text
string | predicted_class
string | confidence
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|---|---|---|
Of the new-borns, 2.2% (14/638) had an Apgar score of < 7, at 5 minutes after delivery, and most affected new-borns were seen in Tanzania (3.7% with a low apgar score). One maternal and three fetal deaths occurred in the study, all of them in the Tanzanian group.
|
study
| 99.94 |
Totally, 5418 samples of AF were collected in the study. 1854 of them at the first vaginal examination after the woman attended delivery ward, 1515 prior to augmentation with oxytocin (638 of them within 30 minutes), and 2049 at the time of delivery. Deliveries with AFL ≥ 10.1 mmol/l correlated with an increased frequency of operative intervention (p < 0.001) and with an active time of delivery >12h (p = 0.04). Significantly more epidurals were used in the high AFL group (p = 0.007), and the group also showed a higher incidence of post-partum fever (>38°C, p = 0.01) and post-partum hemorrhage >1.5L (p = 0.04, Table 3).
|
study
| 100.0 |
According to study design AFL-values within the 30 minutes before augmentation were used in our prediction of labor outcome. The sensitivity for a cesarean section according to high/low AFL values was 39%, the specificity 90%, the PPV 37%, and the NPV was 91%. The overall percentage of correct predictions when AFL was used was 84% (Table 3).
|
study
| 100.0 |
High maternal age, gestational age > 41w, occiput posterior presentation of the fetus, the use of EDA, being delivered in Switzerland and an AFL level ≥10.1 mmol/l at the time of augmentation were all associated with an increased likelihood of operative intervention (Table 4).
|
study
| 99.94 |
High AFL value (≥10.1 mmol/l) and occiput posterior presentation of the fetus had the strongest association with operative intervention after adjusting for the other factors (Table 4). No significant interactions between AFL and each of the other eight factors were detected (p > 0.48 for all test of interactions), which implies that the sensitivity for operative intervention among women with a high AFL value (≥ 10.1 mmol/l) was 4.5 times higher compared to women with a low AFL value, irrespective of the levels of the other factors.
|
study
| 100.0 |
This large observational study of healthy nulliparous women from different countries and settings presents a new way of describing labor dystocia. The lactate values in amniotic fluid analysed just before augmentation with oxytocin provides important information about the uterus. Low levels of AFL may support the decision to continue a prolonged vaginal labor by augmentation with oxytocin as the uterus appears to be receptive to augumentation, whereas high levels of AFL indicate a higher risk not only of cesarean section, but also of post-partum complications.
|
study
| 99.94 |
Labor dystocia is a leading indication for cesarean section worldwide [26–31]. Several publications have linked a number of well-known risk factors for an operative intervention among otherwise low-risk women [29–31]. In this work we have studied some of these risk factors, as well as their relation to the metabolic status of the uterus presented as the AFL value. Our results show that AFL is a new predictor of labor outcome in arrested labors. If a high AFL level is present when augmentation starts, the likelihood of an operative intervention increases 4.5 times irrespective of the levels of the other known risk factors (Table 4).
|
study
| 99.94 |
Another very important observation of this study is that the partogram, recommended by the WHO, did not predict labor outcome. More than half of all deliveries in this study were diagnosed as having labor dystocia according to the partogram, whilst over 60% of them ended in a spontaneously vaginal delivery. Our reflection is that deliveries among primiparas may be different from how they were described by Friedman and his co-workers who created the partogram in the 1950s [18, 19]. Our message is that whilst the partogram is in every way an important tool for keeping records of activity during childbirth, it is not a useful predictor of delivery outcome [16–20].
|
study
| 99.94 |
A study of AFL was just recently performed at Maternity Hospital, Dublin, Ireland, and presented by Murphy et al . In the Dublin study, the first sample of AFL, when a woman attended the delivery ward, was analysed. They concluded that primarily, high levels of lactate in amniotic fluid in spontaneously laboring; single cephalic, nulliparous womans deliveries is a new and independent predictor of labor disorder and cesarean section .
|
study
| 99.4 |
Strength of this project is the understanding why some dystocic deliveries, despite adequate stimulation with oxytocin, do not reach a spontaneous vaginal delivery. It is also a strength that clinics from various settings are included, which makes it possible to confirm that the method of AFL is easy to use, irrespective of geography, culture or clinic size. Some limitations of the study should be noted. As it was not feasible to change clinical guidelines simply for this observational study. Different methods of managing arrested labors were seen in the clinics included, even though the same inclusion criteria were used for the study. The number of deliveries eligible to be included was also limited, due to the strict inclusion criteria: all women included had to be healthy nullipara with a spontaneous onset of labor, in order to avoid bias and interference factors.
|
study
| 100.0 |
The main question in this work is whether the introduction of this new method could help obstetricians/midwives to make a more informed decision in the delivery room. When no information about AFL is available, a good guess would be that arrested delivery will end in a vaginal delivery after a proper administration of oxytocin. If that assumption was correct, the overall percentage of correct classifications of labor outcomes in this study would be 69.4% (Table 4). The sensitivity for an operative delivery would be 0% and the specificity 100%. If the measure of AFL had been used together with the partogram, the overall percentage of correct classifications is better (84%). 39% of the women who had an operative intervention at a later stage would have been identified earlier in the process through their raised AFL levels, hopefully with a higher possibility of avoiding a long and painful parturition. 39% can be perceived as a rather low figure, but in clinical obstetrics of today’s, this prediction cannot be made at all.
|
study
| 100.0 |
The predictive values of AFL may be of more interest for the individual obstetrician/midwife. The NPV imply that more than 90% of cases with a low AFL will have a vaginal delivery. This represents important information. We anticipate that the number of unnecessary cesarean sections in this group can be reduce, as important information about the uterine receptivity to augmentation hopefully lead the to a more individually adapted use of oxytocin and to more normal deliveries. The NPV imply that 37% of deliveries with high AFL would end in an operative intervention. This is also important information for the staff in charge. This is a small group, approximately 15% of the material, but shown to have a higher frequency of complications, probably due to the prolonged time of delivery. This is probably the group described earlier where labor dystocia occurs despite adequate stimulation with oxytocin. By using this new method, very long deliveries can be avoided, caesarean sections that will be carried out can be made at an earlier stage, and postpartum complications will hopefully be reduced. The study has confirmed a new means of classifying dystocic deliveries into an early identification of a normal group with a good receptivity of oxytocin and a possibility of a vaginal delivery after an adequate use of oxytocin, and a group where problems are likely to arise. This is valuable information, especially in countries where referral to a higher level of care may be needed.
|
study
| 99.94 |
One important factor to be taken into consideration is how common an operative intervention is in a specific population. This is problematic as little or no common consensus exists, what labor dystocia really is and how to handle a dystocic delivery. Operative interventions in this study are more common in Switzerland; the predictive value of AFL might therefore be better in this subgroup than among the Tanzanian women, where fewer deliveries end in an operative intervention. On the other hand, more complications such as post-partum infections and post-partum hemorrhages are seen among Tanzanian women in the study. One maternal and three fetal deaths occurred in the Tanzanian group, all of them after extremely long deliveries with high AFL values. AFL is likely to be an even more useful measurement in these settings. The question becomes, whether we are using the correct operative delivery criteria when handling a dystocic delivery. Hopefully this study can help us to reach a new kind of consensus with a reduction in the number of unnecessary operative interventions.
|
study
| 99.94 |
“In conclusion” we have found that the AFL values collected within 30 minutes of augmentation with oxytocin are an early predictor of labor outcome in dysfunctional primiparous deliveries. Given that today many deliveries are unnecessarily long with risks of increased perinatal morbidity; our findings have important implications for public health.
|
study
| 99.94 |
With the continuous increase in the global population, food security problems are increasing, particularly in China and India with 37% of the world population . In China, food production has greatly improved with the increased application of chemical N fertilizer. However, sustainable agricultural is attracting increased interest due to the depletion of fossil fuels and problems with food security. Industrially produced N fertilizer increases the environmental costs of agricultural production and environmental pollution . Additionally, the abuse of N fertilizer further increases the environmental cost and decreases N use efficiency . The inputs of chemical N fertilizer can be reduced by breeding N efficient cultivars, optimizing N nutrient management and choosing a suitable cropping system [4–6].
|
other
| 96.94 |
Intercropping and relay intercropping are used globally as sustainable practices, e.g., in China, India, Southeast Asia, Latin America, and Africa. These practices use land efficiently, are high-yield, and provide efficient control of weeds, diseases and pests [7, 8]. Environmental resources, such as heat and light, can limit cropping systems, but with sufficient contributions of these resources, annual crop harvests increase. Heat and light resources are used efficiently with the practices of intercropping and relay intercropping to increase land output. Maize-soybean intercropping is used in areas with two crops a year (or three crops), e.g., the Huang-Huai-Hai region and in northwest China , whereas in areas with one crop a year (or three crops in two years), e.g., in southwest China , maize-soybean relay intercropping is the practice. In relay intercropping systems, the behaviors of component crops differ from those in sole cropping, and the grain yield and NUE are also affected. In a previous study, relay intercropping with legumes significantly increased the N uptake of the subsequent crop, leading to a 30% increase in grain yield . Compared with the corresponding monocultures, overall nitrogen resources are used 30–40% more efficiently in legume-cereal intercropping . Maize-soybean relay intercropping increases farm land productivity (i.e., the land equivalent ratio of maize-soybean relay intercropping systems ranges from 1.61 to 1.59), in contrast to the monocultures . According to Yamane et al., the land equivalent ratio of relay intercropping is higher than that of double cropping in a legume cropping system .
|
study
| 99.94 |
However, most studies focus on relay intercropping systems in which the legumes play a secondary role, i.e., the legumes are used as a cover crop, and the legume yield is not considered. Previous studies show that facilitation and competition coexist in intercropping systems, particularly in legume/non-legume intercropping systems . Xia et al. reported that legumes facilitate the root system and grain yield of maize considerably , whereas Fan et al. found that the grain yield of fava bean decreased in a wheat/fava bean intercropping system in contrast with a monoculture . Generally, the architecture of a plant influences the relative competitive ability. Soybean seedlings often grow under the shade of the maize canopy and then are transferred to full sunlight after the harvest of maize . Moreover, the distribution of the root system plays a key role in the acquisition of belowground nutrients. The roots of cereals occupy soils both near the surface and in deeper layers, whereas the roots of legumes are distributed in the upper soil layers . The competitive ability of cereals for soil N is stronger than that of legumes in an intercropping system . Furthermore, the separation of the root ecological niche between component crops is well known to affect the total grain yield of intercropping systems. In a maize/fava bean system compared with that of a wheat/fava bean system, the total grain yield was significantly higher . Nitrogen recovery efficiency and uptake efficiency can increase significantly through the interaction of roots between crops. Furthermore, the bi-directional N transfer and positive N competition between crops are advantageous to improve to NUE in the wheat/maize/soybean relay intercropping system .
|
study
| 99.94 |
Early studies demonstrated that the land equivalent ratio (LER) of maize-soybean relay intercropping is greater than 1 , namely, maize-soybean relay intercropping can increase land productivity. However, previous studies focused on resource utilization in maize-soybean relay intercropping, and the distribution of roots belowground remains unclear [12, 19, 20]. Little information is available on the effect of reduced N on the yield advantage and increase in NUE in relay intercropping systems. Additionally, no evidence is available on the effect of reduced N inputs on facilitation in maize-soybean relay strip intercropping. Therefore, the aims of this study were (i) to evaluate the effect of reduced N input on crop grain yield and NUE in the maize-soybean relay strip intercropping system, and (ii) to assess the effect of reduced N input on crop roots distribution in the maize-soybean relay strip intercropping system, and (iii) to analyze the effects of crop roots distribution on crop growth in the maize-soybean relay strip intercropping system.
|
study
| 100.0 |
The experiment was conducted in Renshou County (29°60' N, 104°00' E), Sichuan Province, China. The field climate was subtropical monsoon humid, with average annual temperature of 17.4°C, rainfall of 1009.4 mm, and sunshine of 1196.6 hours. Fig 1 shows the temperature, daylight hours, precipitation and evapotranspiration for the cropping seasons. Total N, total P, total K, alkali hydrolysable N, Olsen-P and exchangeable K in the top 20 cm of soil of the experimental site were 0.90 g kg-1, 0.50 g kg-1, 14.28 g kg-1, 77.35 mg kg-1, 22.83 mg kg-1, and 196.63 mg kg-1, respectively.
|
study
| 100.0 |
The long-term field experiment consisted of three planting patterns, i.e., monoculture maize (MM), monoculture soybean (MS), and maize-soybean relay strip intercropping (IMS), and three application rates of total nitrogen, i.e., no nitrogen (NN), reduced nitrogen (RN) of 180 kg N ha-1, and conventional nitrogen (CN) of 240 kg N ha-1. The compact maize (Zea mays L. cv. Denghai-605) and shade-tolerant soybean (Glycine max L. cv. Nandou-12) were used as experimental crops. Maize was sown on April 1, 2012, April 3, 2013, and April 5, 2014; and harvested on July 29, 2012, August 1, 2013, and August 2, 2014, respectively. Soybean was sown on June 10, 2012, June 11, 2013, and June 15, 2014, with simultaneous application of maize topdressing and soybean base fertilizer; and harvested on October 31, 2012, October 29, 2013, and October 26, 2014, respectively.
|
study
| 99.94 |
Monoculture was planted with row spacing and plant density for maize (MM) of 1.0 m and 58,500 per hectare (ha.) and for soybean (MS) of 0.5 m and 117,000 per ha. The plant spacing was 17 cm for all treatments, with a post-emergence density of 1 maize and 1 soybean plant per hole for the corresponding monocultures. The post-emergence density was 1 maize and 2 soybean plants per hole for maize-soybean relay strip intercropping. The plant density per unit area was equal for intercropping and the corresponding monocultures. All plots were planted with three strips that were 6 m in length and 2 m in width. The total number of plots was 27. In the maize-soybean relay strip intercropping system (IMS), a wide-narrow row planting (160 cm for wide rows and 40 cm for narrow rows) was adopted, resulting in a total ratio of maize to soybean rows of 2:2. Maize plants (IM) were in the narrow rows with row spacing of 40 cm, and soybean plants (IS) were in the wide rows with row spacing of 40 cm. Additionally, the distance between maize and soybean rows was 60 cm (Fig 2).
|
study
| 100.0 |
Potassium chloride, superphosphate, and urea were used as P, K and N fertilizers, respectively. The N fertilizer for maize was divided into two applications, 72 kg N ha-1 for base fertilizer and the rest for topdressing. The P and K fertilizers were used as base fertilizers at 105 kg P2O5 ha-1 and 112.5 kg K2O ha-1 for maize and 63 kg P2O5 ha-1 and 52.5 kg K2O ha-1 for soybean, respectively. All fertilizers were base placement, except for the RN treatments in the maize-soybean relay strip intercropping system. In RN and CN, the N fertilizer for maize was divided into two applications, 72 kg N ha-1 for base fertilizer and the rest for topdressing. Under RN treatment, the base N fertilizer for IM was base placement. The N topdressing for IM was integrated with the soybean base fertilizer and strip placement, with a distance of 25 cm from maize rows to soybean rows in IMS (the optimizing of fertilization methods, unpublished data).
|
study
| 99.94 |
The intercropping advantages in grain yield and N utilization identified in Experiment 1 suggested that belowground facilitation might be responsible for the aboveground growth and efficient use of nutrients. Experiment 2 was conducted with the same three planting patterns and rates of total nitrogen application as in experiment 1. The planting density and fertilization methods and amounts were same as in Experiment 1; however, the row spacing was different, which was 60 cm for all treatments. This experiment was conducted within a rhizo-box, which had a length, width, and height of 1 m, 0.38 m and 1 m, respectively. Each rhizo-box was planted with 2 rows and 2 holes per row, with row spacing and plant spacing of 0.6 m and 0.17 m, respectively. IMS was planted with 2 maize and 4 soybeans; MM was planted with 4 maize plants; and MS was planted with 8 soybeans.
|
study
| 100.0 |
In field experiment 1, samples were collected in the middle row of each plot, and the crop grain yield and straw and root dry matter at physiological maturity were determined. Crop roots were collected from soil blocks, with the length and depth 34 cm (dug in the middle of two plants in a row) and 40 cm, respectively, and the width (dug in the middle of two crop rows) 100 cm for MM, 50 cm for IM, and 50 cm for MS and IS. After manual identification, root samples were hand-washed and oven-dried at 80°C for 72 h before weighing. Crop straw was oven-dried at 80°C for 72 h before weighing. Samples were weighed, and the N content of straw and grain was determined using the Kjeldahl method .
|
study
| 100.0 |
Data on root distribution in soil was obtained by washing off the soil on site , which was a time-consuming and labor-intensive operation to conduct in the field. In a previous study, the vertical distribution was determined using an auger to collect soil cores (5.5 cm diameter) at 10 cm intervals to a maximum depth of 100 cm . We conducted the rhizo-box experiment to study crop root distribution, both during the coexistence period (at the V3 stage of soybean) and at the R2 stage of soybean growth . Both the horizontal and vertical distributions of root systems were investigated (S1 Fig). Crop roots were collected from soil blocks, and after manual identification, root samples were hand-washed and oven-dried at 80°C for 72 h before weighing. Soil block length, width, and height were 10 cm, 0.38 m and 20 cm, respectively. Soil blocks were collected at 10 cm intervals in the horizontal direction and were centered on the crop stem base and sampled from maize row to soybean row in the coexistence period (or from soybean row to maize row at the R2 stage after maize harvest). In the vertical direction, the blocks were divided into depth increments of 20 cm (0–20, 20–40, 40–60, 60–80, and 80–100 cm for maize and 0–20 and 20–40 cm for soybean). Crop root contours were determined using surfer v. 8.0 (Golden Software Inc., Golden, CO, USA), and data-gridding was performed using a natural neighbor method .
|
study
| 100.0 |
Plant N uptake (NA, kg ha-1) was calculated as follows: NA (kg·ha−1) = crop dry matter (kg ha−1) × crop N concentration (g g−1)(1) The N use efficiency (NUE) was calculated as follows : NUE ( %)=UMN+USN−UM0−US0AMN+ASN*100 %(2) where UMN (or USN) (kg N ha-1) is total N accumulation by maize (or soybean) with N application, UM0 (or US0) is total N accumulation by maize (or soybean) without N application, and AMN (or ASN) is the amount of N supplied during the growing season. The NUE of maize (or soybean) was calculated from formula (2) in which USN (or UMN), US0 (or UM0) and ASN (or AMN) were considered zero.
|
study
| 100.0 |
Competition ratio and system productivity index are used to assess interspecific competition and intercropping advantages, respectively. As an indicator, competition ratio is used to measure the degree of competition between crops in an intercropping system and is calculated with the following formula: CRSM=YIS/(YMS×AS)YIM/(YMM×AM)(3) where CRSM is the competitive ratio of maize relative to soybean, YIS and YMS are the yield or nitrogen acquisition per unit area of maize under intercropping and monoculture, respectively, YIM and YMM are the yield or nitrogen acquisition per unit area of soybean under intercropping and monoculture, respectively, and AS and AM are the ratios of the area occupied by maize and soybean under the intercropping system relative to that of the corresponding monoculture, respectively. In this study, AS and AM were the same. A competition ratio greater than 1 indicated the competitive ability of soybean was greater than that of maize in the maize-soybean relay intercropping system. A ratio less than 1 indicated the competitive ability of soybean was less than that of maize in the maize-soybean relay intercropping system. System productivity index, SPI, is another indicator used to assess intercropping that standardizes the yield of the secondary crop in terms of the primary crop and is calculated as follows: SPI=(SSSM)×YM+YS(4) where SS and SM are the average yields of soybean and maize under monoculture, respectively, and YS and YM are the average yields of soybean and maize under intercropping, respectively.
|
study
| 100.0 |
Data were analyzed with analysis of variance (ANOVA) using the SPSS v.22 , and the average values were compared using least significant differences (LSD) at the 5% level. Surfer v.8 was used to draw the figures of root distribution , and Origin Pro 8 was used to draw the figure.
|
study
| 99.9 |
The level of N application affected the competition ratio of soybean relative to maize (CRSM) in the maize-soybean relay intercropping system (Fig 3). The CRSM increased with the increase in N input, and the ratio in all treatments was greater than 1 in 2012, suggesting interspecific facilitation. Although the CRSM decreased in 2013, the CRSM of all treatments remained greater than 1. However, in 2014, the CRSM of the NN treatment was less than 1, whereas that of RN and CN treatments was greater than 1. The trends for CRSM indicated a positive interspecific interaction between component crops in the maize-soybean relay intercropping system, and N application was advantageous and improved the CRSM. With the increase in years of planting, the interspecific facilitation converted to interspecific competition in the NN treatment, although interspecific facilitation was retained under RN and CN, which suggested that N input was required to achieve interspecific facilitation in long-term maize-soybean relay intercropping.
|
study
| 100.0 |
The LER of all treatments was greater than 1, and the LER increased in RN and CN compared with that NN in the three years (Table 1). The LER of RN was lower than that of CN in 2012 but was higher than that of CN in 2013 and 2014. Based on crop yield, the SPI is a simple parameter that directly reflects the intercropping system productivity advantage, and the index increased continually from 2012 to 2014 (Fig 3). However, with increased N application rates, the SPI increased first and then decreased, and the SPI of RN was higher than that of CN by 21.35%, 18.28% and 21.71% in 2012, 2013 and 2014, respectively.
|
study
| 100.0 |
The total N application rates are 180 kg N ha-1 for RN and 240 kg N ha-1 for CN, respectively. RN: reduced nitrogen, CN: conventional nitrogen. Data are mean±S.D., different lower case letters in the same column means significant differences between RN and CN. Values under ANOVA are the F-test, probabilities (P value) and coefficient of variation of the sources of variation (LSD, P < 0.05).
|
study
| 99.94 |
Planting patterns affected crop root growth (S1 Table). The effect of planting pattern on maize root dry matter was not significant, but soybean root dry matter decreased significantly by 12.4% in IS compared with MS. Regarding N levels, both maize and soybean root dry matter increased in RN and CN compared with that in NN, whether in a monoculture or intercropping pattern; the difference between RN and CN was not significant.
|
study
| 100.0 |
Crop root growth was influenced by interspecific competition (Figs 4 and 5; S1 and S2 Tables). The first sampling was during the coexistence period when maize was at the early grain-filling stage and soybean was at the V3 stage of development. At that time, crop root dry matter increased with increased N input and crop root distribution displayed a similar trend. Compared with IM, maize root growth extended farther in the horizontal direction but fewer roots were distributed in the vertical direction under MM (Fig 4). With increased N application, the root dry matter of maize increased in both the upper 40 cm and at the 40–100 cm depth. The increase was particularly striking at the stem base in the upper 40 cm, and the maize root dry matter under IM rapidly increased at the stem base compared with that under MM. The maize root dry matter under IM also increased at the 20–80 cm depth from maize to soybean row (Fig 4).
|
study
| 100.0 |
MM with different N application rates (A), IM with different N application rates (B); the X-axis indicates depth (20 cm per layer) and the Y-axis indicates sampling interval (10 cm per interval); N application rates are 0, 180 kg N ha-1 (shared by soybean and maize), and 240 kg N ha-1 (180 kg N ha-1 for maize and 60 kg N ha-1 for soybean), respectively, the same as below.
|
other
| 99.9 |
During the coexistence period, soybean was at the V3 stage, and planting pattern and level of N significantly influenced the root growth of soybean (S2 Table). When soybean was at the V3 stage, the soybean root dry matter under IS was higher in CN than that in NN and RN (S2 Table). Under IS, soybean root dry matter declined compared with that in MS, but with increased N application, root dry matter increased and that of CN was higher than that of NN and RN (S2 Table). At the R2 stage of soybean, the root dry matter distribution under IS and MS was similar, with more roots at shallow depths, particularly at the stem base and in the upper 20 cm. The soybean root dry matter under IS increased with N application rates, but roots were less distributed both vertically and horizontally (Fig 5).
|
study
| 100.0 |
Planting patterns and N application rates had remarkable effects on crop grain yield (Table 2). From 2012 to 2014, straw dry matter and grain yield of IM declined by 3.4% and 3.5%, respectively, compared with those in MM; however, the difference was not significant. Compared with NN in IM, dry matter accumulation of RN and CN was significantly greater by 29.5% and 33.9% for straw dry matter and 19.6% and 26.6% for grain yield, respectively (Table 2). Effects of planting pattern and N level on soybean dry matter accumulation differed from those for maize. The straw dry matter and grain yield of MS increased and declined, respectively, compared with those in IS. Under different N application rates in MS, the grain yield of RN was greater than that in CN, whereas the straw dry matter of RN was lower. In IS, grain yield and straw dry matter of RN were greater than those of CN.
|
study
| 100.0 |
The total N application rates are 0, 180 kg N ha-1 and 240 kg N ha-1, respectively. MM: monoculture maize, IM: intercropped maize, MS: monoculture soybean, IS: intercropped soybean; NN: no nitrogen, RN: reduced nitrogen, CN: conventional nitrogen. Data are mean±S.D., different lower case letters in the same column means significant differences. Values under ANOVA are the F-test, probabilities (P value) and coefficient of variation of the sources of variation (LSD, P < 0.05).
|
study
| 99.9 |
Nitrogen application significantly increased N uptake under the different planting patterns (Table 3). The N uptake of straw and grain was 7.4% higher and 4.0% lower, respectively, in IM than in MM and 55.3% (significantly) higher and 8.5% lower, respectively, in MS than in IS. Comparing N application rates in MM, the N uptake of grain in CN was greater than that in RN, whereas the straw N uptake in RN was greater than that in CN. In IM, grain and straw N uptake in RN were higher than those in CN. The N uptake of soybean in RN was the highest among all treatments, with N uptake of straw and grain greater in RN than in CN for MS and IS.
|
study
| 100.0 |
The total N application rates are 0, 180 kg N ha-1 and 240 kg N ha-1, respectively. MM: monoculture maize, IM: intercropped maize, MS: monoculture soybean, IS: intercropped soybean; NN: no nitrogen, RN: reduced nitrogen, CN: conventional nitrogen. Data are mean±S.D., different lower case letters in the same column means significant differences. Values under ANOVA are the F-test, probabilities (P value) and coefficient of variation of the sources of variation (LSD, P < 0.05).
|
study
| 99.9 |
With reduced N input, NUE significantly increased for all planting patterns, and the NUE of MM, IMS and MS was 32.1%, 103.7% and 545.8% greater, respectively, in RN than in CN (Table 4). Intercropping maize with soybean increased NUE; the NUE of IMS increased by 105.1% over that of MM, and the NUE of RN was 139.0% greater in IMS than in MM (Table 4).
|
study
| 100.0 |
The total N application rates are 180 kg N ha-1 for RN and 240 kg N ha-1 for CN, respectively. MM: monoculture maize, IM: intercropped maize, MS: monoculture soybean, IS: intercropped soybean, IMS: maize-soybean relay intercropping; RN: reduced nitrogen, CN: conventional nitrogen. Data are mean±S.D., different lower case letters in the same column means significant differences between RN and CN. Values under ANOVA are the F-test, probabilities (P value) and coefficient of variation of the sources of variation (LSD, P < 0.05).
|
study
| 99.94 |
In this study, the increase in crop yield and land productivity with maize-soybean relay intercropping was confirmed. The LER of the maize-soybean relay intercropping varied from 1.85 to 2.36 during the cropping seasons (Table 1). Results were similar for summer soybean-spring maize relay intercropping with an increase in land output and an LER of the relay intercropping that varied from 1.38 to 1.59 . In the U.S., the total grain yield is higher in winter wheat-spring soybean relay intercropping than that with monoculture , and in China, the LER of winter wheat-spring cotton relay intercropping ranged from 1.20 to 1.53 . Interspecific competition for nutrient resources is well known to affect crop growth and grain yield in intercropping systems [7, 17, 25, 26]. Numerous studies show that belowground interactions and competition for nutrients play a key role in intercropping and relay intercropping [6, 13, 22, 30]. Lv et al. demonstrated that competition for nutrients is more important than aboveground competition for light in maize-soybean intercropping . By contrast, Yang et al. found no difference in yield between treatments in which roots were separated or not in maize-soybean relay intercropping . The different conclusions may be the result of the difference in coexistence periods, because the component crops in intercropping have a longer coexistence period than that in relay intercropping; therefore, the following crop in relay intercropping can benefit from the longer recovery period. In this study, the complementary ecological niches of crop roots were advantageous to aboveground growth in the maize-soybean relay intercropping system (Figs 4 and 5; Tables 1 and 2, S1 and S2 Tables). The separation of root ecological niches in intercropping systems avoids interspecific competition for nutrients; for example, the total yield of the maize/fava bean intercropping system was significantly higher than that of the wheat/fava bean intercropping system . During the coexistence period, maize was at the grain-filling stage, whereas soybean seedlings were at the stage with 3 fully developed trifoliate leaves. Maize roots rapidly proliferated underneath the maize plants in the soil top layer (0–40 cm) in relay intercropping compared with that in monoculture. The roots of soybean seedlings were insufficiently developed and primarily distributed underneath the soybean plants. However, after maize harvest (at the full-bloom stage of soybean), the soil volume occupied by soybean roots was similar between relay intercropping and monoculture, likely caused by the changes in root distribution and morphologies in space and time in relay intercropping systems . In previous studies, crop roots show plasticity in response to soil nutrients and the distribution of water. With maize growth, the roots of maize extended to the soybean strip and proliferated underneath soybean . When roots of component crops intermingle with one another, the preceding crop suppresses the growth of roots and decreases the shoot biomass of the following crop . However, with the regrowth of roots of the following crop after harvest of the preceding crop, and fertilizer use efficiency increases, ultimately increasing the grain yield of the following crop [6, 13]. Thus, an optimized root system is advantageous in the acquisition of soil nutrients and provides sufficient nutrition for plant shoot growth.
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| 99.94 |
The belowground benefits contributed to recovery of aboveground growth, thereby improving soybean yield (Table 2). The recovery of growth in soybean is responsible for pod formation, which improves the grain yield of soybean in the relay intercropping system . The N competition ratio (NCR) of soybean relative to maize was greater than 1, indicating a positive interspecific facilitation between component crops in the maize-soybean relay intercropping system (Fig 3). This result is consistent with that of a previous study in which the NCR of a legume was greater than that of maize, and therefore, interspecific complementation between the component crops in fava bean-maize intercropping increased total grain yield . In the present study, the N uptake and NUE of IMS increased through interspecific facilitation. The loss of N by maize in IMS was compensated by a gain in soybean in IMS (Tables 2 and 3). The NUE of IMS increased continually and was higher than that of MS in 2013 and 2014. These results confirmed that after maize harvest, soybean roots recovered growth and proliferated in the soil layer that was previously occupied by maize. Additionally, planting patterns have a large influence on crop root growth and nutrients utilization. For example, the interspecific competition for nutrients is more important than competition for sunlight when the number of maize rows to soybean rows is 1:1 and the distance between soybean and maize is 30 cm . The roots of maize extend to soybean rows, whereas the roots of soybean are distributed primarily underneath the soybean plants when the number of maize rows to soybean rows is 1:3 and the distance between soybean and maize is 30 cm . With the number of maize rows to soybean rows 2:2 and the distance between soybean and maize 60 cm, interactions belowground become weaker than those in the forward intercropping systems . However, no difference is observed in the total grain yield between maize-soybean relay intercropping and maize-soybean intercropping [30, 31].
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| 100.0 |
The availability of nutrients in soil affects crop growth. Relay intercropping maize with soybean can decrease soil pH and increase acid phosphatase activity, thereby increasing maize P uptake and total grain yield . Soybean is well known to increase soil N input, with a rate of N fixation that varies from 100 to 140 kg N ha-1 per year . Relay intercropping that includes legumes can increase N input and maize yield . However, Amuse et al. found that the improvement in N use and grain yield was primarily a result of the biological N fixation of legumes in the previous cropping season , which occurs because during the coexistence period, the growth of legume seedlings is insufficient to compete for N or facilitate with wheat in spring legume-winter wheat relay intercropping . Soil mineral nutrients can be efficiently utilized in intercropping and relay intercropping systems. On one hand, crop root exudates can increase the availability of soil nutrients and use efficiency, e.g., legume exudates can increase soil P availability, whereas those of cereals can increase soil Fe and Zn availability . On the other hand, cereal crops must acquire abundant inorganic N, resulting in decreases in soil N concentration, which can increase the biological nitrogen fixation ability of legume crops . The decrease of soil N concentration is advantageous, because the “suppressing effects” of soil N on N fixation by legumes are alleviated . Additionally, relay intercropping maize with soybean can promote N transfer from legume to nonlegume . Notably, a recent study found that maize root exudates can promote soybean nodule formation and increase soybean biological nitrogen fixation in maize-soybean intercropping . Therefore, a suitable component crop can increase grain yield by increasing soil nutrient availability and utilization efficiency in relay intercropping or intercropping. However, information on the role of rhizosphere processes in interspecific facilitation between component crops in relay intercropping systems remains limited and inconclusive. Long-term location tests are required to understand the development and mechanisms of interspecific facilitation in the maize-soybean relay intercropping system.
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| 99.5 |
Although chemical nitrogen fertilizers have been used worldwide to increase grain yields in the most recent decades , excessive N input may lead to yield loss [20, 39]. Particularly in China, nitrogen fertilizer use has successfully achieved food security in recent decades . However, this high input of N has resulted in serious environmental pollution, which is adverse to the development of sustainable agriculture , e.g., leading to water eutrophication, soil acidification and air pollution [38, 41–43]. Therefore, strategies for the efficient management of N are urgently required. Good et al. identified two ways to increase crop NUE, traditional breeding and transgenic technology . However, cropping techniques are another equally important approach to achieve N efficiency, which can rapidly and further promote the grain yield of highly nutrient-efficient cultivars; for example, intercropping and relay intercropping are resource efficient and environment friendly cropping systems that can increase farm land productivity [20, 31, 45]. In the present study, the NUE was higher in RN than in CN in MM, and in MS, the NUE was significantly higher in RN than in CN. The grain yield of MM increased significantly in CN compared with that in RN, whereas the grain yield of soybean decreased significantly in CN compared with that in RN. The reason for the yield loss might be soil acidification, because multiple fertilizer inputs can result in declines in soil pH , in addition to declines in soil pH that can occur under intercropping soybean with maize . In IMS, the NUE of RN was significantly higher than that of CN, and the maize grain yield was maintained in RN, and soybean grain yield and NUE increased (Tables 1 and 3). These results confirmed that reduced N input led to grain yield loss in MM, in contrast to reduced N supply leading to increased maize yield and NUE in IMS. The results in this study are consistent with those of Yang et al. and Wang et al. who found that relay intercropping can increase resource utilization and land productivity [10, 20] and are also consistent with observations that a reduced N supply can maintain crop yield , whereas excessive N leads to a decline in LER .
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| 99.94 |
Aboveground growth is strongly influenced by belowground processes. Crop root systems have the key role in soil nutrient acquisition, with effects on nutrient balance, and improvements in root distribution increase the potential opportunities for nutrient use. Interspecific root interactions in different cropping systems are manifested as competition or facilitation . A complementary root distribution is a prerequisite for high-yields in intercropping systems . In the present study, the coexistence period between maize and soybean was approximately 8 weeks, with sampling at the grain-filling stage of maize and soybean at approximately the V3 stage, and the results showed that the ecological niche of crop roots was separated (Figs 4 and 5; S2 Table). In the coexistence period, with the increase in N levels, the roots of maize rapidly proliferated underneath the maize plants and in the middle soil layer (20–80 cm), whereas those of soybeans were primarily distributed underneath the soybean plants. Although the soybean seedlings were weak, the seedlings helped to improve soil nutrients and facilitate maize in relay intercropping , which is in contrast to a previous study in which legume seedlings were too weak to compete or facilitate with cereal in relay intercropping . This contrast may be the result of different legumes species. The CR of soybean was stronger than that of maize, and soybean performed better under RN than under CN. In a similar study, the CR of alfalfa was stronger than that of maize, and the yield advantage was greater in maize-alfalfa intercropping than that in the corresponding monocultures . Additionally, N fertilizer input was required in long-term maize-soybean relay intercropping (Tables 1 and 2, Fig 3). During the coexistence period, the demand for N was high in the maize grain-filling stage, and an insufficient supply of N would result in yield loss .
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| 99.94 |
The NUE of IMS increased significantly in RN compared with that in CN (Table 4). Under RN treatment, the nitrogen demand of maize was met, which led to increased NUE and grain yield (Tables 1 and 3). This result is consistent with that of Hasegawa et al. . Additionally, the N uptake of maize led to a decrease in soil N concentration. Gan et al. found that low N levels were advantageous, because the suppressing effect of N on biological nitrogen fixation was removed . Similar results are reported that intercropping with legumes can increase N input and thereby reduce chemical N fertilizer supply [17, 30, 32]. Reduced inputs of N can significantly reduce the residual N in soils and decrease N emissions and leaching losses . The advantage in intercropping is achieved by complementary use of inorganic and atmospheric N and reducing competition for inorganic N, as occurs in pea/barley intercropping . Intercropping advantages also included alleviation of N acquisition and increased sharing between maize and pea when compared with unfertilized intercropping systems . In the present study, the LER and SPI of IMS were higher in RN than in CN, which is consistent with Yang et al. who found that reduced N can increase LER and total grain yield . However, the influence of rhizosphere processes on the effect of interspecific facilitation on biological nitrogen fixation in relay intercropping conditions remains inconclusive. Long-term field location tests are required to explore the mechanisms and changes that promote soybean biological nitrogen fixation in the maize-soybean relay intercropping system.
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| 100.0 |
Our results revealed a positive interspecific facilitation between maize and soybean in the maize-soybean relay strip intercropping system. The separation of the root ecological niche contributed to interspecific facilitation. The selection of the component crop for relay strip intercropping should consider the interspecific complementary characteristics in the case of grain yield loss. For a long-term maize-soybean relay strip intercropping system, N fertilizer input is required. The total grain yield of the maize-soybean relay intercropping system increased under RN compared with that under CN. Furthermore, the nitrogen use efficiency in RN increased notably compared with that in CN in long-term maize-soybean relay strip intercropping.
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| 100.0 |
Setting the maize stem base as origin, root distribution was investigated by hand digging from maize row to soybean row. Numbers above lines are distances from maize to soybean rows. The sample interval from maize base to soybean base in the grid is 10 cm long, while from crops to box edgd was 15 cm long, and soil blocks sampling width and depth were 38 and 20 cm.
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| 99.9 |
The total N application rates are 0, 180 kg N ha-1 and 240 kg N ha-1, respectively. MM: monoculture maize, IM: intercropped maize, MS: monoculture soybean, IS: intercropped soybean; NN: no nitrogen, RN: reduced nitrogen, CN: conventional nitrogen. Data are mean±S.D., different lower case letters in the same column means significant differences. Values under ANOVA are the F-test, probabilities (P value) and coefficient of variation of the sources of variation (LSD, P < 0.05).
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| 99.9 |
The total N application rates are 0, 180 kg N ha-1 and 240 kg N ha-1, respectively. MS: monoculture soybean, IS: intercropped soybean; NN: no nitrogen, RN: reduced nitrogen, CN: conventional nitrogen. Data are mean±S.D., different lower case letters in the same column means significant differences. Values under ANOVA are the F-test, probabilities (P value) and coefficient of variation of the sources of variation (LSD, P < 0.05).
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| 99.9 |
The APOBEC3 (short for “apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like”) family of human cytidine deaminases provides a first line of defense against many exogenous and endogenous retroviruses such as HIV-1 and the retro-element LINE-11–6. APOBEC3 (A3) proteins restrict replication of retroviruses by inducing hypermutations in the viral genome7. A3s deaminate deoxycytidines in ssDNA into uridines during reverse transcription. This results in G to A hypermutations, as adenosines are transcribed across from uridines during second strand DNA synthesis. While all A3 enzymes deaminate deoxycytidines in ssDNA, they have differential substrate specificities that are context dependent, resulting in altered frequencies of mutation for the deoxycytidines. Some A3s deaminate the second deoxycytidine in a sequence containing CC while others deaminate deoxycytidine in a TC context8–10. However, not every cognate dinucleotide motif (CC or TC) in the ssDNA of the HIV genome is deaminated11. Nevertheless, hypermutation in a viral genome results in defective proteins and proviruses, thus decreasing the probability of further viral replication12.
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| 99.94 |
Beyond restricting viral replication, the ability of A3s to deaminate deoxycytidines in ssDNA have made A3s a double-edged sword. When overexpressed, A3s can mutate the host genome resulting in a variety of cancers. The identities and patterns of the mutations observed in cancer genomes can define the source of these mutations. Recently, the search for the deaminase(s) responsible for kataegic mutations found in breast cancer was narrowed down to APOBEC3B, through the comparison of all known APOBEC mutational signatures and eliminating APOBEC3G and other deaminases from potential mutational contributors9,13. Soon after, APOBEC3B was found to be correlated with a variety of other cancers such as ovarian, cervical, bladder lung, head and neck; signature sequence analysis was also a contributing factor that led to these conclusions14,15. Most recently APOBEC3H, which has a different sequence preference than APOBEC3B, has been identified to also play a role in breast and lung cancer16. Thus, defining A3 sequence specificity can be helpful in identifying A3s’ role in viral restriction and in cancer.
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review
| 99.7 |
A3 signature sequences proposed for deaminating deoxycytidines range between di-nucleotide to quad-nucleotide motifs8–11,16–21. A recent high-throughput assay suggested the preferred quad-nucleotide motif for A3A to be CTCG20. Although A3s are known to have varied sequence preference, quantitative and systematic studies of sequence specificity are incomplete. Recently, crystal structures of APOBEC3A (A3A) and APOBEC3B-CTD (an active site A3A chimera) with ssDNA have been solved20,22. However, despite these breakthrough structures, the molecular mechanism underlying substrate sequence specificity flanking the TC dinucleotide sequence remains unclear.
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| 99.9 |
A3A is a single-domain enzyme with the highest catalytic activity among human APOBEC3 proteins23 and a known restriction factor for the retroelement LINE-1 and HPV24,25. A3A can also contribute to carcinogenesis with increased expression or defective regulation26. A3A is the only A3 where both the intact apo and substrate-bound structures have been determined19,20,22,27,28. Initial substrate specificity studies have shown selectivity for DNA over RNA, suggested by NMR chemical shift perturbation19. Since A3A is the best biochemically characterized A3 human cytidine deaminase and thus a critical benchmark within the family, we chose A3A to elucidate the extended characteristics of ssDNA specificity.
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| 100.0 |
To determine the substrate specificity of A3A, we systematically quantified the affinity of A3A for nucleic acid substrates as a function of substrate sequence, length, secondary structure, and solution pH. We identified the A3A preferred ssDNA binding motif, (T/C)TC(A/G) and found binding correlated with enzymatic activity. Also, we determined that A3A can bind RNA in a sequence specific manner. Surprisingly, A3A’s signature sequence was necessary but not sufficient to account for A3A’s high affinity for ssDNA. Significantly, A3A bound more tightly to the motif in longer oligonucleotides, and in the context of a hairpin loop. Using recently published structures of A3As complexed with ssDNA from our lab and others, we propose a structural model for the molecular mechanism for this enhanced affinity where inter-DNA interactions contribute to A3A recognition of the cognate sequence. This model provides insights into how the nucleotides flanking the canonical TC sequence may contribute to substrate sequence preference of A3A.
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| 100.0 |
To interrogate the substrate sequence preference of A3A, we systematically quantified the changes in binding affinity of catalytically inactive A3A bearing the mutation E72A to a library of labeled ssDNA sequences using a fluorescence anisotropy-based DNA binding assay28. First, to ensure that the affinity for substrate was due entirely to the sequence of interest and not due to nonspecific binding or undesired secondary structure effects, an appropriate control background sequence was identified. The dissociation constants (Kd’s) for homo-12-mer ssDNA sequences, Poly A, Poly T, Poly C, were determined (Fig. 1a). Poly G was not tested due its propensity to form secondary structure elements. Poly T (750 ± 44 nM), which had previously been used in background sequences28, bound to A3A with 2-fold higher affinity than Poly C (1,600 ± 117 nM). Thus without a greater context for A3A to target, Poly C was only weakly bound. A3A had the lowest affinity for Poly A with a Kd of >11,000 nM (Table 1). For all subsequent assays, Poly A was used as the background, as there is no detectable binding affinity of A3A to Poly A.Figure 1A3A specificity to ssDNA background and substrate. Fluorescence anisotropy of TAMRA-labeled ssDNA sequences binding to A3A(E72A). (a) Binding of A3A to poly nucleotide (12 mers): Poly A (blue), Poly T (red) and Poly C (green), (b) Binding to Poly A (blue), 5A-C-6A (red), 5A-U-6A (green), (c) Binding to Poly T (blue), 5T-C-6T (red), 5T-U-6T (green).Table 1A3A affinity for DNA sequences used in this analysis.DNA sequenceKd (nM)Poly C (12 C)1,568 ± 117Poly T (12 T)748 ± 44Poly T-C (5T-C-6T)35 ± 2Poly T-U (5T-U-6T)499 ± 23Poly A (12 A)>11,000Poly A-C (5A-C-6A)>5,000Poly A-U (5A-U-6A)>6,500Poly A-TC (4A-TC-6A)143 ± 4Poly A-CC (4A-CC-6A)250 ± 14Poly A-GC (4A-GC-6A)>6,500Poly A-ATUA (3A-ATUA-5A)>5,000Poly A-ATUG (3A-ATUG-5A)328 ± 42Poly A-TTUA (3A-TTUA-5A)306 ± 17Poly A-ATCA (3A-ATCA-5A)145 ± 2Poly A-ATCT (3A-ATCT-5A)209 ± 5Poly A-ATCC (3A-ATCC-5A)163 ± 3Poly A-ATCG (3A-ATCG-5A)154 ± 2Poly A-TTCA (3A-TTCA-5A)90 ± 1Poly A-TTCT (3A-TTCT-5A)127 ± 2Poly A-TTCC (3A-TTCC-5A)114 ± 2Poly A-TTCG (3A-TTCG-5A)92 ± 2Poly A-CTCA (3A-CTCA-5A)85 ± 1Poly A-CTCT (3A-CTCT-5A)122 ± 2Poly A-CTCC (3A-CTCC-5A)101 ± 2Poly A-CTCG (3A-CTCG-5A)86 ± 1Poly A-GTCA (3A-GTCA-5A)150 ± 3Poly A-GTCT (3A-GTCT-5A)218 ± 7Poly A-GTCC (3A-GTCC-5A)152 ± 2Poly A-GTCG (3A-GTCG-5A)150 ± 3Hairpin-TTC (G-CCATC-ATTC-GATGG-G)26 ± 2Hairpin-AAA (G-CCATC-AAAA-GATGG-G)676 ± 399
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| 100.0 |
A3A specificity to ssDNA background and substrate. Fluorescence anisotropy of TAMRA-labeled ssDNA sequences binding to A3A(E72A). (a) Binding of A3A to poly nucleotide (12 mers): Poly A (blue), Poly T (red) and Poly C (green), (b) Binding to Poly A (blue), 5A-C-6A (red), 5A-U-6A (green), (c) Binding to Poly T (blue), 5T-C-6T (red), 5T-U-6T (green).
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| 99.94 |
The specificity of A3A for substrate versus product was measured by binding to Poly A with a single C versus Poly A with a single U (Fig. 1b). Surprisingly, the presence of a single deoxycytidine in a Poly A background was not sufficient for binding with appreciable affinity. The energetics of free ssDNA conformations in solution for Poly A sequences and base stacking propensity29 might be unaltered upon the introduction of a single C. The affinity of A3A for the Poly A-C (5A-1C-6A) (>5,000 nM) is similar to the affinity for Poly A-U (5A-1U-6A) (>6,500 nM) and even the background Poly A. This is in contrast to A3A’s specificity for binding a single C over U in a Poly T background, which is more than ten-fold (35 ± 2 nM and 500 ± 23 nM respectively) (Fig. 1c), as we previously measured28. This strong context dependence differentiating substrate C versus product U within the background of Poly A versus Poly T indicates that A3A heavily relies on the identity of the surrounding nucleotide sequence to recognize and bind substrate deoxycytidine.
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| 100.0 |
A systematic measurement of A3A affinity in a broad range of pH values was performed to verify and quantify the pH dependence of A3A binding to substrate ssDNA21,26,30 and set a reference pH for subsequent experiments. The Kd of A3A for TTC in a Poly A background was determined at pH ranging from 4.0 to 9.0 in 0.5 pH increments (Supplementary Fig. 1 and Supplementary Table 1). A3A had the highest affinity for Poly A-TTC at pH 5.5 with a Kd of 68 ± 3 nM. The isotherms for A3A binding ssDNA at pHs below 6.0 show some secondary binding event that may be due to non-specific binding or aggregation (Supplementary Fig. 1a). A steady decrease was also observed for the affinity of A3A for ssDNA when pH was increased above 6 (Supplementary Fig. 1b), in agreement with decreased deamination activity at higher pH26. A3A affinity also overall correlated with reported deamination activity determined using a different assay at pH 7.531. Interestingly, A3A had no appreciable affinity for Poly A-TTC above pH 8.0. Since A3A is stable at these higher pH values, the lower affinity for ssDNA with increased pH is likely not due to aggregation but due to the protonation of His29, as previously described26 and reported to be responsible for coordinating ssDNA32. Therefore, all of the subsequent binding experiments were performed at pH 6.0 to avoid any potential for secondary binding events or aggregation of the protein.
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| 100.0 |
To study the effect of the nucleotide identity at position −1 relative to target deoxycytidine (NC) on A3A affinity for substrate (Fig. 2a), the Kd values of A3A for (4 A)-TC-(6 A), AC, CC, GC in a Poly A background were determined. A preference for TC (143 ± 4 nM), followed by CC (250 ± 14 nM) was identified. Interestingly, AC and GC had similarly very weak binding affinities for A3A (>5,000 and >6,500 nM respectively), validating a preference for pyrimidines (T or C) over purines (A or G) at −1 position with T as the strongest binder.Figure 2A3A specificity for nucleotides flanking substrate cytidine. Fluorescence anisotropy of TAMRA-labeled ssDNA sequences to A3A(E72A). (a) Binding of A3A to ssDNA with changes at −1 position of substrate C in a poly A background (12 mers): 4A-AC-6A (blue), 4A-TC-6A (red), 4A-CC-6A (green), and 4A-GC-6A (orange). (b) Binding of A3A to ssDNA with changes at −2 position in a TC context in a Poly A background (12 mers): 4A-ATC-6A (blue), 4A-TTC-6A (red), 4A-CTC-6A (green), and 4A-GTC-6A (orange). (c) Binding of A3A to ssDNA with changes at +1 position in a TC context in a Poly A background (12 mers): 4A-TCA-6A (blue), 4A-TCT-6A (red), 4A-TCC-6A (green), and 4A-TCG-6A (orange). (d) Three substrate sequences, TTCA (green), ATCG (red) and ATCA (blue), in closed circles with the corresponding 3 product sequences TTUA (green), ATUG (red) and ATUA (blue) in open circles.
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| 100.0 |
A3A specificity for nucleotides flanking substrate cytidine. Fluorescence anisotropy of TAMRA-labeled ssDNA sequences to A3A(E72A). (a) Binding of A3A to ssDNA with changes at −1 position of substrate C in a poly A background (12 mers): 4A-AC-6A (blue), 4A-TC-6A (red), 4A-CC-6A (green), and 4A-GC-6A (orange). (b) Binding of A3A to ssDNA with changes at −2 position in a TC context in a Poly A background (12 mers): 4A-ATC-6A (blue), 4A-TTC-6A (red), 4A-CTC-6A (green), and 4A-GTC-6A (orange). (c) Binding of A3A to ssDNA with changes at +1 position in a TC context in a Poly A background (12 mers): 4A-TCA-6A (blue), 4A-TCT-6A (red), 4A-TCC-6A (green), and 4A-TCG-6A (orange). (d) Three substrate sequences, TTCA (green), ATCG (red) and ATCA (blue), in closed circles with the corresponding 3 product sequences TTUA (green), ATUG (red) and ATUA (blue) in open circles.
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study
| 100.0 |
The effects of the sequence identity around the cognate dinucleotide deamination motif (TC) on affinity of A3A for ssDNA was determined by first testing the change in affinity for all nucleotide substitutions at −2 position (3A)-NTC-(6 A). A3A has a preference for pyrimidine over purine at −2 position (Fig. 2b) with TTC and CTC having similar affinities (90 ± 1 nM and 85 ± 1 nM respectively) compared to that of purines ATC and GTC (145 ± 2 nM and 150 ± 3 nM respectively). While not as strong as for −1 position, there is a preference for the smaller pyrimidines at position −2. Next, the effect of +1 position on affinity of A3A to TC was determined (Fig. 2c). A3A did not demonstrate a strong preference for any particular nucleotide, although disfavoring T, at the +1 position (145 ± 2 nM for background versus 209 ± 5 nM).
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| 100.0 |
Finally, to identify if there was any interdependency between nucleotide identity at −2 and +1 positions, the affinity of A3A for (3A)-NTCN-(5 A) was determined (Fig. 3, Table 1). A3A displayed preference for pyrimidines at −2 position regardless of the nucleotide at +1. A3A also disfavored T at +1 position regardless of the nucleotide identity at −2. Most interestingly, A3A preferred a pyrimidine at −2 when there was a purine at +1 position. However, the reverse was not true; purine at −2 position with pyrimidine at +1 position did not result in comparable affinities. In fact, the worst binders (ATCT and GTCT) were those that contained purines at −2 with pyrimidines at +1 position. Thus, the substrates can be broadly classified as high (80–130 nM), medium (150–165 nM), and weak (210–220 nM) affinity binders, with (T/C)TC(A/G) identified as the preferred sequence for ssDNA recognition by A3A.Figure 3A3A specificity for poly A NTCN. Binding affinity of A3A(E72A) to TAMRA-labeled ssDNA sequences in a Poly A background. Gray boxes bin sequences by −2 nucleotide identity. Colors represent +1 nucleotide identity: A (blue), T (red), C (green), G (orange). Consensus sequence derived from these Kd values is shown above the graph.
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| 100.0 |
A3A specificity for poly A NTCN. Binding affinity of A3A(E72A) to TAMRA-labeled ssDNA sequences in a Poly A background. Gray boxes bin sequences by −2 nucleotide identity. Colors represent +1 nucleotide identity: A (blue), T (red), C (green), G (orange). Consensus sequence derived from these Kd values is shown above the graph.
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study
| 99.94 |
A3A’s affinity for substrate C was compared to product U in the context of variations of the signature A3A substrate sequence (T/C)TC(A/G). The affinity of three substrate sequences, TTCA, ATCG and ATCA, were compared to the corresponding product sequences (Fig. 2d). For all three sequences, a substantial loss of binding affinity was observed for the corresponding TTUA, ATUG and ATUA, with the most substantial loss with ATUA. Thus, the decrease in affinity for product over substrate was context dependent.
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| 100.0 |
Although enzymatic activity and binding affinity are not expected to be directly correlated, the trends for specificity would likely be similar. The NMR assay is a highly quantitative method of observing product concentration and/or substrate reduction directly by NMR signal volumes throughout the reaction27. Thus A3A’s deamination activity was determined in the context of variations of the signature sequence (T/C)TC(A/G) using a 1H NMR based A3 deaminase activity assay. High (TTCA and TTCG), medium (ATCA, ATCG, GTCA, GTCG, TTCT) and low (ATCT and GTCT) affinity sequences were tested (Table 2) to determine the correlation between binding and activity. Overall, activity by NMR has the same trend as affinity from the binding assay (Fig. 4). This indicates that in general those substrates sequences with varying binding affinity (high, medium and weak) are also processed in a similar order.Table 2A3A enzyme activity for DNA sequences.DNA sequenceActivity (min−1) 40 °CPoly A-ATCA27 ± 1Poly A-ATCG28 ± 1Poly A-ATCT30 ± 2Poly A-GTCA38 ± 2Poly A-GTCG31 ± 2Poly A-GTCT22 ± 2Poly A-TTCA52 ± 2Poly A-TTCG36 ± 1Poly A-TTCT37 ± 2Figure 4Binding affinity versus enzyme activity. The enzyme activity of active A3A measured by NMR-based deamination assay versus the free energy of binding calculated [ΔG = −RTln (Kd)] from the binding affinity for nine 12-mers. These nine represent 2 high binding (green), 5 medium binding (orange) and 2 weak binding (red) sequences.
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| 100.0 |
Binding affinity versus enzyme activity. The enzyme activity of active A3A measured by NMR-based deamination assay versus the free energy of binding calculated [ΔG = −RTln (Kd)] from the binding affinity for nine 12-mers. These nine represent 2 high binding (green), 5 medium binding (orange) and 2 weak binding (red) sequences.
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| 99.94 |
To determine the structural basis for the A3A consensus sequence (T/C)TC(A/G), crystal structures of A3A bound to ssDNA recently determined by our group and others (PDB ID: 5KEG and 5SWW) were analyzed20,22. The target deoxycytidine is well coordinated and buried within the active site of A3A (Supplementary Fig. 2A) in these structures. The thymidine at position −1 has extensive contacts with loop 7 (Y130, D131 and Y132), and van der Waals contacts with loop 5 (W98) (Supplementary Fig. 2B). The Watson-Crick edge of the thymidine base faces the loop 7 residues, and makes three hydrogen bonds: one with the backbone nitrogen of Y132 and the other two, one being water mediated, are with the D131 sidechain. The D131 sidechain further forms a salt bridge to the R189, which stabilizes the overall hydrogen-bonding configuration of loop 7 to the thymine base. This coordination appears critical, as residue 189 is conserved as a basic residue (Arg/Lys) in catalytically active A3 domains. This coordination also explains why −1 nucleotide must be a thymidine. If the −1 position is modeled as a cytidine, the N3 atom lacks the proton to hydrogen bond with D131 (Supplementary Fig. 2C) and would not be as well coordinated thus would be less preferable. Residues Y130 and D131 in loop 7 physically would preclude a larger purine base from fitting in this position (as modelled in Supplementary Fig. 2D). Thus the T specificity at the −1 position is consistent with the crystal structures.
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Although A3A prefers (T/C)TC(A/G), neither of the co-crystal structures has the optimal nucleotide identity at the −2 and +1 positions20,22. Specificity for purine at the −2 position was not evident in the available A3A–ssDNA structures, presumably as neither structure contains an optimal ssDNA sequence. For instance, even though the 5KEG structure contains a preferred pyrimidine in the −2 position, the thymidine is disordered in this complex. However, in both structures20,22, the base at +1 position (pyrimidine T in 5KEG and a purine G in 5SWW) stacks with the critical His29 (Fig. 5a,b)20,22. This type of histidine π-π stacking can occur with either a purine or a pyrimidine. However, protonated histidine prefers to stack with a purine base over pyrimidine, with thymidine stacking being the least preferred33 at pH 6. Thus the base stacking potential with protonated His29 provides strong rationale for the specificity for purines and the disfavoring of thymidine at the +1 position relative to substrate deoxycytidine observed in our biochemical assays (Fig. 3).Figure 5ssDNA is bent within the complex with A3A. Crystal structure of A3A shown in surface and cartoon representation (gray) bound to ssDNA displayed as orange sticks; (a) +1 thymidine (light blue) is interacting with His29 (light green sticks) through aromatic stacking (PDB ID: 5KEG) (b) +1 guanine (light blue) also interacting with His29 through aromatic stacking (light green sticks) (PDB ID: 5SWW). (c) A3A(E72A/C171A) with TTTTTTTTCTTTTTT (PDB ID: 5KEG) (d) A3A(E72A) with AAAAAAATCGGGAAA (PDB ID: 5SWW). Other nucleotides are shown as orange sticks, while water (red), zinc (blue), and chloride (gray) in the active site are shown as spheres. Nitrogen and oxygen of residues and nucleic acids are in blue and red respectively. (e) A schematic of hydrogen bonding between pyrimidine (pink) at −2 and purine (light blue) at +1 position via bending of the DNA by A3A upon binding. (f) Model of inter-DNA base interactions through binding of A3A to ssDNA. A3A(E72A)–ssDNA complex (PDB ID: 5SWW) was used to model A3A signature sequence CTCG bound at the active site. A3A is shown as gray surface and cartoon, His29 as light green sticks, original ssDNA as orange sticks with +1 G in light blue. Adenosine at −1 position was switched to cytosine (pink) with hydrogen bonds to +1 G displayed as yellow dashes.
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ssDNA is bent within the complex with A3A. Crystal structure of A3A shown in surface and cartoon representation (gray) bound to ssDNA displayed as orange sticks; (a) +1 thymidine (light blue) is interacting with His29 (light green sticks) through aromatic stacking (PDB ID: 5KEG) (b) +1 guanine (light blue) also interacting with His29 through aromatic stacking (light green sticks) (PDB ID: 5SWW). (c) A3A(E72A/C171A) with TTTTTTTTCTTTTTT (PDB ID: 5KEG) (d) A3A(E72A) with AAAAAAATCGGGAAA (PDB ID: 5SWW). Other nucleotides are shown as orange sticks, while water (red), zinc (blue), and chloride (gray) in the active site are shown as spheres. Nitrogen and oxygen of residues and nucleic acids are in blue and red respectively. (e) A schematic of hydrogen bonding between pyrimidine (pink) at −2 and purine (light blue) at +1 position via bending of the DNA by A3A upon binding. (f) Model of inter-DNA base interactions through binding of A3A to ssDNA. A3A(E72A)–ssDNA complex (PDB ID: 5SWW) was used to model A3A signature sequence CTCG bound at the active site. A3A is shown as gray surface and cartoon, His29 as light green sticks, original ssDNA as orange sticks with +1 G in light blue. Adenosine at −1 position was switched to cytosine (pink) with hydrogen bonds to +1 G displayed as yellow dashes.
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A common feature between the two A3A–ssDNA complex structures is that the ssDNA forms a “U” shape in the active site (Fig. 5c,d)20,22. This U shape of the bound polynucleotide may be conserved among deaminases, including adenosine deaminases20,34. In both A3A–ssDNA structures, the U shape of the ssDNA orients the −2 and +1 bases in close proximity to each other. Thus, we hypothesized that the observed sequence preference (Fig. 3) for the −2 position is a result of intra-DNA interactions rather than specific interactions with the protein.
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To determine the potential for intra-DNA interactions when A3A is bound to a (T/C)TC(A/G) signature sequence, molecular models were developed based on the crystal structures of A3A bound to ssDNA (PDB ID: 5KEG and 5SWW)20,22. These models orient the bases of the −2 and +1 nucleotides so that they form hydrogen bonds at an angle of approximately 120 degrees and distance of less than 3.5 Å, with the larger purine at +1 position stacking on His29 and the smaller −2 pyrimidine coordinating the +1 base (Fig. 5e,f). The reversal of the nucleotides at +1 and −2 positions would not result in a fit nearly as well, which could explain the lower affinity of purine-TC-pyrimidine. Thus the structural model explains the preference for (T/C)TC(A/G) and suggests stabilizing inter-DNA interactions may further increase the affinity.
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If the bending of the ssDNA is important for substrate recognition, dependence of binding affinity on substrate length may be expected. To determine if the DNA beyond the four-nucleotide signature sequence contributed to the binding, the length of the ssDNA that contained the recognition sequence was varied in Poly A-TTC. A competition assay with different length oligonucleotides was performed to test the effect of ssDNA length on affinity for substrate (Supplementary Fig. 3). Length was varied from 1 nucleotide flanking each end of TTCA (TTCAA and ATTCA) to 3 nucleotides flanking each end, increasing by one nucleotide addition on either end. Surprisingly, a single nucleotide flanking TTCA signature sequence was not enough to permit binding (Supplementary Fig. 3a), and even three nucleotides on either side still did not bring A3A binding to original binding affinity as Poly A-TTC (AAA TTCA AAA AAA) (Supplementary Fig. 3b). Thus, binding affinity is impacted beyond the recognition motif to prefer longer sequences, although the additional nucleotides are not expected to have any direct contacts with A3A, consistent with the model that intra-DNA interactions modulate A3A affinity.
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Another implication of this model would be that pre-bent DNA could be a better substrate for A3A binding, as A3A would not have to pay the entropic cost of bending the DNA. This bending of DNA could be achieved either by the inter-DNA interactions modeled in Fig. 5f, or when within a loop of a hairpin. To determine the significance of the bent DNA structure in the mechanism of A3 binding, we tested A3A affinity to a target deoxycytidine in the loop region of a DNA hairpin. The hairpin sequence was based on a previously identified potential RNA substrate for A3A, from succinate dehydrogenase complex iron sulfur subunit B (SDHB)35. The affinity for TTC in the loop region of this hairpin DNA was higher than that in linear DNA (26 nM vs 90–127 nM respectively). As expected, A3A had a higher affinity for the DNA hairpin with loop region containing TTC compared to one with AAA (26 nM vs ~676 nM respectively) (Fig. 6a). Interestingly, the Kd value for the hairpin (26 nM) is comparable to that for a single C in a polyT background (35 nM)28. This may imply that the Poly T DNA adopts a hairpin structure in solution, as has been reported36.Figure 6A3A specificity for substrate in loop region of stem-loop (hairpin) nucleic acids. Fluorescence anisotropy of TAMRA-labeled hairpin DNA and RNA binding to A3A(E72A). (a) Binding of A3A to a DNA version of the hairpin SDHB RNA containing TTC (dark blue) and AAA (light blue) in the loop region. (b) Binding of A3A to hairpin SDHB RNA (dark orange) and the same RNA sequence replacing the UC with AA in the loop region of the hairpin (light orange).
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A3A specificity for substrate in loop region of stem-loop (hairpin) nucleic acids. Fluorescence anisotropy of TAMRA-labeled hairpin DNA and RNA binding to A3A(E72A). (a) Binding of A3A to a DNA version of the hairpin SDHB RNA containing TTC (dark blue) and AAA (light blue) in the loop region. (b) Binding of A3A to hairpin SDHB RNA (dark orange) and the same RNA sequence replacing the UC with AA in the loop region of the hairpin (light orange).
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A3A affinity to a target cytidine in the loop region of an RNA hairpin was also tested. The exact SDHB hairpin RNA sequence including UC in the loop of this hairpin versus a modified SDHB hairpin RNA replacing the AUC with AAA was compared. A3A had specific affinity for the hairpin RNA containing UC compared to AA (37 nM vs 202 nM respectively) (Fig. 6b). In contrast to what has been previously proposed19, we found that A3A has high affinity and specificity for RNA. Furthermore, A3A has a higher affinity for AUC in the loop region of a hairpin compared to UUC in a linear sequence (Supplementary Fig. 4). The potential UUC substrate sequence in linear RNA has no measurable affinity, comparable to linear RNA without a potential substrate sequence. Overall, A3A has higher affinity for target sequence in the context of a pre-ordered loop region rather than linear DNA, and specific affinity for RNA hairpins with a substrate site.
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A3A is a single-domain enzyme with the highest catalytic activity among the human APOBEC3 proteins23, a known restriction factor24,25, and also likely contributes to carcinogenesis26. In this study we quantified the ssDNA specificity of A3A, and identified the consensus signature sequence as (T/C)TC(A/G). The dinucleotide sequence preference for A3A, TC, which was previously found through activity assays10,20,21 was confirmed and expanded to a preference for pyrimidine-TC-purine. Surprisingly context matters, in that the background nucleotide sequence impacts binding affinity, with essentially no binding observed for Poly A 1 C (Fig. 1b), while Poly T 1 C binds with 35 ± 2 nM affinity28. Furthermore, the length of the ssDNA in which (T/C)TC(A/G) is imbedded within also modulates affinity (Supplementary Fig. 3). Structural analysis of the two A3A–ssDNA complexes containing two distinct, but suboptimal ssDNA sequences have led us to develop a model with intra-DNA interactions for the molecular mechanism for A3A’s specificity to ssDNA. In contrast to previous results27, which implicate the −2 position as defining specificity, the base at this position observed in both A3A–ssDNA co-crystal structures do not make any specific interactions with the protein. Rather, the hydrogen bonding edge of the −2 base is in close proximity to corresponding edge of +1 base, suggesting possible intra-DNA interactions as being determinants of preference. Our molecular modeling confirmed such interactions could stabilize the U-shaped DNA conformation within the A3A active site, explaining the −2 position specificity.
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We found that A3A binds to RNA in a highly specific and structural context-dependent manner. Previous reports19 suggested that A3A bound only weakly and did not deaminate RNA. However, the potential substrate sequence was designed to lack secondary structure, which in light of our results on hairpin versus linear RNAs, may have inadvertently precluded RNA deamination. Recently, A3G and A3A were implicated in deaminating RNA in proposed RNA hairpins in whole cell lysates but the specificity was not quantified35,37. Intriguingly, our data show that A3A binds RNA hairpins with similar affinity as for DNA hairpins, which suggests that RNA-editing activity of A3A might be more prevalent than previously anticipated. Future experiments will identify if A3A’s catalytic efficiency is similar for DNA and RNA hairpins.
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The comprehensive identification of A3A signature sequences and preference for loop structures will enable a more accurate evaluation of A3 activity based on sequence analysis. Previous studies used only a single identified A3 signature sequence to implicate A3’s role in viral restriction or cancer progression. In contrast, our study suggests a more accurate method for determining evidence of A3 activity would be to use a set of sequences. In the case of A3A, we have identified four almost equivalent substrate signature sequences, TTCA, TTCG, CTCA, and CTCG, which should be used for identifying A3A’s involvement in mutagenesis. We also found a positive correlation between A3A’s sequence preference of binding and enzymatic activity. This correlation not only legitimizes the use of a DNA binding assay with inactive enzyme as a reliable method for studying specificity of A3s, it also shows that affinity for substrate is a driving factor for catalysis. Thus, factors that could enhance or perturb binding, such as pH or nucleic acid structure, would result in modulation of deamination activity.
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In addition to using the full A3A signature sequences, the probability of mutagenesis should not be solely based on nucleotide sequence, but should also be weighted by the propensity of the target sequence to be within a structured loop. Secondary structure prediction software could be used to identify the consensus sequence in loop regions of structured DNA or RNA. A3A signature sequences that we identified, (T/C)TC(A/G), not only account for the discrepancies in the A3A target sequences reported in the literature such as TTCA versus CTCG20,21, but also lead us to advocate a new paradigm for identifying A3A’s involvement in mutation of endogenous or exogenous DNA.
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| 100.0 |
Designing inhibitors or activators for A3s has been extremely challenging. Our results implicate a need to incorporate the structural context of the target deoxycytidine in the therapeutic design. Larger macrocycles may serve as more appropriate starting scaffolds in designing cancer therapies targeting A3s, which would mimic the “U” shape of the bound ssDNA. Macrocycles have recently been shown to have good drug-like properties and may be a strategy to target these critical enzymes38.
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| 99.94 |
The pColdII His-6-SUMO-A3A(E72A) was constructed by first cloning the SUMO gene from pOPINS His-6-SUMO into pColdII His-6 vector (Takara Biosciences) using NdeI and KpnI restriction sites. Human APOBEC3A coding sequence from pColdIII GST-A3A(E72A, C171A) was then cloned into the pColdII His-6-SUMO vector with KpnI and HindIII. The C171A mutation in the A3A construct was reverted to wild type residue by site directed mutagenesis resulting in the pColdII His-6-SUMO-APOBEC3A(E72A) catalytically inactive over-expression construct used in this study.
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Escherichia coli BL21 DE3 Star (Stratagene) cells were transformed with the pColdII His-6-SUMO-APOBEC3A(E72A) vector described above. The E72A mutation was chosen to render the protein inactive. Expression occurred at 16 °C for 22 hours in lysogeny broth medium containing 0.5 mM IPTG and 100 µg/mL ampicillin. Cells were pelleted, re-suspended in purification buffer (50 mM Tris-HCl [pH 7.4], 300 mM NaCl, 1 mM DTT) and lysed with a cell disruptor. Cellular debris was separated by centrifugation (45,000 g, 30 min, 4 °C). The fusion protein was separated using HisPur Ni-NTA resin (Thermo Scientific). The His-6-SUMO tag was removed by means of a Ulp1 protease digest overnight at 4 °C. Untagged A3A(E72A) was separated from tag and Ulp1 protease using HisPur Ni-NTA resin. Size-exclusion chromatography using a HiLoad 16/60 Superdex 75 column (GE Healthcare) was used as a final purification step. Purified recombinant A3A was determined to be free of nucleic acid prior to binding experiments by checking OD 260/280 ratios, which was at 0.54.
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Labeled and unlabeled oligonucleotides used in this assay were obtained through Integrated DNA Technologies (IDT). Labeled oligonucleotides used in the fluorescence anisotropy based binding assay contain a 50-TAMRA flourophore at their 5′ end and were resuspended in ultra-pure water at a concentration of 20 µM. Unlabeled oligonucleotides used for the competition assays were resuspended in ultra-pure water to a concentration of 4 mM.
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Fluorescence anisotropy based DNA binding assay was performed as described28 with minor alterations. A fixed concentration of 10 nM 50-TAMRA-labeled oligonucleotides was added to A3A(E72A) in 50 mM MES buffer (pH 6.0), 100 mM NaCl, 0.5 mM TCEP in a total reaction volume of 150 µL per well in nonbinding 96-well plates (Greiner). The concentration of A3A was varied in triplicate wells. Plates were incubated for overnight at room temperature.
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For the pH dependence experiments the buffer reagent used for testing was pH 4.0–5.0 sodium acetate, pH 5.5–6.5 MES, pH 7.0–8.0 HEPES, pH 8.5–9.0 TRIS. Assay was performed as described above. For the competition assays, a fixed concentration of 300 nM A3A(E72A) was used and unlabeled oligonucleotide of varied concentration was added from 0–6.1 μM. A3A(E72A) was pre-incubated with unlabeled oligonucleotide for an hour in assay buffer, then labeled DNA was added and incubated overnight at room temperature.
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For all experiments, fluorescence anisotropy was measured using an EnVision plate reader (PerkinElmer), exciting at 531 nm and detecting polarized emission at 579 nm wavelength. For analyzing data and determining Kd values, Prism (GraphPad) was used for least-square fitting of the measured fluorescence anisotropy values (Y) at different protein concentrations (X) with a single-site binding curve with Hill slope, a nonspecific linear term, and a constant background using the equation \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\rm{Y}}=(({{\rm{Bmax}}}^{\ast }{{\rm{X}}}^{\wedge }{\rm{h}})/({{\rm{Kd}}}^{\wedge }{\rm{h}}+{{\rm{X}}}^{\wedge }{\rm{h}}))+{{\rm{NS}}}^{\ast }{\rm{X}}+{\rm{Background}}$$\end{document}Y=((Bmax⁎X∧h)/(Kd∧h+X∧h))+NS⁎X+Background, where Kd is the equilibrium dissociation constant, h is the Hill coefficient, and Bmax is the extrapolated maximum anisotropy at complete binding.
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Deaminase activity was determined for A3A protein by assaying active enzyme against linear DNA substrates and measuring the product formation using 1H NMR. Active A3A protein (50 nM) was assayed against linear DNA substrates (200 µM) in buffer with 50 mM MES pH 6.0, 100 mM NaCl, 0.5 mM TCEP, and 5% D2O. Experiments were performed on 9-mer substrates containing the target sequences AA(A/G/T)TC(A/G/T)AAA and at 40 °C to prevent the DNA from oligomerizing due to high concentration. Experiments were performed using a Bruker Avance III NMR spectrometer operating at a 1H Larmor frequency of 600 MHz and equipped with a cryogenic probe. Product concentration was estimated from peak integrals with Topspin 3.5 software (Bruker Biospin Corporation, Billerica, MA) using an external standard. Activity was determined from the initial rate of product formation via first-order exponential fitting of the progress curve. Rate errors were estimated by Monte Carlo simulation using 100 synthetic data sets and taking the residuals of the initial fit to the experimental data as the concentration error.
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The crystal structures of A3A bound to ssDNA (PDB ID: 5KEG and 5SWW) were used for molecular modeling20,22. The DNA sequence was first mutated using Coot39. The complex structure was then prepared, energy minimized with ProteinPrep Wizard in Maestro (Schrödinger) using the OPLS3 force field, at pH 6.0 with all other settings kept as default.
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The olfactory system is a specific area of the central nervous system (CNS) capable of supporting neurogenesis throughout the life of mammals by forming new olfactory receptor neurons (ORNs) 1, 2, 3. These neurons are then able to spread axons from the peripheral nervous system of the olfactory epithelium into the CNS environment of the olfactory bulb 4. The capacity of ORNs to stimulate neurogenesis in the adult olfactory system may be due to both the neural stem cells present in the olfactory epithelium and the glial cells known as olfactory ensheathing cells (OECs) 5, 6. Olfactory ensheathing cells, originally described by Golgi and Blanes 7, 8, 9 at the end of the 19th century, are a special glial cell population sharing properties with both Schwann cells and the astrocytes 10, 11. Similarly to Schwann cells, OECs express some characteristic markers such as the low affinity neurotrophin receptor (p75NTR) and adhesion molecules such as laminin, L1 and N‐CAM; likewise to astrocytes they express the S‐100 protein and the glial fibrillary acidic protein (GFAP), a member of the intermediate filament family that offers support to glial cells 12, 13.
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| 99.2 |
Furthermore, OECs are able to secrete high level of growth factors, such as nerve growth factor (NGF), basic fibroblast growth factor (bFGF), brain derived neurotrophic factor (BDNF), glial derived neurotrophic factor (GDNF), ciliary neurotrophic factor (CNTF), neurotrophins NT4, NT5 and neuregulins, which exhibit important functions as neuronal supporting elements 14, 15, 16, 17, 18, 19.
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| 99.9 |
Gap junctions (GJs) are specialized intercellular channels that directly connect cytoplasms of adjacent cells, enabling direct exchanges of small molecules (less than 1200 Da). Gap junctions are composed by two docked hemichannels (HCs), also named connexons, one on each cell. Hemichannels are hexamers of homotypic or heterotypic connexins (Cxs), which are the transmembrane proteins encoded by a multigene family of approximately 20 members in mammals 20, 21, 22, 23, 24, 25 that form respectively homotypic or heterotypic GJs.
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Besides constituting ‘GJs plaques’ that allow GJ intercellular communication (GJIC), Cxs also constitute free HCs throughout the plasma membrane, allowing exchange of a number of autocrine and paracrine signalling molecules between the cytoplasm and the extracellular environments 26, 27, 28.
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| 99.0 |
Gap junctions, as well as HCs, play a crucial role in a wide range of cellular activities, including cell signalling, differentiation, growth, pro‐apoptotic signalling, either as anti‐apoptotic gates or pathogenic pores depending on conditions and cell type 29, 30, 31, 32. Gap junctions and free HCs are extensively distributed in different tissues and organs 33 in which different cell type usually expresses specific Cxs profile involved in the selective permeability of channels formed, according to the metabolic or functional needs 34, 35, 36, 37, 38. Also in the CNS, GJs and HCs are extensively distributed among neurons and glial cells 39, 40, 41, where, contributing to GJIC and cell‐extracellular communication, they provide specific exchange pathways under resting conditions, and also play a context‐dependent role in contradictory cell survival or cell death phenomena 42.
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| 99.44 |
In recent years, much attention has been paid to the exploitation of neuroprotective effects in combatting the progression and chronicity of neurodegeneration. In particular, evidence shows altered Cxs expression and functions under pathological conditions, suggesting a central role of glial GJs and HCs in the development of various neurodegenerative diseases 21, 43, 44, 45, 46. Conversely, the therapeutic potential of OECs is attracting considerable interest owing to their exceptional ability to promote functional recovery of the damaged CNS 3, 47, 48, 49, 50. However, the molecular mechanisms underlying this protective function are not yet known. The aim of this study was firstly to investigate the neuroprotective effects of OEC conditioned medium (OEC‐CM) on two different neuroblastoma cell lines (SH‐SY5Y and SK‐N‐SH) exposed to hypoxic/reoxygenation (H/R) injury. Towards this goal we explored the relationship between OEC‐CM and Cxs, HCs and GJs following H/R injury in cultures grown with and without OEC‐CM. We found that: (1) OEC‐CM offers a neuroprotective effect to the SH‐SY5Y and SK‐N‐SH cells exposed to H/R injury; (2) injured SH‐SY5Y and SK‐N‐SH cells show higher Cx43 levels compared to normoxic cultures; and (3) H/R cultures grown in the presence of GJ and/or HC chemical inhibitors, such as carbenoxolone (CBX, non‐selective GJIC inhibitor) 51, ioxynil octanoato (IO, Cx43 homotypic GJ inhibitor) 52, and Gap19 (Cx43 homotypic HC inhibitor) 53, show higher viability compared to control cultures. Our results, showing the neuroprotective effects of OEC‐CM likely afforded by inhibition of Cx43 GJ/HC signalling pathways, suggest potential new therapeutic tools against neurodegenerative diseases.
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| 99.94 |
Primary OECs were isolated from post‐natal day 0 (P0) CD1 mice olfactory bulbs. Experiments were performed in compliance with current guidelines for animal care and in accordance with the European Community Council Directive (86/609/EEC). All efforts were made to minimize animal suffering and to use the fewest animals possible.
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| 99.94 |
Ten P0 pups were decapitated, the entire brain was exposed and bulbs removed and dissected out in cold (+4°C) Leibowitz L‐15 medium (Sigma‐Aldrich, Saint Louis, Missouri, USA). Subsequently, collected olfactory bulbs were digested twice in fresh minimum essential medium‐H (MEM‐H; Sigma‐Aldrich, Saint Louis, Missouri, USA) containing 0.03% collagenase (Sigma‐Aldrich, Saint Louis, Missouri, USA) and 0.25% trypsin (Sigma‐Aldrich, Saint Louis, Missouri, USA) for 15 min at 37°C. Suspension was mechanically triturated and filtrated through a 80 μm nylon filter and centrifuged at 600 g for 10 min. Cells were suspended with fresh complete Dulbecco's modified Eagle's medium (DMEM, Sigma‐Aldrich, Saint Louis, Missouri, USA) supplemented with 10% foetal bovine serum (FBS, Sigma‐Aldrich, Saint Louis, Missouri, USA), 2 mM L‐glutamine (Sigma‐Aldrich, Saint Louis, Missouri, USA), penicillin (50 U/ml, Sigma‐Aldrich, Saint Louis, Missouri, USA) and streptomycin (50 mg/ml, Sigma‐Aldrich, Saint Louis, Missouri, USA) and plated in 25 cm2 flasks. Cytosine arabinoside (Sigma‐Aldrich, Saint Louis, Missouri, USA) at final concentration of 10−5 M was added 24 hours (hrs) after plating to reduce the number of dividing fibroblasts. After two passages, purity of OECs was verified by using immunofluorescence with p75 and S‐100 (data not shown). Media were replaced twice a week for three passages and then cultures were used to collect the conditioned medium. OEC‐CM was removed from the cultures and filtered through a membrane filter (0.22 μm pore diameter) to remove cells and debris.
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| 100.0 |
The human neuroblastoma cell lines, SH‐SY5Y and SK‐N‐SH, were purchased from ATCC (Rockville, MD, USA) and grown as previously described 54. Briefly, cells were incubated at 37°C in a humidified 5% CO2/95% air atmosphere and maintained in DMEM/F12 (Sigma‐Aldrich) supplemented with 10% FBS (Sigma‐Aldrich), 2 mM L‐glutamine (Sigma‐Aldrich), penicillin (50 U/ml, Sigma‐Aldrich) and streptomycin (50 mg/ml, Sigma‐Aldrich, Saint Louis, Missouri, USA). The medium was replaced twice a week.
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| 99.9 |
Cells at a density of 2.5 × 104 cells/cm2 were seeded on appropriate supports for analysis 16 hrs before performing the experiments (time 0). The following experimental culture groups were established: untreated control groups (CTRL Normoxia) and hypoxic groups (CTRL Hypoxia) at 0, 3, 8 and 24 hrs. Each group was grown in normal culture conditions for 16 hr, after which they were subjected to hypoxia (1% O2) for 3 hrs, followed by reoxygenation to 24 hrs. All cultures were grown with DMEM‐F12 (89%), FBS (10%) and P/S (1%).
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| 100.0 |
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