA, gene set enrichment analysis; IPA, ingenuity pathway analysis; NES, normalized enrichment score; PI, propidium iodide; RNA-seq, RNA sequencing; WTp53, wild-type p53.(EPS)Click here for additional data file.S19 Fig(A) PCR validation of additional clones. Blue primer sets span the insertion, and red primer sets have forward primer within the insertion and the reverse primer outside the insertion. Lack of 500-bp band with blue primers indicated homozygous insertion. PCR experiments were completed on genomic DNA isolated from each of the indicated cell lines. Non-edited MCF10A cells were tested as an additional control. (B) Western blot validation of each genome-edited cell line, using a p53 antibody (DO-1). As designed, each tethered construct (Δ40p53:WTp53 or WTp53:WTp53) migrated at around 100 kDa, indicative of dimer expression as a single transcript. (C) Differences between the different WTp53, WTp53:WTp53, or Δ40p53:WTp53 clones were minimal as shown by the relative induction (RT-qPCR) for p21 . (D) Cell cycle analysis (PI) of the alternative clone for each genome-edited cell line. Cell cycle was performed at 20 hours of 0.1% DMSO treatment (control) or 20 hours of Nutlin-3a (treatment). Underlying FACS data in the BioStudies database under accession number S-BSST672. Raw data for panels C and D in S18 Data. See  for uncropped gels. FACS, fluorescence-activated cell sorting; PI, propidium iodide; RT-qPCR, reverse transcription quantitative PCR; WTp53, wild-type p53.(EPS)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 TableRNA-seq, RNA sequencing.(PDF)Click here for additional data file.S6 TableChIP-seq, chromatin immunoprecipitation sequencing.(PDF)Click here for additional data file.S7 TablePRO-seq, precision nuclear run-on sequencing.(PDF)Click here for additional data file.S1 DataFig 1. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S2 DataFig 2. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S3 DataFig 3. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S4 DataFig 4. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S5 DataS3 Fig. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S6 DataS4 Fig. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S7 DataS5 Fig. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S8 DataS7 Fig. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S9 DataS8 Fig. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S10 DataS9 Fig. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S11 DataS10 Fig. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S12 DataS12 Fig. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S13 DataS13 Fig. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S14 DataS14 Fig. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S15 DataS16 Fig. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S16 DataS17 Fig. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S17 DataS18 Fig. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S18 DataS19 Fig. File contains tabs with labels corresponding to relevant panel.Associated with (XLSX)Click here for additional data file.S1 Raw Images(EPS)Click here for additional data file.S2 Raw Images(EPS)Click here for additional data file.