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Introduction, physical structure of the barley grain, chemical composition of barley grain, functional compounds of barley and their health benefits, the pathways for improving health components in barley grains, author contributions, conflict of interest.

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Barley: a potential cereal for producing healthy and functional foods

These authors contributed equally to the article.

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La Geng, Mengdi Li, Guoping Zhang, Lingzhen Ye, Barley: a potential cereal for producing healthy and functional foods, Food Quality and Safety , Volume 6, 2022, fyac012, https://doi.org/10.1093/fqsafe/fyac012

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Barley is the fourth largest cereal crop in the world. It is mainly used for feeding, beer production and food. Barley is receiving more attention from both agricultural and food scientists because of its special chemical composition and health benefits. In comparison with other cereal crops, including wheat, rice and maize, barley grains are rich in dietary fiber (such as β-glucan) and tocols, which are beneficial to human health. It is well proved that diets rich in those chemicals can provide protection against hypertension, cardiovascular disease, and diabetes. Barley has been widely recognized to have great potential as a healthy or functional food. In this review, we present information about studies on the physical structure of the barley grain and the distribution of its main chemical components, nutrient and functional composition of barley grain and their health benefits, and the approaches of improving and utilizing the nutrient and functional chemicals in barley grain. With the development of processing technologies, functional components in barley grains, especially β-glucan, can be efficiently extracted and concentrated. Moreover, nutrient and functional components in barley grains can be efficiently improved by precise breeding and agronomic approaches. The review highlights the great potential of barley used as healthy and functional foods, and may be instructive for better utilization of barley in food processing.

Barley is the fourth largest cereal crop in the world. Wild barley was collected and used by human ancestors as early as 10 000 years ago, and evolved into a cultivated crop around 7000 years ago ( Dai et al. , 2012 ; Haas et al. , 2019 ). In comparison with other cereal crops, such as wheat, rice, and maize, barley is characterized by higher barren, salt, and drought tolerance, allowing it to have wide environmental adaptability and distribution across the world. Barley is well known for its multiple uses, and at present is mainly used as feed and brewing material, although it is still a staple food for humans in some areas, including Tibet, China ( Sakellariou and Mylona, 2020 ). In the food industry, naked (hulless) barley is considered more valuable than hulled barley, as the absence of the hull increases the nutrient content (including starch, protein, and β-glucan) in barley grains ( Pejcz et al. , 2017 ; Sterna et al. , 2017 ). The highland barley planted on the Qinghai–Tibet Plateau, called ‘qingke’ locally, is unique in its growth habits and chemical components in grains, rich in nutrients such as bioactive carbohydrates, polyphenols, minerals, vitamins, phenols, flavonoids, and β-glucan ( Obadi et al. , 2021 ). Therefore, the highland barley attracts great attention as a potential healthy food. Moreover, with better understanding of the chemical composition in barley grains and their functions in human health, the use of barley as a health food has been intensively addressed. In 2006, the U.S. Food and Drug Administration gave the final approval of a health claim for barley, based on research demonstrating that regular consumption of barley could prevent or control cardiovascular disease by lowering blood cholesterol ( FDA, 2006 ). The healthy functions of barley are mainly attributed to its higher contents of dietary fiber (such as β-glucan) and tocols. Barley is characterized by its high grain β-glucan content, almost 10-fold higher than wheat on average ( Geng et al. , 2021 ). Indeed, it is well documented that diets rich in β-glucan can improve immunity of human bodies, providing protection against hypertension, stroke, cardiovascular disease, and type 2 diabetes ( Maheshwari et al. , 2019 ; Tosh and Bordenave, 2020 ).

Currently, the challenges issued to barley and other cereal crop researchers are to develop varieties with high content of these healthy chemical components or produce crops suitable for utilization of healthy foods, and to food scientists to produce barley food with high nutrition which is commonly accepted in markets. The improvement of barley quality is helpful for producing better healthy food, while understanding of the chemical and functional components as well as their genetic regulation in barley grains and their health benefits is fundamental to improve relevant quality and meet the requirement by market. Therefore, in this paper we review the chemical components and healthy functions of barley grains, and some possible ways to improve the functional components, addressing the viewpoint that barley is a potentially ideal crop for producing healthy food.

Generally, the barley grain (kernel) is spindle-shaped and 7–12 mm in length ( Jadhav et al. , 1998 ). The mature barley kernel consists of hull, caryopsis, and rachilla, and its transverse section is shown in Figure 1 .

The transversal section of mature barley grain. (A) Detailed description of the whole grain from outside to inner; (B) The structure of embryo and attached scutellum.

At a late stage of barley grain development, the lemma and palea develop into the hull, the outermost component of the mature grains, which mainly consists of cellulose, lignin, and silicon. The hulls are closely adhered to the caryopsis in hulled barley, whereas they are separated in naked barley.

A cementing layer is generated during the late grain milk stage, at 9–29 d after pollination (DAP), which adheres the hull to the caryopsis ( Brennan et al. , 2017 ). There are abundant octadecanol, tritriacontane, campesterol, and beta-sitosterol in the cementing layer, and the composition and quantity of these chemicals are related to adhesive ability ( Brennan et al. , 2017 ). Moreover, the adhesive ability is dependent on the environmental conditions during grain development due to their influence on the synthesis of these chemicals. Warm pre-anthesis and cool post-anthesis conditions cause the decrease of the adhesive ability ( Brennan et al. , 2017 ).

The caryopsis accounts for the major part of the whole mature cereal grain ( Evers et al. , 1999 ). Barley caryopsis mainly comprises embryo and endosperm, which is in turn surrounded by the nucellar layer, testa (seed coat), and pericarp (fruit coat; Evers et al. , 1999 ; Figure 1 ). The interval of the nucella layer and testa is the thinnest, while the interval between the testa and pericarp is the thickest ( Brennan et al. , 2017 ).

Endosperm occupies most of grain composition, and consists of aleurone, sub-aleurone, and starchy endosperm ( Zheng and Wang, 2014 ). Barley aleurone comprises multilayered cells, generally three cell layers ( Bacic and Stone, 1981 ; Becraft and Yi, 2011 ). Aleurone is rich in nutrients such as lipids, proteins, minerals, B-type vitamins, such as niacin and folates, and some other micronutrients ( Brouns et al. , 2012 ; Zheng and Wang, 2014 ; Li DQ et al. , 2021 ). Storage protein is accumulated in the form of aleurone granules in aleurone cells ( Zheng and Wang, 2014 ). Starch granules are primarily accumulated in the inner starchy endosperm, while more proteins are accumulated in the intermediate sub-aleurone between the outer aleurone and the inner starchy endosperm ( Zheng and Wang, 2014 ).

The embryo is the most important component of grains for filial generation ( Evers and Millar, 2002 ), which attaches to one side of the rachis and is located on the dorsal side of the caryopsis ( Figure 1B ). The embryo mainly consists of embryonic axis, plumule, and radicle, supplying nutrients for the growth and development of plants ( Evers and Millar, 2002 ). The embryo is well-protected and surrounded by the coleoptile and coleorhiza. The scutellum is a flat-shaped and external recessed protective tissue, connecting with the embryonic axis and endosperm on both sides ( Evers and Millar, 2002 ).

The chemical composition of barley grains is directly related to its end use. Barley with high protein content is suitably used for human food and animal feed, while low protein content is expected for barley used for malting or brewing. On the whole, the major constituents of barley grains are carbohydrates, proteins, lipid, and minerals, in addition to various secondary metabolites, such as vitamin and phenolic compounds ( Table 1 ).

Constituents of barley grains

DW, dry weight.

Carbohydrates

Carbohydrates occupy the most composition in barley grains, generally about 78%–83% of total dry weight ( Henry, 1988 ). Barley carbohydrates are mainly categorized into low molecular weight carbohydrates, non-structural polysaccharides, and cell wall polysaccharides ( Henry, 1988 ). Low molecular weight carbohydrates include glucose, fructose, sucrose, maltose, and raffinose. Non-structural polysaccharides consist of fructans and starch, and cell wall polysaccharides are mainly cellulose, β-glucan, and arabinoxylans (AXs).

Low molecular weight carbohydrates

Monosaccharides, disaccharides, and oligosaccharides are known as the low molecular weight carbohydrates. Monosaccharides are the simplest carbohydrates, including glucose and fructose, and normally they represent 3%–8% of the total sugar content in barley mature grains ( Henry, 1988 ). Sucrose and maltose are the main representatives of disaccharides, consisting of two sugar molecules with glycosidic linkage ( Evers et al. , 1999 ). Sucrose, as the most important disaccharide involved in photosynthesis, is converted into starch as a storage substance ( Evers et al. , 1999 ). There is no maltose during starch synthesis; it is considered to be produced during germination of barley ( Henry, 1988 ). Oliogosaccharides may be defined as carbohydrates consisting of 2–10 monomeric residues linked by O -glycosidic bonds ( Henry, 1988 ). In barley mature grains, raffinose accounts for about 25% of the total sugar content, and it declines rapidly during germination ( Henry, 1988 ).

Non-structural polysaccharides

In barley grains, the non-structural polysaccharides mainly consist of fructans and starch. Starch, as a main source of energy, is synthesized in the endosperm, and functions for grain germination and plant growth ( Collins et al. , 2021 ). Starch is the largest constituent in barley grains, and its content is largely controlled by genetic factors, although environmental conditions also have a dramatic impact on starch synthesis and accumulation ( Savin and Nicolas, 1996 ; Cuesta-Seijo et al. , 2019 ). Asare et al. (2011) reported that starch content of 10 hulless barley genotypes varied from 58.1% to 72.2%. We measured 100 barley genotypes collected from different areas in the world, and found the total starch content ranged from 50.36% to 72.46% ( Li MD et al. , 2021 ).

The starch in barley grains is mainly divided into amylose and amylopectin according to their structures. Amylose consists of 100–10 000 glucose residues linked by linear chains of α-(1–4) linkage ( Martin and Smith, 1995 ; Blennow et al. , 2013 ) and accounts for 20%–30% of the total starch in the non-waxy grains ( Collins et al. , 2021 ). Amylose is mainly synthesized by adenosine diphosphate (ADP)-glucose pyrophosphorylase (AGPase) and granule-bound starch synthase (GBSS; James et al. , 2003 ; Crofts et al. , 2017 ). Waxy gene encoding GBSS is responsible for amylose synthesis in barley and the mutated waxy gene affects amylose content and starch granules morphology ( Li et al. , 2019 ). The high amylose mutant Glacier AC38 was first reported by Merritt (1967) , and amo1 is responsible for the high amylose phenotype ( Schondelmaier et al. , 1992 ). Barleys containing high amylose content are always characterized by shrunken kernels with much lower kernel weight ( Morell et al. , 2003 ; Clarke et al. , 2008 ).

Amylopectin is highly branched by 20 α-(1–4)-linked glucose residues and branched by α-(1–6) linkages ( Martin and Smith, 1995 ; Blennow et al. , 2013 ). Amylopectin accounts for the majority of the total starch, usually 70%–80% ( Collins et al. , 2021 ). Amylopectin biosynthesis requires the coordination of a series of enzymatic reactions involving AGPase, soluble starch synthases (SS), starch branching enzymes (SBE), and starch de-branching enzymes (DBE; James et al. , 2003 ; Crofts et al. , 2017 ). The loss of SSIIa enzyme activity (referred to sex6 mutants) leads to a decrease in amylopectin synthesis and shortened chain length distribution ( Morell et al. , 2003 ). RNA interference method was used to silence SBE genes, resulting in a novel amylose-only starch ( Carciofi et al. , 2012 ).

The quantity and proportion of amylose and amylopectin can be changed through genetic manipulation according to the requirement of the end use for barley ( Sarka and Dvoracek, 2017 ). In wheat, bread made of high-amylose wheat flour showed greater hardness and springiness, and brighter appearance ( Li JY et al. , 2021 ). Waxy rice, also called glutinous rice, is more suitable for thickening soups, sauces, gravies, baby foods, and puddings because of its stickiness ( Sarka and Dvoracek, 2017 ). Waxy barley is favorable for processing food as its flour has freeze–thaw and anti-staling properties ( Sarka and Dvoracek, 2017 ).

In terms of size of starch granules, there are the large A-type and small B-type granules in barley grains, with diameters of 10–25 μm and less than 6 μm, respectively ( Morrison et al. , 1986 ; Andersson et al. , 1999 ). Small granules are much more abundant, accounting for 97% of the total granules ( De Schepper et al. , 2020 ). The mutation of the Waxy gene can change the shape of starch granules, leading to shrunken and irregular B-type starch granules ( Li et al. , 2019 ). The size distribution of starch granules greatly affects the end use of barley ( Sarka and Dvoracek, 2017 ; Jaiswal et al. , 2020 ). During malting, the hydrolysis rate of small starch granules is faster than in the large starch granules ( De Schepper et al. , 2020 ). Barley with more small starch granules in the endosperm is suitable for making paper and cosmetics, while barley with more large starch granules is favorable for malting and brewing ( Wei et al. , 2008 ). In food use, small starch granules can produce a creamy and smooth texture for making low-fat and fat-free frozen desserts, cookies, and cheesecakes ( Lindeboom et al. , 2004 ).

Fructan is one of the main storage carbohydrates in cereal grains ( Lim et al. , 2020 ). In comparison with starch, fructan content in barley grains is quite small, only 0.9%–4.2% of the total dry weight ( Burton and Fincher, 2012 ; Nemeth et al. , 2014 ). Fructans typically consist of 10 fructose residues in barley. Fructans are synthesized based on sucrose as a substrate ( Krahl et al. , 2009 ) and it has been intensively highlighted in health food production ( Vijn and Smeekens, 1999 ) because it is recognized as dietary fiber and prebiotics ( Verspreet et al. , 2015 ). In barley, fructans are widely associated with malting and brewing quality. Cozzolino et al. (2016) found the fructan content in malts is higher than that in raw grains, and the fructan content in malt was positively correlated with hot water extract rate (HWE) and negatively correlated with viscosity. However, different results were reported that fructans content had little change during malting and brewing ( Krahl et al. , 2009 ).

Non-starch polysaccharides (NSPs)

NSPs in barley grains belong to the structural compounds, distributed over the whole caryopsis, aleurone layer, and endosperm cell wall. AXs and mixed linkage (1,3;1,4)-β-glucan (MLG) are the major components of the NSPs in barley grains ( Singh et al. , 2017 ). AX content in barley grains ranges from 4.30% to 9.75% ( Messia et al. , 2017 ). AXs consist of a linear β-(1→4)-linked xylan backbone, which is linked with α-l-arabinofuranose units at the position of O -2 and/or O -3 linkages as side residues ( Izydorczyk and Biliaderis, 1995 ; Messia et al. , 2017 ). Much research has been done on isolation and evaluation of AXs ( Yadav and Hicks, 2015 ; Acar et al. , 2020 ), but still little is known about its genetic regulation and the relevant genes encoding the enzymes of AX synthesis. β-(1,4)-Xylosyltransferase (XylTase) is reported to be involved in AX synthesis in barley endosperms ( Urahara et al. , 2004 ).

MLGs are linear polymers of high molecular weight consisting of d-glucose molecules linked by β-(1–4) and β-(1–3) linkages ( Messia et al. , 2017 ). MLG content differs greatly among barley genotypes and analysis methodology, ranging from less than 2.0% to over 10% ( Zhang et al. , 2002 ; Geng et al. , 2021 ). On the whole, the waxy varieties, with a low amylose content, contain higher MLG content ( Messia et al. , 2017 ; Fastnaught et al. , 1996 ; Li et al. , 2019 ). The key enzyme involved in MLGs synthesis is generally considered to be cellulose synthase-like (CSL) enzyme ( Houston et al. , 2014 ). In addition, environmental conditions also have a great impact on MLG content ( Stuart et al. , 1988 ; Narasimhalu et al. , 1995 ; Zhang et al. , 2002 ). Higher temperature and less rainfall during the grain filling stage, and water stress post-anthesis could result in increased MLG ( Fastnaught et al. , 1996 ; Zhang et al. , 2001 ).

Protein and lysine

In barley grains, proteins are responsible for many functions, such as structural functions, metabolic activity, and providing nitrogen for the developing embryo. A survey of more than 1000 barley genotypes showed that the total protein content in barley grains ranged from 7% to 25% ( Lorz, 2003 ). The barley protein content is highly affected by both genotype and environment ( Zale et al. , 2000 ; Lorz, 2003 ; Qi et al. , 2006 ).

According to the biological functions of proteins, the barley grain proteins are mainly divided into seed storage proteins and structural proteins. Non-storage proteins are found primarily in aleurone and embryo, while storage proteins are mainly located in endosperm. The major storage protein in barley grains is hordein, which accounts for 35%–50% of the total protein ( Tanner et al. , 2019 ). Hordein can be divided into A, B, C, and D groups on the basis of molecular mass and amino acid composition ( Shewry et al. , 1984 ; Pan et al. , 2007 ).

Like other cereals, lysine is the most limited amino acid in barley. Thus, increasing lysine content has been an important objective in the breeding of food barley. High-lysine cultivars of different cereals have been developed. In contrast with the normal maize and sorghum of 2% lysine content, high lysine maize reached 3.4% and high lysine sorghum contains 3.33% lysine content ( Mertz et al. , 1964 ; Singh and Axtell, 1973 ). In barley, the high lysine cultivars, Hiproly and Risø Mutant 1508, contain 4.1% and 5% lysine content, respectively, while the normal barleys were 3.5% ( Munck et al. , 1970 ; Newman et al. , 1990 ).

Lipids are a group of biomolecules distributed throughout all plant tissues. They play different roles in the composition of the cell membrane, storing molecules of metabolic energy, and responding to stresses ( Kuczynska et al. , 2019 ). In plants, lipids usually exert functions as triacylglycerol (TAG) droplets for offering dietary phytochemicals ( Kuczynska et al. , 2019 ). The lipids concentration in barley grains is around 3.0%–3.5% ( Price and Parsons, 1974 ), although some mutants had much higher lipid content. For example, the lipid content of the high-lysine mutant Risø 1508 is 4.1% ( Tallberg, 1977 ). In barley, the lipid distribution in embryo, endosperm, and hull fractions account for 17.9%, 77.1%, and 5% of total lipid content, respectively ( Price and Parsons, 1979 ). Lipids can be divided into neutral lipid, glycolipid, and phospholipid, and their distributions vary greatly in the grain tissues, with neutral lipids being predominant in all fractions, and glycolipids rich in hull and phospholipids rich in endosperm ( Price and Parsons, 1979 ).

Plant lipid transfer proteins (LTPs) have the function of delivering lipids intercellularly and intracellularly, and maintaining the lipid composition of organelles and membrane ( Vignols et al. , 1997 ). For instance, LTP2 protein, belonging to the pathogenesis-related (PR) protein family, have the ability to bind to linear lipid molecules and sterols.

Generally, vitamins are classified into two groups, i.e. fat-soluble and water-soluble vitamins. Fat-soluble vitamins are vitamins A, D, E, and K, and water-soluble vitamins include inositol, and vitamins C and B. In general, vitamin B is abundant in cereals. Vitamin E (also called tocols), one of the antioxidants, is considered to be beneficial to human health in lowering the risk of diseases ( Do et al. , 2015 ; Idehen et al. , 2017 ). Vitamin E is a collection of eight isomers, including four tocopherols and four tocotrienols ( Pryma et al. , 2007 ). Barley is rich in vitamin E compared to other cereals ( Moreau et al. , 2007 ; Temelli et al. , 2013 ). Moreover, the content varies greatly with genotypes, ranging from 8.5 to 68.8 μg/g dry weight ( Andersson et al. , 2008 ; Do et al. , 2015 ). Compared with hulless barley, hulled barley contains higher tocols content ( Cavallero et al. , 2004 ). However, a hulless waxy variety, Washonubet, has high tocols content, even higher than hulled barley varieties ( Ehrenbergerova et al. , 2006 ). In general, tocotrienol and tocopherol occupy 76.8% and 23.2% of total tocols, respectively ( Andersson et al. , 2008 ). In barley grains, the majority of tocopherols is located in embryo, whereas tocotrienols are mostly present in endosperm and pericarp ( Sen et al. , 2006 ; Idehen et al. , 2017 ).

In general, ash content of barley ranges from 2.5% to 3.1% ( Cieslik et al. , 2017 ). Mineral elements are distributed over the whole grain, but are mainly concentrated in the outer layers of grains ( Liu et al. , 1974 ; Weaver et al. , 1981 ; Marconi et al. , 2000 ). Therefore, the mineral content is much higher in hulled barley than the hulless type. On the basis of concentration in plant tissues, minerals are divided into two groups, i.e. macro- and micro-elements. The macro-elements are Ca, P, K, Mg, Na, Cl, and S, and the micro-elements are Co, Cu, Fe, I, Mn, Se, and Zn.

K and P are the most abundant mineral elements in barley grains, accounting for 0.37%–0.50% and 0.33%–0.60% of dry matter, respectively ( Rasmusson et al. , 1971 ; Cieslik et al. , 2017 ). K is an essential macronutrient for maintaining electrical potential, hydrostatic pressure, and biochemical activity for many enzymes ( Britto and Kronzucker, 2008 ). P is another essential macronutrient and its scarcity can affect growth and development of plants. Phytic acid (PA) is the main storage form of phosphorus in barley and cereal grains, accounting for 65%–85% of the total phosphorus in seeds ( Raboy et al. , 2001 ). PA is a nutrition-limiting factor of feed for animals, and moreover excrements with PA may cause contamination of water ( Erdman, 1981 ; Raboy, 2001 ). Thus, low grain PA content is expected for feed barley. Mutants with low phytic acid ( lpa ) have been isolated in barley ( Larson et al. , 1998 ; Dorsch et al. , 2003 ; Oliver et al. , 2009 ).

Phenolic acids

Phenolic compounds, such as phenolic acid, flavonoid, and proanthocyanidins, are the major source of antioxidant compounds in whole grains ( Shao and Bao, 2015 ; Tohge et al. , 2017 ). In barley grain, these compounds are mainly distributed in husks, pericarp, testa, and aleurone ( Nordkvist et al. , 1984 ). The total phenolics content ranged from 130 to 481 mg gallic acid equivalents (GAE)/100 g dry weight ( Han et al. , 2018 ; Shen et al. , 2018 ). The total flavonoid and total proanthocyanidin content ranged from 50 to 150 mg rutin equivalents (RE)/100 g and 29–65.26 mg/100 g dry weight, respectively ( Nordkvist et al. , 1984 ; Verardo et al. , 2015 ; Han et al. , 2018 ; Shen et al. , 2018 ). Ferulic acid (FA) and p -coumaric acid ( p -CA) are the major phenolic acids in barley and account for 1.13–4.04 μg/g and 0.19–3.53 μg/g ( Cai et al. , 2016 ), respectively. As important secondary metabolites in plants, phenolic compounds not only play an important role in plant growth and development and resistance to stress, but are also beneficial to human health due to their strong antioxidant effect ( Cheynier et al. , 2013 ; Patel et al. , 2017 ). In the brewing industry, phenolic compounds affect the quality of beer, such as taste, flavor, haze stability, and appearance ( Mikyska et al. , 2002 ; Vanderhaegen et al. , 2006 ).

Barley grains are rich in a variety of health beneficial functional compounds, such as β-glucan, tocols, and resistant starch. Barley β-glucan can reduce serum cholesterol and blood glucose levels and improve intestinal function. Tocols have the effect of lowering serum cholesterol, and resistant starch can lower blood sugar and promote intestinal function. The schematic is shown in Figure 2 . The detailed health beneficial functions and special chemicals of barley grains are listed in Table 2 .

Health benefits of barley grain

LDL, low-density lipoprotein.

Health beneficial components and functions of barley grain.

Cholesterol-lowering effect

High blood cholesterol is a major risk factor for cardiovascular disease. It is well documented that soluble dietary fiber, especially β-glucan, is associated with the prevention of heart disease ( Maheshwari et al. , 2019 ; Tosh and Bordenave, 2020 ). Barley and oats contain much higher β-glucan content than other cereals, such as rice, maize, and wheat. Many clinical trials showed that barley is the same as or even better than oats as a cholesterol-lowering food. Delaney et al. (2003) reported an increase in fecal neutral cholesterol and a decrease in aortic cholesterol levels in hamsters fed with 8 g/100 g β-glucan from barley or oats. A study on 44 high cholesterol men showed substituting barley meal (7 g β-glucan per day) for rice could reduce visceral organ fat as well as low-density lipoprotein (LDL) cholesterol and total cholesterol levels ( Shimizu et al. , 2008 ). These studies suggest that barley foods have the same health benefits as oatmeal foods in reducing the risk of coronary heart disease. Foods made from suitable barley should contain 0.75g of β-glucan (soluble fiber) per serving. As mentioned above, β-glucan is thought to have cholesterol-lowering effects. It increases the viscosity in the small intestine, thus slowing the absorption of used oils. In addition, β-glucan can bind to bile acids and excrete them, eventually breaking down and replacing cholesterol in the body. Intake of pure β-glucan, extracted from oats or barley seeds, may lower blood cholesterol levels, particularly LDL levels ( Tosh and Bordenave, 2020 ).

The cholesterol-lowering effect of tocopherol and tocotrienol has been proven in chickens ( Qureshi et al. , 1991a ), pigs ( Qureshi et al. , 1991a ), and humans ( Chin et al. , 2016 ). These compounds have been observed to significantly lower serum cholesterol levels. They also have the function in the prevention and treatment of cardiovascular diseases and cancer in the application of health food. Tocols content in barley and oats is much higher than that of other cereals, although there is a significant difference among barley varieties ( Suriano et al. , 2020 ). Extract barley oil and brewery barley waste have higher tocols content than the whole grains, and are also recommended as sources of tocols for additive food use. In addition, rolling and grinding produce oil- and tocols-rich fragments, which are also potential sources of these compounds. Cholesterol content in the egg yolk of the chicken was reduced by feeding laying hens with barley oil ( Walde et al. , 2014 ). Qureshi et al. (1991b) showed that people fed with 200 mg palm vitamin E showed obvious reduction of serum total cholesterol, LDL cholesterol, and glucose concentrations during a 4-week treatment period. Moreover, a separate hypocholesterolemia group fed with 200 mg γ-tocotrienol showed greater total cholesterol reduction. A study of 19 patients with type 2 diabetes and hyperlipidemia who were supplemented with tocotrienol-rich ingredients (3 mg/kg body weight) for 60 d showed that tocotrienol-rich ingredients can reduce total lipids, total cholesterol, and LDL cholesterol ( Baliarsingh et al. , 2005 ). In short, tocols have a significant effect on lowering cholesterol content. Barley food with high tocols content has a great potential for preventing and controlling the related diseases.

Blood sugar-lowering effect

In recent years, many studies have shown that intake of barley foods has positive effects on glucose metabolism in humans ( Fuse et al. , 2020 ). The glycemic index (GI) of barley (34–70) is generally lower than other cereals (55–85 for rice, 52–75 for wheat, and 46–80 for maize), although the GI value of food can be affected by processing or combining with other foods ( Lal et al. , 2021 ). Research has shown that barley grains have a high potential to produce foods with very low GI, especially amylose-only barley grains containing a 99% amylose starch ( Sagnelli et al. , 2018 ).The effect of barley foods in lowering blood sugar is attributed to both β-glucan and amylose/amylopectin ratio. In many cases, the glycemic response to barley foods is the result of a combination of these two factors. Ikegami et al. (1991) examined the glycemic response of normal and diabetic mice to barley feeding, and found all mice increased glycemic tolerance, and diabetic mice had normal fasting glucose levels after being fed the diet consisting of 70.83% barley. Narain et al. (1992) studied the response of healthy people to barley meals in India. The result showed that the increase in the area of the 3-h blood glucose curve declined from 107.9 to 91.5 mg/dL after 4 weeks of consuming 100 g whole barley flour. Minehira et al. (2001) investigated the mechanism of β-glucan in regulating prandium-glucose metabolism in healthy men. They suggested that the reduced glycemic response and increased intestinal viscosity finally caused delayed or decreased glucose absorption after eating a β-glucan diet ( Minehira et al. , 2001 ). Foods containing β-glucan slow down carbohydrate absorption by affecting intestinal viscosity, so that the corresponding blood sugar peak is low and flat.

In addition, the amylose/amylopectin ratio can also significantly affect blood glucose levels, resulting in changes of insulin responses. Resistant starches are macromolecular polymer-like starch combinations that have antidigestive enzymes and remain intact in the large intestine of healthy humans ( Lockyer and Nugent, 2017 ). The resistant starch content of barley breads was consistent with a decrease in blood sugar content ( Lal et al. , 2021 ). Obviously, the effect of barley foods in lowering blood sugar is attributed to the functions of β-glucan and resistant starch in slowing digestion and absorption.

Improving intestinal function

Amylose and amylopectin, the main types of starch, are organized into semicrystalline structures with amorphous and crystalline zone. The crystalline zone of starch particles is due to the linear portion of the amylopectin chain. The semicrystalline characteristic of starch granule is an important determinant of its digestibility ( Perla et al. , 2017 ). Starch was classified into three types based on its digestion rate in the small intestine: rapidly digestible starch, slowly digestible starch, and resistant starch ( Englyst et al. , 1992 ). Slowly digestible starch and resistant starch have been widely studied due to their beneficial health effects. The benefits of slowly digestible starch have been explained by its low GI, because slowly digestible starch is slowly digested throughout the small intestine, resulting in a slow and prolonged release of glucose into the blood ( Bello-Perez et al. , 2020 ). Resistant starch resists digestion in small intestine; instead, it reaches the large intestine ( Perla et al. , 2017 ). β-Glucan and resistant starch are fermented in the large intestine to produce short-chain fatty acids (SCFA), especially butyrate and propionate. The effect of these fatty acids on the intestine is to provide an energy source for epithelial cells that form a healthy colonic mucosa ( Demartino and Cockburn, 2020 ). A mouse feeding study showed that all mice fed with high-amylose barley and whole barley grains (containing 50 g/100 g barley extrudates) had better growth and intestinal quality than the control group fed with a commercial highly resistant corn starch ( Gerhard et al. , 2002 ). Bird et al. (2008) found the indices of bowel health (stool weight, concentrations of butyric acid and fecal p -cresol, SCFA excretion) differed significantly between the two groups of participants feeding with barley and wheat, respectively, and the persons who ate barley food including 103 g of the test cereal showed improvement in gastrointestinal integrity ( Bird et al. , 2008 ). These benefits are mainly attributed to the fermentation properties of resistant starch.

Heart disease, diabetes, metabolic syndrome, and cancer have become public health challenges. These diseases are not only occurring in the elderly, but are rising in the younger age group. Clinical trials demonstrated that lifestyle choices, including food choices, have a profound impact on the prevention or control of these diseases. A large body of evidence has shown that barley has a positive effect on blood cholesterol and blood sugar control as well as colon integrity ( Gustavo et al. , 2019 ).

Breeding approaches

Along with the deeper understanding of the mechanisms for genetic controlling of the health or nutrition components in barley grains, it has become possible for breeders to develop barley varieties with high nutrition or health qualities ( Loskutov and Khlestkina, 2021 ). Wild barley germplasm may contribute to improvement of nutritional qualities by crossbreeding, as it showed lower glycemic loads in the form of lower relative starch content with higher relative protein, fiber, and mineral contents compared with domesticated barley ( Hebelstrup, 2017 ). In recent years, molecular markers and genetic mapping have been widely used for improvement of barley grain nutritional qualities. Association mapping and favorable allele discovery of barley physicochemical properties including total phenolics, amylose content, and β-glucan content have provided the foundation for the improvement of barley breeding strategies using molecular markers ( Mohammadi et al. , 2014 ). Mahalingam et al. (2020) conducted genome-wide association studies (GWAS) to identify marker-trait associations (MTAs) and key candidate genes involved in the biosynthesis of tocols. The related results provided a valuable resource for barley breeding programs targeting specific isoforms of grain tocols. GWAS have also been widely used to map loci and genetic regions responsible for total starch, amylose, amylopectin, and β-glucan content, which can highlight markers for breeding of barley varieties with suitable content of amylose and β-glucan ( Geng et al. , 2021 ; Li MD et al. , 2021 ). It was found that overexpression of the CslF6 gene in transgenic increased β-glucan content by at least 80% ( Burton et al. , 2011 ). By silencing all the SBE genes, Carciofi et al. (2012) found that only amylose was produced in barley grain endosperm, and resistant starch content (90%) was dramatically increased. Moreover, the use of chemical mutagenesis can also improve the quality of barley varieties. For example, a naked barley variety with high resistant starch named Himalaya292 was developed by the use of chemical mutagenesis ( Bird et al. , 2004 ). In addition, the genetic control of tocopherol synthesis in barley has also been intensively investigated. The gene encoding the geranylgeranyl transferase (HGGT) required for tocotrienol synthesis was isolated and cloned in barley ( Cahoon et al. , 2003 ). Overexpression of barley HGGT gene in maize can result in a sixfold increase in tocotrienol content in seeds ( Cahoon et al. , 2003 ). Recently, multiple quantitative trait loci (QTLs) associated with barley tocopherol have been identified through analysis of parental mapping populations and genome-wide association analysis ( Oliver et al. , 2014 ; Mahalingam et al. , 2020 ). With the completion of barley genome sequencing and rapid development of gene-editing technologies, the precise breeding of improving the specific nutrient and functional components will be efficiently performed to develop the barley varieties required by functional food production.

Cultivation approaches

In order to meet the market demand for barley, it is particularly important to use suitable cultivation practices to regulate barley quality. First of all, selection of a suitable variety is a prerequisite based on the end use of barley. For processing health foods, the varieties should be rich in functional nutrients, including protein, β-glucan, and tocols. As environmental conditions have a great impact on nutrition and health components, planting areas and soils should also be reasonably selected. Grains of barley growing at low soil water content and high air temperature, in particular during the filling stage, have high protein and β-glucan content ( Hong and Zhang, 2020 ; Ni et al. , 2020 ). In general, the content of protein and β-glucan in barley grains increased with nitrogen fertilizer level ( Conry, 1994 ; Guler, 2003 ; Wroblewitz et al. , 2013 ). In addition, potassium (K) fertilizer also has a great impact on protein and β-glucan content in barley grains. In general, a high K level tends to increase both protein and β-glucan content in barley grains. Protein content in barley grains is affected by sowing time, and late sowing may increase protein content in winter barley grains ( Yin et al. , 2002 ; Xue et al. , 2008 ).

Processing approaches

According to the location of various nutrients in barley grains, people can enrich and semi-separate these nutrients by conventional grain processing methods. As a food ingredient, hulled barley is usually required to remove the outermost fiber layer. A variety of nutrients in the products are enriched by skin grinding and fine grinding. For example, as phenolic compounds are rich in the outside of grains, high quantities can be found in the bran produced from barley flour and the fine grinding powder ( Andersson et al. , 2003 ). Barley bran also contains high levels of other nutrients such as non-starch polysaccharides, starch, and protein ( Tufail et al. , 2021 ). Air classification and screening are effective ways to produce nutrient (mainly protein and β-glucan) from coarse milling powder, a dry milling form used to produce whole grain flour and whole grain foods. As β-glucan is known for its functions in the control and prevention of cardiovascular disease in humans, many investigators have enriched β-glucan using conventional dry milling processes, such as air classification ( Messia et al. , 2020 ). In the process of fine grinding and skin grinding, the outer seed coat and endosperm are separated, and the products are enriched in these two parts of the grain, which can be further air-classified and screened. Air classification separates coarse ground powder into powders with large, medium and small particle sizes, and each type of powder has different nutritional composition. By further air classification and screening of the three powders, the products rich in certain nutrients can be produced. Larger particles tend to contain more dietary fiber, while smaller particles often contain higher levels of starch and protein.

Many researchers have tried to extract, separate, and purify the health components (mainly β-glucan and tocols) from barley grains. At present, four methods are commonly used to isolate β-glucan from barley grains: HWE, alkaline extraction, acid extraction, and enzyme extraction ( Maheshwari et al. , 2017 ). Among these methods, the order of barley β-glucan extraction rate is hot water>enzyme>acid>alkaline ( Maheshwari et al. , 2017 ). High purity of β-glucan can be extracted by the hot water method. The temperature of extraction is generally higher than 90 °C, which makes the starch polymer to be extracted along with it. Thus, a heat-resistant α-amylase is added to remove starch during HWE ( Papageorgiou et al. , 2005 ). The acid method can easily make cereal starch over hydrolysis, causing a large amount of glucose to be mixed into β-glucan products and increasing the difficulty of separation process ( Ahluwalia and Ellis, 1984 ). In the alkaline hydrolysis process, partial seed coat fibers will be decomposed, and more β-glucan combined with cellulose is dissociated into aqueous solution, increasing the yield of β-glucan. However, pectin may be easily formed in the extraction process, which affects the extraction rate and properties of β-glucan. β-Glucan can also be enzymatically extracted from barley grains, but many other enzymes should be simultaneously added during extraction to remove impurities such as starch and protein ( Ahmad et al. , 2009 ). Furthermore, the molecular weight of β-glucan extracted by enzymatic methods is the highest ( Babu and Joy, 2016 ). In addition, ultrasound has also been used in the extraction of β-glucan. It was reported that the extraction rate of β-glucan could be improved by using ultrasonic-assisted extraction ( Liu et al. , 2021 ). Although these methods have been commonly used in the laboratory to extract barley β-glucan, there are still many problems to be solved in order to realize the industrialization of barley β-glucan extraction and processing. The imperfect industrial extraction technology, low extraction rate, and high cost restrict production of barley β-glucan.

Compared with β-glucan in barley grains, tocols is less studied in its extraction and concentration in barley grains. Supercritical fluid extraction is a new extraction and separation technology developed in recent years. This technique has been widely used to extract tocopherol ( King et al. , 1996 ; Ibanez et al. , 2000 ). Tocols are lipid-soluble; therefore, they can be extracted together with lipids. Lipids mainly exist in the outer layers of barley kernel. Temelli et al. (2013) showed that tocols content in pearl flour was higher than that in whole grains ( Temelli et al. , 2013 ). They also found that naked varieties contained higher levels of tocols in pearl flour than shelled varieties. Obviously, barley pearl powder is a suitable raw material for the recovery of tocopherol-rich oil.

Barley is a major cereal crop with multiple uses, and has been receiving more attention from both agricultural and food scientists because of its special chemical composition and health benefits. In particular, barley can serve as a food that meets the needs of a diet low in calories, high in fiber, and rich in probiotics, which has led to barley being listed as a desirable healthy food. The constituent characters, nutritional components and health beneficial components of barley grain have been well understood. Further effort should be concentrated on the better utilization of these traits in barley food processing, development of the new food products well accepted by markets and consumers, and improvement of barley quality more suitable for food processing. Important physical characters to be considered in the barley food processing include grain weight, size, hardness, etc. The important chemical components closely associated with functional food processing include starch, protein, minerals, β-glucan, tocols, etc. With the development of processing technologies, functional components in barley grains, such as β-glucan and tocols, can be efficiently extracted and concentrated, and with the development of barley genome sequencing and gene-editing technologies, the barley varieties rich in nutrient and functional components can be precisely obtained.

La Geng, Mengdi Li, and Linzhen Ye conceived and designed the research. L. Geng, M.D. Li, L.Z. Ye, and G.P. Zhang wrote the article. In detail, M.D. Li wrote Sections 2 and 3, L. Geng wrote Sections 4 and 5. G.P. Zhang and L.Z. Ye directed and revised the review. All authors read and approved the final article.

This work was funded by the Science and Technology Program of Zhejiang Province of China (LGN20C130007, 2021C02064-3, 2020C02002), the National Natural Science Foundation of China (No.32171917), and the earmarked fund for China Agriculture Research System (CARS-05).

The authors declare no conflict of interest.

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Health-promoting properties of barley: A review of nutrient and nutraceutical composition, functionality, bioprocessing, and health benefits

Affiliations.

• 1 School of Food and Biological Engineering, Jiangsu University, Zhenjiang, China.
• 2 Department of Food Science, University of Massachusetts Amherst, Amherst, Massachusetts, USA.
• 3 Department of Food Science & Bioengineering, Zhejiang Gongshang University, Hangzhou, Zhejiang, China.
• PMID: 36394558
• DOI: 10.1080/10408398.2021.1972926

Barley is one of the world's oldest cereal crops forming an important component of many traditional diets. Barley is rich in a variety of bioactive phytochemicals with potentially health-promoting effects. However, its beneficial nutritional attributes are not being fully realized because of the limited number of foods it is currently utilized in. It is therefore crucial for the food industry to produce novel barley-based foods that are healthy and cater to customers' tastes. This article reviews the nutritional and functional characteristics of barley, with an emphasis on its ability to improve glucose/lipid metabolism. Then, recent trends in barley product development are discussed. Finally, current limitations and future research directions in glucolipid modulation mechanisms and barley bioprocessing are discussed.

Keywords: Anti-obesity; barley; fermentation; food processing; nutrition.

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Peer-reviewed

Research Article

Barley systematics and taxonomy foreseen by seed morphometric variation

Roles Formal analysis, Methodology, Writing – original draft

* E-mail: [email protected]

Affiliation ISEM, CNRS, EPHE, IRD, Univ Montpellier, Montpellier, France

Roles Conceptualization, Supervision, Validation, Writing – review & editing

Roles Conceptualization, Visualization, Writing – review & editing

Affiliations ISEM, CNRS, EPHE, IRD, Univ Montpellier, Montpellier, France, Athéna, Lacamp, Roquedur, France

Roles Resources, Writing – review & editing

Affiliation UCA, INRAE, GDEC, Clermont-Ferrand, France

Roles Data curation, Resources

Roles Methodology, Validation, Visualization, Writing – review & editing

Roles Supervision, Validation, Writing – review & editing

Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

• Angèle Jeanty, 
• Laurent Bouby, 
• Vincent Bonhomme, 
• François Balfourier, 
• Clément Debiton, 
• Camille Dham, 
• Sarah Ivorra, 
• Jérôme Ros, 
• Allowen Evin

• Published: May 17, 2023
• https://doi.org/10.1371/journal.pone.0285195
• Peer Review
• Reader Comments

Since its Neolithic domestication in the Fertile Crescent, barley has spread to all continents and represents a major cereal in many modern agrarian systems. Current barley diversity includes thousands of varieties divided into four main categories corresponding to 2-row and 6-row subspecies and naked and hulled types, each of them with winter and spring varieties. This diversity is associated to different uses and allow cultivation in diverse environments. We used a large dataset of 58 varieties of French origin, (1) to assess the taxonomic signal in barley grain measurements comparing 2-row and 6-row subspecies, and naked and hulled types; (2) to test the impact of the sowing period and interannual variation on the grains size and shape; (3) to investigate the existence of morphological differences between winter and spring types; and finally (4) to contrast the relationship between the morphometric and genetic proximity. Size and shape of 1980 modern barley caryopses were quantified through elliptic Fourier Transforms and traditional size measurements. Our results indicate that barley grains record morphological diversity of the ear (89.3% classification accuracy between 2-row/6-row subspecies; 85.2% between hulled and naked type), sowing time of the grains (from 65.6% to 73.3% within barley groups), and environmental conditions during its cultivation and varietal diversity. This study opens perspectives for studying archaeological barley seeds and tracing the barley diversity and evolution since the Neolithic.

Citation: Jeanty A, Bouby L, Bonhomme V, Balfourier F, Debiton C, Dham C, et al. (2023) Barley systematics and taxonomy foreseen by seed morphometric variation. PLoS ONE 18(5): e0285195. https://doi.org/10.1371/journal.pone.0285195

Editor: Muhammad Abdul Rehman Rashid, Government College University Faisalabad, PAKISTAN

Received: October 4, 2022; Accepted: April 17, 2023; Published: May 17, 2023

Copyright: © 2023 Jeanty et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All raw data are fully available on a public Figshare repository [ https://doi.org/10.6084/m9.figshare.21940241 ].

Funding: This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 852573). https://erc.europa.eu/homepage The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Barley ( Hordeum vulgare L.) is one of the most important cereal crops in the world with a global production of 160 million tons every year ( http://www.fao.org/ ). Barley tolerates drier, colder and poorer soils than wheat, which explains its wider geographical distribution in Eurasia [ 1 , 2 ]. Today, barley grains are mainly used for both animal and human consumption while straw is dedicated to livestock, and starch is used in food production and chemical industry [ 3 – 5 ]. At the global scale, several hundreds of barley varieties are recorded today [ 6 ].

Two Hordeum subspecies can be distinguished based on spike morphology and number of fertile spikelets present at each node of the rachis. Six-row barley ( Hordeum vulgare subsp. vulgare also called Hordeum hexastichum ) has 3 fertile spikelets while in two-row barley ( Hordeum vulgare subsp. distichum ) only the central spikelet is fertile [ 1 , 2 ]. Within these two subspecies, hulled and naked types are distinguished on the basis of the adherence or non-adherence of the protective envelopes of the caryopses. Hulled barley is the most widely grown type, mainly for animal feeding and malt production for brewing. Naked barley is more scarcely cultivated nowadays and mainly serves as a human food source [ 1 , 4 , 5 , 7 ]. In addition, barley varieties can be divided into winter- or spring-sown varieties [ 1 , 4 ] that differ mainly in their vernalization requirements [ 8 ]. Winter barley has a cycle of about 10 months. Sown in autumn, it needs vernalisation, i . e . period of low temperatures, for its development and closes its cycle before the summer droughts. The need of vernalization ensures that ear development takes place after the risk of frost damage has passed [ 9 , 10 ]. In spring barleys, flowering is not inhibited (no vernalization) because this stage takes place during the good season [ 11 ]. They have a short growth cycle, and are well adapted to northern regions where winter conditions are too harsh for the cultivation of winter varieties. Spring barleys, however, usually provide lower yields than winter barleys.

Barley diversity has been partially studied using molecular markers, and the genotyping of 570 French accessions (784 SNPs spread on the whole genome) shows that the main variation is caused by the difference between the sowing season (winter vs . spring), prior to the number of spike rows (6 vs . 2) [ 12 ].

Current barley diversity reflects its past history, which is, at least partially, known through the study of archaeobotanical remains as well as recent genetic studies. Barley is one of the founder crops of the Old World food production [ 2 ]. It was domesticated from its wild progenitor Hordeum vulgare subsp. spontaneum around 10.000 years ago in the Fertile Crescent. Only the central spikelet of this ancestor is fertile and produce a kernel. Barley domestication has long been thought to be monophyletic [ 13 ], but recent genetic analyses of current varieties support the hypothesis of a polyphyletic and multiregional origin of barley domestication [ 14 – 17 ] and discerned European and Asian routes of barley spread based on Simple Sequence Repeat (SSR) markers [ 18 , 19 ]. Nevertheless, the biogeographical history of barley can be traced mainly from the macro-remains (grain, spikelet or chaff) found in archaeological sites [ 2 ]. They give evidence of the spread of barley in Europe and Asia as early as the Neolithic. Palaeogenetic analyses gave some insights into the early domestication and spread of barley [ 18 , 20 , 21 ]. However, most of the cereal macro-remains are, especially in the Mediterranean area, preserved by charring which is detrimental to ancient DNA preservation and palaeogenetic studies [ 22 – 24 ]. Tracing the ancient history of barley from the study of macro-remains must therefore primarily rely on morphological characteristics. The ratio of straight and twisted caryopses has long been used to identify the presence of two-row and six-row barley. In addition, the presence of naked or hulled barley types can be inferred from the general aspect of the grain and of its outer surface, naked barley grains being more roundish, especially in cross-section, and having cross-ripples on their surface [e.g. 25 ]. In the Near-East, morphological traits allow to document the presence of six-row hulled and naked barleys soon after the domestication of the two-row morphotype, which is closer to the wild H . vulgare subsp. spontaneum [ 2 ]. However, it is difficult to apply systematically and consistently between studies using qualitative morphological criteria. As a consequence, important issues in the history of barley spread and in the cultivation dynamics of barley types remain unclear or under debate. Hordeum types are not always distinguished in archaeobotanical studies. However, in Western Europe, it is often considered that the Early Neolithic agriculture relied mostly on naked six-row barley [e.g. 26 , 27 ]. Two-row barley is however occasionally identified, in particular by chaff, and hulled six-row barley seems to be predominant in Italy and North-Eastern Spain [ 28 , 29 ].

For these reasons, it is crucial to investigate quantitative (and therefore more objective) methods for discriminating and identifying barley types from seed morphology.

Several studies have shown the interest of morphometrics to discriminate barley types. Traditional morphometrics, based on the study of the length, width and thickness of archaeological barley grains, has been used to confirm the presence of 2-row barley during Roman times in France [ 30 ]. More recently, geometric morphometric analysis of grain’s outline shape of a sample of 10 present-day varieties evidenced that 2-row and 6-row types could be differentiated even when the grains were experimentally charred [ 31 ]. This discrimination was later confirmed by [ 32 ] who also showed that within 2-row and 6-row barley, a selection of British and Scandinavian varieties could be distinguished. This last study also gave evidence that differences between barley types are independent of environmental conditions. In addition, it has been suggested that barley size was related to culinary systems and traditions in prehistoric Asia [ 33 ].

The north-western Mediterranean basin host an important barley diversity and France is the 5 th largest producer of barley in the world according to FAO data available in 2020, with hundreds of very diverse varieties recorded [ 34 , 35 ], providing a favourable context to investigate morphometric variation within and among barley types.

Based on the quantification of the size and shape variation of barley grains belonging to a set of 58 French varieties, the present study aims to (1) assess more globally how the morphological variability of caryopses is structured according to the different categories of barley (2-row vs six-row, hulled vs naked, varieties), (2) take into account the impact of inter-annual variation, (3) investigate the existence of morphological differences between winter and spring types, and (4) explore the relationship between morphometric and genomic proximities.

Material and methods

Morphometric data.

A total of 1980 barley seeds corresponding to 58 varieties were studied ( S1 Table ). The varieties are divided into four taxonomical groups ( Table 1 ): two-row naked (N = 13), two-row hulled (N = 23), six-row naked (N = 10) and six-row hulled (N = 12). The varieties are also divided into three different sowing periods: spring (N = 24), winter (N = 31), and alternative (N = 3) without seasonal preference ( Table 1 ). So-called alternative varieties corresponding to varieties that can be sown in spring or winter.

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https://doi.org/10.1371/journal.pone.0285195.t001

An accession includes grains from a single variety grown and sampled the same year in the same field. A total of 66 accessions were analysed. This includes 54 varieties collected a single year and 4 varieties repeatedly harvested on three different years (varieties 10004-CFL33 and 10024-ESV are 6-row-hulled-winter barley; varieties 12510-DLG and 12900-CHI are 2-row-hulled-spring barley). These last 12 accessions (4 varieties x 3 years sown) were used to test inter-annual grain size and shape variation.

All the seeds originated from the Biological Resources Centre (BRC) small grain cereals at INRAE in Clermont Ferrand (France) were they were cultivated under the similar growing conditions and stored under strictly controlled conditions.

Once received at the laboratory, the grains were placed for 48 hours in a freezer, followed by 24 hours at 38°C in an oven in order to avoid germination and eliminate pests. Each grain was then manually peeled to remove the husks. Only complete and undeformed grains were selected.

Because the analysis of samples of 50 grains from two varieties revealed that a sample size of 30 grains was sufficient to capture the size and shape variation of a population, 30 barley seeds were analysed per accession. This preliminary analysis was performed on variance estimation using rarefaction curves [ 36 – 38 ] ( S2 Fig ).

The grains were positioned on plasticine and photographed in their dorsal and lateral views using Olympus SZ-ET microscope and DP26 Olympus Camera. The lateral view documents the shape of the grains taken in their thickness while the ventral view documents the shape of the grains visualised with the furrow facing upwards. The combination of the two views allows to document the shape of the grains from two different points of view, which somehow allows to study the grains in 2.5D, approaching a 3D description. A centimetric scale was included in all pictures. The background of each picture was removed, and the grain was converted to a black mask using Adobe Photoshop CS6 software. The 2D outline coordinates was then extracted using R v. 4.1.1 and the package Momocs v. 1.3.0 ( https://github.com/MomX/Momocs/ ; [ 39 ]). The outline coordinates were scaled using two landmark coordinates manually localized 1 cm apart on the original picture using Image J [ 40 ]. The length, width, thickness and centroid size of the grains were calculated using the Momocs package ( S1 Fig ).

Genetic data

Genetic data were available for 51 of our varieties [ 12 ], corresponding to 784 SNPs markers covering the 7 chromosomes of the genome, and were used to compute Sokal and Michener genetic distances [ 41 ] between varieties. The genetic distance matrix was compared to the morphometric distance matrix using a Mantel test.

Statistical analysis

Differences between groups were first explored for size, shape and form (size + shape) separately. Different levels of investigation were explored: between accessions, varieties, systematic and taxonomical groups, between sowing period, and between year of collect for 4 varieties.

For size analysis, differences in length, width, thickness and centroid size of the grains were tested using Kruskall-Wallis rank tests and visualised with boxplots. Pairwise differences were tested using Wilcoxon rank tests.

To analyse shape, outlines coordinates were centred and scaled. Subsequently, the elliptical Fourier transforms (EFT) were calculated. Outline of the grains are decomposed into a series of coefficients of trigonometric functions, the harmonics. The shape of the studied object is reconstructed using the inverse transform. In our case, the lateral view of the barley grain is described by 5 harmonics and the ventral view by 7. The number of harmonics was determined using the harmonic power criterium in the Momocs package [ 39 ]. These harmonic coefficients correspond to shape variables and are analysed first using a Principal Component Analysis (PCA) in order to reduce the dimensionality of the data, assess the overall grain shape variation and detect potential outliers. Then, differences between groups were tested using Multivariate Analyses of Variance (MANOVA). Pairwise differences were assessed using pairwise multilevel comparison (vegan & pairwiseAdonis R packages). Subsequently, discriminant analyses were used to separate groups and to accuracies presented were calculated using leave-one-out cross-validation (CVP) and their confidence intervals (MASS R package, [ 42 ]). Neighboor Joining (NJ) dissimilarity networks, based on the Mahalanobis distances, were computed. Differences in mean shapes were visualised using the MSHAPES function of the Momocs R package.

Overall morphometric variation between varieties

Overall, barley varieties differ in their grain size (length, width, thickness, centroid sizes), shape and form of both their lateral and ventral views (all p < 2.2e-16). The centroid sizes of the ventral and lateral views appeared highly correlated with each other and with grain length ( Fig 1A ). Consequently, centroid sizes were not analysed further. Conversely, length, width and thickness show significative (all p-value < 2.2e-16) small correlation ( Fig 1A ) and were analysed separately.

Correlation between the size indices of length, width, thickness and centroid sizes (CS) of the ventral (VV) and lateral (VL) views of the grains (A). Boxplot of length(B) width (C) and thickness (D) showing variation between varieties. Dissimilarity network between varieties for the lateral (E) and ventral (F) views of the grain, and the shape of the two views combined (G). The colors differenciate the hulled (red) and naked (orange) 2-row varieties and hulled (purple) and naked (blue) 6-row varieties.

https://doi.org/10.1371/journal.pone.0285195.g001

Visualisation of between-varieties size variation ( Fig 1B–1D ) shows a strong general overlap with however some varieties showing larger or smaller values, neither appear related to the 2-row or 6-row, not to hulled or naked categories.

The between-varieties dissimilarity networks for the lateral and ventral shapes (respectively Fig 1E and 1F ) appeared, at least partially, taxonomically structured. The ventral shape network ( Fig 1F ) is mainly structured by a 2-row vs 6-row varieties opposition with only few varieties not clustering within their categories, while the lateral shape network ( Fig 1E ) appeared less structured. When the lateral and ventral shapes were combined ( Fig 1G ), the network showed a clearer pattern, with a clear structuring, first between the 2-row and 6-row varieties, then between naked and hulled varieties within the two main groups. Only three exceptions can be noted corresponding to three 2-row varieties being clustered with the 6-row varieties.

Interannual variability

Four varieties were sampled for three different years, but not necessarily in the same years, which limits direct comparisons. The varieties show different pattern of interannual size variation with one variety (10004-CFL33) showing no variation, two varieties (10024-ESV and 12900-CHI) showing interannual differences for all comparisons, and one (12510-DLG) showing significant differences only for grain thickness ( Table 2 and Fig 2A–2C ). The boxplots and dissimilarity network evidenced the interannual variation in grain size ( Fig 2A–2C ) and shape ( Fig 2D , both views combined) to be weaker than variation between varieties.

Boxplot of the length (A), width (B) and thickness (C) of the grain for visualising size differences between the sampled years for four varieties. Dissimilarity network based on shape (two views combined) variation between years for four varieties. Dissimilarity network based on shape (two views combined) between the four varieties and their three sampled years (D).

https://doi.org/10.1371/journal.pone.0285195.g002

https://doi.org/10.1371/journal.pone.0285195.t002

Genetic information and grain morphometrics

Genetic and morphometric datasets revealed no correlation whether it be for the lateral (p = 0.889) and dorsal (p = 0.326) shape of the grains or their length, width, and thickness (p > 0.005).

Morphometric differences between systematic and taxonomic groups

All pairwise comparisons of the size indices between the four main categories appeared significant (all p < 2.2e-16), except length difference between 6-row hulled and naked types, and thickness difference between the 2-row and 6-row hulled types ( Table 3 , Fig 3A–3C ). Size measurements greatly varied between sowing seasons with hulled spring varieties showing larger measurements than winter varieties when the differences were significant ( Fig 3A–3C ) and winter varieties showing larger measurements than winter varieties in naked types. For 6-row naked, winter varieties showed larger measurements than varieties characterized as “alternative”.

Boxplot of length (A) width (B) and thickness (C) showing variation between categories. Dissimilarity network between categories for the lateral (D) and ventral (E) views of the grain. The colors differentiate hulled (h, orange) and naked (n, red) 2-row varieties and hulled (h, purple) and naked (n, blue) 6-row varieties.

https://doi.org/10.1371/journal.pone.0285195.g003

https://doi.org/10.1371/journal.pone.0285195.t003

Regarding dissimilarity networks, the same patterns were observed with the naked vs hulled dichotomy for the lateral shape ( Fig 3D ), and the 2-row vs 6-row dichotomy for the ventral shape ( Fig 3E ). The two sowing periods of the same category clustered together in both networks, with the 6-row (both naked and hulled) categories showing more differences between sowing periods than their 2-row counterparts ( Fig 3D and 3E ). The network pooling the shape of the two views (not shown) was highly similar to those based on the ventral shape.

Discriminating power of the different morphometric parameters

All varieties were then grouped, first according to the four initial categories (2-row/6-row, hulled/naked) to which was then added the sowing period.

For all comparisons of the four main categories ( Fig 4 , Table 4 ), shape and form performed equally, and the mean CVP obtained when the lateral and ventral views of the grains are combined always performed better than their separate analyses for comparing the taxonomic groups ( Fig 4A ). Results are more contrasted regarding the sowing season for which the ventral view performs equally or better than the combined analyses ( Fig 4B ). The three size indices provided always much lower CVP than shape and form analyses.

Mean Cross-Validation Percentages (CVP) computed from width, thickness and length size indices, as well as from lateral and ventral shape and form of the grain analysed separately and combined contrasting the categories mentioned along the x-axis (A). The ‘4groups’ (4gp) comparison includes the 2-row hulled (2H) and naked (2N), and 6-row naked (6N) categories. The sowing periods were contrasted for each category separately comparing winter vs. spring varieties for the 2H and 2N, and winter vs. alternative for the 6N (B). All mean CVP values and their confidence intervals can be found in ( Table 5 ).

https://doi.org/10.1371/journal.pone.0285195.g004

https://doi.org/10.1371/journal.pone.0285195.t004

At best, grains are identified to the correct variety with a mean CVP of 48.1% (Confidence Interval (CI): 47.5–48.6%, shape of both views combined).

The ventral shape performed better in discriminating the 2-row vs 6-row types, while the lateral shape performed better for hulled vs naked discrimination. Visualisation of mean shape differences ( Fig 5 ) revealed shorter and wider grains for naked compared to hulled barley in ventral view, but also an apex mismatch in lateral view. The differences between 2-row and 6-row barley are mainly in the lower part of the grain, with 2-row barley being shorter, wider and rounder than 6-row barley in ventral view. In the lateral view, there is also a shift in the apex of the grain and the 2-row barleys are more rounded in the furrow of the grain.

Outlines indicate mean shapes for each of barley types: hulled (A) and 2-row (B) in blue, naked (A) and 6-row (B) in purple. Warmer colours indicate the largest morphological differences between barley features.

https://doi.org/10.1371/journal.pone.0285195.g005

The grains can be attributed to the 2-row or 6-row category with a mean CVP of 89.3% (CI: 88.8–89.9%, form of both views combined) and to the naked or hulled category with a mean CVP of 85.3 (CI: 84.6–86.1%, shape of both views combined).

When the four categories are considered simultaneously, the lateral and ventral views performed equally, less efficiently than their combined analysis that allow attributing the grain correctly with a mean CVP of 76.7% (CI: 75.7–77.6%, shape of both views combined).

Within each of the four main categories, the sowing period can be identified with relatively high CVP ranging from 65.1% (CI: 62.2–68.6%, shape of both views combined) for two-row hulled barleys to 77.6% (CI: 73.3–81.1%, shape of both views combined) for six-row naked barleys. The sowing season of hulled barleys was more discriminated than that of naked barleys. While both views performed relatively equally for hulled barleys, the ventral view performed better for naked barleys for discriminating the sowing seasons.

Spring, winter or alternative barley

For each of the four groups, spring varieties have longer, wider and thicker grains than winter varieties ( Table 5 , Fig 3 ). The only exception is 2-row naked, showing no length differences between spring and winter varieties.

https://doi.org/10.1371/journal.pone.0285195.t005

Morphometric methods

Geometric morphometric analysis of barley grains belonging to 58 French varieties show that 6-row/2-row, hulled/naked types and even varieties differ in their grain size, shape and form. As expected, form and shape variables (lateral, ventral outlines and the combination of the two) are more discriminant than size variables (length, width, thickness, lateral and ventral centroid size).

Barley varieties differ in the size, shape and form of the grains. At best, 48.1% of the grains are correctly re-assigned to the correct variety using a combination of lateral and ventral shapes. Our results are coherent to previous studies, [ 31 ] obtained 53.7% of correct cross-classification but using only 10 varieties and a different methodological approach based on sliding semi-landmarks on the ventral view of the grains. In 2019, [ 32 ] obtained 61.5% of correct classification based on the study of 54 landraces using an EFT analysis of the dorsal outline only. It appeared therefore possible that outline analysis, using the EFT method, better discriminate barley varieties than Procrustes approaches, though a quantitative comparison of the methods is still lacking.

The combination of the lateral and ventral grain outlines also provides a clear accuracy gain to analyses based on a single view. Here, the combination of lateral and ventral shapes increases the correct reclassification rate by about 5% compared to lateral shape alone. On the other hand, the use of a size variable in combination with lateral and ventral shapes (form) does not significantly improve the performance of the model. The better performance of the analyses based on the combination of lateral and ventral shapes is confirmed at all levels (6-rows/2-rows, hulled/naked).

A possible explanation for the non-correlation between genetics and morphometrics may be the low number of SNPs used. To find grain measurements causative SNPs, a Genome-Wide Association Study (GWAS) would have to be performed with hundreds of thousands of SNPs [e.g. 43 ].

Inter-annual variation and influence of environmental conditions in grain morphology

Our results show that grain size, shape and form vary from one year to another. This interannual variability appeared specific to each variety and no common variation emerged. That being said, interannual variation in grain shape is here shown to be lower than varietal variation. This conclusion echoes with the findings of [ 32 ]. In their study, 54 accessions were sown in two different environments and they show that the effect of growing conditions and grain morphology was minor compared to genetic factors. Evidencing that grain morphology is more strongly determined by genetics than by environmental conditions is an important milestone with the prospect of applying these models established on modern accessions to identify barley types to which belong archaeological grains. The role of genetics in the control of grain morphology is supported by the identification of QTL involved in grain form determination [ 44 , 45 ] that do not seem associated with environmental variability.

Environmental conditions interact with genetics to influence barley grain size and weight [ 46 ]. The environmental effects vary according to seasonality and barley cycle [ 47 ]. The conditions during pre-anthesis (flowering) period directly influence grain weight and size through determining assimilates, while conditions during post-anthesis period influence cellular division, grain filling or deposits of starch grains. Grain width and thickness are controlled by cell division and grain length by the elongation within the developing endosperm, a process that ceases 20–25 days after initiation of flowering [ 47 ]. According to [ 48 ], barley grain length is weakly affected by the environment. On the other hand, width and thickness vary more strongly according to environmental factors [ 47 , 49 ]. Our results showed differences in length, width and thickness for some varieties sown in several different years.

Patterns of grain morphological variability in relation to spike anatomy

More broadly, the dissimilarity networks calculated on the lateral and ventral grain shapes combined indicates that our dataset is firstly structured by the number of spike rows (2-rows vs. 6 rows), then according to hulled/naked types. The discrimination of 6-row/2-row varieties is mainly determined by the ventral shape while the discrimination of hulled/naked varieties is determined by the lateral outline. This structuration is readily explainable by the constraints imposed by the anatomy of the spike on the morphology of the grains. These constraints are very different for the 6 and 2-row types. In 2-row barley only the central spikelet is fertile at each node of the rachis whereas the 6-row barley has three fertile spikelets at each node. This difference in the number of fertile grains attached to a rachis node induces a twisting of the lateral grains in the 6-row type, which will have less place to develop in the ear and thus will have a different shape from the untwisted central grains [ 31 , 48 ]. A mutation on the vrs1 gene of the chromosome 2 is responsible for these differences in the fertility of lateral spikelets [ 50 – 52 ]. Morphological differences between hulled and naked barley grains are related to husks adherence to the grain after ripening, tight in hulled barleys and much looser in naked barley varieties [ 31 , 48 ]. These differences, here quantified and visualized, are well known to archaeobotanists and especially the fact that naked barley grains are rounder than grains from hulled varieties [ 25 ]. Here we can see that the naked grains are more particularly rounded on the lateral view.

Cross-validation percentages are coherent with the observed structuration, with the accuracy for discriminating 2-row from 6-row barley being higher for the ventral shape, whereas the CVP for discriminating naked from hulled barley is higher for the lateral shape. Additionally, the combination of lateral and ventral views allows a slightly better discrimination of 6-row/2-row types than hulled/naked types. Grains can be attributed to 2-row or 6-row categories with a mean CVP of 89.3%, and to naked or hulled categories with a CVP of 85.3%, and, combining the four categories, to a CVP of 76.7%. The CVP values for 6-row/2-row types are close to those obtained in previous studies by [ 31 ] (CVP = 91%) and [ 32 ] (CVP = 87.6%). In their study, [ 32 ] only included the central grains of 6-row varieties to compare them to 2-row grains. The strong discrimination they obtained show that even the morphology of central, “untwisted”, grains is constrained by the pressure of lateral spikelets in 6-row varieties. Here we decided to consider the whole population of 6-row grains, “twisted” and “untwisted”, in order to develop approaches that can be used to identify archaeological barley grains which cannot always be easily sorted according to “twisted” and “untwisted” categories. In this regard it is important to note that geometric morphometrics gives encouraging results to distinguish the four types 6-row-hulled, 6-row-naked, 2-row-hulled and 2-row-naked when uncharred grains are studied.

Morphological variation and sowing period

Contrary to genotypic data, which evidence a structuration primarily dependent on the sowing period [ 12 ], grain morphometric variation appeared less structured between winter and spring varieties. This is consistent with the fact that spike anatomy does not impose different morphological constraints between spring barley and winter barley, unlike the differences between 6-row/2-row and hulled/naked types. A weak morphological structuration nevertheless exists and is strong enough to identify winter/spring types when looking at the combined lateral-ventral shapes of the grain. Within each of the 6-row/2-row vs hulled/naked groups, relatively high CVPs, from 65.1% to 77.6%, are obtained for the distinction between winter/spring types.

Several hypotheses could be suggested to explain morphological differences between winter/spring types, since size and shape differences could be linked to genetic, physiologic or environmental influence during life cycle.

A first hypothesis could be genetic with the association between grain form QTLs [ 11 , 44 , 45 , 53 , 54 ] and loci related to phenological traits. Flowering time genes are classified into at least three families [ 55 ]: photoperiod genes (e.g. Ppd-H1), vernalization genes (e.g. Vrn-H1, Vrn-H2 and Vrn-H3, sgh1, sgh2, sgh3) and earliness per se (eps) genes, the last controlling flowering independently from photoperiod and temperature (e.g. Sdw1 for semi-dwarfing genes). Barley size QTLs are reported to be associated to Ppd-H1 locus [ 47 , 56 ], eps2 locus [ 47 , 56 , 57 ], swd1 locus, which are linked to late maturity, reduced plant height, increased tillers number and biomass production [ 47 ].

The second hypothesis to explain the differences between spring and winter barley grains morphology could be ecological and linked to resources trade-offs. The plant has limited resources for its growth until maturity, which necessarily induces trade-offs in the allocation of these resources to the different plant sinks. For example, tillers formation could limit the resources available for grain filling, which could limit the size of the grains even though their number per plant would be greater. Tillering is the production of multiple stems (tillers) starting from the initial single seedling. This ensures the formation of dense tufts and multiple ears [ 8 ]. Tillering is influenced by genetic variation [ 8 , 58 – 60 ] but also by environmental variation during pre-anthesis phase [ 58 , 61 , 62 ]. Tillering should be higher for winter barley than spring barley according to [ 8 ]. Thus, spring varieties that have less tillers than winter varieties could provide more resources for seed filling, which would explain why spring grains are wider and thicker than winter ones.

Another explanation to differences between spring and winter barley can be variation in environmental conditions during barley growth cycle. Several critical growth periods, as tillering, flowering, filling and ripening, are sensitive to rainfall [ 63 ], drought [ 64 , 65 ], available soil water [ 66 ]. The impact of environmental variability could be increased in our dataset as in the BRC of Clermont-Ferrand varieties were not always sown in the season they were supposed to be, i.e. spring varieties were occasionally sown in December because droughts during the spring sowing period are more frequent and winters are less harsh due to climate change. Unfortunately, this data was not always recorded, it would therefore be important to further investigate this question based on a detailed record of sowing time and environmental conditions along the life-cycle, and to grow the same accession at different periods.

This study of 1980 grains from 58 modern varieties demonstrated the possibility of determining barley characteristics (2 rows/6 rows, naked/hulled, spring/winter) using morphometric analysis of caryopses. Despite inter-annual variability, the characteristics related to varietal differences allow varieties to be distinguished with a cross-validation percentage of 48.1%. The higher identification percentages for the distinction between 2 rows and 6 rows (CVP = 89.3%), between naked and hulled barley (CVP = 85.3%) and between sowing periods (between 65.6% and 77.4%) are promising for documenting the characteristics of archaeological barley. Indeed, barley is found charred in archaeological contexts, preventing the study of grains taxonomy using genetics. Further studies should include charring experiment of the modern diversity in order to build a reference collection of known characteristics (such as 2-row/6-row or naked/hulled types) directly comparable with archaeological charred grains. I would be then possible to explore the diachronic evolution of barley and the factors shaping its diversity over time.

Supporting information

S1 table. barley accessions information as varieties, subspecies, hulled or naked barley, sowing period, agronomic selection number of grains sampled per accession..

https://doi.org/10.1371/journal.pone.0285195.s001

S1 Fig. Morphometric protocol applied to barley seeds.

https://doi.org/10.1371/journal.pone.0285195.s002

S2 Fig. Rarefaction curves of barley seeds in X and percentage of population variation recorded in Y.

https://doi.org/10.1371/journal.pone.0285195.s003

Acknowledgments

We gratefully thank the Biological Resource Center-INRAE Clermont Ferrand for providing the studied grains.

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• Published: 17 October 2012

A physical, genetic and functional sequence assembly of the barley genome

The international barley genome sequencing consortium.

Nature volume  491 ,  pages 711–716 ( 2012 ) Cite this article

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• Functional genomics
• Plant genetics

Barley ( Hordeum vulgare L.) is among the world’s earliest domesticated and most important crop plants. It is diploid with a large haploid genome of 5.1 gigabases (Gb). Here we present an integrated and ordered physical, genetic and functional sequence resource that describes the barley gene-space in a structured whole-genome context. We developed a physical map of 4.98 Gb, with more than 3.90 Gb anchored to a high-resolution genetic map. Projecting a deep whole-genome shotgun assembly, complementary DNA and deep RNA sequence data onto this framework supports 79,379 transcript clusters, including 26,159 ‘high-confidence’ genes with homology support from other plant genomes. Abundant alternative splicing, premature termination codons and novel transcriptionally active regions suggest that post-transcriptional processing forms an important regulatory layer. Survey sequences from diverse accessions reveal a landscape of extensive single-nucleotide variation. Our data provide a platform for both genome-assisted research and enabling contemporary crop improvement.

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Cultivated barley, derived from its wild progenitor Hordeum vulgare ssp. spontaneum , is among the world’s earliest domesticated crop species 1 and today represents the fourth most abundant cereal in both area and tonnage harvested ( http://faostat.fao.org ). Approximately three-quarters of global production is used for animal feed, 20% is malted for use in alcoholic and non-alcoholic beverages, and 5% as an ingredient in a range of food products 2 . Barley is widely adapted to diverse environmental conditions and is more stress tolerant than its close relative wheat 3 . As a result, barley remains a major food source in poorer countries 4 , maintaining harvestable yields in harsh and marginal environments. In more developed societies it has recently been classified as a true functional food. Barley grain is particularly high in soluble dietary fibre, which significantly reduces the risk of serious human diseases including type II diabetes, cardiovascular disease and colorectal cancers that afflict hundreds of millions of people worldwide 5 . The USA Food and Drug Administration permit a human health claim for cell-wall polysaccharides from barley grain.

As a diploid, inbreeding, temperate crop, barley has traditionally been considered a model for plant genetic research. Large collections of germplasm containing geographically diverse elite varieties, landraces and wild accessions are readily available 6 and undoubtedly contain alleles that could ameliorate the effect of climate change and further enhance dietary fibre in the grain. Enriching its broad natural diversity, extensive characterized mutant collections containing all of the morphological and developmental variation observed in the species have been generated, characterized and meticulously maintained. The major impediment to the exploitation of these resources in fundamental and breeding science has been the absence of a reference genome sequence, or an appropriate enabling alternative. Providing either of these has been the primary research challenge to the global barley community.

In response to this challenge, we present a novel model for delivering the genome resources needed to reinforce the position of barley as a model for the Triticeae, the tribe that includes bread and durum wheats, barley and rye. We introduce the barley genome gene space, which we define as an integrated, multi-layered informational resource that provides access to the majority of barley genes in a highly structured physical and genetic framework. In association with comparative sequence and transcriptome data, the gene space provides a new molecular and cellular insight into the biology of the species, providing a platform to advance gene discovery and genome-assisted crop improvement.

A sequence-enriched barley physical map

We constructed a genome-wide physical map of the barley cultivar (cv.) Morex by high-information-content fingerprinting 7 and contig assembly 8 of 571,000 bacterial artificial chromosome (BAC) clones ( ∼ 14-fold haploid genome coverage) originating from six independent BAC libraries 9 . After automated assembly and manual curation, the physical map comprised 9,265 BAC contigs with an estimated N50 contig size of 904 kilobases and a cumulative length of 4.98 Gb (Methods, Supplementary Note 2 ). It is represented by a minimum tiling path (MTP) of 67,000 BAC clones. Given a genome size of 5.1 Gb 10 , more than 95% of the barley genome is represented in the physical map, comparing favourably to the 1,036 contigs that represent 80% of the 1 Gb wheat chromosome 3B 11 .

We enhanced the physical map by integrating shotgun sequence information from 5,341 gene-containing 12 , 13 and 937 randomly selected BAC clones (Methods, Supplementary Notes 2 and 3 , and Supplementary Table 4 ), and 304,523 BAC-end sequence (BES) pairs ( Supplementary Table 3 ). These provided 1,136 megabases (Mb) of genomic sequence integrated directly into the physical map ( Supplementary Tables 3 and 4 ). This framework facilitated the incorporation of whole-genome shotgun sequence data and integration of the physical and genetic maps. We generated whole-genome shotgun sequence data from genomic DNA of cv. ‘Morex’ by short-read Illumina GAIIx technology, using a combination of 300 base pairs (bp) paired-end and 2.5 kb mate-pair libraries, to >50-fold haploid genome coverage ( Supplementary Note 3.3 ). De novo assembly resulted in sequence contigs totalling 1.9 Gb. Due to the high proportion of repetitive DNA, a substantial part of the whole-genome shotgun data collapsed into relatively small contigs characterized by exceptionally high read depths. Overall, 376,261 contigs were larger than 1 kb (N50 = 264,958 contigs, N50 length = 1,425 bp). Of these, 112,989 (308 Mb) could be anchored directly to the sequence-enriched physical map by sequence homology.

We implemented a hierarchical approach to further anchor the physical and genetic maps (Methods, Supplementary Note 4 ). A total of 3,241 genetically mapped gene-based single-nucleotide variants (SNV) and 498,165 sequence-tag genetic markers 14 allowed us to use sequence homology to assign 4,556 sequence-enriched physical map contigs spanning 3.9 Gb to genetic positions along each barley chromosome. An additional 1,881 contigs were assigned to chromosomal bins by sequence homology to chromosome-arm-specific sequence data sets 15 ( Supplementary Note 4.4 ). Thus, 6,437 physical map contigs totalling 4.56 Gb (90% of the genome), were assigned to chromosome arm bins, the majority in linear order. Non-anchored contigs were typically short and lacked genetically informative sequences required for positional assignment.

Consistent with genome sequences of other grass species 16 the peri-centromeric and centromeric regions of barley chromosomes exhibit significantly reduced recombination frequency, a feature that compromises exploitation of genetic diversity and negatively impacts genetic studies and plant breeding. Approximately 1.9 Gb or 48% of the genetically anchored physical map (3.9 Gb) was assigned to these regions ( Fig. 1 and Supplementary Fig. 11 ).

Track a gives the seven barley chromosomes. Green/grey colour depicts the agreement of anchored fingerprint (FPC) contigs with their chromosome arm assignment based on chromosome-arm-specific shotgun sequence reads (for further details see Supplementary Note 4 ). For 1H only whole-chromosome sequence assignment was available. Track b , distribution of high-confidence genes along the genetic map; track c , connectors relate gene positions between genetic and the integrated physical map given in track d . Position and distribution of track e class I LTR-retroelements and track f class II DNA transposons are given. Track g , distribution and positioning of sequenced BACs.

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Repetitive nature of the barley genome

A characteristic of the barley genome is the abundance of repetitive DNA 17 . We observed that approximately 84% of the genome is comprised of mobile elements or other repeat structures ( Supplementary Note 5 ). The majority (76% in random BACs) of these consists of retrotransposons, 99.6% of which are long terminal repeat (LTR) retrotransposons. The non-LTR retrotransposons contribute only 0.31% and the DNA transposons 6.3% of the random BAC sequence. In the fraction of the genome with a high proportion of repetitive elements, the LTR Gypsy retrotransposon superfamily was 1.5-fold more abundant than the Copia superfamily, in contrast to observations in both Brachypodium 18 and rice 19 . However, gene-bearing BACs were slightly depleted of retrotransposons, consistent with Brachypodium 18 where young Copia retroelements are preferentially found in gene-rich, recombinogenic regions from which inactive Gypsy retroelements have been lost by LTR–LTR recombination. Overall, we see reduced repetitive DNA content within the terminal 10% of the physical map of each barley chromosome arm ( Fig. 1 ). Class I and II elements show non-quantitative reverse-image distribution along barley chromosomes ( Fig. 1 ), a feature shared with other grass genomes 16 , 20 and shown by fluorescence in situ hybridization (FISH) mapping 17 . Not surprisingly, the whole-genome shotgun assembly shows a lower abundance of LTR retrotransposons (average 53%) than gene-bearing BACs. That LTR retrotransposons are long ( ∼ 10 kb), highly repetitive and often nested 21 supports our assumption that short reads either collapsed or did not assemble. Short interspersed elements (SINEs) 22 , short (80–600 bp) non-autonomous retrotransposons that are highly repeated in barley, showed no differential exclusion from the assemblies. However, miniature inverted-repeat transposable elements (MITEs), small non-autonomous DNA transposons 23 , were twofold enriched in the whole-genome shotgun assemblies compared with BES reads or random BACs, consistent with the gene richness of the assemblies and their association with genes 23 . Both MITEs and SINEs are 1.5 to 2-fold enriched in gene-bearing BACs which could indicate that SINEs are also preferentially integrated into gene-rich regions, or because they are older than LTR retroelements, may simply remain visible in and around genes where retro insertions have been selected against.

Transcribed portion of the barley genome

The transcribed complement of the barley gene space was annotated by mapping 1.67 billion RNA-seq reads (167 Gb) obtained from eight stages of barley development as well as 28,592 barley full-length cDNAs 24 to the whole-genome shotgun assembly (Methods, Supplementary Notes 6, 7 and Supplementary Tables 20–22 ). Exon detection and consensus gene modelling revealed 79,379 transcript clusters, of which 75,258 (95%) were anchored to the whole-genome shotgun assembly ( Supplementary Notes 7.1.1 and 7.1.2 ). Based on a gene-family-directed comparison with the genomes of Sorghum , rice, Brachypodium and Arabidopsis , 26,159 of these transcribed loci fall into clusters and have homology support to at least one reference genome ( Supplementary Fig. 16 ); they were defined as high-confidence genes. Comparison against a data set of metabolic genes in Arabidopsis thaliana 25 indicated a detection rate of 86%, allowing the barley gene-set to be estimated as approximately 30,400 genes. Due to lack of homology and missing support from gene family clustering, 53,220 transcript loci were considered low-confidence ( Table 1 ). High-confidence and low-confidence barley genes exhibited distinct characteristics: 75% of the high-confidence genes had a multi-exon structure, compared with only 27% of low-confidence genes ( Table 1 ). The mean size of high-confidence genes was 3,013 bp compared with 972 bp for low-confidence genes. A total of 14,481 low-confidence genes showed distant homology to plant proteins in public databases ( Supplementary Notes 7.1.2, 7.1.4 and Supplementary Fig. 18 ), identifying them as potential gene fragments known to populate Triticeae genomes at high copy number and that often result from transposable element activity 26 .

A total of 15,719 high-confidence genes could be directly associated with the genetically anchored physical map ( Supplementary Note 4 ). An additional 3,743 were integrated by invoking a conservation of synteny model ( Supplementary Note 4.5 ) and a further 4,692 by association with chromosome arm whole-genome shotgun data ( Supplementary Note 4.4 and Supplementary Table 15 ). Importantly, the N50 length of whole-genome shotgun sequence contigs containing high-confidence genes was 8,172 bp, which is generally sufficient to include the entire coding sequence, and 5′ and 3′ untranslated regions (UTRs). Overall 24,154 high-confidence genes (92.3%) were associated and positioned in the physical/genetic scaffold, representing a gene density of five genes per Mb. Proximal and distal ends of chromosomes are more gene-rich, on average containing 13 genes per Mb ( Fig. 1 ).

In comparison with sequenced model plant genomes, gene family analysis ( Supplementary Note 7.1.3 ) revealed some gene families that exhibited barley-specific expansion. We defined the functions of members of these families using gene ontology (GO) and PFAM protein motifs ( Supplementary Table 25 ). Gene families with highly overrepresented GO/PFAM terms included genes encoding (1,3)-β-glucan synthases, protease inhibitors, sugar-binding proteins and sugar transporters. NB-ARC (a nucleotide-binding adaptor shared by APAF-1, certain R gene products and CED-4 27 ) domain proteins, known to be involved in defence responses, were also overrepresented, including 191 NBS-LRR type genes. These tended to cluster towards the distal regions of barley chromosomes ( Supplementary Fig. 17 ), including a major group on barley chromosome 1HS, co-localizing with the MLA powdery mildew resistance gene cluster 28 . Biased allocation to recombination-rich regions provides the genomic environment for generating sequence diversity required to cope with dynamic pathogen populations 29 , 30 . It is noteworthy that the highly over-represented (1,3)-β-glucan synthase genes have also been implicated in plant–pathogen interactions 31 .

Regulation of gene expression

Deep RNA sequence data (RNA-seq) provided insights into the spatial and temporal regulation of gene expression ( Supplementary Note 7.2 ). We found 72–84% of high-confidence genes to be expressed in all spatiotemporal RNA-seq samples ( Fig. 2a ), slightly lower than reported for rice 32 where ∼ 95% of transcripts were found in more than one developmental or tissue sample. More importantly, 36–55% of high-confidence barley genes seemed to be differentially regulated between samples ( Fig. 2b ), highlighting the inherent dynamics of barley gene expression.

a , Barley gene expression in different spatial and temporal RNA-seq samples ( Supplementary Notes 6, 7 ). Numbers refer to high-confidence genes. b , Dendrogram depicting relatedness of samples and colour-coded matrix showing number of significantly upregulated high-confidence genes in pairwise comparisons. Σ, total number of non-redundant high-confidence genes upregulated in comparison to all other samples. Height, complete linkage cluster distance (log 2 (fragments per kilobase of exon per million fragments mapped)); see Supplementary Note 7.2.5.1. c , Distribution and overlap of alternatively spliced barley transcripts between RNA-seq samples. d , Distribution and overlap of alternative splicing transcripts fulfilling criteria for PTC+ as detected in different spatial and temporal RNA-seq samples ( Supplementary Note 7.4 ).

Two notable features support the importance of post-transcriptional processing as a central regulatory layer ( Supplementary Notes 7.3 and 7.4 ). First, we observed evidence for extensive alternative splicing. Of the intron-containing high-confidence barley genes, 73% had evidence of alternative splicing (55% of the entire high-confidence set). The spatial and temporal distribution of alternative splicing transcripts deviated significantly from the general occurrence of transcripts in the different tissues analysed ( Fig. 2c ). Only 17% of alternative splicing transcripts were shared among all samples, and 17–27% of the alternative splicing transcripts were detected only in individual samples, indicating pronounced alternative splicing regulation. We found 2,466 premature termination codon-containing (PTC+) alternative splicing transcripts (9.4% of high-confidence genes) ( Fig. 2d and Table 2 ), similar to the percentage of nonsense-mediated decay (NMD)-controlled genes in a wide range of species 33 , 34 . Premature termination codons activate the NMD pathway 35 , which leads to rapid degradation of PTC+ transcripts, and have been associated with transcriptional regulation during disease and stress response in human and Arabidopsis , respectively 34 , 36 , 37 , 38 , 39 . The distribution of PTC+ transcripts was strikingly dissimilar, both spatially and temporally, with only 7.4% shared and between 31% and 40% exclusively observed in only a single sample ( Fig. 2d ). Genes encoding PTC+-containing transcripts show a broad spectrum of GO terms and PFAM domains and are more prevalent in expanded gene families. These observations support a central role for alternative splicing/NMD-dependent decay of PTC+ transcripts as a mechanism that controls the expression of many different barley genes.

Second, recent reports have highlighted the abundance of novel transcriptionally active regions in rice that lack homology to protein-coding genes or open reading frames (ORFs) 40 . In barley as many as 27,009 preferentially single-exon low-confidence genes can be classified as putative novel transcriptionally active regions ( Supplementary Note 7.1.4 ). We investigated their potential significance by comparing the homology of barley novel transcriptionally active regions with the rice and Brachypodium genomes that respectively represent 50 and 30 million years of evolutionary divergence 18 . A total of 4,830 and 2,450 novel transcriptionally active regions yielded a homology match to the Brachypodium and rice genomes, respectively (intersection of 2,046; BLAST P value ≤ 10 −5 ), indicating a putative functional role in pre-mRNA processing or other RNA regulatory processes 41 , 42 .

Natural diversity

Barley was domesticated approximately 10,000 years ago 1 . Extensive genotypic analysis of diverse germplasm has revealed that restricted outcrossing (0–1.8%) 43 , combined with low recombination in pericentromeric regions, has resulted in modern germplasm that shows limited regional haplotype diversity 44 . We investigated the frequency and distribution of genome diversity by survey sequencing four diverse barley cultivars (‘Bowman’, ‘Barke’, ‘Igri’ and ‘Haruna Nijo’) and an H. spontaneum accession (Methods and Supplementary Note 8 ) to a depth of 5–25-fold coverage, and mapping sequence reads against the barley cultivar ‘Morex’ gene space. We identified more than 15 million non-redundant single-nucleotide variants (SNVs). H. spontaneum contributed almost twofold more SNV than each of the cultivars ( Supplementary Table 28 ). Up to 6 million SNV per accession could be assigned to chromosome arms, including up to 350,000 associated with exons ( Supplementary Table 29 ). Approximately 50% of the exon-located SNV were integrated into the genetic/physical framework ( Fig. 3 , Supplementary Table 30 and Supplementary Fig. 31 ), providing a platform to establish true genome-wide marker technology for high-resolution genetics and genome-assisted breeding.

Barley chromosomes indicated as inner circle of grey bars. Connector lines give the genetic/physical relationship in the barley genome. SNV frequency distribution displayed as five coloured circular histograms (scale, relative abundance of SNVs within accession; abundance, total number of SNVs in non-overlapping 50-kb intervals of concatenated ‘Morex’ genomic scaffold; range, zero to maximum number of SNVs per 50-kb interval). Selected patterns of SNV frequency indicated by coloured arrowheads (for further details see Supplementary Note 8 ). Colouring of arrowheads refers to cultivar with deviating SNV frequency for the respective region.

We observed a decrease in SNV frequency towards the centromeric and peri-centromeric regions of all barley chromosomes, a pattern that seemed more pronounced in the barley cultivars. This trend was supported by SNV identified in RNA-seq data from six additional cultivars mapped onto the Morex genomic assembly ( Supplementary Note 8.2 ). We attribute this pattern of eroded genetic diversity to low recombination in the pericentromeric regions, which reduces effective population size and consequently haplotype diversity. Whereas H. spontaneum may serve here as a reservoir of genetic diversity, using this diversity may itself be compromised by restricted recombination and the consequent inability to disrupt tight linkages between desirable and deleterious alleles. Surprisingly, the short arm of chromosome 4H had a significantly lower SNV frequency than all other barley chromosomes ( Supplementary Fig. 33 ). This may be a consequence of a further reduction in recombination frequency on this chromosome, which is genetically (but not physically) shortest. Reduced SNV diversity was also observed in regions we interpret to be either the consequences of recent breeding history or could indicate landmarks of domestication ( Fig. 3 ).

The size of Triticeae cereal genomes, due to their highly repetitive DNA composition, has severely compromised the assembly of whole-genome shotgun sequences and formed a barrier to the generation of high-quality reference genomes. We circumvented these problems by integrating complementary and heterogeneous sequence-based genomic and genetic data sets. This involved coupling a deep physical map with high density genetic maps, superimposing deep short-read whole-genome shotgun assemblies, and annotating the resulting linear, albeit punctuated, genomic sequence with deep-coverage RNA-derived data (full-length cDNA and RNA-seq). This allowed us to systematically delineate approximately 4 Gb (80%) of the genome, including more than 90% of the expressed genes. The resulting genomic framework provides a detailed insight into the physical distribution of genes and repetitive DNA and how these features relate to genetic characteristics such as recombination frequency, gene expression and patterns of genetic variation.

The centromeric and peri-centromeric regions of barley chromosomes contain a large number of functional genes that are locked into recombinationally ‘inert’ genomic regions 45 , 46 . The gene-space distribution highlights that these regions expand to almost 50% of the physical length of individual chromosomes. Given well-established levels of conserved synteny, this will probably be a general feature of related grass genomes that will have important practical implications. For example, infrequent recombination could function to maintain evolutionarily selected and co-adapted gene complexes. It will certainly restrict the release of the genetic diversity required to decouple advantageous from deleterious alleles, a potential key to improving genetic gain. Understanding these effects will have important consequences for crop improvement. Moreover, for gene discovery, forward genetic strategies based on recombination will not be effective in these regions. Whereas alternative approaches exist for some targets (for example, by coupling resequencing technologies with collections of natural or induced mutant alleles), for most traits it remains a serious impediment. Some promise may lie in manipulating patterns of recombination by either genetic or environmental intervention 47 . Quite strikingly, our data also reveal that a complex layer of post-transcriptional regulation will need to be considered when attempting to link barley genes to functions. Connections between post-transcriptional regulation such as alternative splicing and functional biological consequences remain limited to a few specific examples 48 , but the scale of our observations suggest this list will expand considerably.

In conclusion, the barley gene space reported here provides an essential reference for genetic research and breeding. It represents a hub for trait isolation, understanding and exploiting natural genetic diversity and investigating the unique biology and evolution of one of the world’s first domesticated crops.

Methods Summary

Methods are available in the online version of the paper.

Online Methods

Building the physical map.

BAC clones of six libraries of cultivar ‘Morex’ 9 , 49 were analysed by high information content fingerprinting (HICF) 7 , 9 . A total of 571,000 edited profiles was assembled using FPC v9.2 8 ( Supplementary Table 2 ) (Sulston score threshold of 10 −90 , tolerance = 5, tolerated Q clones = 10%). Nine iterative automated re-assemblies were performed at successively reduced stringency (Sulston score of 10 −85 to 10 −45 ). A final step of manual merging of FPC contigs was performed at lower stringency (Sulston score threshold 10 −25 ) considering genetic anchoring information for markers with a genetic distance ≤ ± 5 cM. This produced 9,265 FPcontigs (approximately 14-fold haploid genome coverage) ( Supplementary Table 2 ).

Genomic sequencing

BAC-end sequencing (BES). BAC insert ends were sequenced using Sanger sequencing ( Supplementary Note 2.1 ). Vector and quality trimming of sequence trace files was conducted using LUCY 50 ( http://www.jcvi.org/cms/research/software/ ). Short reads (that is, < 100 bp) were removed. Organellar DNA and barley pathogen sequences were filtered by BLASTN comparisons to public sequence databases ( http://www.ncbi.nlm.nih.gov/ ).

BAC shotgun sequencing (BACseq). Seed BACs of the FPC map were sequenced to reveal gene sequence information for physical map anchoring. 4,095 BAC clones were shotgun sequenced in pools of 2 × 48 individually barcoded BACs on Roche/454 GS FLX or FLX Titanium 51 , 52 . Sequences were assembled using MIRA v3.2.0 ( http://www.chevreux.org/projects_mira.html ) at default parameters with features ‘accurate’, ‘454’, ‘genome’, ‘denovo’. An additional 2,183 gene-bearing BACs ( Supplementary Note 3.2 ) were sequenced using Illumina HiSeq 2000 in 91 combinatorial pools 13 . Deconvoluted reads were assembled using VELVET 53 . Assembly statistics are given in Supplementary Table 4 .

Whole-genome shotgun sequencing. Illumina paired-end (PE; fragment size ∼ 350 bp) and mate-pair (MP; fragment size ∼ 2.5 kb) libraries were generated from fragmented genomic DNA 54 of different barley cultivars (‘Morex’, ‘Barke’, ‘Bowman’, ‘Igri’) and an S3 single-seed selection of a wild barley accession B1K-04-12 55 ( Hordeum vulgare ssp. spontaneum ). Libraries were sequenced by Illumina GAIIx and Hiseq 2000. Genomic DNA of cultivar ‘Haruna Nijo’ (size range of 600–1,000 bp) was sequenced using Roche 454 GSFLX Titanium chemistry.

Whole-genome shotgun sequence assembly

PE and MP whole-genome shotgun libraries were calibrated for fragment sizes by mapping pairs against the chloroplast sequence of barley (NC_008590) using BWA 56 . Sequences were quality trimmed and de novo assembled using CLC Assembly Cell v3.2.2 ( http://www.clcbio.com/ ). Independent de novo assemblies were performed from data of cultivars ‘Morex’, ‘Bowman’ and ‘Barke’.

Transcriptome sequencing

Eight tissues of cultivar ‘Morex’ (three biological replications each) earmarking stages of the barley life cycle from germinating grain to maturing caryopsis were selected for deep RNA sequencing (RNA-seq). Plant growth, sampling and sequencing is detailed in Supplementary Information ( Supplementary Note 6 ). Further mRNA sequencing data was generated from eight additional spring barley cultivars within a separate study and was used here for sequence diversity analysis ( Supplementary Note 8.2 ).

Genetic framework of the physical map

The genetic framework for anchoring the physical map of barley was built on a single-nucleotide variation (SNV) map 57 ( Supplementary Note 4.3 ) which provided the highest marker density (3,973) and resolution ( N = 360, RIL/F8) for a single bi-parental mapping population in barley. Additional high-density genetic marker maps ( Supplementary Note 4.3 ) were compared and aligned on the basis of shared markers. Furthermore, we used genotyping-by-sequencing (GBS) 58 to generate high-density genetic maps comprising 34,396 SNVs and 21,384 SNVs as well as 241,159 and 184,796 dominant (presence/absence) tags for the two doubled haploid populations Oregon Wolfe Barley 14 and Morex × Barke 45 , respectively. Altogether 498,165 marker sequence tags were used ( Supplementary Table 11 ).

Genetic anchoring

Genetic integration of the physical map involved procedures of direct and indirect anchoring.

Direct anchoring. Genetic markers were assigned to BAC clones/BAC contigs by three different procedures ( Supplementary Note 4.3 and Supplementary Table 9 ). 2,032 PCR-based markers from published genetic maps 59 , 60 were PCR-screened on custom multidimensional (MD) DNA pools ( http://ampliconexpress.com/ ) obtained from BAC library HVVMRXALLeA 9 . A single haploid genome equivalent of these MD pools was used for multiplexed screening of 42,302 barley EST-derived unigenes represented on a custom 44K Agilent microarray as previously described 61 . 27,231 barley unigenes, comprising 1,121 with a genetic map position 45 , 62 , could be assigned to 12,313 BACs. 14,600 clones from BAC library HVVMRXALLhA were screened with 3,072 SNP markers on Illumina GoldenGate assays 45 leading to 1,967 markers directly assigned to BACs 13 ; approximately one third of this information has been included in the present work.

Indirect anchoring. Sequence resources associated with the FPCmap framework provided the basis for extensive in silico integration of genetic marker information ( Supplementary Note 4.3 and Supplementary Table 11 ). Repeat masked BES sequences, sequences of anchored markers and 6,295 sequenced BACs allowed integration of 307 Mb of ‘Morex’ whole-genome shotgun contigs into the FPC map. Genetic markers and barley gene sequences were positioned to this reference by strict sequence homology association. Overall 8,170 ( ∼ 4.6 Gb) BAC contigs received sequence and/or anchoring information ( Supplementary Note 4 ). 4,556 FPC contigs (Σ = 3.9 Gb) were anchored to the genetic framework.

Analysis of repetitive DNA and repeat masking

Repeat detection and analysis was undertaken as previously described 18 , 20 with the exception of an updated repeat library complemented by de novo detected repetitive elements from barley ( Supplementary Note 5 ).

Gene annotation, functional categorization and differential expression

Publically available barley full-length cDNAs 24 and RNA-seq data generated in the project ( Supplementary Note 6 ) were used for structural gene calling ( Supplementary Note 7 ). Full-length cDNAs and RNA-seq data were anchored to repeat masked whole-genome shotgun sequence contigs using GenomeThreader 63 and CuffLinks 64 , respectively, the latter providing also information of alternatively spliced transcripts. Structural gene calls were combined and the longest ORF for each locus was used as representative for gene family analysis ( Supplementary Note 7.1.2 ).

Gene family clustering was undertaken using OrthoMCL ( Supplementary Note 7.1.3 ) by comparing against the genomes of Oryza sativa (RAP2), Sorghum bicolor , Brachypodium distachyon (v 1.4) and Arabidopsis thaliana (TAIR10 release).

Analysis of differential gene expression ( Supplementary Note 7.2 ) was performed on RNA-seq data using CuffDiff 65 .

Analysis of sequence diversity

Genome-wide SNV was assessed by mapping (BWA v0.5.9-r16 56 ) the original sequence reads of sequenced genotypes to a de novo assembly of cultivar ‘Morex’. Sequence reads from RNA-seq were mapped against the ‘Morex’ assembly. Details are provided in Supplementary Note 8 .

Accession codes

Data deposits.

Sequence resources generated or compiled in this study have been deposited at EMBL/ENA or NCBI GenBank. A full list of sequence raw data accession numbers as well as URLs for data download, visualization or search are provided in Supplementary Note 1 and Supplementary Table 1 .

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Acknowledgements

This work has been supported from the following funding sources: German Ministry of Education and Research (BMBF) grant 0314000 “BARLEX” to K.F.X.M., M.P., U.S. and N.S.; Leibniz Association grant (Pakt f. Forschung und Innovation) to N.S.; European project of the 7th framework programme “TriticeaeGenome” to R.W., A.S., K.F.X.M., M.M. and N.S.; SFB F3705, of the Austrian Wissenschaftsfond (FWF) to K.F.X.M.; ERA-NET PG project “BARCODE” grant to M.M., N.S. and R.W.; Scottish Government/BBSRC grant BB/100663X/1 to R.W., D.M., P.H., J.R., M.C. and P.K.; National Science Foundation grant DBI 0321756 “Coupling EST and Bacterial Artificial Chromosome Resources to Access the Barley Genome” and DBI-1062301 "Barcoding-Free Multiplexing: Leveraging Combinatorial Pooling for High-Throughput Sequencing" to T.J.C. and S.L.; USDA-CSREES-NRI grant 2006-55606-16722 “Barley Coordinated Agricultural Project: Leveraging Genomics, Genetics, and Breeding for Gene Discovery and Barley Improvement” to G.J.M., R.P.W., T.J.C. and S.L.; the Agriculture and Food Research Initiative Plant Genome, Genetics and Breeding Program of USDA-CSREES-NIFA grant 2009-65300-05645 “Advancing the Barley Genome” to T.J.C., S.L. and G.J.M.; BRAIN and NBRP-Japan grants to K.S., Japanese MAFF Grant (TRG1008) to T.M. A full list of acknowledgements is in the Supplementary Information .

Author information

Authors and affiliations.

MIPS/IBIS, Helmholtz Zentrum München, D-85764 Neuherberg, Germany.,

Klaus F. X. Mayer, Thomas Nussbaumer, Heidrun Gundlach, Mihaela Martis, Klaus F. X. Mayer ( leader ), Manuel Spannagl, Matthias Pfeifer, Heidrun Gundlach, Klaus F. X. Mayer ( leader ), Heidrun Gundlach, Klaus F. X. Mayer ( co-leader ), Matthias Pfeifer, Manuel Spannagl, Klaus F. X. Mayer ( co-leader ) & Klaus F. X. Mayer ( co-leader )

The James Hutton Institute, Invergowrie, Dundee DD2 5DE, UK.,

Robbie Waugh, Pete Hedley, Hui Liu, Jenny Morris, Robbie Waugh, Robbie Waugh ( leader ), Pete Hedley, Jenny Morris, Joanne Russell, Arnis Druka, David Marshall, Micha Bayer, Robbie Waugh ( leader ), David Marshall, Micha Bayer, Robbie Waugh ( co-leader ), Robbie Waugh ( co-leader ) & John W. S. Brown

Australian Centre for Plant Functional Genomics, University of Adelaide, Glen Osmond 5064, Australia.,

Peter Langridge, Bujun Shi, Peter Langridge & Peter Langridge

Department of Botany & Plant Sciences, University of California, Riverside, California 92521, USA.,

Timothy J. Close, Kavitha Madishetty, Prasanna Bhat, Matthew Moscou, Josh Resnik, Timothy J. Close, Steve Wanamaker, Timothy J. Close ( co-leader ), Timothy J. Close, Steve Wannamaker & Timothy J. Close

Department of Plant Pathology & Microbiology, USDA-ARS, Iowa State University, Ames, Iowa 50011-1020, USA.,

Roger P. Wise, Roger P. Wise, Roger P. Wise & Roger P. Wise

Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), D-06466 Seeland OT Gatersleben, Germany.,

Andreas Graner, Nils Stein, Ruvini Ariyadasa, Daniela Schulte, Naser Poursarebani, Ruonan Zhou, Burkhard Steuernagel, Martin Mascher, Uwe Scholz, Andreas Graner, Nils Stein ( leader ), Burkhard Steuernagel, Uwe Scholz, Nils Stein ( leader ), Burkhard Steuernagel, Uwe Scholz, Axel Himmelbach, Nils Stein ( leader ), Daniela Schulte, Burkhard Steuernagel, Nils Stein, Ruvini Ariyadasa, Naser Poursarebani, Burkhard Steuernagel, Uwe Scholz, Nils Stein, Nils Stein, Burkhard Steuernagel, Thomas Schmutzer, Martin Mascher, Uwe Scholz, Nils Stein ( leader ) & Nils Stein ( leader )

National Institute of Agrobiological Sciences, 2-1-2, Kannondai, Tsukuba Ibaraki 305-8602, Japan.,

Takashi Matsumoto, Takashi Matsumoto & Tsuyoshi Tanaka

Okayama University, Kurashiki 710-0046, Japan.,

Kazuhiro Sato, Kazuhiro Sato, Kazuhiro Sato & Kazuhiro Sato

MTT Agrifood Research and Institute of Biotechnology, University of Helsinki, FIN-00014 Helsinki, Finland.,

Alan Schulman, Cédric Moisy, Jaakko Tanskanen, Alan Schulman ( leader ) & Alan Schulman

Department of Agronomy and Plant Genetics, Department of Plant Biology, University of Minnesota, St Paul, Minnesota 55108, USA.,

Gary J. Muehlbauer, Gary J. Muehlbauer, Gary J. Muehlbauer & Gary J. Muehlbauer

Institute of Evolution, University of Haifa, Haifa 31905, Israel.,

Zeev Frenkel & Avraham Korol

INRA-CNRGV, Auzeville CS 52627, France.,

Hélène Bergès

Leibniz Institute of Age Research- Fritz Lipmann Institute (FLI), D-07745 Jena, Germany.,

Stefan Taudien, Marius Felder, Marco Groth, Matthias Platzer, Stefan Taudien, Marius Felder, Matthias Platzer, Stefan Taudien, Matthias Platzer & Matthias Platzer

Department of Computer Science & Engineering, University of California, Riverside, California 92521, USA.,

Stefano Lonardi, Denisa Duma, Matthew Alpert, Francesa Cordero, Marco Beccuti, Gianfranco Ciardo & Yaqin Ma

Istituto di Genomica Applicata, Via J. Linussio 51, 33100 Udine, Italy.,

Federica Cattonaro, Simone Scalabrin, Michele Morgante, Simone Scalabrin, Andrea Zuccolo & Michele Morgante

Dipartimento di Scienze Agrarie ed Ambientali, Università di Udine, 33100 Udine, Italy.,

Vera Vendramin, Slobodanka Radovic, Michele Morgante, Vera Vendramin & Michele Morgante

University of Arizona, Arizona Genomics Institute, Tucson, 85721, Arizona, USA

USDA-ARS Hard Winter Wheat Genetics Research Unit and Kansas State University, Manhattan, 66506, Kansas, USA

Jesse Poland

The Genome Analysis Centre, Norwich Research Park, Norwich NR4 7UH, UK.,

David Swarbreck, Dharanya Sampath, Sarah Ayling, Melanie Febrer & Mario Caccamo

Division of Plant Sciences, University of Dundee at The James Hutton Institute, Invergowrie, Dundee DD2 5DA, UK.,

John W. S. Brown

ARC Centre of Excellence in Plant Cell Walls, University of Adelaide, Waite Campus, Glen Osmond, South Australia 5064, Australia.,

Geoffrey B. Fincher

Department of Computer Science, Corso Svizzera 185, 10149 Torino, Italy.,

Francesa Cordero

Principal investigators

• Klaus F. X. Mayer
• , Robbie Waugh
• , Peter Langridge
• , Timothy J. Close
• , Roger P. Wise
• , Andreas Graner
• , Takashi Matsumoto
• , Kazuhiro Sato
• , Alan Schulman
• , Gary J. Muehlbauer
•  & Nils Stein

Physical map construction and direct anchoring

• Ruvini Ariyadasa
• , Daniela Schulte
• , Naser Poursarebani
• , Ruonan Zhou
• , Burkhard Steuernagel
• , Martin Mascher
• , Uwe Scholz
• , Bujun Shi
• , Kavitha Madishetty
• , Jan T. Svensson
• , Prasanna Bhat
• , Matthew Moscou
• , Josh Resnik
• , Pete Hedley
• , Jenny Morris
• , Zeev Frenkel
• , Avraham Korol
• , Hélène Bergès
•  & Nils Stein ( leader )

Genomic sequencing and assembly

• Burkhard Steuernagel
• , Stefan Taudien
• , Marius Felder
• , Marco Groth
• , Matthias Platzer

BAC sequencing and assembly

• , Axel Himmelbach
• , Stefano Lonardi
• , Denisa Duma
• , Matthew Alpert
• , Francesa Cordero
• , Marco Beccuti
• , Gianfranco Ciardo
• , Steve Wanamaker
• , Timothy J. Close ( co-leader )

BAC-end sequencing

• Federica Cattonaro
• , Vera Vendramin
• , Simone Scalabrin
• , Slobodanka Radovic
• , Michele Morgante
• , Nils Stein
•  & Robbie Waugh ( leader )

Integration of physical/genetic map and sequence resources

• Thomas Nussbaumer
• , Heidrun Gundlach
• , Mihaela Martis
• , Ruvini Ariyadasa
• , Jesse Poland
•  & Klaus F. X. Mayer ( leader )

Gene annotation

• Manuel Spannagl
• , Matthias Pfeifer

Repetitive DNA analysis

• Heidrun Gundlach
• , Cédric Moisy
• , Jaakko Tanskanen
• , Andrea Zuccolo
• , Klaus F. X. Mayer ( co-leader )
•  & Alan Schulman ( leader )

Transcriptome sequencing and analysis

• Matthias Pfeifer
• , Manuel Spannagl
• , Joanne Russell
• , Arnis Druka
• , David Marshall
• , Micha Bayer
• , David Swarbreck
• , Dharanya Sampath
• , Sarah Ayling
• , Melanie Febrer
• , Mario Caccamo
• , Tsuyoshi Tanaka
• , Steve Wannamaker

Re-sequencing and diversity analysis

• , Thomas Schmutzer
• , Robbie Waugh ( co-leader )

Writing and editing of the manuscript

• Klaus F. X. Mayer ( co-leader )
• , John W. S. Brown
• , Geoffrey B. Fincher

Contributions

See list of consortium authors. R.A., D.S., H.L., B.S., S.T., M.G., F.C., T.N., M.S., M.P., H.G., P.H., T.S., K.F.X.M., R.W. and N.S. contributed equally to their respective work packages and tasks.

Corresponding authors

Correspondence to Klaus F. X. Mayer , Robbie Waugh , Nils Stein , Robbie Waugh , Nils Stein , Nils Stein , Nils Stein , Nils Stein , Robbie Waugh , Nils Stein , Klaus F. X. Mayer , Klaus F. X. Mayer , Klaus F. X. Mayer , Nils Stein , Klaus F. X. Mayer , Robbie Waugh , Robbie Waugh , Nils Stein , Klaus F. X. Mayer , Robbie Waugh or Nils Stein .

Ethics declarations

Competing interests.

The author declare no competing financial interests.

Supplementary information

Supplementary information.

This file contains Supplementary Text, Supplementary Figures 1-33, Supplementary Tables 1-24 and 26-33 (see separate file for Supplementary Table 25) and Supplementary References – see Contents for more details. (PDF 6676 kb)

Supplementary Data

This file contains Supplementary Table 25, which shows GO terms and PFAM domains over- and underrepresented in barley-expanded gene clusters. (XLS 117 kb)

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The International Barley Genome Sequencing Consortium. A physical, genetic and functional sequence assembly of the barley genome. Nature 491 , 711–716 (2012). https://doi.org/10.1038/nature11543

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Issue Date : 29 November 2012

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Wheat and Barley Production Trends and Research Priorities: A Global Perspective

• First Online: 27 March 2022

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• Surabhi Mittal 8  

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Global food security is impacted by climate change that further is linked with the challenge of low productivity, issues of diseases and pests, varietal replacement and sustainable input utilization. Given the role of wheat and barley in human food, animal feed and livelihood through industrial use, it is important to manage its production in a sustainable manner. It is important to increase both the yields and reduce the yield gaps through efficient use of inputs, weed and pest management and improving the role of extension. This paper discusses the global production trends and productivity issue and lays out the emerging issues in wheat and barley production and productivity. Further the chapter lists the research priorities for enhancing wheat and barley production, keeping in view the given challenges and increased demand in the future. There is a need to increase the yield potential of wheat and barley and reduce the yield gap by improving interdisciplinary linkages, enhancing the role of extension and varietal adoption.

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Mittal, S. (2022). Wheat and Barley Production Trends and Research Priorities: A Global Perspective. In: Kashyap, P.L., et al. New Horizons in Wheat and Barley Research . Springer, Singapore. https://doi.org/10.1007/978-981-16-4449-8_1

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Thus, a white paper author would not “brainstorm” a topic. Instead, the white paper author would get busy figuring out how the problem is defined by those who are experiencing it as a problem. Typically that research begins in popular culture--social media, surveys, interviews, newspapers. Once the author has a handle on how the problem is being defined and experienced, its history and its impact, what people in the trenches believe might be the best or worst ways of addressing it, the author then will turn to academic scholarship as well as “grey” literature (more about that later).  Unlike a school research paper, the author does not set out to argue for or against a particular position, and then devote the majority of effort to finding sources to support the selected position.  Instead, the author sets out in good faith to do as much fact-finding as possible, and thus research is likely to present multiple, conflicting, and overlapping perspectives. When people research out of a genuine desire to understand and solve a problem, they listen to every source that may offer helpful information. They will thus have to do much more analysis, synthesis, and sorting of that information, which will often not fall neatly into a “pro” or “con” camp:  Solution A may, for example, solve one part of the problem but exacerbate another part of the problem. Solution C may sound like what everyone wants, but what if it’s built on a set of data that have been criticized by another reliable source?  And so it goes. 

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History and future perspectives of barley genomics

Kazuhiro sato.

Institute of Plant Science and Resources, Okayama University, Kurashiki 710-0046, Japan

Barley ( Hordeum vulgare ), one of the most widely cultivated cereal crops, possesses a large genome of 5.1 Gbp. Through various international collaborations, the genome has recently been sequenced and assembled at the chromosome-scale by exploiting available genetic and genomic resources. Many wild and cultivated barley accessions have been collected and preserved around the world. These accessions are crucial to obtain diverse natural and induced barley variants. The barley bioresource project aims to investigate the diversity of this crop based on purified seed and DNA samples of a large number of collected accessions. The long-term goal of this project is to analyse the genome sequences of major barley accessions worldwide. In view of technical limitations, a strategy has been employed to establish the exome structure of a selected number of accessions and to perform high-quality chromosome-scale assembly of the genomes of several major representative accessions. For the future project, an efficient annotation pipeline is essential for establishing the function of genomes and genes as well as for using this information for sequence-based digital barley breeding. In this article, the author reviews the existing barley resources along with their applications and discuss possible future directions of research in barley genomics.

1. Introduction

1.1. origin of genomic diversity in barley.

Before barley ( Hordeum vulgare ssp. vulgare ) was domesticated, hunter–gatherers used the ancestral wild form ( H. vulgare ssp. spontaneum ) of domesticated barley as a human food source. Both wild and domesticated barley were found in archaeological sites in the Fertile Crescent dating back about 10,000 years, 1 which is believed to be the origin of barley domestication. Wild barley kernels can be distinguished from those of domesticated barley by their brittle and smooth rachis. 2 The ancestral wild form of barley shares the same genome as domesticated barley and is classified as a barley subspecies. 3 Millions of generations of this wild barley provide a source of diversity to the present-day cultivated form, although domestication partially narrowed the diversity. Soon after domestication, mutations responsible for agronomically valuable traits, such as the jump from two to six row spike, 4 spring growth habit, 5 and hull-less caryopsis, 6 were selected for and spread quickly to all cultivated barley within a few thousand years. Allelic diversity at these loci corresponds with ecological conditions and with different uses of the cereal (human food, animal feed, and malt production) and made barley landraces well suited for cultivation throughout the world in most conditions, except in the tropics. Naturally occurring diversity in locally adapted landraces was the only source of available diversity until barley crossbreeding and mutation induction started in the early 20th century. 7

2. Relationships of plant species within the Poaceae family

Barley ranks fourth among grain cereals (Poaceae species) after maize ( Zea mays ), wheat ( Triticum aestivum ), and rice ( Oryza sativa ) in terms of global production. Barley is self-pollinating with a diploid genome consisting of seven chromosomes (2 n  =   2 x  =   14). Barley, common wheat, rye ( Secale cereale ), and their wild relatives (e.g. Aegilops spp.) are closely related and are included in the Triticeae tribe, which evolved some 12 million years ago within the Pooideae subfamily of the Poaceae (grasses). 8 The estimated barley genome size is 5.1 Gbp 9 with >80% of repetitive elements. Each sub-genome of hexaploid wheat is characterized by the same genetic components as barley in terms of genome size, gene content, and repetitive elements. 10 Although it has a large genome, barley can be considered a good genomic model for cultivated hexaploid wheat due to its simple diploid genome. Large differences in genome sizes exist among Poaceae species. Both Brachypodium distachyon (272 Mbp) and rice (430 Mbp) have small genomes and share a common ancestor with the Triticeae tribe, with divergence times of 32–39 and 40–53 million years ago, respectively. 11 Their evolutionary relationships reflect the sequence similarity within Poaceae species and facilitate the identification of orthologous genes of importance across crop genomes. Among Poaceae species, barley has been well studied genetically. Several mutant traits have practical importance and can be used as model traits in cereal crops. In particular, many barley and wheat genes exhibit similar functions: information about a gene in barley can therefore be readily applicable to estimating the genes responsible for similar traits in wheat.

3. Genetic and genomic resources

3.1. seed collection for barley.

Barley is the only cultivated species of the genus Hordeum , which includes about 32 species and about 45 taxa. 3 More than 485,000 accessions for the genus Hordeum are preserved at more than 200 different institutions worldwide. 12 These collections include 299,165 accessions of H. vulgare ssp. vulgare (primarily new and old cultivars and landraces), 32,385 accessions of H. vulgare ssp. spontaneum , 4,681 accessions of wild species, and a substantial representation of genetic stocks, breeding lines, and mapping populations. 13 Many accessions are duplicated between gene banks for safety or to avoid quarantine problems. The world’s largest seed storage facility is the Svalvard Seed Vault, which preserves over 1 million crop-related accessions, including 92,075 Hordeum accessions as of July 2020 ( https://www.nordgen.org/sgsv/ ).

As an East Asian Center of barley genetic resources, Okayama University maintains 10,980 cultivars and landraces, 2,498 genetic stocks, and 628 wild barleys. Their collection, preservation, and distribution are partly supported by the National Bioresource Project ( nbrp.jp ). These materials have been collected since the 1940s for crop evolutionary studies at Okayama University. About 5,300 cultivated barley accessions have been intensively characterized both by genomic markers and for some agronomically relevant traits, such as resistance to powdery mildew. The data sets are available online from Barley DB (http://www.shigen.nig.ac.jp/barley/).

These collections are part of the South and East Asian subset of the international barley core collection, which constitutes the entire ex situ (stored) barley genetic diversity. Okayama University is responsible for the distribution of the South and East Asian subset, comprising 380 accessions. Other materials can be requested from the United States Department of Agriculture (USDA) small grain collection (Americas), the International Center for Agricultural Research in the Dry Areas (ICARDA, West Asia and North Africa and ssp. spontaneum ), Leibniz-Institut für Pflanzengenetik und Kulturpflanzenforschung (IPK, Germany), and the Nordic Genetic Resource Center in Sweden (for wild Hordeum ).

Barley has been the subject of much mutation research and breeding. These mutants have been mainly collected at the USDA small grain collection, located in Aberdeen, Idaho, USA, and at the Nordic Genetic Resource Center, in Alnarp, Sweden. 14 To enhance the utility and accessibility of these mutants, >400 mutant alleles have been introgressed into the cultivar Bowman. 15 Information on the Bowman introgression lines is available in the Barley Genetics Newsletter, Vol. 26 ( http://wheat.pw.usda.gov/ggpages/bgn/26/bgn26tc.html ). These historical mutants provide a rich resource for functional studies and gene cloning.

Okayama University also maintains mutants, tetraploid lines, linkage testers, and near isogenic lines for barley. These lines were largely developed through the research activities at Okayama University and used for the development of linkage maps and genetic analysis of mutant traits. 15 Tetraploids helped to develop a series of trisomic lines that can be used to identify the chromosome location of a given locus of interest. Currently, three mapping populations 16–18 have been deposited from the North American barley genome mapping project. Another mapping population (Haruna Nijo × H602) was developed at Okayama University. 19 These populations were instrumental in developing molecular genetic maps for barley and are being exploited for high-throughput mapping by single-nucleotide polymorphism (SNP) arrays. Recombinant chromosome substitution lines (backcross introgression lines) are also useful resources to identify genes in specific genomic regions to precisely study and map a locus of interest. Several sets of populations are available for distribution. 20 , 21

3.2. Genomic resources

Multiple genomic resources have been developed to analyse partial or total genomic sequences and their associated functions in barley.

Since 2000, several large barley expressed sequence tag (EST) projects have generated large numbers of sequences from expressed genes ( http://harvest.ucr.edu/ ). Eight different genotypes provided the material for these projects. The polymorphisms identified between genotypes contributed to promoting high-throughput SNP genotyping, genetic mapping, and marker generation. The first comprehensive barley full-length cDNA (FL-cDNA) sequences were collected by Sato et al. 22 mRNA samples were isolated from 15 organs and treatments from the Haruna Nijo cultivar and later pooled to develop a FL-cDNA library using the Cap-trapper method. 23 A total of 5,006 clones were sequenced ( http://www.shigen.nig.ac.jp/barley/ ). Another set of about 25,000 clones was generated and sequenced from the same cultivar, from 40 different organs and treatments. 24 These sequences have allowed the annotation of genes on contigs 25 or chromosome-scale genome sequences. 26 , 27

Barley geneticists have developed high-quality genetic maps, based on mutant phenotypes, using classical three-point linkage tests. 15 These efforts are being updated and complemented with genome-wide genetic maps generated from molecular markers. Stein et al. 28 developed a consensus barley map with 1,032 EST-based loci assayed using a combination of marker assays. Sato et al. 19 developed a high-resolution barley EST map with 2,890 loci using a single-mapping population. High-throughput and high-quality multiplex PCR-based genotyping assays based on Golden Gate technology (Illumina Inc., CA, USA) involve the allele-specific detection of SNPs. A consensus genetic linkage map containing >2,900 gene-based SNP markers has been developed ( http://harvest.ucr.edu/ ). 29 , 30

Several bacterial artificial chromosome (BAC) libraries have been constructed. The first library was developed from the Morex cultivar 31 ; it has been joined by other five Morex BAC libraries that collectively cover >25 genome equivalents. 32 A library from the cultivar Haruna Nijo consists of 294,912 clones with an average insert size of 115.2 kbp and a coverage of about 6.6 genome equivalents. 33 Another BAC library from wild barley accession H602 was developed at Okayama University (unpublished). Libraries from Haruna Nijo (coded as HNB) and H602 are available from the National Bioresource Project ( nbrp.jp ).

4. Genome sequencing

4.1. the nuclear genome.

The International Barley Genome Sequencing Consortium (IBSC) was established in 2006 to generate a high-quality barley genome sequence. 34 Two general approaches have been undertaken for the analysis of genome structure in barley: (i) identifying a minimum tiling path of 87,075 genetically mapped BAC clones and sequencing these clones (BAC-by-BAC strategy) and (ii) performing whole-genome shotgun sequencing.

The development of a BAC-based physical map for genome sequencing is a large-scale and worldwide effort. Madishetty et al. 35 developed a high-throughput approach to use overgo probes to identify gene-containing BAC clones. They used >10,000 overgo probes derived from EST sequences to identify 83,381 gene-containing clones from the initial Morex BAC library. 31 Fingerprinting of these clones resulted in contigs that comprise roughly two-thirds of all barley genes. Four new Morex BAC libraries have been prepared that cover an estimated 25 haploid genome equivalents. 34 From these libraries, approximately 550,000 clones (covering about 14 genome equivalents) have been fingerprinted and assembled in contigs. 32 To complement these efforts, BAC-end sequencing will be conducted on 350,000 BACs. In addition, there are efforts to integrate the resulting BAC contigs with the SNP-based genetic maps. Thus, a robust BAC-based physical map was integrated with the genetic map.

Of the 5.10 Gbp of the barley genome, IBSC developed a physical map of 4.98 Gbp, with more than 3.90 Gbp anchored to a high-resolution genetic map. 9 Projecting a deep whole-genome shotgun assembly, EST, FL-cDNA, and newly developed RNA-seq data onto this framework supports 79,379 transcript clusters, including 26,159 high-confidence genes with homology support from other plant genomes. 9 More than 80% of the genome is occupied by repeat sequences.

For chromosome-scale, high-quality assembly, each BAC clone from the minimum tiling path was barcoded and sequenced by Illumina short-read sequencing. Then, a high-resolution genetic map created by population sequencing methodology 36 and a highly contiguous optical map were combined to construct super-scaffolds composed of merged assemblies from individual BACs. Finally, chromosome conformation capture sequencing (Hi-C) was used to order and orient BAC-based super-scaffolds. The final chromosome-scale assembly represents 4.79 Gbp (∼95%) of the genome. 37 Mapping of transcriptome data identified 39,734 high-confidence loci and 41,949 low-confidence loci on the basis of sequence homology to related species.

4.2. The organellar genomes

Middleton et al. 38 determined that the chloroplast sequences from cultivated and wild barley were closely related (sequence identity 99.98%). The divergence time of these haplotypes is estimated to be 80,000 ± 20,000 years using semi-penalized likelihood. A comparison of the chloroplast genome from cultivated barley and common wheat identified four insertions and five deletions >50 bp relative to the common wheat chloroplast genome. The extent of chloroplast sequence similarity indicates that cultivated and wild barleys are more closely related to each other than they are to cultivated wheat.

Hisano et al. 39 assembled the complete nucleotide sequences of the mitochondrial genomes from wild and cultivated barley. Two independent circular maps of the 525,599-bp barley mitochondrial genome were constructed by de novo assembly of high-throughput sequencing reads from the wild accession H602 and from the cultivar Haruna Nijo. These two maps detected only three SNPs between the two haplotypes. Both mitochondrial genomes contained 33 protein-coding genes, three ribosomal RNAs, 16 transfer RNAs, 188 new ORFs, six major repeat sequences, and several types of transposable elements. Mitochondrial genome sequencing is essential for annotating the barley nuclear genome; indeed, these mitochondrial sequences identified a significant number of fragmented mitochondrial sequences in the reported nuclear genome sequences. 37

4.3. Transcriptomes

Deep sequencing of the transcriptome (RNA-seq) from the cultivar Morex and FL-cDNAs from the cultivar Haruna Nijo helped to annotate the reference genome of the cultivar Morex. 9 , 37 The recent development of a single-molecule sequencing technique may also support the sequencing of long transcripts. To this end, an international collaborative project on full transcript sequencing for major haplotypes is underway (Waugh et al., unpublished). These long transcript sequences will also contribute to the annotation of pan-genome assemblies (Stein et al., unpublished). As it is essential to isolate intact mature transcripts (mRNA) to obtain FL-cDNA sequences, techniques are being developed to maintain transcript integrity.

A de novo RNA-seq-based genotyping procedure for barley strains used in breeding programs has been implemented. 40 Using RNA samples from several tissues, reads were mapped onto transcribed regions, which correspond to ∼590 Mbp out of the ∼4.8 Gbp reference genome. Using 150 samples from 108 strains, this approach detected 181,567 SNPs and 45,135 indels, located in 28,939 transcribed regions distributed throughout the Morex genome. 37 The quality of this polymorphism detection method was validated by analysing 387 RNA-seq-derived SNPs by amplicon sequencing. These results demonstrated that this RNA-seq-based de novo polymorphism detection system can generate genome-wide markers, even in the closely related barley genotypes used in breeding programs.

4.4. Genomic information and available databases

The barley research community maintains a diverse array of databases that house information pertaining to barley genetics and genomics that can be easily accessed. Table 1 lists such databases and their respective information type.

Barley genome databases

For genome assembly and annotation, EnsemblPlants provides easy access to the most updated barley genome assembly, including chromosome sequences, genes, transcripts, and predicted proteins. The same website also supports Basic Local Alignment Search Tool (BLAST) searches against the barley genome. Similarly, IPK allows the user to conduct BLAST searches against all sequence resources published by the worldwide barley community, including activities related to the International Barley Sequencing Consortium.

Several databases are specifically related to transcripts. PLEXdb is a public resource for gene expression analysis of plants and plant pathogens. This website currently hosts microarray data sets from a range of species, including barley and wheat. HarvEST is principally an EST database viewing platform that emphasizes gene function and is geared towards comparative genomics and oligonucleotide design, in support of activities such as microarray content design, functional annotation, and physical and genetic mapping. The site also allows the display of consolidated maps of approximately 3,000 SNP markers from four barley mapping populations 29 and offers a rice synteny viewer. A subset of these mapped SNPs is integrated with the minimum BAC clone tiling path. barleyGenes provides access to predicted genes from an assembly of whole-genome shotgun sequences from barley (cultivar Morex). These genes were predicted from the mapping of RNA-seq data to the genome assembly. Gene expression levels were also calculated from the RNA-seq data and are available in the form of FPKM values associated with the predicted genes. bex-db provides 5′- and 3′-end sequences for 175,000 FL-cDNA clones as well as their expression data in a searchable database. The database also provides a genome browser, showing the locations of cDNA sequences on the barley genome. 9

Several databases contain barley genomic resource information. GrainGenes, the database of choice for legacy and classical genetics data, 41 gathers information such as genetic maps, genes, alleles, genetic markers, phenotypic data, quantitative trait loci studies, experimental protocols, and publications about Triticeae species. Barley DB includes information on barley germplasm and barley genome resources from Okayama University. The database contains 5,006 FL-cDNAs and 134,928 EST entries. The barley germplasm collection from Okayama University and associated information are also available on the site.

5. Applied use of the genomic information of barley

5.1. towards the digital bioresource project of barley.

The ultimate goal of barley genomics is to de novo sequence all natural and induced germplasm. The collection of seed samples with unchanged sequences can become a digital bioresource. 42 , 43 However, even the current state of techniques and cost reduction are not sufficient to make sequencing more than a few thousand barley haplotypes possible. After the analysis of sequence data sets, the TRITEX pipeline 27 can assemble chromosome-scale sequences for one haplotype in 3–4 weeks for barley. A parallel sequencing and computational analysis may save time, but it is not feasible to sequence thousands of accessions by high-quality chromosome-scale assembly with the current technical standards. For these reasons, partial sequencing of genomes may give useful preliminary information for digital bioresource development in barley. Here, The author summarizes some of the activities and tools used to estimate diversity in natural and induced barley accessions.

5.2. Sequence accessions to summarize natural sequence variation

The IPK crop gene bank has a major world collection of barley seed samples. To estimate the diversity in genome sequences in wild and cultivated barleys, a genotyping-by-sequencing platform was applied to single plants from 21,405 accessions in the IPK barley collection. 44 All haplotype reads were aligned to the reference genome from the barley cultivar Morex, 37 which allowed the detection of 171,263 bi-allelic SNPs. A principal component (PC) analysis of cultivated barleys indicated that PC2 separated Eastern and Western barleys, whereas PC1 set Ethiopian barleys apart. Figure 1 shows that the geographical origin of collection agreed well with the SNP analysis. Representative accessions based on PC analysis are being used to analyse the barley pan-genome (Stein et al., unpublished), which may present a full complement of sequence variation within the barley genome.

Principal component analysis of 19,778 domesticated barleys based on 76,102 genotyping-by-sequencing markers from the IPK Bridge Web Portal ( https://bridge.ipk-gatersleben.de/bridge/ ). 45 Accessions from Germany (left), Japan (middle), and Ethiopia (right) are plotted on PC1 ( y axis) and PC2 ( x axis). The proportion of variance explained by the PCs is indicated on the axes.

5.3. Exome sequencing

Plant biologists and breeders concentrate most of their efforts on deciphering the functions of genes. Exome sequencing specifically targets coding sequences from the genome, and hence alleviating the need to sequence entire genomes and their repeat content, while bringing the computational needs and cost down to a reasonable range. The barley exome capture system selectively enriches for 61.6 Mbp of coding sequences based on the Morex cultivar HC (high-confidence gene model) sequences, FL-cDNA sequences, and de novo assembled RNA-seq consensus sequence contigs by hybridizing the gene-related fragments (or exons) from genomic libraries. 46

The platform provides a highly specific and targeted capture of barley exons and closely related species. Using this platform, Russel et al. 47 sequenced the exomes of a collection of 267 geo-referenced landraces and wild accessions of barley. A combination of genome-wide analyses demonstrated that the patterns of variation in barley have been strongly shaped by geography and that variant-by-environment associations for individual genes are prominent. A high-density 50 K SNP array with the Illumina Infinium whole-genotyping array was also developed, based on SNP design on exome capture data from 170 lines from a barley diversity panel. 48

5.4. Mapping of mutations for gene annotation

The use of pooled sequencing approaches may accelerate genetic mapping and identification of causal mutations. Mascher et al. 49 applied exome sequencing to pooled samples from a barley mapping population segregating for the phenotype caused by the mnd ( many noded dwarf ) mutant, which increases the rate of leaf initiation. The pool of mutant plants should display a local and specific peak in read depth corresponding to the mutant genomic background around the candidate locus, which can be confirmed by the analysis of independent mutant alleles exhibiting the same phenotype.

Another example of mapping by exome sequencing borrowed from an existing pipeline for QTL-seq, 50 which was initially developed for genome sequencing in rice. This ‘exome QTL-seq’ approach allowed the mapping of the causal locus underlying the black lemma and pericarp ( Blp ) phenotype from a segregating population derived from doubled haploid barley lines. 51 Exome sequences were assembled into pseudo-contigs by first-ordering exomes based on the genomic coordinates of their respective genes and then limiting each locus to 200 bp in the pseudo-map (but including all relevant SNPs). Short reads generated by the sequencing of the exome capture library are then analysed through this QTL-seq pipeline. The causal loci responsible for the trait of interest are identified based on the relative enrichment in SNP allele frequencies from their original genomic background, as described above for QTL-seq.

5.5. Identification of useful genes for the genetic and genomic resources established

Natural diversity remains a major source of agronomically relevant traits for barley breeding programs. Genetic and genomic resources have been capitalized on to isolate genes of interest that control agronomic and industrial traits. Some such genes were isolated through homology-based cloning, whereas others were isolated via positional cloning strategies. In other cases, several approaches were combined to map genes: positional cloning, synteny with related grasses, and homology-based approaches. Here, we provide two examples of genes underlying important phenotypes that were isolated by our group.

The most important step that allowed barley domestication is linked to mutations in the two adjacent, dominant, and complementary genes Brittle rachis ( Btr ) Btr1 and Btr2 . Their loss of function caused barley grains to remain on the inflorescence at maturity, enabling easier and effective harvesting. 2 To identify the btr1 and btr2 genes, we crossed the cultivars Kanto Nakate Gold (carrying a btr1 -type allele) and Azumamugi (bearing a btr2 -type allele) to produce a mapping population segregating at both btr loci. We mapped two candidate genes genetically from >10,000 segregating individuals. We then identified BAC clones using some of the genetic markers used for mapping and sequenced positive clones. We confirmed the identification of both genes via complementation tests by transforming functional Btr alleles in the respective haplotypes.

Dormancy allows wild barley grains to survive dry summers in the Near East. After domestication, barley was selected for shorter dormancy periods. Sato et al. 52 isolated the major seed dormancy gene QTL for Seed Dormancy 1 ( Qsd1 ) from wild barley, which encodes an alanine aminotransferase (AlaAT). We first built a high-resolution genetic map between the cultivar Haruna Nijo and the wild barley accession H602, narrowing the mapping interval down to two Haruna Nijo BAC clones, which we then annotated, utilizing information from barley EST and FL-cDNA sequences. The candidate gene was knocked down by RNA interference and subjected to complementation tests to determine the phenotypic effects on seed dormancy. The seed dormancy gene is expressed specifically in the embryo. The two Qsd1 alleles responsible for long and short dormancy periods encode proteins that differ by a single amino acid.

5.6. Variations in the proteomes useful for trait analysis

A unique example of an application of transcript/protein sequence information in barley is related to its industrial product: malt and beer. Iimure et al. 53 conducted a two-dimensional gel-based proteome analysis to identify proteins associated with quality traits related to malt and beer production. Several protein species were identified in malt, wort (the first extraction step after malt maceration and mashing), and beer by gel electrophoresis, followed by trypsin digestion and mass spectrometry analyses and/or liquid chromatography tandem mass spectrometry. In addition, low-molecular-weight polypeptides were isolated from beer by the combination of non-enzymatic digestion and mass analysis. Collectively, these data sets of polypeptides from barley proteomes provide a platform for analysing protein functions in beer. Several novel proteins related to beer quality traits such as foam stability and haze formation have been identified through analysing these proteomes. Some of the proteins have also been turned into efficient protein or DNA markers for trait selection in malting barley breeding. 54

5.7. Transformation, genome editing, and functional validation of identified genes

As described above (see description of the brt1 / brt2 and Qsd1 loci), introducing a gene of interest by stable transformation is the standard technique for validating the gene responsible for the target trait. Targeted genome modification technology (so-called genome-editing) offers tremendous promise as a technology that can efficiently produce mutations in desired genes, and several cases of barley genome-editing by clustered regularly interspaced short palindromic repeats (CRISPR) and the CRISPR-associated nuclease Cas9 have already been reported. 55 However, transformation and genome-editing experiments may suffer from some limitations resulting from the low transformation potential of some accessions. Indeed, the old Scottish malting cultivar Golden Promise is one of the few reliable haplotypes for Agrobacterium ( Agrobacterium tumefaciens )-mediated transformation. Hisano and Sato 56 identified loci controlling transformation amenability in the regions of chromosomes 2H and 3H in an F 2 population derived from a cross between the cultivars Golden Promise and Haruna Nijo. Introducing these genomic regions in target haplotypes may increase their transformation efficiency and genome-editing capabilities.

5.8. The digital bioresource project for digital breeding

Natural and induced variation provides opportunities to analyse traits of interest. However, how to combine sequence and trait information remains a challenging question that needs to be addressed. High-quality genome sequences are essential to establish a digital bioresource centre for world barley ex situ collections but are unfortunately also insufficient. To better understand the contribution of genomic sequence variation to various traits, automatic sequence annotation must become faster and more efficient, since this process currently relies on a slow and manual data curation step. Automated phenotyping is also emerging as an essential goal to diminish the bottleneck associated with the characterization of increasing numbers of accessions. Systematic gene inactivation or modification by genome editing may provide a functional picture of candidate genes of interest in the target haplotype. However, agronomically important traits are often controlled by multiple interacting genes, which demands a deep knowledge of trait-based genetics. Finally, the combined information collected from genomic sequences and the systematic functional analysis of genes may provide novel strategies for trait improvement, for example, the sequence-based digital breeding of barley.

6. Conclusion and future perspectives

The genomes of various organisms have been sequenced and analysed for some 40 years. During this period, development and application of new technologies including computer software frequently changed sequencing strategies. Initially, most plant genome sequencing projects had to rely on a single haplotype to establish a reference genome. However, it has become clear that to sequence multiple haplotypes in parallel would be essential for our understanding of the genetic and genomic features underlying the natural and induced variations. Newly developed technologies of various sorts have allowed us to make it quite efficient to analyse multiple accessions and lines simultaneously, even for species possessing a large genome size such as barley.

However, we realize at the same time that we need to establish genetically stable reference accessions to obtain consistent results for genomic and proteomic sequencings. In these regards, a digital bioresource including sequence and related information of all accessions appears to be quite challenging. In the meantime, however, sequencing of a limited number of accessions that cover most of the major sequence variations in a crop species may provide an alternative and efficient strategy to establish the ‘pan-genome’.

High-quality sequence assembly will certainly be an indispensable component of the pan-genome infrastructure. The ongoing attempt of a barley pan-genome project aims to construct chromosome-scale sequence assembly for 20 genotypes, consisting of landraces, cultivars, as well as a wild barley accession selected to represent the global barley diversity. The details of the project will be published in the near future which is expected to present an advanced view of barley genomics.

Acknowledgments

The author thanks the National Bioresource Project, Japan, for collection, preservation and distribution of barley resources stored at Okayama University described in this review.

Conflict of interest

The author declare that there is no competing interest.

3L Annabelle Lincoln Presents Pioneering Paper at SportsLand Summit

Third-year law student Annabelle Lincoln recently presented her research at the inaugural SportsLand Summit, held at the Cleveland Browns Stadium. The summit gathered prominent figures in sports, healthcare, technology and human performance.

Lincoln's presentation focused on a research paper she co-authored with fellow CWRU Law students Nathaniel Arnholt and Trey Quillin. The research began in the fall of 2023, with the students exploring varying sports law topics. Ultimately, Lincoln, Arnholt and Quillin were encouraged to further explore their research in the spring semester, culminating in a research paper that was finalized over the summer. 

Their paper explores the critical issue of collegiate athletes’ control over their personal data within college athletics. With rapid advancements in wearable technology, the ability to collect vast amounts of data on athletes both on and off the field has grown exponentially. This data is increasingly valuable to organizations, raising important questions about ownership of that information. 

The legal frameworks governing athlete data rights are complex and vary depending on the athlete's status—whether professional, collegiate or amateur. While the research of Lincoln and her team primarily focused on collegiate athletes, they believe their findings may have broader implications for athletes at all levels. Those interested in the topic can find the full paper on the CWRU Law Athlete Data Lab website .

Reflecting on her experience, Lincoln expressed her gratitude for the opportunity to speak at such a prestigious event, which featured influential leaders such as David Jenkins, COO of the Cleveland Browns, and the CEO of the Rock Entertainment Group, Nic Barlage, along with top team physicians and HealthTech experts. She also thanked Sports Data Labs founders Stan Mimoto and Mark Gorski, and commended Professor Craig Nard for his guidance throughout her research journey.

Lincoln concluded by emphasizing the importance of their research, stating, "Nathan, Trey and I researched this topic extensively and hope our paper will be helpful to players and industry leaders moving forward."  

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Positive energy districts: fundamentals, assessment methodologies, modeling and research gaps.

1. Introduction

State of the art on positive energy districts, 2. methodology.

• Setting: a café-like environment with small, round tables, tablecloths, colored pens, sticky notes and any interaction tool available.
• Welcome and Introduction: the host offers a welcome, introduces the World Café process, and sets the context.
• Small-Group Rounds: three or more twenty-minute rounds of conversations occur in small groups. Participants switch tables after each round, with one person optionally remaining as the “table host” to brief newcomers.
• Questions: each round starts with a context-specific question. Questions may remain constant or be built upon each other to guide the discussion.
• Harvest: participants share their discussion insights with the larger group, often visually represented through graphic recording.
• Objectives of the workshop and preparation. The first step of the World Café approach is to identify the main objectives. For this workshop, there was the need to investigate the current landscape of PED research, as well as to have a benchmark and collect feedback on the current research activities within Annex 83. Questions were structured in order to frame the current state-of-the-art understanding of the topic. A mapping of the potential different stakeholders in the PED design and implementation process was carried out at this stage. As a result, municipalities, community representatives, energy contractors, real estate companies and commercial facilitators, as well as citizens, were identified as main target groups. Later, the follow-up discussions were built around these main actors. Further, the mapping of the stakeholders’ involvement was carried out for better understanding the complexity of relationships, roles and synergies as well as the impact on the design, implementation and operation stages of PEDs.
• Positive Energy Districts’ definitions and fundamentals ( Section 3.1 ).
• Quality-of-life indicators in Positive Energy Districts ( Section 3.2 ).
• Technologies in Positive Energy Districts: development, use and barriers ( Section 3.3 ).
• Positive Energy Districts modeling: what is further needed to model PEDs? ( Section 3.4 ).
• Sustainability assessment of Positive Energy Districts ( Section 3.5 ).
• Stakeholder engagement within the design process ( Section 3.6 ).
• Tools and guidelines for PED implementation ( Section 3.7 ).

3.1. Positive Energy Districts Definitions and Fundamentals

3.2. quality-of-life indicators in positive energy districts, 3.3. technologies in positive energy districts: development, use and barriers, 3.4. positive energy districts modeling: what is further needed to model peds, 3.5. sustainability assessment of positive energy districts, 3.6. stakeholder engagement within the design process, 3.7. tools and guidelines for ped implementation, 4. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Kozlowska, A.; Guarino, F.; Volpe, R.; Bisello, A.; Gabaldòn, A.; Rezaei, A.; Albert-Seifried, V.; Alpagut, B.; Vandevyvere, H.; Reda, F.; et al. Positive Energy Districts: Fundamentals, Assessment Methodologies, Modeling and Research Gaps. Energies 2024 , 17 , 4425. https://doi.org/10.3390/en17174425

Kozlowska A, Guarino F, Volpe R, Bisello A, Gabaldòn A, Rezaei A, Albert-Seifried V, Alpagut B, Vandevyvere H, Reda F, et al. Positive Energy Districts: Fundamentals, Assessment Methodologies, Modeling and Research Gaps. Energies . 2024; 17(17):4425. https://doi.org/10.3390/en17174425

Kozlowska, Anna, Francesco Guarino, Rosaria Volpe, Adriano Bisello, Andrea Gabaldòn, Abolfazl Rezaei, Vicky Albert-Seifried, Beril Alpagut, Han Vandevyvere, Francesco Reda, and et al. 2024. "Positive Energy Districts: Fundamentals, Assessment Methodologies, Modeling and Research Gaps" Energies 17, no. 17: 4425. https://doi.org/10.3390/en17174425

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