Overexpression of various genes in cotton that reported to enhance drought tolerance.
Abstract
Aldehyde dehydrogenases (ALDHs) contribute to cellular protection against oxidative stress. These enzymes are crucial to organisms’ ability to cope with environmental stress. The ALDH21 gene was introduced into upland cotton (Gossypium hirsutum L.) from desiccant-tolerant Syntrichia caninervis moss, created stable genetic transgenic lines. As a result, drought tolerance is increased and yield penalty is reduced in those transgenic lines. The first study to demonstrate overexpression of ALDH21 enhances drought tolerance in cotton under multi-location field experiments is presented here. Cotton genotypes containing ScALDH21 exhibit significant morphological, physiological, and economic benefits. ScALDH21 functions in the physiology of cotton plants to protect them by scavenging ROS and reducing osmotic stress. The yield of transgenic cotton in northern Xinjiang showed up to 10% improvement under full irrigation and up to 18% improvement in deficit irrigation conditions on fields with purple clay loam soils. Additionally, transgenic cotton can be grown in sandy loam soil in southern Xinjiang with an average yield increase of 40% on different irrigation levels in the desert-oasis ecotone. Using ScALDH21 as a candidate gene for cotton improvement in arid and semi-arid regions was demonstrated. In addition, we assessed different irrigation protocols and optimized irrigation methods with minimal water requirements for ScALDH21-transgenic cotton that could be used in production agriculture.
Keywords
- transgenic cotton
- molecular breeding
- ScALDH21
- drought tolerance
- yield improved
1. Introduction
Plants are restricted in their habitat range and productivity by adverse environmental conditions [1]. Drought is the foremost constraint on agricultural production. Cotton (Gossypium spp.) is a major source of textile fibers and oil around the world. More than 32 million ha of cotton are produced in 76 countries [2]. In terms of cotton production, China is ranked among the top two countries in the world [3]. However, cotton production in China, as well as other countries, has recently declined due to increasing drier environments [3, 4]. Chinese agriculture consumes 62% of the country’s annual water consumption, and the country is in a moderate water shortage [4]. In agriculture, cotton is the crop with the highest water consumption. In China, cotton is grown mainly in the Xinjiang-Uygur Autonomous Region, an area characterized by very low air humidity and a severe water shortage.
Cotton is the most important crop in China, accounting for around 25% of global fiber production. There is more than one-third of all agricultural land in the Xinjiang-Uyghur Autonomous Region dedicated to cotton plantations [5]. This region has a warm climate with average temperatures of 11.4°C and 49 mm precipitation annually, low groundwater levels, sandy soils, and severe soil salinization [6, 7, 8, 9]. In southern Xinjiang, cotton has low germination rates, low survival rates, and low yields [10].
Plants are able to generate significant amounts of reactive aldehydes when faced with a variety of abiotic stresses (such as salinity, desiccation, and cold) [11], which can impair plant growth and crop productivity. Cotton varieties that survive droughts and other adversities must be developed urgently to combat these conditions. In arid lands where freshwater scarcity is a severe constraint on agricultural production, it is necessary to develop more tolerant varieties of plants. It is often difficult to obtain drought-tolerant crops through traditional breeding programs because of the time and labor involved, in addition to the need for large-scale facilities, such as rainout shelters. Interestingly, biotechnological improvements have been attempted since the 1990s, which is inspiring. However, the majority of transgenic cotton is aimed at controlling insect pest damage by expressing a variety of insecticidal proteins from
Plants that are known as bryophytes (mosses, hornworts, liverworts, etc.) are among the oldest species in the world’s flora; they are thought to be small, non-vascular, and green plants. Many bryophytes survive even with a total loss of water in their vegetative tissues [21, 22]. The study of drought-tolerant mosses is of particular interest because their genetic engineering properties can be used to increase drought tolerance in arid-zone crops. The desiccation-tolerant moss
Aldehyde dehydrogenase (ALDH) genes show promise as candidate genes to increase plant resistance, especially
2. Cotton drought tolerance breeding with transgene technology
2.1 The overexpression gene types in current drought-tolerant cotton
It is possible to improve cotton drought tolerance using transgenic technology. The drought tolerance of transgenic cotton has recently been enhanced by using several genes (Table 1). As an example, AtLOS5, encoding an aldehyde oxidase cofactor sulfurase; GhAnn1, an annexin gene; isopentenyl transferase (IPT), an enzyme responsible for cytokinin biosynthesis; and 14-3-3 genes involved in plasma membrane H+-ATPase activity [17, 24, 25, 26, 27]. Increased drought tolerance was also observed in transgenic cotton overexpressing the OsSIZ1 gene from
Gene name | Gene types/function | Gene source | References |
---|---|---|---|
Molybdenum cofactor sulfurase gene/aldehyde oxidase activity | Yue et al. [24] | ||
Annexin gene | Zhang et al. [25] | ||
Isopentenyl transferase gene/rate-limiting enzyme for cytokinin biosynthesis | Kuppu et al. [26]; Zhu et al. [17] | ||
Regulate the activity of plasma membrane H+-ATPase | Yan et al. [27] | ||
bZIP transcription factor family gene | Liang et al. [28] | ||
Homeodomain-START transcription factor gene | Yu et al. [29] | ||
Related to ABA insensitive3/viviparous1 | Mittal et al. [30] | ||
Transcription factor gene | Liu et al. [31] | ||
Zinc-finger protein gene | Hozain et al. [32] | ||
Vacuolar proton-pumping pyrophosphatase (H+-PPase) gene | Pasapula et al. [33]; Zhang et al. [34] | ||
Vacuolar Na+/H+ antiporter gene | Shen et al. [35] | ||
SUMO E3 protein gene/participates in a sumoylation reaction | Mishra et al. [36] | ||
Aldehyde dehydrogenases gene | Yang et al. [37, 38] | ||
Histone H2B monoubiquitination E3 ligase gene | [39] | ||
Dehydration-responsive element binding (DREB) transcription factors | [40] | ||
bZIP AREB/ABF transcription factor orthologs | [41] | ||
Heat-shock proteins gene | [42] |
2.2 Transgenic ScALDH21 cotton significantly improve drought tolerance in southern and northern Xinjiang
A number of fiber quality parameters and yield were improved with cotton
A variety of irrigation protocols were evaluated and optimized to use
2.2.1 The aldehyde dehydrogenase (ALDH) enzyme superfamily and its functions
As ROS are generated, oxidative stress is induced, lipid membranes are destroyed, and 200 types of aldehydes are accumulated, many of which are highly reactive and toxic. Aldehydes must be effectively removed and detoxified in arid environments to improve plant productivity. Plants have developed many enzymatic and non-enzymatic mechanisms to scavenge these toxic compounds [24]. Aldehyde dehydrogenase (ALDH) superfamily proteins may also play a role in scavenging ROS enzymatically [43]. Aldehyde dehydrogenases (ALDHs) have been found to play a central role in plants exposed to stressful conditions in the detoxification of aldehyde [44]. This superfamily of enzymes metabolizes endogenous and exogenous aldehydes to their carboxylic acids by using the coenzyme NAD(P)+, producing NAD(P)H and thereby reducing oxidative/electrophilic stress [45]. ALDHs belong to a group of NAD(P)+
2.2.2 The genetic background of transgenic ScALDH21 cotton
2.2.2.1 The plant expression vector and the plant transformation
To make
2.2.2.2 PCR, RT-PCR detection, and Southern blot analysis
Using the cetyltrimethylammonium bromide method, genomic DNA was isolated from cotton seedlings at the five-leaf stage. PCR was used to detect the
2.2.2.3 The transcriptome background of transgenic ScALDH21 cotton
We collected root samples from cotton seedlings after 1month of growth. Three biological replicates of each treatment were carried out. The total RNA was extracted using the RNAprep Pure Plant Kit (Tiangen, Beijing, China) following the manufacturer’s instructions. In accordance with the manufacturer’s instructions, sequencing libraries were prepared using the NEB Next Ultra RNA Library Prep Kit for Illumina (NEB, Beverly, CA, USA). Illumina HiSeq 4000 platform was used for sequencing with 150 bp paired-end reads. Based on the length of the gene and the number of reads mapped to the gene (Novogene company, China), the expected fragments per kilobase of genes per million mapped reads (FPKM) of each gene were calculated. Differentially expressed genes were defined using the DESeq R package with an adjusted P-value (q-value) of 0.05. We used the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/) to test the statistical enrichment of DEGs in KEGG pathways. Five hundred and seventy-eight co-expressed genes were detected in the two
2.2.3 The phenotype of ScALDH21 -transgenic cotton
Following the identification of
2.2.4 The physiological character of ScALDH21-transgenic cotton
Various biotic and abiotic stresses trigger ROS accumulation in plant cells, which leads to oxidative stress with lipid peroxidation, which also causes free radical reactions involving membrane polyunsaturated fatty acids [67]. ROS are produced when toxic aldehydes accumulate from lipid peroxidation [68]. Aldehyde-detoxifying enzymes ALDH3I1 and ALDH7B4 are both significant ROS scavengers and proteins that inhibit lipid peroxidation in Arabidopsis. In Arabidopsis, overexpression of these genes reduced lipid peroxidation under drought and salt stress [60].
It remains to be seen whether overexpression of
2.2.5 The yield and fiber performance of ScALDH21 -transgenic cotton
From 2013 to 2018, transgenic lines of cotton were grown in northern and southern Xinjiang to determine whether the
TC lines in northern Xinjiang have been found to be better in growth and development than NT lines to some extent, after applying six different water retention treatments at different growth stages. Water deficit stress during the bud stage will cause the plant stalk length and boll number to decrease. Cotton yields were significantly decreased if twice deficit stresses were met during the bud or flower stage. Cotton growth and yield are critically dependent on water availability during the bud stage. Fiber parameters such as fiber strength, ginning out-turn of the fibers, fiber length, and length uniformity of the
During harvesting season, boll weight, seed index, cotton yield, and fiber yield were measured in managed treatment plots under full and deficit irrigation conditions in northern Xinjiang in 2014 and 2016. Under drought stress, both
To determine the performance of the
In addition to treatments, cotton yields vary by year. As with the boll number per plant, the cotton yield per hectare, fiber yield per hectare, and cotton yield per plant were significantly higher in the transgenic lines than in the NT lines. The average seed yield for all treatments was 68% (variable from 14–128%) and 41% (variable from 10–102%) in 2017 and 2018, respectively [37]. Fiber elongation was increased in transgenic lines. Fiber strength also increased in transgenics after irrigation. There were no significant differences in fiber uniformity and micronaires between genotypes.
2.2.6 The irrigation strategy of ScALDH21 -transgenic cotton
Cotton productivity and yield are largely influenced by a variety of factors, including genetics and irrigation methods. In this study, the TC lines performed better than the NT lines, despite soil, air humidity, and temperature affecting plant yield [71, 72, 73]. This study evaluated the drought tolerance ability of
The lack of rainfall makes irrigation vital for agricultural production in arid and semi-arid lands. In arid zones, for example, normal irrigation above 600 mm during the vegetation period is sufficient for stable cotton harvesting [5]. Our desert oasis drought experiments in southern Xinjiang with sandy loam soils designed the 75% deficit irrigation and less than 600 mm different irrigation strategies to conserve more irrigation water and keep cotton yield constant. We used three irrigation schedules: DSSIS (Decision Support System for Irrigation Scheduling) forecasts (F), soil moisture sensors (S), and experience irrigation (E). Full irrigation (FI) and deficit irrigation (DI, 75% of full) were applied from 2016 to 2018 (Figure 2). Different irrigation protocols and water consumption affected the growth and yield of cotton, and the “Smart Irrigation” irrigation scheme based on the Root zone water quality model (RZWQM2) was found to be the best irrigation scheme for sustainable cotton production in an arid land. The results indicated that deficit irrigation schemes can be utilized in the desert-oasis ecotone, and in conjunction with the use of
Moreover, through mixed model analysis, we found that the cotton line always has a significant effect on plant phenotype, physiology, and yield components in southern Xinjiang, and cotton line and irrigation scheduling both have significant effects on cotton growth and development separately. In addition, irrigation scheduling and irrigation levels have a significant interaction effect. The relationship between yield and crop water use was calculated as overall water use efficiency (WUE). The EI schedule consumed more water (EFI, 547 mm, and EDI, 409 mm) than either the FI (FFI, 385 mm, and FDI, 288 mm) or SI (SFI, 254 mm, and SDI, 186 mm) schedules. Compared to NT plants, WUE was higher in
The individual irrigation level and timing significantly affected vegetative growth parameters, plant height, and leaf area, but the differences did not differ substantially between years despite differing precipitation levels. In each irrigation treatment, the TC, and especially the L16, grew significantly higher than the NT controls from 2016 to 2018. There was a dramatic reduction in the leaf area of NT under SDI in all years, but there was no difference in the leaf area of TC [37]. Therefore, the irrigation treatments can be ranked as forecast irrigation > flood irrigation > soil moisture irrigation based on their ability to maintain high instantaneous water use efficiency (IWUE).
We also used managed treatment plot experiments and field-scale in purple clay loam soil sites at Manas Experimental Station, northern Xinjiang. The two experiments differed in terms of growth space and water consumption. In the managed plot experiment, 50% less of full irrigation significantly reduced cotton vegetative growth and cotton yield (*50% loss of cottonseed and lint yield compared with full irrigation), whereas, in the field, 30% less of full irrigation did not affect cotton vegetative growth or yield. The reason for this can be explained by the amount of water used, which was 675 L of water m−2 with full irrigation and 472 L m−2 with deficient irrigation in the field, 566 L m−2 (control), and 283 L m−2 (stress) in the managed treatment areas.
The study also provides guidelines for optimal irrigation protocol and minimum water requirements for the use of
3. Conclusions and perspectives
It has been widely used in plant biotechnology to improve crop traits with CRISPR/Cas9 technology [75, 76] and it will be applied to crop breeding in the near future [77], especially for gene knock out in crops. To overcome complex and changing adversity, crop breeding must be multi-resistant because climate change leads to an increase in both biotic and abiotic stresses. The pursuit of homologous genes from extreme xerophytes plants, but with a low degree of identity to crop, will significantly increase drought tolerance.
Acknowledgments
This work was supported by grants from the Tianshan Youth Program (Grant No. 2019Q035), the National Natural Science Foundation of China (Grant No. 31700289), The third comprehensive scientific investigation in Xinjiang (2021xjkk0502), and the West Light Talents Cultivation Program of Chinese Academy of Sciences (2016-QNXZ-B-20). We are grateful to Xin Wei and Professor Jianhui Xu from the Research Institute of Economic Crops, Xinjiang Academy of Agricultural Sciences, China, for their support in the fieldwork. We thank Fanjiang Zeng and Xiangyi Li from Cele National Station of Observation and Research for Desert-Grassland Ecosystem, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences for providing experimental conditions. We are also thankful to the anonymous reviewers who put a great deal of effort into reviewing and editing this chapter.
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