Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\\n\\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\\n\\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\\n\\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\\n\\n
Thank you all for being part of the journey. 5,000 times thank you!
\\n\\n
Now with 5,000 titles available Open Access, which one will you read next?
Preparation of Space Experiments edited by international leading expert Dr. Vladimir Pletser, Director of Space Training Operations at Blue Abyss is the 5,000th Open Access book published by IntechOpen and our milestone publication!
\n\n
"This book presents some of the current trends in space microgravity research. The eleven chapters introduce various facets of space research in physical sciences, human physiology and technology developed using the microgravity environment not only to improve our fundamental understanding in these domains but also to adapt this new knowledge for application on earth." says the editor. Listen what else Dr. Pletser has to say...
\n\n\n\n
Dr. Pletser’s experience includes 30 years of working with the European Space Agency as a Senior Physicist/Engineer and coordinating their parabolic flight campaigns, and he is the Guinness World Record holder for the most number of aircraft flown (12) in parabolas, personally logging more than 7,300 parabolas.
\n\n
Seeing the 5,000th book published makes us at the same time proud, happy, humble, and grateful. This is a great opportunity to stop and celebrate what we have done so far, but is also an opportunity to engage even more, grow, and succeed. It wouldn't be possible to get here without the synergy of team members’ hard work and authors and editors who devote time and their expertise into Open Access book publishing with us.
\n\n
Over these years, we have gone from pioneering the scientific Open Access book publishing field to being the world’s largest Open Access book publisher. Nonetheless, our vision has remained the same: to meet the challenges of making relevant knowledge available to the worldwide community under the Open Access model.
\n\n
We are excited about the present, and we look forward to sharing many more successes in the future.
\n\n
Thank you all for being part of the journey. 5,000 times thank you!
\n\n
Now with 5,000 titles available Open Access, which one will you read next?
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"690",leadTitle:null,fullTitle:"Gamma Knife Radiosurgery",title:"Gamma Knife Radiosurgery",subtitle:null,reviewType:"peer-reviewed",abstract:"Gamma knife radiosurgery is a minimally-invasive treatment alternative for intracranial disorders, including tumors, vascular malformations, facial pain and epilepsy. 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\n
1. Introduction
\n
\n
1.1. Amount and distribution of precipitation required for sugar beet development
\n
Required amount of precipitation for successful production of sugar beet is 600 mm per year [1]. Furthermore, during the winter period, sugar beet requires around 230 mm and during the period of vegetation (from April to October) approximately 370 mm of precipitate. However, based on perennial average yield data, sugar beet production may achieve high outcome even in the presence of lower (500 mm per year) or higher (1000 mm per year) amount of rain. Water requirement of plant, during the period of vegetation, depends on precipitation. The water loss due to evaporation is most intensive from June to August when the temperatures are high and the air is dry. The average potential evapotranspiration (ET) for period of 30 years in case of sugar beet is 576 mm, but it may vary between 528 and 625 mm due to weather conditions. Approximately 10–20% of total water requirement of sugar beet is fulfilled from the soil water reserves and the rest is obtained by precipitation and irrigation. The amount of water lost by transpiration is 392 mm in average, and it varies from 198 mm during dry years to 542 mm during rainy years. The average precipitation during vegetation (April–September) is 359 mm and it varies from 138 to 521 mm in certain years [2].
\n
Having in mind the above-mentioned facts, amount and distribution of precipitation, in combination with the light and amount of heat, mostly determine quality and yield of sugar beet. On the territory of Serbia, it is common that the lack of soil water, typical for summer months, sometimes occurs during moderately rained years. Lack of soil moisture outcomes 100–200 mm per year, but rarely exceeds 300 mm per year. Currently, less than 1% of irrigation-suitable agricultural land in Serbia is intensively irrigated.
\n
Climate of Serbia is continental or moderate continental. The most important sugar beet production area is Vojvodina region situated in the north of the country. Climate of Vojvodina is moderated continental, determined by the presence of Alps on western border of Pannonian basin, Carpathian Mountains, the Dinarides, and the Balkan Mountains [3]. Precipitation regime is continental, typical for Danube region, with precipitation maximum in summer (June) and minimum in winter. According to the Köppen classification [4], for the period of 1961–1990, dominant climate type in Vojvodina is Cfwbx″ [C = mild temperate climate; f = significant precipitation during all seasons; w = dry winters; b = warmest month averaging below 22°C (but with at least 4 months averaging above 10°C) and x″ = second precipitation maximum occurs in autumn] [5].
\n
Brief analysis of current and expected precipitation distribution during winter, spring, and sugar beet growing season in Vojvodina is made using the data from two meteorological stations located in southern and northwestern part of the Vojvodina region, Novi Sad (Rimski Sancevi) and Sombor, respectively. Climatological data for 1971–2000 refers to the climatological periods derived from the database of the Republic Hydrometeorological Service of Serbia (RHMSS). Future climatic conditions were obtained from the Eta Belgrade University (EBU)—Princeton Ocean Model (POM) model for the A1B scenario for 2001–2030 and A2 for 2071–2100 integration periods [6]. Obtained results lead to expected shift in climate types from Cfwbx to “Cfwax” in the prevailing part of the country indicating temperature of the warmest month above 22°C (letter a in the Köppen formula) [5].
\n
Overview of the average precipitation for the selected past climatological period indicates that during winter time of 1971–2000 reference period, the amount of precipitation is twice less than optimum ones, while growing period of precipitation was slightly below optimal values.
\n
According to climate model simulations for 2001–2030 integration periods, expected average annual precipitation during first decades of the twentieth century, at selected locations, will not vary significantly in relation to the 1971–2000 precipitation records (Table 1). Inspection of the precipitation amounts for 2001–2014 period (Table 2) witnesses in favor to this expectation, with 699.8 mm in Novi Sad and 668.0 mm in Sombor in comparison with the expected 2001–2030 average (Table 1). However, it is important to notice significant variability of precipitation in this period which is in accordance with climate simulations for 2001–2030 [3]. In regards to winter and spring precipitations, for all integration periods, climate model simulates slightly higher average precipitation in comparison to reference climatology. Less optimistic is the expectation that growing period precipitation supposed to decrease towards end of the century, with particularly vulnerable summer period and increasing variability.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Climatol. period
\n
Annual
\n
Winter (DJF)
\n
Spring (MAM)
\n
Growing season
\n
\n
\n
Precipitation (mm)
\n
Precipitation (mm)
\n
Variability coeff. (%)
\n
Precipitation (mm)
\n
Variability coeff. (%)
\n
Precipitation (mm)
\n
Variability coeff. (%)
\n
\n\n\n
\n
\n
Novi Sad (Rimski Sancevi)
\n
\n
\n
1971–2000
\n
604.1
\n
108.1
\n
46.2
\n
146.6
\n
33.3
\n
359.2
\n
28.8
\n
\n
\n
2001–2030
\n
641.8
\n
131.3
\n
56.1
\n
159.7
\n
34.7
\n
369.2
\n
26.1
\n
\n
\n
2071–2100
\n
560.5
\n
131.0
\n
54.5
\n
147.4
\n
37.0
\n
282.2
\n
32.0
\n
\n
\n
\n
Sombor
\n
\n
\n
1971–2000
\n
580.3
\n
107.6
\n
41.9
\n
133.3
\n
29.6
\n
339.4
\n
22.7
\n
\n
\n
2001–2030
\n
629.4
\n
127.3
\n
56.7
\n
144.6
\n
37.2
\n
356.5
\n
29.0
\n
\n
\n
2071–2100
\n
565.8
\n
127.2
\n
53.6
\n
148.1
\n
41.3
\n
277.8
\n
31.9
\n
\n\n
Table 1.
Past (1971–2000) and future (2001–2030 and 2071–2100) climate precipitation data for Novi Sad and Sombor locations in Serbia.
\n
\n
\n
\n
\n\n
\n
Year
\n
Novi Sad (Rimski Sancevi)
\n
Sombor
\n
\n\n\n
\n
2000
\n
252
\n
231
\n
\n
\n
2001
\n
929
\n
749
\n
\n
\n
2002
\n
412
\n
448
\n
\n
\n
2003
\n
491
\n
434
\n
\n
\n
2004
\n
797
\n
816
\n
\n
\n
2005
\n
726
\n
753
\n
\n
\n
2006
\n
640
\n
585
\n
\n
\n
2007
\n
754
\n
683
\n
\n
\n
2008
\n
539
\n
598
\n
\n
\n
2009
\n
617
\n
615
\n
\n
\n
2010
\n
1035
\n
994
\n
\n
\n
2011
\n
382
\n
401
\n
\n
\n
2012
\n
480
\n
446
\n
\n
\n
2013
\n
723
\n
692
\n
\n
\n
2014
\n
989
\n
932
\n
\n
\n
Average
\n
679.8
\n
653.3
\n
\n\n
Table 2.
Annual amount of precipitation for 2001–2014 in Novi Sad (Rimski Sancevi) and Sombor locations.
\n
\n
\n
1.2. The impact of water deficiency on sugar beet production
\n
Water shortage during vegetation is a frequent and a significant issue in agricultural production. Possible solution to this problem is selection of genotypes, which do not show decreased yield under economically acceptable level, in the presence of water shortage. Great challenge in the process of genotype selection is to choose the convenient plant idiotype for the present agroecological conditions. Water deficiency has complex impact on plant physiology. First indicators of water deficiency in plants are the loss of turgor pressure and stomatal closure [7]. Photosynthesis is also highly dependable on the plant’s water supply. Many studies showed that disruption of water flow causes decrease in water content in assimilation tissue, which leads to photosynthetic depression [8]. Based on this, soil moisture, as well as relative air humidity, determines photosynthetic intensity. A decrease in chloroplast size, an increase in stomatal density, and disruption of thylakoid membrane structure were reported as consequences of water deficit [9]. Besides decrease in tissue water content, water shortage may cause synthesis of specific compounds in the roots, during the early growth phase. According to this, roots are very significant sensors of soil changes (not only in terms of water, but also texture changes), which alert the aboveground tissues by “chemical drought signals” which are transported to leaves. These signals mostly refer to plant hormones such as abscisic acid (ABA) [10].
\n
\n
\n
1.3. Sugar beet tolerance to water deficiency
\n
Adaptation of plant metabolism on stress conditions is species specific and was the subject of numerous studies [11]. Plants more tolerant to drought have longer root system with bigger absorptive area, better developed photosynthetic parenchyma, thicker cuticle, smaller leaf area (LA) and number of stomata per leaf area, and higher density of conductive elements [12]. They also possess highly expandable protoplasm, higher content of bound water and osmolytes, enhanced accumulation of ABA, free proline, and alanine. The following indicators point out to higher phenotypic tolerance of sugar beet to water shortage: more shiny leaves, higher turgor pressure of petiole, and more sensitive leaves to expansion [12]. Even though there is genotypic variability with respect to response to drought in sugar beet (i.e., [13]), structural and morphological mechanisms still remain unclear.
\n
Stress occurrence during early stages of growth and development may adversely affect sugar beet root growth, which may result in yield loss by 46% [14]. In addition, later stress occurrence may cause decreased leaf area and also number of leaves and by that, the efficiency in light usage becomes decreased [15]. Water deficiency significantly increases concentrations of potassium and sodium, which disturb sugar extraction from roots. Plant response to water stress can partially be explained by disorders in mineral nutrition. Water deficiency actually may retard or even stop ion assimilation, which results in perturbation in ion ratios in specific tissues. This trend is manifested through ion deficiency symptoms in plants. The adverse effect of water stress in later phenophases is less pronounced, since plants already developed root system and canopy which completely covers the soil. Well-developed root system increases efficiency in water extraction and usage, which results in higher tolerance to water deficiency [16]. However, first signs of water stress are usually seen on leaves. Minor drop in leaf water potential may cause significant decrease of total leaf area, and the low water potential enhances emergence of new leaves and accelerates senescence of old leaves. Drought stress results in stomatal closure, limits the transpiration which increases leaf temperature [15]. Both, lower stomatal density and heat stress decrease photosynthetic outcome [17]. Sugar beet leaves have higher number of smaller stomata on their abaxial side. Higher density and smaller size of stomata is a form of adaptation to drought, because it allows plants to be more efficient in regulation of water transport and transpiration [18]. Varieties more efficient in tolerating lack of water are proven to have decreased stomatal density (70–150 stomata/mm2) [19]. During drought, when negative turgor pressure in guard cells generates, small epidermal cells with tightened cell walls increase plant resistance towards water stress [20]. Response of sugar beet genotypes to drought may also be affected by percentage of adaxial and abaxial epidermis and palisade tissue thickness [21].
\n
\n
\n
1.4. Chemical response of sugar beet to drought stress
\n
Plants also osmotically adapt to drought [22]. Exposure to water deficiency results in accumulation of osmolytes, such as betaine, proline, and fructans. These substances often accumulate in the form of compatible solutes in plants (compounds which do not take part in chemical reactions in plants, but affect cell water potential), which generate expression of genes encoding relevant enzymes. Osmolyte production, as well as change in osmotic pressure, may increase sugar beet tolerance to abiotic stress. Proline and glycine betaine help in the preservation of cell [23], which makes them suitable for further investigation with purpose of increase stress tolerance of many species including sugar beet [24, 25]. They are not only involved in maintenance of cell turgor and osmotic balance but also in protection of cell structure from stress [26]. However, it still remains unclear whether the plants, which accumulate osmolytes, better tolerate lack of water or not [27].
\n
\n
\n
1.5. Proline accumulation
\n
Free proline is a key metabolite which accumulates in sugar beet exposed to drought [28]. Change of the free proline concentrations in tissues is an indicator of other kinds of stress, such as temperature, environmental pollution, and misbalanced nutrition. The same factors may affect glucose accumulation and yield. In some cases, stress conditions may increase sugar beet root quality and potential of recovery if plants were not highly damaged by water deficiency [29].
\n
Higher nitrogen supply increases proline content and may also increase leaf area index (LAI) and drought stress impact. Positive and significant correlation among proline and glucose content in sugar beet root indicates the relationship between the response to stress, carbohydrates, and proline and glucose accumulation ratio. This is supported by the effect of treatment with di-1-p-mentene (anti-transpirant) and DMDP (2,5-dihydroxymetil-3,4-dihydroxypyrolidine, glycosidase inhibitor), which decreased proline content in roots of irrigated sugar beet [29]. Presence of compounds such as proline and glucose adversely affect sugar crystallization and lead to the formation of colored components, thus reducing industrial quality of beet roots [30].
\n
Proline accumulated in sugar beet root, as a nitrogen compound, reduces the quality of roots. Both, the stress and an excess of nitrogen lead to the mobilization of accumulated carbohydrates, which are the source of energy essential for adaptation to the stress conditions. Moreover, chemicals containing nitrogen (e.g., proline) reduce the yield of sucrose and the quality of the roots [29]. The importance of the accumulation of proline in osmotic adjustment is still debatable and varies from species to species [31]. The highest proline accumulation was observed at the end of beet root growth [29]. Correlation between drought and proline content suggests, however, that alteration in proline concentration is useful stress indicator in sugar beet [28]. Proline may act as a signal molecule which alters mitochondrial function, affects cell division and gene expression. This role of proline may be very significant for plant recovery when favorable conditions are regained [32].
\n
\n
\n
1.6. The use of plant biotechnology to increase tolerance to water deficiency
\n
Basic need for sustainable food production directed research programs towards improving traits of crops despite the size and complexity of their genome [33]. Plant biotechnology is a process in which the use of molecular and cytological techniques help to increase the productivity of the plants, to improve the quality of plant products, to prevent the damage caused under the influence of various biotic and abiotic stresses. Plant breeding reliving on the employment of molecular markers [Marker Assisted Selection (MAS)] is one of the promising techniques to improve crop resilience. A prerequisite for the success of MAS is defining the genes which regulate traits of interest and to test relationships between potential markers and those traits. Only when this link is defined, i.e., when the marker is physically located in the vicinity of or even within the gene of interest, it is possible to use it efficiently in breeding [34].
\n
In sugar beet, development of breeding programs aimed to increase drought tolerance is further complicated by the fact that several types of abiotic stresses often occur at the same time during the growing season, and approach which involves a manipulation of a group of genes for tolerance to drought seems necessary to solve this complex problem [35].
\n
In an era of rapid progress in the identification and characterization of complete segments of plant genome, proteins, transcripts, metabolites, as well as their interactions in a biological system, new discoveries will lead to better understanding and possibly to manipulation of physiological responses to water deficit [36]. Evaluation of the relative contribution of genes conferring tolerance to the dehydration and elimination of those which do not affect the tolerance to stress is a major challenge.
\n
Although the yield is the basic goal of the breeders, it is very difficult to accurately predict the possibility of water utilization and identify candidate genes for further cloning [37]. Several studies have identified quantitative trait loci (QTLs) associated with a specific component of the response to drought. Although the development of molecular markers and genome sequencing should expedite positional cloning [38], genome areas associated with individual QTLs are still very large and usually not suitable for testing in the breeding program. With the rapid development of genomic technology and the suitable statistical methods, there is an increased interest in the use of mapping strategies for the identification of genes encoding quantitative traits which have agricultural or evolutionary significance [11]. Another major challenge is how to apply knowledge to improve crop tolerance to stress conditions. There is a problem between high yield and tolerance to stress since very often genotypes with higher stress tolerance have lower yield under optimal conditions. One of the strategies for sugar beet phenotyping was proposed by Ries et al. [39].
\n
On the cellular level plant adaptation to stress includes regulation of the beginning of protein synthesis (e.g., H+ pumps and Na+/H+ antiporter), an increase in antioxidant level, transient increase of the concentrations of ABA, the reduction of the energy consumption ways, as well as accumulation of the solution, and protective protein [40]. All of these changes at the cellular level are of great importance for the maintenance of homeostasis after ion imbalance caused by abiotic stress [26]. The deficit of water causes the synthesis and accumulation of ABA in plant cells and the genes corresponding to this has been defined. Most of these genes contain conserved cis-activating promoter elements, called Abre (ABA-responsive element, PyACGTGG/TC) [41]. Great progress to clarify the response of plants to abiotic stress has been made in the last decade [11].
\n
In order to achieve a combination of high yield and tolerance to stress in one variety, it is necessary to establish a connection of development of individual characteristics and mutual reactions, which can be achieved only through co-operation among molecular biologists, physiologists, and breeders [11, 42]. It is necessary to assess the relationship between different morphological, anatomical, physiological, and biochemical traits of sugar beet tissues in different phases of their growth and development during different periods of water shortage, in order to categorize genotypes with respect to their tolerance to drought which was in the focus of our previous [21] and present study.
\n
\n
\n
\n
2. Material and methods
\n
\n
2.1. Plant material
\n
The study involved 11 genotypes (marked from 1 to 11) of sugar beet (Beta vulgaris ssp. vulgaris, L.) differing in levels of drought tolerance, according to observation test conducted in the field. According to this test, genotypes were divided into three groups: (1) sensitive genotypes: 2, 5, 6, and 8; (2) moderately tolerant: 3, 7, 9, and 11; and (3) tolerant: 1, 4, and 10.
\n
Experiment was conducted in three stages:
Under semi-controlled conditions in greenhouse.
In in vitro conditions of tissue culture.
Gene expression analyses of water regime responsible genes in leaves (plants from the greenhouse experiment).
\n
\n
2.1.1. Experiment under semi-controlled conditions in greenhouse
\n
Sugar beet seeds were sown in growth substrate Potgrond H (Klasmann), mixed with river sand (17.5:1) in plastic pots (31 × 37 × 13 cm). A single pot contained 12 plants. During 90-day period, soil moisture was kept at 80% field capacity. Plant watering was conducted on the basis of evapotranspiration. When the plants were at the 6–12 leaves stage, they were exposed to water deficit by cessation of watering, while control plants were watered. Five days later, molecular and physiological analyses were done.
\n
After drying plant material on 105–130°C to its constant mass, % of dry matter was determined. Activity of photosynthetic apparatus was assessed by monitoring of F0(initial), Fm(maximal), Fv(variable), Fv/Fm, and t1/2 using plant stress meter (PSM, BioMonitor S.C.I. AB). Free proline concentration was measured in the both in vitro and in vivo conditions [43]. Concentration of chloroplast pigments was determined spectrophotometrically [44, 45]. Leaf area (LA) was measured by automatic leaf area meter LI-3000 (LI-COR, USA).
\n
\n
\n
2.1.2. Experiment in tissue culture
\n
In this experiment, MS basic substrate was used [46] with 0.3 mg/l BA (benzyldenine) and 0.01 mg/l GA3 (gibberellic acid). In order to obtain sufficient number of axillary shoots (64), equal in size, subcultivation was done every 3 weeks. Lack of water was caused by addition of polyethylene glycol to the substrate. Obtained shoots were set on a substrate for micropropagation with 0, 3, and 5% of polyethylene glycol (PEG 6000, Duchefa, Netherlands). Plants were cultivated on this substrate for 4 weeks and afterwards fresh weight of shoots, as well as dry matter and free proline content were determined. The temperature during the experiment in air conditioning chamber was 21–23°C, with a photoperiod of 16 h of light and 8 h of dark.
\n
\n
\n
2.1.3. Gene expression analyses of water regime responsible genes in leaves (plants grew in the first experiment)
\n
The changes in gene expression were analyzed in the leaves of the sugar beet plants grown in the greenhouse experiment. Candidate genes were selected from the previous studies [47, 48, 49, 50]. For 13 candidate genes which are, considered to be, involved in osmotic and salt stress responses, primers were constructed and used to screen for polymorphisms at the DNA and gene expression levels. Ten selected candidate genes were homologous probes (BI543470, BI096135, AW697770, BI543640, BG932913, BI096146, BQ060651, BF011094, BI096078, and BF011254), and heterologous probes from maize (X15290), alfalfa (BI543243), and carrot (BI073246). Samples for DNA/RNA analysis (leaves) were taken 5 days after the last watering (experiment 1) and used for DNA/mRNA extractions. mRNA was used to synthesize cDNA, and this cDNA was template in further PCR reactions [42].
\n
\n
\n
\n
2.2. Statistical analyses
\n
Statistical analyses of data were performed by different statistical methods. ANOVA was applied, to photosynthetic pigments (MCMCglmm Methods, [51]), using Package R (http://www.jstatsoft.org/v33/i02/). Logarithmic and Jonson’s transformations (Minitab) were performed for parameters with large data variability, in order to normalize their distribution. Confidence intervals for fitted mean responses were calculated as quantiles of simulated distributions of the expected response values. Analyses were done with the R environment [52] and the contributed packages lme4 [53] and ggplot2 [54].
\n
\n
\n
\n
3. Results and discussion
\n
As previously indicated (Tables 1 and 2), climatic conditions in our region suggest the need for research, which has the potential to enhance selection of genotypes more tolerant to drought.
\n
\n
3.1. Experiment under semi-controlled conditions in greenhouse
\n
\n
3.1.1. Sugar beet genotype classification based on physiological tests in semi-controlled conditions
\n
Sugar beet genotypes in semi-controlled conditions showed different reactions to 5-day water deficiency. As expected, decline in turgor was observed in all genotypes. Number of leaves was significantly different between treatments and respective controls. Concentrations of photosynthetic pigments and leaf area varied between genotypes and standard normal distribution was not observed here. Therefore, the data were subjected to Johnson’s data transformation which proved to be very effective [55]. This procedure allowed assessing differences in concentrations of photosynthetic pigments between different genotypes (Figure 1).
\n
Figure 1.
Genotype separation on the basis of pigment concentration and leaf area for variables normalized according to Johnson’s data transformation (jlarea—leaf area; jcar—carotenoids; jca—chlorophyll a; jcb—chlorophyll b; jcab—chlorophyll a + b).
\n
Secession in water supply caused water loss from plant tissues within both sensitive and tolerant genotypes. Due to this fact, sugar beet genotypes may be divided on the basis of tested parameters and following treatments (Figure 2).
\n
Figure 2.
The separation of the sugar beet genotypes based on experiments in greenhouse with a highlighting treatment (control, drought) for a variable normalized by Johnson’s transformation (jrwc—relative water content; jpcdw—dry weight; jdwproli—free proline; jpcdwleaf—leaf dry matter; jpcdwstem—stem dry matter; jpcdwroot—root dry matter).
\n
The results obtained in semi-controlled conditions (experiment 1) were compared to previous field observations (Figure 3). Proline concentration increased in all genotypes after exposure to water deficit as well as % of DM (except for genotypes 9 and 11). Changes within treatments with respect to control, referring to dry weight were less pronounced than changes referring to % of DM and RWC of root, stem, and leaf. Plants subjected to stress conditions had in average three leaves less, 4% higher % of DM, and seven times higher proline content.
\n
Figure 3.
Effects of drought stress on growth traits and proline production of greenhouse grown plants of sugar beet genotypes (1–11) from three classes of visually field-assessed drought tolerance (DT). Observed values of three replicates (circles, 10 plants each), average genotype positions (numbered gray lines), and class means with 95% confidence intervals (crossbars). The drought stress was induced by suspension of watering to test plots after 3 months of culture and observations were made after 5 more days [28].
\n
The relationships between two effects on measured traits were assessed by mixed model (Figure 4). Crossed pink lines in diagrams represent average impact on genotypes in control (x axis) and stress effect (y axis). There is a nearly perfect negative correlation between the unstressed value and the response to stress for root DM and a similar, but weaker one, for leaf number. Genotypes showing positive scores for the slope effect (y axis) are less affected than the average by (more tolerant to) water stress for the involved trait, and vice-versa. Genotypes showing positive scores for both effects are both higher scoring in absence of stress and less affected than the average by (more tolerant to) stress for the involved trait, and vice-versa.
\n
Figure 4.
Genotype effects from mixed model analyses for traits of greenhouse experiment (deviations for values in unstressed condition on the x axis, deviations for stress effects on the y axis. Genotypes are identified by the numbers).
\n
Conventionally, results of chlorophyll fluorescence indicate a high sensitivity to influence of ecological factors. Therefore, it is often used as an indicator of functioning of photosystem II.
\n
According to Fv/Fm, sugar beet genotypes were compared on the basis of photosynthetic characteristics. Water deficit did not cause significant variations in fluorescence indicators (Figure 5).
\n
Figure 5.
Maximal (Fm) and variable (Fv) chlorophyll fluorescence and their ratio (Fv/Fm) in sugar beet genotypes grouped according to their field-assessed drought tolerance (ctrl—control; drought; DT—drought tolerance).
\n
Effects of drought were observed in case of Fv and Fm, but not for Fv/Fm ratio, where the largest differences between genotypes were obtained. In addition, overlap of intervals of interaction between stress and genotype indicates stress, which caused differences, similar for all genotypes. The influence of water deficiency on fluorescence may be related to plant tolerance towards water deficit in field conditions (Figure 5).
\n
Plant development may be inhibited in different ways in field conditions. It may be affected by interactions among drought and other ecological stresses, precipitation, and temperature availability as well as interactions with different micro-organisms [36]. On the contrary, semi-controlled conditions may only eliminate interference of other factors with plant development. Therefore, it is necessary to compare results obtained in the greenhouse with those obtained in the field.
\n
\n
\n
\n
3.2. Experiment in tissue culture (in vitro)
\n
Increased PEG concentration decreased growth of axillary buds with respect to control (Figure 6).
\n
Figure 6.
PEG effect on growth traits and free proline concentration of plants cultivated in tissue culture [28].
\n
Number of axillary shoots may be indicator of the influence of different PEG concentrations, which cause water deficit, on micropropagation potential of genotypes. Average number of axillary shoots of 11 subjected genotypes showed 2.2 times decreased number of shoots in the presence of 3% PEG and 2.7 times in the presence of 5% of PEG.
\n
The degree of tolerance to drought observed in the field corresponded to tolerance recorded in the experiments performed in the greenhouse and in tissue culture (Figure 6). The most prominent criterion for estimation of genotype tolerance to drought was found to be concentration of free proline [28]. Proline concentration was significantly increased in leaves exposed to drought and axillary buds and it was positively correlated with PEG concentration, which is in accordance with the results of other researches [56].
\n
PEG treatment decreased total dry weight and number of axillary shoots by more than twice, while presence of 3% PEG in the substrate increased total fresh weight. Furthermore, PEG caused decrease in water content in tissues and decreased number of buds, but increased bud weight and % of DM. The highest values were recorded in control (0% PEG) for total fresh weight, in the presence of 3% PEG for proline concentration and fresh weight of axillary buds and in presence of 5% PEG for % of DM. Fresh weight of plants grown in presence of 3 and 5% PEG decreased (Figure 6). Average dry weight of the plants was the highest in the presence of 3% PEG. However, in the presence of 5% PEG, it was almost in line with the control. Higher variability in dry weight was recorded in the group of drought sensitive group (according to field observations), but the same trend as in the other two groups of genotypes remained. Tissue water content linearly decreased following the increase in PEG concentration, the average drop in presence of 5% PEG was 6%, and was followed by the low average difference among groups of different tolerance and higher difference among genotypes of one group (Figure 6).
\n
Proline accumulation under stress conditions increased under treatments in both experiments. In tissue culture, it was 6 times increased and in greenhouse 16 times with respect to corresponding controls.
\n
If taking into account the genotypes tolerance in the field, in relation to the parameters obtained from the analysis of plants in tissue culture and in experiment in the greenhouse, dry matter, in relation to the water content and the concentration of proline is not significantly different among groups of the tolerance (Figure 7).
\n
Figure 7.
Water deficit effect on dry weight, water content, and free proline concentration in greenhouse and in tissue culture experiment [28].
\n
Recorded differences between genotypes show that there are two approaches for the separation of sugar beet genotypes in relation to response to water stress, which cannot substitute each other. On one hand, proline content in plants grown in tissue culture enabled to match their grouping with respect to observations in the field. On the other hand, experiment in greenhouse was less efficient in that sense (Figure 3). The main cause of this may be the fact that stress in the field was not continuous as it was in the greenhouse.
\n
\n
\n
3.3. Analyses of changes in expression of genes involved in reactions to water stress (plants from greenhouse experiment)
\n
Changes in the expression of 13 candidate genes in 11 different sugar beet genotypes were followed in leaves of plants grown in the greenhouse. Expression pattern corresponding to BI543243 differed in plants exposed to drought in comparison with corresponding controls in genotypes 1, 10, and 11 (Figure 8). Therefore, it may serve to develop molecular marker useful to differentiate genotypes with respect to drought.
\n
Figure 8.
Expression pattern of gene corresponding to BI543243 in sugar beet leaves (c—template cDNA deriving from control plants; d—template cDNA deriving from plants exposed to drought). Amplification on genomic DNA served as additional control (g). M—100 bp DNA ladder size marker.
\n
\n
\n
\n
4. Conclusion
\n
Tolerance to drought is very complex. Experiments in three different environments (tissue culture, greenhouse, and field) with 11 genotypes, where many different parameters were followed, revealed that it is not easy to find single criteria for classification with respect to drought tolerance. However, the results suggest that free proline accumulation may be used as a reliable parameter. The classification based on changes in concentration of free proline in plants exposed to drought in greenhouse and tissue culture corresponded to classification made on the bases on field observations. Therefore, similar fast tests, conducted with young plants and possibly aided by the use of molecular markers, can be useful for estimation of breeding material with respect to tolerance to water deficiency, which will significantly enhance sugar beet breeding for expected future changes in climate.
\n
\n
Acknowledgments
\n
We thank European Union’s Horizon 2020 research and innovation program under Grant Agreement No. 691998 and Ministry of Education, Science and Technological Development of the Republic of Serbia for financial support.
Faculty of Agriculture, University of Novi Sad, Serbia
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Shashidhar, Adnan Kanbar, Mahmoud Toorchi, G.M.\nRaveendra, Pavan Kundur, H.S. Vimarsha, Rakhi Soman, Naveen G.\nKumar, Berhanu Dagnaw Bekele and P. Bhavani",authors:[{id:"157332",title:"Dr.",name:"Halagappa",middleName:null,surname:"Shashidhar",fullName:"Halagappa Shashidhar",slug:"halagappa-shashidhar"}]},{id:"44529",title:"Breeding to Improve Symbiotic Effectiveness of Legumes",slug:"breeding-to-improve-symbiotic-effectiveness-of-legumes",signatures:"Vladimir A. Zhukov, Oksana Y. Shtark, Alexey Y. Borisov and Igor A.\nTikhonovich",authors:[{id:"73360",title:"Dr.",name:"Alexey",middleName:"Y.",surname:"Borisov",fullName:"Alexey Borisov",slug:"alexey-borisov"},{id:"81134",title:"Dr.",name:"Vladimir",middleName:null,surname:"Zhukov",fullName:"Vladimir Zhukov",slug:"vladimir-zhukov"},{id:"81139",title:"Dr.",name:"Oksana",middleName:"Yurievna",surname:"Shtark",fullName:"Oksana Shtark",slug:"oksana-shtark"},{id:"81142",title:"Prof.",name:"Igor",middleName:null,surname:"Tikhonovich",fullName:"Igor Tikhonovich",slug:"igor-tikhonovich"}]},{id:"44504",title:"Opium Poppy: Genetic Upgradation Through Intervention of Plant Breeding Techniques",slug:"opium-poppy-genetic-upgradation-through-intervention-of-plant-breeding-techniques",signatures:"Brij Kishore Mishra, Anu Rastogi, Ameena Siddiqui, Mrinalini\nSrivastava, Nidhi Verma, Rawli Pandey, Naresh Chandra Sharma and\nSudhir Shukla",authors:[{id:"156649",title:"Dr.",name:"Sudhir",middleName:null,surname:"Shukla",fullName:"Sudhir Shukla",slug:"sudhir-shukla"}]},{id:"44016",title:"Castor Breeding",slug:"castor-breeding",signatures:"Máira Milani and Márcia Barreto de Medeiros Nóbrega",authors:[{id:"74313",title:"M.Sc.",name:"Maira",middleName:null,surname:"Milani",fullName:"Maira Milani",slug:"maira-milani"},{id:"156611",title:"Dr.",name:"Márcia",middleName:null,surname:"Barreto De Medeiros Nóbrega",fullName:"Márcia Barreto De Medeiros Nóbrega",slug:"marcia-barreto-de-medeiros-nobrega"}]},{id:"41024",title:"Participatory Plant Quality Breeding: An Ancient Art Revisited by Knowledge Sharing. 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1. Introduction
Over the past decades, the livestock industry has been revolutionized toward the use of microbial feed additives due to an increasing awareness of the stockholders on the beneficial role of probiotics in production and gut health status [1, 2]. There are several probiotic products that are commercially available and marketed for animal use [3]. Most probiotic products at the moment do not go through pre-market approvals and are commonly used for a much wider range of scenarios in which their efficacy is not well established. Similarly, latest molecular methods such as gene sequencing and phylogenetic analysis are not used to identify the probiotic strains as feed supplements. For the selection of best probiotic product, it is highly important to determine the real probiotic potential of the microbial strain by using latest molecular methods. In this contract, locally isolated and validated probiotic strains will be better than any unauthorized local available strain. The competitive advantage and adaptability to local microbial ecosystem will allow local probiotic strain to grow and adhere well in the local animal breed. Literature showed that probiotic strains should specifically prepare according to purpose and function related to the milk enhancement in local breed [4, 5]. Nowadays, it is highly accepted that probiotic yeast is highly productive in terms of milk and meat for large animals [6, 7]. Probiotic yeast improves the ruminal gut microbiota which may increase the nutrient digestibility and leads to improve animal productivity [8]. In large animals, ingested feed digested by numerous microbial species is present along the gastrointestinal tract [9]. This microbial community consists of 1014 members, mainly composed of fibrolytic bacterial species [10]. Literature highlighted that gut microbiota plays important role in the feed digestion and utilization. The gut microbial populations in cow have been identified in almost 90% of the total microbial community [11]. On the other hand, a certain fraction of the GI tract bacterial community has yet to be identified due to less knowledge of the microbial community in gut microbial ecosystem because majority of the 16S rRNA gene sequences from feces are taken from unidentified species, and many modern methods of genomic analysis of communities to determine changes in microbiota have been used by many scientists [12]. Studies have utilized culture-independent sequencing techniques, 16S rDNA bacterial tag-encoded FLX amplicon pyrosequencing and many more have added a new era to determine the microbial diversity of the GI tract [13]. Research noted that the culture-independent methods deliver a comprehensive assessment of the microbial community composition, while the culture-dependent methods provide the structural and functional diversity of the microbial strains [14]. In this chapter, a detailed discussion on the effects of probiotic yeast in ruminant’s well being, production performance, uses of different omics methodologies for the discovery of ideal animal probiotic strains and development of indigenous probiotic yeast for ruminant will be employed.
2. Yeast: an ideal microbial feed supplement for ruminants
The Saccharomyces cerevisiae (baker’s yeast) is the first eukaryotic sequenced genome. The sequencing of first whole eukaryotic genome was a challenging task for the scientists, but the efforts of more than 600 scientists from Europe, North America, and Japan made it possible. The entire sequence of the yeast was released in 1996. The size of the baker’s yeast genome is 12.1 Mb containing 16 chromosomes and 5400 coding genes approximately. The sequence information of yeast is available at Saccharomyces Genome Database (SGD), Yeast Protein Database (YPD), and Munich Information Center for Protein Sequences (MIPS) [15] (Table 1).
Yeast genome
Genome size
12.1 Mb
Chromosomes
16
Genes
5300–5400
Base pairs
12 million base pairs
Databases
SGD, MIPS, YPD
Table 1.
Details of first eukaryotic sequenced genome (yeast).
Ruminant nutritionists have been pondering to improvise new methodologies for ameliorating the roles of microflora in ruminants and enhance processes of digestion and fermentation along with augmented nutrients usage and bioavailability using feed supplementation. One of the commonly used methods was the use of growth promoters (antibiotics) to restrict the pathogenic effect on productivity of ruminants [16]. Nevertheless, antibiotics have been reported to cause serious health challenges to consumers and environmental implications. Thus, their usage has been banned in 2006 due to emerging antibiotic resistance. In the light of these concerns, consumer preferred more natural product. A super alternate of feed additives was the use of probiotics [17]. Probiotics are living microorganisms confined in animal feed that affect the host by improving the digestion [18]. Other definition includes probiotics as microorganisms (viable) that functions in gaining weight and feed conversions along with reducing diarrheal incidence [19]. Probiotics have been deployed as one of the recent exploited proposals in ensuring efficiency of production systems and safety to both consumers and environment [20, 21]. In ruminant nutrition, yeast probiotics are commonly being used because of their efficient roles in rumen stabilization and maintaining microbial communities specifically fibrolytic bacteria [22]. The yeast cells function in maintaining throughout viability of the digestive tract [23]. Yeast supplementation as probiotics enhanced feed conversion, efficient fermentation, and fiber digestion in the rumen, maintained ruminal pH, increased milk production [24, 25] and feed intake and production of organic acids and vitamins to activate the growth of the lactic acid bacteria (LAB) [26]. The commonly used yeast probiotic is Saccharomyces cerevisiae. Numerous literatures on Saccharomyces cerevisiae as supplement are available that dated back to the 1950s and continued under study till today [27]. Significant role of yeast supplementation (live) in diet has been stated for lactating and growing ruminants. Recent studies confirmed that they increase the ruminant’s milk production early lactation period by altering the fermentation of food inside the GIT of ruminants[28]. Latest beef and dairy production systems demand active muscle growth and high milk yield via feeding animal at high ruminal ferment ability rates. This would result in increased risk of metabolic disorders such as acidosis due to dysbiosis in ruminal microbial environment resulting in abnormal functioning in rumen which further leads to poor feed intake, health, and decreased productivity [29]. Therefore, yeast supplementation in ruminant diet is beneficial in the ruminal functioning and overall animal health and maintenance. The ameliorating functions of yeast probiotic on digestibility of high forage diets also underscore the potential use of yeast supplementation to optimize the use of lower quality feeds.
3. Understanding of the ruminant microbial community for development of ideal probiotic yeast for ruminants
Rumen microbial manipulation by using the probiotics to improve the ruminant feed digestion is a promising production improvement strategy. A better understanding of the rumen microbiology is an important step to select and prepare a new yeast strain affecting on functional specific microbes. Latest molecular techniques have provided the opportunity to study the rumen microbiota in detail for development of the ideal probiotic.
3.1 Digestive system of ruminants
Digestive system of ruminant is composed of four parts: reticulum, rumen, omasum and abomasums. The rumen is that part of the digestive system in which fermentation is carried out [30]. The rumen can also be defined as a complex ecosystem in which nutrients consumed by different microorganisms are digested anaerobically. Microbial biomass and volatile fatty acids are most common end products of fermentation which are then used by ruminant host. Interaction of host animal and microorganisms is a symbiotic relationship that helps the ruminant hosts in digestion of fiber-rich and protein-low diets. Rumen microorganisms provide enzymes that are necessary for fermentation processes, which in turn allow ruminants to obtain energy contained in forage [31]. Growth and activity of ruminal microorganisms are influenced by different factors including pH, temperature, osmotic pressure, buffering capacity, and redox potential. These factors are determined by environmental factors. Temperature of the rumen is in the range of 39–39.5°C. But when animal eats, fermentation occurs that generates heat due to which temperature increases up to the limit of 41°C [32, 33]. Short-chain fatty acid generation along with their absorption, saliva production, feed intake level and type, as well as exchange of phosphates and bicarbonates through epithelium of the rumen are the factors that affect pH [34]. In the reticule ruminal environment, these factors determine the buffering capacity as well as pH. There is a constant change in pH but mostly it remains in the range of 5.5–7.0 [35]. When there is an acidic environment in the cell, bacterial intracellular pH decreases. Microbial enzymes are very much sensitive to pH, i.e., bacterial growth is inhibited when there is an acidic pH. This is due to the disproportion of intracellular hydrogen ions [36]. In the rumen, ions and molecules affect osmotic pressure due to which gas tension is created. Fermentation process in the rumen depends upon the environmental factors and the diet due to which these factors also affect rumen osmotic pressure [37] (Figure 1).
Figure 1.
Rumen ecosystem: different types of microbial flora present inside the rumen. The most abundant microbes are bacteria.
3.2 Microbial community of GIT
Bacteria are more in number than any other microbes. It is noted that there are five groups of rumen bacteria: (1) free-living in liquid phase, (2) loosely attached with feed, (3) firmly attached with feed, (4) attached with rumen epithelial lining, and (5) attached with protozoa/fungi. The bacterial species inside the rumen are 99.5% obligatory anaerobic. Mostly rumen bacteria are involved in the fermentation of fibers, starch, and sugar present in the feed and converted into volatile fatty acid, H2, and CO2 [38]. Most of the bacteria are responsible for degradation of different types of dietary components [39] (Table 2).
Bacterial diversity of the rumen microbial ecosystem.
Majority of anaerobic rumen fungi is from order Neocallimastigales within the phylum Neocallimastigomycota. On the phylogeny basis, six genera have been identified, which are Piromyces, Neocallimastix, Caecomyces, Anaeromyces, Orpinomyces, and Cyllamyces [40]. In fiber digestion, fungi play a very important role because of the vegetative thallic rhizoids. The main functions of the rumen fungi are the lignin and fiber degradation by producing different types of enzymes [41] (Table 3).
Bacteria, fungi, and archaea present inside the rumen and feces of dairy cows.
3.3 Mechanism of action of probiotic yeast in the rumen
The rumen is the first part of the ruminant stomach which has a well-developed microbial ecosystem containing different types of microbes (bacteria, fungi, protozoa, and bacteriophages). These microbes coexist in ecological equilibrium in unique symbiotic relationship between cows and rumen microbes. The cows supply food to the rumen microbes which in turn digest the feedstuff to provide cows the essential nutrients in the form of microbial protein as organic acid energy sources. The microscopic view of rumen ecosystem showed that it is consisted of a number of bacteria, protozoa and fungi [42]. Bacteria make the largest population in this diverse microbial world. Their function is to digest the fibers, starch, sugar acids, and protein to give useful compounds and elements necessary for the growth and productivity of the cows. The role of protozoa and fungi is less clear. However, these microbes do provide help in digestion of feed. The structure and function of microbial community are influenced by feed composition and mainly by the host genetic potential. Prevotella and Succinivibrionaceae are the dominated rumen bacterial communities, cellulolytic and fibrolytic genera; Neocallimastigaceae are the dominant fecal and rumen fungal communities; and Methanobrevibacter are the dominant fecal and rumen archaeal communities in the adult ruminants. Bacteroidetes and Firmicutes are the dominant phyla of bacterial communities. Bacteroidaceae, Lachnospiraceae, Prevotellaceae, Ruminococcaceae, Succinivibrionaceae, and Veillonellaceae are the most abundant bacterial families in adult ruminant [43]. The term “yeast” is originally derived from the Dutch word gist, which basically refers to the foam that formed during beer fermentation. A variety of roles is played by yeast in veterinary practices, livestock feeding, and medicine as well as in biomedical and pharmaceutical industries [44]. Hayduck first discovered the inhibitory activity of yeast. Probiotics such as yeast or fungi have been extensively used in ruminant feed for the improvement of growth, health, and lactation due to their impact on rumen pH, intake of dry matter, and digestibility of nutrients [45]. Probiotic yeast has potential beneficial effects on the rumen. In the cattle, the ability of live yeast for enhancement of milk yield as well as weight gain is due to the fact that yeast is responsible for stimulating bacterial activity in the rumen [46]. Mechanism of action of yeast mainly stimulates the growth of cellulatic and hemicellulatic bacteria [47]. Increase in the number of bacteria in the rumen is due to the reproducible effects of probiotic yeast. Yeasts remove oxygen from the rumen due to which bacterial performance improves in the rumen. To maintain the metabolic activity, yeast cells consume available oxygen on the surface of freshly ingested feed in the rumen. Few studies showed that there is a significant decrease in redox potential, up to -20 mV by providing yeast supplementation (Figure 2).
Figure 2.
Representative scheme of effect of live yeast on the microbial flora of the gastrointestinal tract in ruminants: live yeast improves carbohydrate, protein, and lipid digestion rates by improving the production of cellulolytic, hemi-cellulolytic, and proteolytic and lipolytic bacteria and fungi.
Better conditions have been created by this change for the growth of anaerobic cellulolytic bacteria which in turn stimulates their attachment to forage particles as well as increases the initial rate of cellulolysis. Recalcitrant plant lignocellulosic material is not degraded by ruminants on its own. They rely on rumen microbial flora for its degradation [48]. The main components of the fiber are cellulose, hemicellulose, and lignin. It has been estimated that 20–70% of the ruminant feed is composed of the cellulose and hemicellulose [49]. The most abundant carbohydrate in plant cell wall is the cellulose which makes up to 40% of the plant cell wall. The microbial cellulolytic enzymes have the capability to digest the β-1,4 links present inside the cellulose, glucose molecules [50] (Figure 3).
Figure 3.
A scheme describing the mode of action of yeast culture: improved the gut microbial balance is related to the O2 slavering by live yeast cells.
3.4 Mechanism of action of probiotic yeast in the lower gut
The lower gut microbial population is affected by dietary supplementation of the probiotic yeast. The probiotics provide a desirable microbial balance due to shift in the balance of friendly and pathogenic microbiota. The GIT having healthy microbial populations are often related with improved host performance and its immune system. In the lower gut, the pathogenic microbial species reduces due to the production of the antimicrobial material (bacteriocin) and the attachment of the friendly microbes to the gut wall, via the competitive exclusive method. The most common modulation of the GIT microflora is provided by probiotics [51].
4. Modern methods to understand and develop fibrolytic probiotics for ruminants
Latest researches have improved our understanding related to the mode of action of probiotic yeast inside the rumen. Well-designed animal studies have verified that target-specific probiotic strains have health and production benefits in the ruminants. These studies have made the livestock industry to accept and understand the probiotic concept [52]. On the other hand, current probiotic has not been chosen for definite purposes in the animal feed. Therefore, some unique molecular methods are needed for selection and characterization of target-specific probiotic strains [53]. It has been noted that during stress conditions, some portion of the live probiotic microbial strain enters in the dormant but metabolically active state called viable but nonculturable (VBNC) state. These microbial cells have an ability to replicate when acclimated to a favorable condition inside the host [54]. Uses of molecular techniques have changed the study of the rumen ecosystem. First is the PCR which is more sensitive than growth on traditional selective media in determining small differences in population sizes in response to dietary changes or upon the inclusion of an additive to the diet and thus may identify changes or shifts within levels of the microbial population which may have been previously overlooked [55] (Figure 4).
Figure 4.
Probiotic preparation: general steps for the isolation and characterization of probiotic yeast strains for local animal breed.
In response to various feeding sources, changes within the microbial population can be studied by DNA fingerprinting (DGGE, TTGE, and TGGE). Probiotic can be classified into three different types, like mono-probiotic, poly probiotics, and combined probiotics depending on the probiotic strain function [55] (Figure 5).
Figure 5.
Potential characteristics of typical animal probiotic yeast.
5. Common methods used to identify indigenous probiotic yeast
Yeasts and fungi are the ideal organisms and have been used in vast genetic studies and comparative genomic studies in eukaryotes because of their small and compact genomes.
We have sketched sampling approaches and finalized the protocols that will guide researchers in identifying the most ideal probiotics for animal use. Livestock is under increasing threat of antimicrobial resistance genes; therefore, continued optimization of protocols is urgently needed so that these threats can be reduced through the use of probiotics. Two sequence-based methods are commonly used for the identification of yeast. The first and the most common method used for the identification is PCR amplification of internal transcribed spacer (ITS) of nuclear ribosomal variable region that has been recognized as the universal barcode for the identification of fungi. The second and the advanced approach to identify fungal species or strains is shotgun metagenomics [56]. Microbes are very vital to life present on the earth. Their significance is increasing day by day as their beneficiary potential has been recognized in the field of health and medicine. There are two methods which have been utilized till now for the identification of the microorganisms present in microbial community.
Culture-dependent method
Culture-independent method
Both approaches have their own significance. Culture-based methods are considered effective for the morphological, physiological, and functional characterizations of a particular strain, while culture-independent technology is preferred to unravel the microbial diversity along with genomic and genetic identification of microbial communities. Studies have also indicated that there is a loss of 99% microbes in the laboratory-dependent culturing methods. Culturing-independent method has been recognized as an effective and efficient method to isolate the DNA of a number of microbes from an environmental sample which seems impossible using the cultural methods. The linkage of culture-dependent and culture-independent data has been recognized as a crucial step for the identification of probiotics [57]. For identification of the potential probiotic strains, researchers should use the latest molecular methods, and the probiotic strains should be deposited in some recognized microbial culture collection. Proteomics and metabolomics may also be used for choosing the best yeast species [58]. By utilizing strain’s proteome and metabolome, which are argued to yield a positive influence upon ruminal fermentation, it may be possible to identify specific traits, characteristics, and secondary growth metabolites that play a potential role to enhance the growth of target-specific microorganisms inside the rumen. Even accounting for the potential bias of latest molecular methods, it is obvious that these methods are the dominant tools recently accessible for monitoring the gut for bacterial diversity of dairy animals and developing new yeast strain [59]. Extensive use of molecular methodologies may give insights into the new era where such microbial studies are no longer limited to a handful of laboratories with an abundance of funding and labor. It is noted that the specific yeast strains of known origin act more precisely and efficiently as compared to the yeast strain obtained from any unknown origin [60]. As we note all ruminates live in different parts of the world; therefore, upon the ruminal fermentation different yeast strains may exhibit markedly different effects. Therefore, we should identify new yeast strains for getting best results on the rumen fermentation. Uses of molecular techniques have changed the study of the rumen ecosystem. First is the PCR which is more sensitive than growth on traditional selective media in determining small differences in population sizes in response to dietary changes or upon the inclusion of an additive to the diet and thus may identify changes or shifts within levels of the microbial population which may have been previously overlooked. In response to various feeding sources, changes within the microbial population can be studied by DNA fingerprinting (DGGE, TTGE, and TGGE). To select best yeast strains, proteomics and metabolomics may also be used. By characterizing the proteome and metabolome of microbial isolates endowed with the ability to have a positive impact on the rumen fermentation, it may be possible to identify specific traits, characteristics, and secondary growth metabolites which play genuine role in the improvement of the growth of some important microbial species [61] (Figure 6).
Figure 6.
Interlinked factors involved in the application of probiotic in the ruminant nutrition.
5.1 Culture-dependent techniques
Cultural approach is the widely used method in microbiology to grow a microbe in a laboratory. Sampling is the basic and the crucial step for the identification of the indigenous probiotic yeast. The second step is isolation of the pure yeast strain under laboratory conditions which requires a series of inoculation steps of the microbes on the selective media. After purification of the yeast isolate on the OGA media, the biochemical tests are performed to identify the distinct features of the pure isolates. Morphological features of the isolate are determined by using electron microscope. The next step is the molecular identification of the yeast via 18S rRNA gene sequencing. The probiotic characterization is usually performed according to the standards defined by the WHO [62]. The best probiotic strain is retrieved among all the selected potential candidates, and in vivo experiments are performed using an animal model. After functional testing, all technological and safety measures are accessed, and the probiotic yeast strain is ready for probiotic product and packaging [63].
5.2 Culture-independent techniques
The use of omics approach has been emphasized to study the microbiome of microbes. To identify the potential probiotic strains among the microbial community present in any environment, it is very important to identify all the microorganisms in microbiota and determine their structural and functional differences at genomic level. Below are the currently available omics approaches for the identification, screening, and selection of probiotic strains of indigenous yeast [64] (Figure 7).
Figure 7.
Omics approaches to identify the probiotic.
5.2.1 18S amplicon sequencing
Amplicon sequencing refers to the sequencing of a specific fragment of interest of a microbe using high-throughput sequencing technique. 18S amplicon sequencing is specifically used to determine the most prevalent fungal yeast species present in microbiota [65]. The methodologies used in the recent researches for the identification of bacterial probiotics can be applied in the recognition of indigenous probiotic yeast strains. The comparative and detailed analysis of 18S amplicon sequencing data can help the scientists in the isolation of potential probiotic after the identification of functional and structural characteristics of the indigenous yeast in microbiota. Further experiments and testing would be required to maximize the production and ability of probiotic yeast in the gut of an animal [66]. Furthermore, the 18S amplicon sequencing does not only help in the indigenous yeast identification, but it also reveals the diversity of microeukaryotes when 18S rRNA gene is sequenced [67].
5.2.2 Shotgun metagenomics
Shotgun metagenomics is one of the most advanced techniques of sequencing in which the entire microbiome of microbiota is sequenced. The data generated using this method provides all the information about the genome of an organism [68]. Metagenomics information unravels the composition of microbial community and also indicates the genes, their functions, and associated genetic pathways. The identification of the indigenous yeast and their probiotic potential and capabilities can also be determined using the metagenomics data. Their relationship within the microbial community and their effect on the host can also be studied on the basis of the retrieved information [69].
5.2.3 Metatranscriptomics
Scientists and researchers are using metatrancriptomics to study and analyze the expression profiles of mRNA in a microbial community. The identification of genes, genetic pathways and their regulation, host-microbe interaction, and the symbiotic relation among microbes can easily be determined by using the mRNA expression data. Metatranscriptome approach can be pursued in the identification of indigenous probiotic yeast within the microbiota of an animal. For this purpose the sampling methods and molecular techniques should be improved [70].
5.2.4 Metabolomics
Metabolomics refers to the study of the metabolites or final cellular products. This is also considered one of the useful and efficient methods for the identification of probiotic potential of a microorganism within a microbiota of an animal or selected biological sample [71]. Indigenous probiotic potential of yeast can also be determined using this technique. Studies are still needed to fully understand the function of metabolites in context of probiotic potential and other inhibitory functions of metabolic compounds. As metabolites vary in structure and function, so they could be used in the comparative studies of species and populations. A number of species with high probiotic potential could be approached using metabolomics [72].
6. Challenges in preparation of suitable probiotic yeast
Yeast probiotics not only help to improve the performance factor of cattle, but it also enhances nutrient digestibility. However, the effectiveness of yeast-supplemented products is variable. Therefore, future studies are required to estimate the potency of these diet products as supplements for finishing beef cattle, with an objective to have healthier and productive animals without negotiating their efficiency and costs.
The animal body is a “supraorganism” and refers to the gastrointestinal tract as a virtual organ of the human body. The ongoing research is mainly on probiotics that are used chiefly for the GI tract, whereas there is an impetus need to evaluate the progress on other regions of the body as well.
Yeast supplementation is an effective strategy; thus, it is vital to ensure the stability and viability of yeast-supplemented diet products by developing practicable and cost-effective technologies (e.g., storage, microencapsulation, etc.), which poses marketing and technological challenges for producers at industrial level. Polysaccharides, lipids, and proteins are chiefly used for encapsulation materials in food industry. However, cost-effective production remains a challenge for production of future probiotics and formulation technologies.
Role of yeast probiotics in combating antibiotic-associated diseases has been extensively reported through control trials and ingestion of yeast probiotics (Saccharomyces boulardii) and has positive therapeutic effects specifically in preventing antibiotic-associated diarrhea (ADD), but validated biomarkers for numerous target diseases are probiotic or antibiotic deficient. Therefore, in the field of probiotic investigation, the defining of validated biomarkers needs to be advanced.
There is a dire need to understand the composition and relationship of microbial community within an animal gut for improving the production of dairy products. Advances in the high-throughput technologies, computational tools, and omics approaches give insights into the molecular and genetic potential of an organism. Studies in the omics arena are still needed to fully understand the genetic mechanisms and pathway analysis.
7. Conclusions and future research
Every living organism is different in terms of their genetic makeup. The current progresses in sequencing and functional omics techniques have delivered better understandings into the precise mechanisms underlying probiotic functionality. The emerging understanding of the animal gut microbiota allowed accurate characterization of probiotic effects on the commensal microbiota of animal in vivo. Identification of genes vital to probiotic functionality is providing scientists the capacity to genetically tailor probiotics to encounter the requirements for precise applications. The livestock sector has a larger proportion of land consumption than agriculture keeping in view both grain feed intake and grazing. This trend is expected to rise, putting pressure and competencies on land resources in the agriculture sector. Moreover, there is a high demand for quality production which Cannot be attained by traditional practices for feeding ruminants. Quality cereal feed costs high and is uneconomical for large production. Consequently, this creates an imbalance in nutrition which drastically reduces dairy production. Probiotic yeast can overcome dairy production disparity. It augments nutrient uptake and increases Immunity, overall better health and production. Utilization of probiotic yeast for health and production is influenced by many different factors including probiotic strains, age, and breed of cattle. Essentially, yeast probiotics enhance assimilation by balancing the microflora of the rumen. It facilitates fiber digestion via inducing fermentation and stabilizing high pH. Facilitating an environment that flourishes rumen microbes is one factor. Other avenues need to be explored for probiotic yeast. More probiotic yeast strains are needed to be identified. For the preparation of probiotic feed, a complete nutritional profile generation is required. Furthermore, the amino acid profile of milk produced by dairy heifers fed on yeast probiotic should be analyzed.
8. Recommendations
The recommendations are outlined as follows:
Sampling source should be indigenous for isolation of the probiotic strains.
The identification of the probiotic strains must be based on the international validated molecular methods.
The identified strain name should be deposited in validated microbial culture collection.
The probiotic as well as genetic properties of the probiotic strains should be studied. Good manufacturing practices must be applied with quality assurance and shelf-life conditions established and labeling made clear to include minimum dosage and verifiable health claims.
\n',keywords:"indigenous probiotic yeast, lower gut, microbiota, molecular methods",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/64889.pdf",chapterXML:"https://mts.intechopen.com/source/xml/64889.xml",downloadPdfUrl:"/chapter/pdf-download/64889",previewPdfUrl:"/chapter/pdf-preview/64889",totalDownloads:754,totalViews:46,totalCrossrefCites:0,dateSubmitted:"September 22nd 2018",dateReviewed:"November 27th 2018",datePrePublished:"May 20th 2019",datePublished:"August 7th 2019",dateFinished:null,readingETA:"0",abstract:"Probiotic yeast enhanced the ruminal gut microbial balance by producing intercellular effectors and important metabolites. The impact of yeast addition on animal health is influenced by different interlinked factors including animal genomics, its gut microbiota, and environment. Therefore, all factors should be considered regarding achieving the maximum outputs from animal probiotic yeast. In the situation of a high feeding cost, microbial feed supplements provide a suitable nutritional approach, which allows increased nutrient digestion rate and accordingly improves animal performance. Many yeast products are commercially available, but their efficiency as probiotic dietary addition in a particular breed is mostly questionable. Therefore, identification of ideal probiotic yeast strain is of great interest in this context. Innovative methods in relation to develop new probiotic are mainly focused on the exploring novel microbial strains from indigenous sources. It has been noted that for the identification of best probiotic strain for the host, a linkage between culture-independent and culture-dependent methods is a functional step. In this chapter, we will discuss the mode of action of probiotic yeast on animal lower gut microbiota and identification of ideal probiotic yeast by using advanced molecular methods.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/64889",risUrl:"/chapter/ris/64889",signatures:"Shakira Ghazanfar, Aayesha Riaz, Ghulam Muhammad Ali, Saima Naveed, Irum Arif, Sidra Irshad, Naeem Riaz and Khanzadi Nazneen Manzoor",book:{id:"8107",title:"Yeasts in Biotechnology",subtitle:null,fullTitle:"Yeasts in Biotechnology",slug:"yeasts-in-biotechnology",publishedDate:"August 7th 2019",bookSignature:"Thalita Peixoto Basso",coverURL:"https://cdn.intechopen.com/books/images_new/8107.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"139174",title:"Ph.D.",name:"Thalita",middleName:null,surname:"Peixoto Basso",slug:"thalita-peixoto-basso",fullName:"Thalita Peixoto Basso"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"202370",title:"Dr.",name:"Shakira",middleName:null,surname:"Ghazanfar",fullName:"Shakira Ghazanfar",slug:"shakira-ghazanfar",email:"shakira_akmal@yahoo.com",position:null,institution:{name:"Quaid-i-Azam University",institutionURL:null,country:{name:"Pakistan"}}},{id:"286878",title:"Dr.",name:"Ghulam",middleName:null,surname:"Muhammad Ali",fullName:"Ghulam Muhammad Ali",slug:"ghulam-muhammad-ali",email:"drgmali5@gmail.com",position:null,institution:null},{id:"286879",title:"Ms.",name:"Irum",middleName:null,surname:"Arif",fullName:"Irum Arif",slug:"irum-arif",email:"irum_arif56@yahoo.com",position:null,institution:null},{id:"286880",title:"Ms.",name:"Sidra",middleName:null,surname:"Irshad",fullName:"Sidra Irshad",slug:"sidra-irshad",email:"sidrairshad29@ymail.com",position:null,institution:null},{id:"286881",title:"Ms.",name:"Khanzadi",middleName:null,surname:"Nazneen Manzoor",fullName:"Khanzadi Nazneen Manzoor",slug:"khanzadi-nazneen-manzoor",email:"knazneen02@gmail.com",position:null,institution:null},{id:"297912",title:"Dr.",name:"Naeem",middleName:null,surname:"Riaz",fullName:"Naeem Riaz",slug:"naeem-riaz",email:"naeem_nibge@yahoo.com",position:null,institution:null},{id:"304921",title:"Dr.",name:"Aayesha",middleName:null,surname:"Riaz",fullName:"Aayesha Riaz",slug:"aayesha-riaz",email:"aayeshariaz@uaar.edu.pk",position:null,institution:null},{id:"304923",title:"Dr.",name:"Saima",middleName:null,surname:"Naveed",fullName:"Saima Naveed",slug:"saima-naveed",email:"saimamahad@uvas.edu",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Yeast: an ideal microbial feed supplement for ruminants",level:"1"},{id:"sec_3",title:"3. Understanding of the ruminant microbial community for development of ideal probiotic yeast for ruminants",level:"1"},{id:"sec_3_2",title:"3.1 Digestive system of ruminants",level:"2"},{id:"sec_4_2",title:"3.2 Microbial community of GIT",level:"2"},{id:"sec_5_2",title:"3.3 Mechanism of action of probiotic yeast in the rumen",level:"2"},{id:"sec_6_2",title:"3.4 Mechanism of action of probiotic yeast in the lower gut",level:"2"},{id:"sec_8",title:"4. Modern methods to understand and develop fibrolytic probiotics for ruminants",level:"1"},{id:"sec_9",title:"5. Common methods used to identify indigenous probiotic yeast",level:"1"},{id:"sec_9_2",title:"5.1 Culture-dependent techniques",level:"2"},{id:"sec_10_2",title:"5.2 Culture-independent techniques",level:"2"},{id:"sec_10_3",title:"5.2.1 18S amplicon sequencing",level:"3"},{id:"sec_11_3",title:"5.2.2 Shotgun metagenomics",level:"3"},{id:"sec_12_3",title:"5.2.3 Metatranscriptomics",level:"3"},{id:"sec_13_3",title:"5.2.4 Metabolomics",level:"3"},{id:"sec_16",title:"6. Challenges in preparation of suitable probiotic yeast",level:"1"},{id:"sec_17",title:"7. Conclusions and future research",level:"1"},{id:"sec_18",title:"8. Recommendations",level:"1"}],chapterReferences:[{id:"B1",body:'Vohra A, Syal P, Madan A. Probiotic yeasts in livestock sector. Animal Feed Science and Technology. 2016;219:31-47'},{id:"B2",body:'McCann JC, Elolimy AA, Loor JJ. Rumen microbiome, probiotics, and fermentation additives. 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Using “omics” and integrated multi-omics approaches to guide probiotic selection to mitigate chytridiomycosis and other emerging infectious diseases. Frontiers in Microbiology. 2016;7:68'},{id:"B67",body:'Findley K et al. Topographic diversity of fungal and bacterial communities in human skin. Nature. 2013;498(7454):367'},{id:"B68",body:'Ghazanfar S et al. Metagenomics and its application in soil microbial community studies: Biotechnological prospects. Journal of Animal & Plant Sciences. 2010;6(2):611-622'},{id:"B69",body:'Lindahl BD et al. Fungal community analysis by high-throughput sequencing of amplified markers–A user\'s guide. New Phytologist. 2013;199(1):288-299'},{id:"B70",body:'Qin J et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464(7285):59'},{id:"B71",body:'Jung JY et al. Metatranscriptomic analysis of lactic acid bacterial gene expression during kimchi fermentation. 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National Institute of Genomics and Advance Biotechnology (NIGAB), NARC, Pakistan
National Institute of Genomics and Advance Biotechnology (NIGAB), NARC, Pakistan
'}],corrections:null},book:{id:"8107",title:"Yeasts in Biotechnology",subtitle:null,fullTitle:"Yeasts in Biotechnology",slug:"yeasts-in-biotechnology",publishedDate:"August 7th 2019",bookSignature:"Thalita Peixoto Basso",coverURL:"https://cdn.intechopen.com/books/images_new/8107.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"139174",title:"Ph.D.",name:"Thalita",middleName:null,surname:"Peixoto Basso",slug:"thalita-peixoto-basso",fullName:"Thalita Peixoto Basso"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},profile:{item:{id:"176472",title:"Prof.",name:"Rossana",middleName:null,surname:"Melo",email:"rossana.melo@ufjf.edu.br",fullName:"Rossana Melo",slug:"rossana-melo",position:null,biography:null,institutionString:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",totalCites:0,totalChapterViews:"0",outsideEditionCount:0,totalAuthoredChapters:"1",totalEditedBooks:"0",personalWebsiteURL:null,twitterURL:null,linkedinURL:null,institution:{name:"Universidade Federal de Juiz de Fora",institutionURL:null,country:{name:"Brazil"}}},booksEdited:[],chaptersAuthored:[{title:"TEM as an Important Tool to Study Aquatic Microorganisms and their Relationships with Ecological Processes",slug:"tem-as-an-important-tool-to-study-aquatic-microorganisms-and-their-relationships-with-ecological-pro",abstract:"Microorganisms are critically important for ecological processes in aquatic environments. Bacteria and viruses are key components of the microbial loop and are central for biogeochemical cycles in aquatic ecosystems. Our group has been using transmission electron microscopy (TEM) to study aquatic microorganisms in both natural tropical ecosystems and cultures. In this review, we highlight structural aspects of freshwater bacteria, based on TEM findings that have provided insights into the functional capabilities of these cells in aquatic tropical ecosystems. First, we focus on TEM applied to the study of the ultrastructural diversity and morphological alterations of bacteria in response to environmental stress. Second, we address the relationship between viruses and bacteria in freshwater ecosystems. Third, we demonstrate by TEM that outer membrane vesicles (OMVs), structures associated with cell secretion and cell communication, are released by aquatic bacteria into natural ecosystems and cultures. Thus, TEM has proven to be a powerful technique to study aquatic microorganisms, contributing to the understanding of ecological processes, including regulation of bacterial populations, during different environmental conditions.",signatures:"Thiago P. Silva, Juliana P. Gamalier and Rossana C.N. 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His expertize includes transmission and scanning electron microscopy and electron diffraction and analytical techniques of microstructure investigation and mechanical properties of light metals and alloys. He was a visiting research fellow at the University of Manitoba in Winnipeg, Canada, and at the INP Grenoble, France, and a visiting professor at the Technical University in Clausthal, Germany.\nHe has published more than 130 scientific papers in referred journals and about 50 conference papers and 2 book chapters. He graduated and obtained his PhD degree at the Charles University in Prague. Currently he is the head of the Department of Physics of Materials at the Faculty of Mathematics and Physics, Charles University in Prague. His current scientific interests include investigation of microstructure, mechanical and physical properties of ultrafine-grained materials processed by severe plastic deformation, and phase transformations in metastable Ti alloys. 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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
Wellcome Trust (Funding available only to Wellcome-funded researchers/grantees)
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