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AIMS Biophysics

2018, Volume 5, Issue 3: 182-193. doi: 10.3934/biophy.2018.3.182
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Review

Cytosine hydroxymethylation by TET enzymes: From the control of gene expression to the regulation of DNA repair mechanisms, and back

  • Univ Rennes, CNRS, IGDR-UMR 6290, F35000 Rennes, France
  • Received: 06 July 2018 Accepted: 18 September 2018 Published: 25 September 2018

  • Chromatin is a complex multi-scale structure composed of DNA wrapped around nucleosomes. The compaction state is finely regulated mainly by epigenetic marks present not only on nucleosomes but also on the DNA itself. The most studied DNA post-transcriptional modification is 5-methylcytosine (5-mC). Methylation of the cytosine at CpG islands localized at the promoter is associated with repression of transcription. On the contrary, enrichment of 5-hydroxymethylcytosine (5-hmC), one of the oxidation products of 5-mC by TET (ten-eleven translocation) enzymes, on promoters and enhancers promotes transcription activation. Recently, a new role of 5-hmC has been proposed in the context of DNA repair. 5-hmC was found to be enriched at DNA lesions and knockdown of TET led to impaired repair efficiency. Here, we review our current knowledge regarding the role of the regulation of the 5-mC/5-hmC balance by TET enzymes in the context of transcription modulation as well as DNA repair processes. In a final section, we speculate on the potential involvement of TET proteins in DNA repair mechanisms associated with transcription activation.

    Citation: Audrey Lejart, Gilles Salbert, Sébastien Huet. Cytosine hydroxymethylation by TET enzymes: From the control of gene expression to the regulation of DNA repair mechanisms, and back[J]. AIMS Biophysics, 2018, 5(3): 182-193. doi: 10.3934/biophy.2018.3.182

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  • Chromatin is a complex multi-scale structure composed of DNA wrapped around nucleosomes. The compaction state is finely regulated mainly by epigenetic marks present not only on nucleosomes but also on the DNA itself. The most studied DNA post-transcriptional modification is 5-methylcytosine (5-mC). Methylation of the cytosine at CpG islands localized at the promoter is associated with repression of transcription. On the contrary, enrichment of 5-hydroxymethylcytosine (5-hmC), one of the oxidation products of 5-mC by TET (ten-eleven translocation) enzymes, on promoters and enhancers promotes transcription activation. Recently, a new role of 5-hmC has been proposed in the context of DNA repair. 5-hmC was found to be enriched at DNA lesions and knockdown of TET led to impaired repair efficiency. Here, we review our current knowledge regarding the role of the regulation of the 5-mC/5-hmC balance by TET enzymes in the context of transcription modulation as well as DNA repair processes. In a final section, we speculate on the potential involvement of TET proteins in DNA repair mechanisms associated with transcription activation.


    1.   Introduction

    Models can be defined as a simple or abstract representation of the real system. Model simulations typically go through a calibration phase, where the modeler is tuning a model by making comparisons with the observed data. Biological systems such as crops cultivation are very complex systems, making them a challenge to the model. However, because of crops, annuals crops, go through their complete life cycle in one year or a growing season, they belong to a repetitive biological system.

    Barley (Hordeumvulgare L.) is the fourth largest cereal important crop worldwide, and it is also an excellent model species for the genetic and physiological studies [1]. Also, it's the unique genetic adaptation and the tolerance to a biotic stress is providing insights relevant to improvement in other cereal crops. Nevo and Chen mentioned that drought restrains of the crop production and the global food supply of crops often experience periods of the atmospheric or the soil water deficit, which are often accompanied by the high temperatures, poor nutrient uptake, and the outraged soil salinity stress [2]. The repeatable and reoccurring real systems can be validated independently making it possible to the develop models and continue to build on them year after year. The development of crop growth models began in the 1960s and have advanced and become more refined since, [3]. Three different simulation models, AquaCrop, SALTMED and SWAP, were used to predict the effective impact of both good irrigation management and irrigation water quality on crop yield, in this study, the accuracy of the use of these models on the maize crop was proven, [5]. The next section will provide the review of AquaCrop model used in this study.

    The AquaCrop model is defined by Steduto et al. as canopy-level and the engineering type of the model, mainly focusing on simulating an attainable crop biomass and the harvestable yield in the response to water available [4]. The model was developed for purpose to using the fewer parameters in the balance of the simplicity, accuracy, and robustness. Water is used as main driver in AquaCrop for simulating yield production. Water is very important for crop production and was proven early on to be one of major limiting factors in crop growth. Crops use the water to carry the minerals, the sucrose and the hormones through the plant. Water is also very critical factor in the chemical reaction of photosynthesis [6]. Water-limiting conditions will result in the lower yields at the end of the season, so it is an important factor for crop modeling. The objectives of this research work are to evaluatethe water productivity of Mediterranean barley varieties under normal irrigation and water stress conditions by AquaCrop model.

    2.   Material s and methods

    2.1.   Experimental field

    Field experiment was carried out in the Experimental Research Station of Nubaria, during two seasons of 2016/2017 and 2017/2018. The study conducted to characterize the Mediterranean barley (Egyptian, Tunisian, Algerian and Morocco) varieties grown under water stress condition (40% of water holding capacity) compared to normal one (75% of water holding capacity).

    Field experiment was carried out in the Experimental Research Station of Nubaria, during two seasons of 2016/2017 and 2017/2018. The study conducted to assess the water productivity of the Mediterranean barley (Egyptian, Tunisian, Algerian and Morocco) varieties grown under water stress condition (40% of water holding capacity) compared to normal irrigation (75% of water holding capacity) by Aquacrop model. The metrological data required by AquaCrop model are daily values of minimum and maximum air temperature, reference evapotranspiration (ETo), rainfall and mean annual carbon dioxide concentration (CO2). ETo was estimated using ETo calculator using the daily maximum and minimum temperature, wind speed at 2 m above ground surface and mean relative humidity (RH). Rainfall depths of 2.1 mm, 2.0 mm and 0.0 mm during the barley growth, all data obtained from the closest meteorological station at Wadi Al-Natroun City, El Buhaira Govornorate, Egypt.

    2.2.   Methods for data collection

    Barley crop data was obtained from an experimental field. The experiment was laid in Randomized Complete Block Design (RCBD). Plots were 2.5 m × 6 rows with 0.20 m row spacing and sowing density was adjusted to 300 g m2. The crop component divided to 4 subcomponents including initial canopy, canopy development, flowering and yield formation and rooting depth. Both of yield formation and rooting depth were observed visually while the canopy was observed in field at regular intervals.

    Geerts et al. and Farahani et al. used the following equation for estimate canopy cover [7,8]:

    $ {\rm{CC}} = 1{\exp ^{( - 0.65{\rm{LAI}})}} $ (1)

    Where CC is canopy cover as shown in Figures 1 and 2. The relationship between Barley biomass water productivity and transpiration/ETo as shown in Figures 3 and 4. LAI is the leaf area index. LAI was calculated as LAP × NPM2, LAP being the leaf area per plant (m2), and NPM2 the number of plants per m2 [9]. Straw and grain yield were obtained from all plots after maturity from an area of 6 m2 in all cropping seasons.

    Figure 1.  Canopy cover, flowering and yield formation of barley varieties by AquaCrop model growing under Egyptian conditions.
    DownLoad: Full-Size Img PowerPoint
    Figure 2.  Effective root depth of barley varieties by AquaCrop model growing under Egyptian conditions.
    DownLoad: Full-Size Img PowerPoint
    Figure 3.  The relationship between Barley biomass water productivity and transpiration/ETo.
    DownLoad: Full-Size Img PowerPoint
    Figure 4.  The relationship between Barley water productivity and CO2.
    DownLoad: Full-Size Img PowerPoint

    2.3.   The agronomic model (Aqua crop)

    AquaCrop has four sub-model components: (ⅰ) the soil (water balance); (ⅱ) the crop (development, growth and yield); (ⅲ) the atmosphere (temperature, rainfall, evapotranspiration (ET) and carbon dioxide (CO2) concentration); and (ⅳ) the management (major agronomic practices such as planting dates, fertilizer application and irrigation if any).

    AquaCrop calculates a daily water balance that includes all the incoming and outgoing water fluxes (infiltration, runoff, deep percolation, evaporation and transpiration) and changes in soil water content. The advantage with AquaCrop is that it requires only a minimum of input data, which are readily available or can easily be collected. AquaCrop has default values for several crop parameters that it uses for simulating different crops including wheat, however, some of these parameters are not universal and thus have to be adjusted for local conditions, varieties and management practices.

    $ {\rm{Deviation}}\% = 100 - (({\rm{ Oi}}{\rm{. }}100)/{\rm{Si}}) $ (2)

    Where Oi: Observed values and Si: Simulated values.

    The AquaCrop model uses the yield response to water equation (Eq 3) as a starting point for the model. AquaCrop evolves from this approach (Eq 3) by separating the evapotranspiration into crop transpiration and soil evaporation to develop a final yield as a function of the final biomass of the crop (Eq 4). This separation allows for distinguishing the effects on the non-productive consumptive use of water, soil evaporation, to better simulate crop growth. The water productivity (WP, biomass produced per unit of cumulative transpiration) is a conservative parameter, which is constant for given climatic conditions in Eq 3.

    $ \left( {Yx - Ya} \right)/Yx = \left( {\left( {ETx - ETa} \right)} \right) $ (3)

    Where Yx and Ya are maximum and actual yield, ETx and ETa are maximum and actual evapotranspiration and key is the proportionality factor between relative yield loss and relative reduction in evapotranspiration.

    $ B = WP*\Sigma Tr $ (4)

    Where B is the final biomass, WP is the water productivity (biomass per unit of cumulative transpiration), and Tr is the crop transpiration.

    The WP parameter is based on the atmospheric evaporative demand and the atmospheric CO2 concentration for the purpose of being applicable to diverse locations and simulating future climate scenarios. Equation 5 shows the procedure for calculating the normalized WP based on adjustments to annual CO2 concentrations. This approach tends to over-simulate future crop yields caused by CO2 fertilization when compared to free air CO2 enrichment (FACE) experiments [10].

    $ WP = (B\Sigma (TrETo))C{O_2} $ (5)

    Where CO2 is the mean annual CO2 concentration and ETo is the atmospheric evaporative demand. The CO2 outside the bracket is the normalization concentration for a given year. Once the final biomass is calculated at harvest, the final yield output is the function of the final biomass (B) and the Harvest Index (HI). HI is the ratio between the harvested product and the total above ground biomass [11].

    2.4.   Calibration and validation model

    AquaCrop is effective for modeling yields under a limited number of site locations. The current version of AquaCrop (6.0) has been assessed by the creation of two external utility programs called AquaData and AquaGIS [12]. AquaData acts as a database that contains all data necessary for creating input files used in AquaCrop. FAO have developed an AquaCrop plug-in program that will run AquaCrop without a user interface, which allows an application like AquaData to automatically run multiple crop simulations much more efficiently [13]. The AquaCrop plug-in program can be used for iterative runs for calibration purposes or for inputting into a Geographical Information System (GIS) for subsequent spatial analysis. Using similar methods, AquaCrop can be used for calibrating and analyzing long-term climate change impacts on crop yields in southern Alberta.

    The drip irrigation system consists of the following components, as following:

    Control head: It is located at the water inlet and consists of: Pump: Centrifugal electric pump (0.75 HP), n ≈ 2900 rpm and discharge 3 m3/h., Filter: Screen filter 1.5" (one unit), 155 mesh, Max. Flow 7.2 m3/h and maximum pressure 150 (PSI). Injection unit: Venture PE of 1" , rang of suction capacity 34–279 l/h., Measurement units: Spring brass non-return valve 2" , Pressure gauges, control valves and flow meter.

    Main line: PVC pipe of 63 mm diameter-6 bar, connects the control unit to convey the water to sub main line: PVC 32 mm diameter line, delivered from the main line to feed the group of the laterals which represent treatments.

    Pibars et al. and Mansour et al. designed drip irrigation system: Laterals: It is 16 mm diameter PE tubes, with 30 cm apart, built in drippers of 4 lph discharge at 1bar operating pressure. Distance between laterals was 0.9 m. Irrigation system design [14,15].

    3.   Results

    3.1.   Canopy cover (CC%)

    Figure 5 shows the effect of water stress and normal water (without water stress) on observed and simulated values by the AquaCrop program on the canopy cover (CC%) of barley varieties grown in Egypt and exported from four different Mediterranean countries (Egypt, Tuisia, Algeria and Morocco) under the drip irrigation system. The measured and simulated values up to maximum at the end of February and began to decline at the end of March.

    Figure 5.  Canopy cover of mean barley varieties under water stress and normal water. (a) CC % of Egyptin varities under water stress. (b) CC % of Egyptin varities under normal water. (c) CC % of Tuisian varities under water stress. (d) CC % of Tuisian varities under normal water. (e) CC % of Algerian varities under water stress. (f) CC % of Algerian varities under normal water. (g) CC % of Morocco varities under water stress. (h) CC % of Morocco varities under normal water.
    DownLoad: Full-Size Img PowerPoint

    The average of all Egyptian barley varieties observed and simulated values of (CC%) were recorded under water stress 72.79%, while 89.96% under normal water were recorded, respectively.

    The average of all Tunisian barley varieties observed and simulated values of (CC%) under water stress recorded 77.85%, on the other hand under normal water, 88.98%, respectively.

    The average of the Algerian barley varieties observed and simulated values of (CC%) recorded under water stress 80.88%, while under normal water recorded 87.98%, respectively.

    The average of all Moroccan varieties observed and simulated values of (CC%) recorded under water stress 76.58% recorded 90.97%, respectively.

    3.2.   Egyptian varieties at harvest

    Table 2 and Figure 6 showed the effect of both the use of the measurement method and the simulated use of the AquaCrop model on the water productivity estimated for the Egyptian barley varieties. Data on hand revealed that using the method of observed water productivity was the highest values in the case of normal drip irrigation system (75% WHC) or that exposed to water stress (40% WHC) (2.84 and 2.51 kg/m3) were recorded at Giza 131 variety, followed by values (2.65 and 2.17 kg/m3); (2.64 and 2.31) obtained after Giza 123 and Giza 127 varieties, respectively while, the lowest values of water productivity were recorded (1.95 and 2.34 kg/m3) after using Giza 125 variety.

    Table 1.  Conservative and non-conservative crop parameters for barley obtained from various sources.
    Non-conservative parameters Barley
    Base temperature (℃) below which crop development does not progress 0.0
    Upper temperature (℃) above which crop development no longer increases with an increase in temperature 15.0
    Number of plants per hectare 1,500,000
    Maximum effective rooting depth (m) 1.3
    Harvest Index (HIo) (%) 33
    Conservative parameters
    Water Productivity normalized for ETo and CO2 (WP*) (gram/m2) 15.0
    Water Productivity normalized for ETo and CO2 during yield formation (as % WP*) 100
    Maximum air temperature above which pollination starts to fail (heat stress) (℃) 35.0
    Minimum air temperature below which pollination starts to fail (cold stress) (℃) 5.0
    Excess of potential fruits (%) 100
    Canopy growth coefficient (CGC): Increase in canopy cover (fraction soil cover per day) 0.1241
    Maximum canopy cover (CCx) in fraction soil cover 0.8
    Canopy decline coefficient (CDC): Decrease in canopy cover (in fraction per day) 0.07697
    Soil surface covered by an individual seedling at 90 % emergence (cm2) 1.5
    Crop coefficient when canopy is complete but prior to senescence (Kcb, x) 1.10
    Maximum root water extraction (m3water/m3soil.day) in top quarter of root zone 0.019
    Maximum root water extraction (m3water/m3soil.day) in bottom quarter of root zone 0.006
    Effect of canopy cover in reducing soil evaporation in late season stage 50
    Soil water depletion factor for pollination (p-pol)-Upper threshold 0.55
    Shape factor for water stress coefficient for canopy expansion (0.0 = straight line) 3.0
    *Note: Source: [16,17,18].
     | Show Table
    DownLoad: CSV
    Table 2.  Effect of observed simulated water normal and stress on water productivity of Egyptian barley varieties.
    Observed Observed Simulated by Aqua crop
    Grain yield (ton/fed) WP (Kg/m3) WP (Kg/m3)
    Barley varieties Normal Stress Normal Stress Normal Stress
    Avg
    G123 2653.98 2166.67 2.21 3.01 2.34 2.11
    G125 1953.33 1233.33 1.63 1.71 2.12 1.48
    G126 2007.00 1343.33 1.67 1.87 2.46 1.94
    G127 2640.00 2310.00 2.20 3.21 2.03 1.49
    G130 2330.00 1940.00 1.94 2.69 2.22 2.14
    G131 2843.33 2510.00 2.37 3.49 2.21 1.83
    G2000 2440.00 2070.00 2.03 2.88 2.54 2.63
    El-Arich 2136.67 1410.00 1.78 1.96 2.01 1.47
    Ksar 2243.33 1540.00 1.87 2.14 2.33 2.46
     | Show Table
    DownLoad: CSV
    Figure 6.  Effect of observed water (a) and simulated water (b) normal and stress on water productivity of Egyptian barley varieties.
    DownLoad: Full-Size Img PowerPoint

    On the other side: By simulated using AquaCrop model, results found that in case of normal condition of irrigation, the three highest yielding values of water productivity (2.54, 2.46 and 2.34 kg/m3) were recorded after Egyptian varieties; Giza 2000, Giza 126 and Giza 123, respectively. While the lowest one of simulated water productivity using the AquaCrop model was recorded at Arish variety (2.01 kg/m3). Whereas, under water stress condition, the highest simulated values using the AquaCrop model were recorded (2.63, 2.46 and 2.14kg/m3) at the varieties Giza 2000, Kasr and Giza 130, respectively.

    3.3.   Tunisian varieties at harvest

    Table 3 and Figure 7 show the effect of both the use of the measurement method and the simulated use of the AquaCrop model on the water productivity of the Tunisian barley varieties. It was found that using the method of measurement was the highest values in case of normal condition under water stress condition (2.35 and 2.23kg/m3) were recorded at Kebili 3 and Sidi-Bou varieties, respectively, followed by values (2.44 and 2.39kg/m3) obtained at Kebili 3 and Sidi-Bou varieties while the lowest values of water productivity were recorded (2.06 and 1.92 kg/m3) after Manel varieties.

    Table 3.  Effect of observed simulated water normal and stress on water productivity of Egyptian barley varieties.
    Observed Observed Simulated by Aqua crop
    Grain yield (ton/fed) WP (Kg/m3) WP (Kg/m3)
    Barley varieties Normal Stress Normal Stress Normal Stress
    Avg
    Kebili 1 2070.0 1750.0 1.73 2.43 2.90 4.09
    Tozeur-2 2133.8 1870.0 1.78 2.60 2.99 4.37
    Kebili 3 2350.0 2060.0 1.96 2.86 3.30 4.82
    Kairouan 2050.0 1400.0 1.71 1.94 2.88 3.27
    Manel 2110.0 1480.0 1.76 2.06 2.96 3.46
    Raihane 1930.0 1502.8 1.61 2.09 2.71 3.51
    Sidi-Bou 2230.0 1920.0 1.86 2.67 3.13 4.49
    Sabra 1930.0 1310.0 1.61 1.82 2.71 3.06
    Tombari 1870.0 1240.0 1.56 1.72 2.62 2.90
    Lemsi 1970.0 1400.0 1.64 1.94 2.76 3.27
     | Show Table
    DownLoad: CSV
    Figure 7.  Effect of observed water (a) and simulated water (b) normal and stress on water productivity of Tunisian barley varieties Algerian varieties at harvest.
    DownLoad: Full-Size Img PowerPoint

    With respect to the AquaCrop model, the three highest yielding values of water productivity (3.61, 3.36 and 2.86) kg/m3 were recorded using Tunisian varieties (Tombari, Kebili 3 and lemsi), respectively under normal condition (unstressed). While the lowest value of water productivity using the model AquaCrop model was recorded (2.40) kg/m3 at Manel variety. While under water stress condition, the highest values using the model AquaCrop model were recorded (4.12, 4.03 and 3.93 kg/m3) at the varieties Kebili 3, Tozeur-2 and Tombari, respectively. These results are obtained using the AquaCrop simulation model.

    Table 4 and Figure 8 showed the effect of both the use of the yield estimated data and the simulated by AquaCrop model on the water productivity of the Algerian barley varieties. It was found that using the method of measurement was the highest values in case without water stress or in the presence of water stress (2.27 and 2.18 kg/m3) were recorded at Ras El-Mouche and Nailia varieties, followed by values (2.18 and 2.15 kg/m3) obtained using Techedrett and Ksar-Megrine varieties. While the lowest values of water productivity were recorded (1.87 and 2.04 kg/m3) by at Sedi Mahdi and Temacine varieties. On the other side: After using AquaCrop model, It was found that in case of use without water stress, the three highest yielding values of water (3.85, 3.45 and 3.18) kg/m3 were recorded at Algerian varieties Ras El-Mouche, Techedrett and Saida, respectively. Whereas, the lowest value of water productivity after the AquaCrop model was gained at Sedi Mahdivariety (2.62 kg/m3). While under water stress, the highest values using the model AquaCrop model were recorded (3.18, 3.06 and 3.02) kg/m3 at the varieties Ras El-Mouche, Techedrett and Ksar-Megrine, respectively.

    Table 4.  Effect of observed simulated water normal and stress on water productivity of Algerian barley varieties.
    Observed Observed Simulated by Aqua crop
    Barley varieties Grain yield (ton/fed) WP (Kg/m3) WP (Kg/m3)
    Normal Stress Normal Stress Normal Stress
    Avg
    Temacine 2040.0 1750.0 1.70 2.43 2.86 4.09
    Ksar-Megrine 2152.5 1545.6 1.79 2.15 3.02 3.61
    Techedrett 2180.0 1940.0 1.82 2.69 3.06 4.54
    Saida 2129.0 1710.0 1.77 2.38 2.99 4.00
    Sedi Mahdi 1870.0 1313.9 1.56 1.82 2.62 3.07
    Ras El-Mouche 2268.3 1410.0 1.89 1.96 3.18 3.30
    Naïlia 2209.4 1950.0 1.84 2.71 3.10 4.56
     | Show Table
    DownLoad: CSV
    Figure 8.  Effect of observed water (a) and simulated water (b) normal and stress on water productivity of Morocco barley varieties.
    DownLoad: Full-Size Img PowerPoint

    3.4.   Morocco varieties at harvest

    Table 5 and Figure 9 showed the effect of both the use of the measurement method and the simulated using of the AquaCrop model on the water productivity of the Morocco barley varieties. It was found that the highest values in case without water stress or in the presence of water stress measurement were (2.73 and 2.47 kg/m3) were recorded at Laanaceur variety, followed by Amira variety (2.69 and 2.42 kg/m3) while the lowest values of water productivity were recorded at Tamellalet variety (2.35 and 1.76 kg/m3).

    Table 5.  Effect of observed simulated water normal and stress on water productivity of Morocco barley varieties.
    Observed Observed Simulated by Aqua crop
    Barley varieties Grain yield (ton/fed) WP (Kg/m3) WP (Kg/m3)
    Normal Stress Normal Stress Normal Stress
    Avg
    Adrar 2510.0 1930.0 2.09 2.68 3.52 4.51
    Oussama 2540.0 2220.0 2.12 3.08 3.56 5.19
    Amalou 2470.0 2130.0 2.06 2.96 3.47 4.98
    Massine 2510.0 2240.0 2.09 3.11 3.52 5.24
    Taffa 2530.0 1880.0 2.11 2.61 3.55 4.40
    Firdaws 2420.0 1860.0 2.02 2.58 3.39 4.35
    Amira 2689.3 2420.0 2.24 3.36 3.77 5.66
    Tamellalet 2350.0 1760.0 1.96 2.44 3.30 4.12
    Laanaceur 2730.0 2470.0 2.28 3.43 3.83 5.78
     | Show Table
    DownLoad: CSV
    Figure 9.  Comparison between barley varieties from different Mediterranean countries.
    DownLoad: Full-Size Img PowerPoint

    On the other side: By using AquaCrop model, it was found that in case of use without water stress, the three highest yielding values of water productivity (2.92, 2.75 and 2.67) kg/m3 were recorded at varieties Oussama, Firdaws and Amira, respectively while the lowest value of water productivity using the model AquaCrop model was recorded Laanaceur variety (1.82 kg/m3) while under water stress, the highest values using the model AquaCrop model were recorded (3.98, 3.26 and 3.23) kg/m3 at the varieties Oussama, Massine and Amalou, respectively.

    3.5.   Comparison between barley varieties from some different Mediterranean countries

    Figures 9 and 10 showed the comparison between different barley varieties from different African Mediterranean countries under water normal and stress or water deficit condition. One can notice that the most tolerable varieties of water stress treatment are Algerian varieties was 40.84%, followed by Tunisian varieties which recorded 40.83%. While the percentage of absolute difference between the percentage of variation in the case of water stress and the case without water stress treatment was 5.36% under Egyptian varieties, followed by Morocco varieties was 12.78%. Egyptian varieties are the least tolerant of water stress treatment where the percentage difference in the absolute difference between the cases is equal to 25%.

    Figure 10.  Deviation of water productive barley between different barley varieties from different Mediterranean countries.
    DownLoad: Full-Size Img PowerPoint

    4.   Discussions

    In Figure 11, the mean of deviation (%) between observed and simulated data of non-treated water stress showing that under varieties El-Arich and Giza 130 were obtained the low values (1.11 and 2.20%), respectively. In Egyptian varieties, the water productivity simulated by AquaCrop model under normal and/or stress condition was higher than the measurement cases under same Egyptian climate, water, soil, etc, of all condition. Whereas, the highest value was obtained at Giza 131 variety and the lowest one at Al-Arish variety.

    Figure 11.  The mean of deviation between observed and simulated water productivity of Egyptian barley (a) and Tunisian barley varieties (b).
    DownLoad: Full-Size Img PowerPoint

    Deviation of normal (%) between observed and simulated data showing that Kasr and Giza 2000 varieties were achieved the low values (3.72 and 3.94 %), respectively. On the other hand, deviation of stress (%) between observed and simulated data showing that El-Arich and Giza 130 varieties were achieved values (4.08 and 9.35 %), respectively, they were superior varieties for deficit tolerance. These results are obtained using the AquaCrop simulation model [4,16,17,18].

    Regarding to the relationship between the observed values and the values of the AquaCrop simulation model as showed in Figure 11, it is possible to notice that in all Tunisian varieties, the water productivity in the AquaCrop model under both studied conditions was higher than the cases of measurement where the highest value obtained at the Kebili-3 verity and lower one at Manel variety. The mean of deviation (%) between observed and simulated data of water productivity showing that under varieties Sidi-Bou and Manel were the low values (27.93 and 31.16%), respectively. On the other hand, the deviation of unstressed (%) between observed and simulated data showing that Manel and Sidi-Bou varieties were achieved lowest values (12.04 and 14.08 %), and deviation of stress values were (2.63 and 3.13%) under Manel and Sabra varieties, they were the better varieties of deficit tolerance by drip irrigation system design [6,19,20,21,22,23,24,25,26,27,28].

    The relationship between the observed values and the simulated model values as showed in Figure 12, it can be observed that in all Algerian varieties, the water efficiency of the Aquacrop model was reduced without stress and in the presence of water stress was higher than the measurement cases in the presence of stress and without water stress. The higher value was obtained under the Techetrett verity was obtained and the lowest value was obtained by using Nailia variety. The mean of deviation (%) between stress and normal water data of water productivity showing that under varieties Tamacine and Ksar-Megrine were the low values (34.61 and 34.33%), respectively. Deviation (%) of normal water and stress water between observed and simulated data showing that (Nailia and Ksar-Megrine); (Nailia and Sidi mahdi) varieties were achieved values (0.95; 13.32) and (39.12; 46.14 %), they were the better varieties of deficit tolerance [4,13,28,29,30].

    Figure 12.  The mean of deviation between observed and simulated water productivity of Algerian barley (a) and Morocco barley varieties (b).
    DownLoad: Full-Size Img PowerPoint

    In Figure 12, the mean of deviation (%) between observed and simulated data of water productivity showing that under Morocco varieties Amira and Adrar were the low values (1.21 and 12.30 %), respectively. Deviation normal (%) between observed and simulated data showing that the three lowest varieties were achieved values (0.54, 0.99 and 1.27) recorded under Amira, Amalou and Taffa. On the other hand, the three lowest values of deviation stress (%) were (2.97, 10.04 and 18.18) under Morocco varieties Amira, Laanaceur and Tamellalet, they were the better varieties of deficit tolerance. The water productivity of the Aquacrop model was reduced without stress and in the presence of water stress was higher than the measurement cases in the presence of stress and without water stress. The highest value was obtained when cultivating Oussama verity and lower value using Laanaceur verity. These results are obtained using the AquaCrop simulation model [28,29,31,32,33].

    5.   Conclusions

    AquaCrop model was applied to estimate water productivity of Barley varieties derived from Mediterranean countries (Egypt, Tunisia, Algeria and Morocco) under water deficit treatments. The study showed that very clear convergence of all data resulting from the use of the AquaCrop simulation program for the water productivity values with the corresponding quantities that were actually observed in the field during two successful seasons on examined barley varieties under drought conditions and water stress compared to the conditions natural water availability. So it is possible to recommend that using AquaCrop simulation program for different barley varieties in the future to predict the productivity of the water unit under semi-arid areas, especially in the case of water shortage using this program is based on the input of the most important (climate, soil and water data) outputs for the production data of the water unit, therefore it is possible to predict which will help to find suitable scenarios in case of climatic changes, water shortage or changes in soil characteristics.

    Acknowledgments

    The authors warmly thank the Agricultural Research in the Mediterranean Area 2 (ARIMNet 2) and Academy of Scientific Research and Technology (ASRT) and National Research Centre (NRC), Egypt who have funded this research work as a part of barley stress tolerance best project.

    [1] Geiman TM, Robertson KD (2002) Chromatin remodeling, histone modifications, and DNA methylation-how does it all fit together? J Cell Biochem 87: 117–125. doi: 10.1002/jcb.10286
    [2] Bonev B, Cavalli G (2016) Organization and function of the 3D genome. Nat Rev Genet 17: 661–678. doi: 10.1038/nrg.2016.112
    [3] Bannister AJ, Kouzarides T (2011) Regulation of chromatin by histone modifications. Cell Res 21: 381–395. doi: 10.1038/cr.2011.22
    [4] Tessarz P, Kouzarides T (2014) Histone core modifications regulating nucleosome structure and dynamics. Nat Rev Mol Cell Bio 15: 703–708.
    [5] Koch A, Joosten SC, Feng Z, et al. (2018) Analysis of DNA methylation in cancer: Location revisited. Nat Rev Clin Oncol 15: 459–466. doi: 10.1038/s41571-018-0004-4
    [6] Watt F, Molloy PL (1988) Cytosine methylation prevents binding to DNA of a HeLa cell transcription factor required for optimal expression of the adenovirus major late promoter. Gene Dev 2: 1136–1143. doi: 10.1101/gad.2.9.1136
    [7] Csankovszki G, Nagy A, Jaenisch R (2001) Synergism of Xist RNA, DNA methylation, and histone hypoacetylation in maintaining X chromosome inactivation. J Cell Biol 153: 773–784. doi: 10.1083/jcb.153.4.773
    [8] Li E, Beard C, Jaenisch R (1993) Role for DNA methylation in genomic imprinting. Nature 366: 362–365. doi: 10.1038/366362a0
    [9] Okano M, Bell DW, Haber DA, et al. (1999) DNA methyltransferases Dnmt3a and Dnmt3b are essential for de novo methylation and mammalian development. Cell 99: 247–257. doi: 10.1016/S0092-8674(00)81656-6
    [10] Hashimoto H, Liu Y, Upadhyay AK, et al. (2012) Recognition and potential mechanisms for replication and erasure of cytosine hydroxymethylation. Nucleic Acids Res 40: 4841–4849. doi: 10.1093/nar/gks155
    [11] Nabel CS, Jia H, Ye Y, et al. (2012) AID/APOBEC deaminases disfavor modified cytosines implicated in DNA demethylation. Nat Chem Biol 8: 751–758. doi: 10.1038/nchembio.1042
    [12] Bochtler M, Kolano A, Xu GL (2017) DNA demethylation pathways: Additional players and regulators. Bioessays 39: 1–13. doi: 10.1002/bies.201670013
    [13] Tahiliani M, Koh KP, Shen Y, et al. (2009) Conversion of 5-methylcytosine to 5-hydroxymethylcytosine in mammalian DNA by MLL partner TET1. Science 324: 930–935. doi: 10.1126/science.1170116
    [14] Tamanaha E, Guan S, Marks K, et al. (2016) Distributive processing by the iron(II)/α-ketoglutarate-dependent catalytic domains of the TET enzymes is consistent with epigenetic roles for oxidized 5-methylcytosine bases. J Am Chem Soc 138: 9345–9348. doi: 10.1021/jacs.6b03243
    [15] Müller U, Bauer C, Siegl M, et al. (2014) TET-mediated oxidation of methylcytosine causes TDG or NEIL glycosylase dependent gene reactivation. Nucleic Acids Res 42: 8592–8604. doi: 10.1093/nar/gku552
    [16] Weber AR, Krawczyk C, Robertson AB, et al. (2016) Biochemical reconstitution of TET1-TDG-BER-dependent active DNA demethylation reveals a highly coordinated mechanism. Nat Commun 7: 10806. doi: 10.1038/ncomms10806
    [17] Wang J, Hevi S, Kurash JK, et al. (2009) The lysine demethylase LSD1 (KDM1) is required for maintenance of global DNA methylation. Nat Genet 41: 125–129. doi: 10.1038/ng.268
    [18] Szwagierczak A, Bultmann S, Schmidt CS, et al. (2010) Sensitive enzymatic quantification of 5-hydroxymethylcytosine in genomic DNA. Nucleic Acids Res 38: e181. doi: 10.1093/nar/gkq684
    [19] Spruijt CG, Gnerlich F, Smits AH, et al. (2013) Dynamic readers for 5-(hydroxy)methylcytosine and its oxidized derivatives. Cell 152: 1146–1159. doi: 10.1016/j.cell.2013.02.004
    [20] Matarese F, Carrillode SPE, Stunnenberg HG (2011) 5-Hydroxymethylcytosine: A new kid on the epigenetic block? Mol Syst Biol 7: 562.
    [21] Koh KP, Yabuuchi A, Rao S, et al. (2011) Tet1 and Tet2 regulate 5-hydroxymethylcytosine production and cell lineage specification in mouse embryonic stem cells. Cell Stem Cell 8: 200–213. doi: 10.1016/j.stem.2011.01.008
    [22] Ito S, D'Alessio AC, Taranova OV, et al. (2010) Role of Tet proteins in 5mC to 5hmC conversion, ES-cell self-renewal and inner cell mass specification. Nature 466: 1129–1133. doi: 10.1038/nature09303
    [23] Caron G, Hussein M, Kulis M, et al. (2015) Cell-cycle-dependent reconfiguration of the DNA methylome during terminal differentiation of human B cells into plasma cells. Cell Rep 13: 1059–1071. doi: 10.1016/j.celrep.2015.09.051
    [24] Costa Y, Ding J, Theunissen TW, et al. (2013) NANOG-dependent function of TET1 and TET2 in establishment of pluripotency. Nature 495: 370–374. doi: 10.1038/nature11925
    [25] Kriaucionis S, Heintz N (2009) The nuclear DNA base 5-hydroxymethylcytosine is present in Purkinje neurons and the brain. Science 324: 929–930. doi: 10.1126/science.1169786
    [26] Jin SG, Wu X, Li AX, et al. (2011) Genomic mapping of 5-hydroxymethylcytosine in the human brain. Nucleic Acids Res 39: 5015–5024. doi: 10.1093/nar/gkr120
    [27] Nestor CE, Ottaviano R, Reddington J, et al. (2012) Tissue type is a major modifier of the 5-hydroxymethylcytosine content of human genes. Genome Res 22: 467–477. doi: 10.1101/gr.126417.111
    [28] Szulwach KE, Li X, Li Y, et al. (2011) 5-hmC-mediated epigenetic dynamics during postnatal neurodevelopment and aging. Nat Neurosci 14: 1607–1616. doi: 10.1038/nn.2959
    [29] Sherwani SI, Khan HA (2015) Role of 5-hydroxymethylcytosine in neurodegeneration. Gene 570: 17–24. doi: 10.1016/j.gene.2015.06.052
    [30] Jeschke J, Collignon E, Fuks F (2016) Portraits of TET-mediated DNA hydroxymethylation in cancer. Curr Opin Genet Dev 36: 16–26. doi: 10.1016/j.gde.2016.01.004
    [31] Haffner MC, Chaux A, Meeker AK, et al. (2011) Global 5-hydroxymethylcytosine content is significantly reduced in tissue stem/progenitor cell compartments and in human cancers. Oncotarget 2: 627–637.
    [32] Mahé EA, Madigou T, Sérandour AA, et al. (2017) Cytosine modifications modulate the chromatin architecture of transcriptional enhancers. Genome Res 27: 947–958. doi: 10.1101/gr.211466.116
    [33] Nan X, Ng HH, Johnson CA, et al. (1998) Transcriptional repression by the methyl-CpG-binding protein MeCP2 involves a histone deacetylase complex. Nature 393: 386–389. doi: 10.1038/30764
    [34] Ng HH, Bird A (1999) DNA methylation and chromatin modification. Curr Opin Genet Dev 9: 158–163. doi: 10.1016/S0959-437X(99)80024-0
    [35] Schübeler D (2015) Function and information content of DNA methylation. Nature 517: 321–326. doi: 10.1038/nature14192
    [36] Williams K, Christensen J, Pedersen MT, et al. (2011) TET1 and hydroxymethylcytosine in transcription and DNA methylation fidelity. Nature 473: 343–348. doi: 10.1038/nature10066
    [37] Sérandour AA, Avner S, Oger F, et al. (2012) Dynamic hydroxymethylation of deoxyribonucleic acid marks differentiation-associated enhancers. Nucleic Acids Res 40: 8255–8265. doi: 10.1093/nar/gks595
    [38] Song CX, He C (2013) Potential functional roles of DNA demethylation intermediates. Trends Biochem Sci 38: 480–484. doi: 10.1016/j.tibs.2013.07.003
    [39] Sepulveda H, Villagra A, Montecino M (2017) Tet-mediated DNA demethylation is required for SWI/SNF-dependent chromatin remodeling and histone-modifying activities that trigger expression of the Sp7 osteoblast master gene during mesenchymal lineage commitment. Mol Cell Biol 37.
    [40] Yildirim O, Li R, Hung JH, et al. (2011) Mbd3/NURD complex regulates expression of 5-hydroxymethylcytosine marked genes in embryonic stem cells. Cell 147: 1498–1510. doi: 10.1016/j.cell.2011.11.054
    [41] Neri F, Incarnato D, Krepelova A, et al. (2013) Genome-wide analysis identifies a functional association of Tet1 and Polycomb repressive complex 2 in mouse embryonic stem cells. Genome Biol 14: R91. doi: 10.1186/gb-2013-14-8-r91
    [42] Deplus R, Delatte B, Schwinn MK, et al. (2013) TET2 and TET3 regulate GlcNAcylation and H3K4 methylation through OGT and SET1/COMPASS. EMBO J 32: 645–655. doi: 10.1038/emboj.2012.357
    [43] Kong L, Tan L, Lv R, et al. (2016) A primary role of TET proteins in establishment and maintenance of De Novo bivalency at CpG islands. Nucleic Acids Res 44: 8682–8692. doi: 10.1093/nar/gkw529
    [44] Mendonca A, Chang EH, Liu W, et al. (2014) Hydroxymethylation of DNA influences nucleosomal conformation and stability in vitro. BBA-Gene Regul Mech 1839: 1323–1329.
    [45] Deplus R, Delatte B, Schwinn MK, et al. (2013) TET2 and TET3 regulate GlcNAcylation and H3K4 methylation through OGT and SET1/COMPASS. EMBO J 32: 645–655. doi: 10.1038/emboj.2012.357
    [46] Guan W, Guyot R, Samarut J, et al. (2017) Methylcytosine dioxygenase TET3 interacts with thyroid hormone nuclear receptors and stabilizes their association to chromatin. P Natl Acad Sci USA 114: 8229–8234. doi: 10.1073/pnas.1702192114
    [47] Zhang YW, Wang Z, Xie W, et al. (2017) Acetylation enhances TET2 function in protecting against abnormal DNA methylation during oxidative stress. Mol Cell 65: 323–335. doi: 10.1016/j.molcel.2016.12.013
    [48] Cimmino L, Dawlaty MM, Ndiaye-Lobry D, et al. (2015) TET1 is a tumor suppressor of hematopoietic malignancy. Nat Immunol 16: 653–662. doi: 10.1038/ni.3148
    [49] An J, González-Avalos E, Chawla A, et al. (2015) Acute loss of TET function results in aggressive myeloid cancer in mice. Nat Commun 6: 10071. doi: 10.1038/ncomms10071
    [50] Kafer GR, Li X, Horii T, et al. (2016) 5-Hydroxymethylcytosine marks sites of DNA damage and promotes genome stability. Cell Rep 14: 1283–1292. doi: 10.1016/j.celrep.2016.01.035
    [51] Mahfoudhi E, Talhaoui I, Cabagnols X, et al. (2016) TET2-mediated 5-hydroxymethylcytosine induces genetic instability and mutagenesis. DNA Rep 43: 78–88. doi: 10.1016/j.dnarep.2016.05.031
    [52] Jiang D, Zhang Y, Hart RP, et al. (2015) Alteration in 5-hydroxymethylcytosine-mediated epigenetic regulation leads to Purkinje cell vulnerability in ATM deficiency. Brain 138: 3520–3536. doi: 10.1093/brain/awv284
    [53] Jiang D, Wei S, Chen F, et al. (2017) TET3-mediated DNA oxidation promotes ATR-dependent DNA damage response. EMBO Rep 18: 781–796. doi: 10.15252/embr.201643179
    [54] Blackford AN, Jackson SP (2017) ATM, ATR, and DNA-PK: the trinity at the heart of the DNA damage response. Mol Cell 66: 801–817. doi: 10.1016/j.molcel.2017.05.015
    [55] Sellou H, Lebeaupin T, Chapuis C, et al. (2016) The poly(ADP-ribose)-dependent chromatin remodeler Alc1 induces local chromatin relaxation upon DNA damage. Mol Biol Cell 27: 3791–3799. doi: 10.1091/mbc.e16-05-0269
    [56] Smith R, Sellou H, Chapuis C, et al. (2018) CHD3 and CHD4 recruitment and chromatin remodeling activity at DNA breaks is promoted by early poly(ADP-ribose)-dependent chromatin relaxation. Nucleic Acids Res 46: 6087.
    [57] Ciccarone F, Valentini E, Zampieri M, et al. (2015) 5mC-hydroxylase activity is influenced by the PARylation of TET1 enzyme. Oncotarget 6: 24333–24347.
    [58] Ciccarone F, Valentini E, Bacalini MG, et al. (2014) Poly(ADP-ribosyl)ation is involved in the epigenetic control of TET1 gene transcription. Oncotarget 5: 10356–10367.
    [59] Chou DM, Adamson B, Dephoure NE, et al. (2010) A chromatin localization screen reveals poly (ADP ribose)-regulated recruitment of the repressive polycomb and NuRD complexes to sites of DNA damage. P Natl Acad Sci USA 107: 18475–18480. doi: 10.1073/pnas.1012946107
    [60] Luijsterburg MS, Dinant C, Lans H, et al. (2009) Heterochromatin protein 1 is recruited to various types of DNA damage. J Cell Biol 185: 577–586. doi: 10.1083/jcb.200810035
    [61] Abu-Zhayia ER, Awwad SW, Ben-Oz B, et al. (2017) CDYL1 fosters double-strand break-induced transcription silencing and promotes homology-directed repair. J Mol Cell Biol 1: 1.
    [62] D'Alessandro G, Fagagna FDD (2017) Transcription and DNA damage: holding hands or crossing swords? J Mol Biol 429: 3215–3229. doi: 10.1016/j.jmb.2016.11.002
    [63] Puc J, Aggarwal AK, Rosenfeld MG (2017) Physiological functions of programmed DNA breaks in signal-induced transcription. Nat Rev Mol Cell Bio 18: 471–476.
    [64] Ju BG, Lunyak VV, Perissi V, et al. (2006) A topoisomerase IIbeta-mediated dsDNA break required for regulated transcription. Science 312: 1798–1802. doi: 10.1126/science.1127196
    [65] Madabhushi R, Gao F, Pfenning AR, et al. (2015) Activity-induced DNA breaks govern the expression of neuronal early-response genes. Cell 161: 1592–1605. doi: 10.1016/j.cell.2015.05.032
    [66] Perillo B, Ombra MN, Bertoni A, et al. (2008) DNA oxidation as triggered by H3K9me2 demethylation drives estrogen-induced gene expression. Science 319: 202–206. doi: 10.1126/science.1147674
    [67] Puc J, Kozbial P, Li W, et al. (2015) Ligand-dependent enhancer activation regulated by topoisomerase-I activity. Cell 160: 367–380. doi: 10.1016/j.cell.2014.12.023
    [68] Baranello L, Wojtowicz D, Cui K, et al. (2016) RNA polymerase II regulates topoisomerase 1 activity to favor efficient transcription. Cell 165: 357–371. doi: 10.1016/j.cell.2016.02.036
    [69] Bunch H, Lawney BP, Lin YF, et al. (2015) Transcriptional elongation requires DNA break-induced signalling. Nat Commun 6: 10191. doi: 10.1038/ncomms10191
    [70] Marnef A, Cohen S, Legube G (2017) Transcription-coupled DNA double-strand break repair: active genes need special care. J Mol Biol 429: 1277–1288. doi: 10.1016/j.jmb.2017.03.024
    [71] Huertas P, Aguilera A (2003) Cotranscriptionally formed DNA:RNA hybrids mediate transcription elongation impairment and transcription-associated recombination. Mol Cell 12: 711–721. doi: 10.1016/j.molcel.2003.08.010
    [72] Sollier J, Stork CT, García-Rubio ML, et al. (2014) Transcription-coupled nucleotide excision repair factors promote R-loop-induced genome instability. Mol Cell 56: 777–785. doi: 10.1016/j.molcel.2014.10.020
    [73] Métivier R, Gallais R, Tiffoche C, et al. (2008) Cyclical DNA methylation of a transcriptionally active promoter. Nature 452: 45–50. doi: 10.1038/nature06544
    [74] Li J, Wu X, Zhou Y, et al. (2018) Decoding the dynamic DNA methylation and hydroxymethylation landscapes in endodermal lineage intermediates during pancreatic differentiation of hESC. Nucleic Acids Res 46: 2883–2900. doi: 10.1093/nar/gky063
    [75] Zhang Y, Zhang D, Li Q, et al. (2016) Nucleation of DNA repair factors by FOXA1 links DNA demethylation to transcriptional pioneering. Nat Genet 48: 1003–1013. doi: 10.1038/ng.3635
    [76] Boque-Sastre R, Soler M, Oliveira-Mateos C, et al. (2015) Head-to-head antisense transcription and R-loop formation promotes transcriptional activation. P Natl Acad Sci USA 112: 5785–5790. doi: 10.1073/pnas.1421197112

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