27.1 · Northwest A & F University Show more authors Abstract odon usage patterns of 23 Poaceae chloroplast genomes were analysed in this study. Neutrality analysis indicated that the codon usage patterns have significant correlations with GC12 and GC3 and also showed strong bias towards a high representation of NNA and NNT codons. The Nc-plot showed that although a large proportion of points follow the parabolic line of trajectory, several genes with low ENc values lie below the expected curve, suggesting that mutational bias played a major role in the codon biology of the Poaceae chloroplast genome. Parity Rule 2 plot analysis showed that T was used more frequently than A in all the genomes. Correspondence analysis of relative synonymous codon usage indicated that the first axis explained only a partial amount of variation of codon usage. Furthermore, the gene length and expression level were also found to drive codon usage variation. These findings revealed that besides natural selection, other factors might also exert some influences in shaping the codon usage bias in Poaceae chloroplast genomes. The optimal codons of these 23 genomes were also identified in this study.

Discover the world's research A,B,C,F A State Key Laboratory of Crop Stress Biology in Arid Areas, Northwest A&F University, Yangling 712100, Shaanxi, China. College of Agronomy, Northwest A&F University, Yangling 712100, Shaanxi, China. C Yangling Branch of China Wheat Improvement Centre, Northwest A&F University, Yangling 712100, Shaanxi, China. College of Animal Science, Northwest A&F University, Yangling 712100, Shaanxi, China. E These authors contributed equally to this work. F Corresponding author. Email: sweining2002@yahoo.com Abstract. Codon usage patterns of 23 Poaceae chloroplast genomes were analysed in this study. Neutrality analysis indicated that the codon usage patterns have signiﬁcant correlations with GC 12 3 and also showed strong bias towards a high representation of NNA and NNT codons.

The Nc-plot showed that although a large proportion of points follow the parabolic line of trajectory, several genes with low ENc values lie below the expected curve, suggesting that mutational bias played a major role in the codon biology of the Poaceae chloroplast genome. Parity Rule 2 plot analysis showed that T was used more frequently than A in all the genomes. Correspondence analysis of relative synonymous codon usage indicated that the ﬁrst axis explained only a partial amount of variation of codon usage. Furthermore, the gene length and expression level were also found to drive codon usage variation. These ﬁndings revealed that besides natural selection, other factors might also exert some inﬂuences in shaping the codon usage bias in Poaceae chloroplast genomes. The optimal codons of these 23 genomes were also identiﬁed in this study. Additional keywords: mutational bias, optimal codon.

Received 27 March 2012, accepted 26 May 2012, published online 13 August 2012 Introduction The genetic code is the set of rules by which information encoded in genetic material (DNA or mRNA sequences) is translated into proteins (amino acid sequences) and there are 64 codons coding 20 different amino acids. With the exception of Met and Trp that are encoded by unique codons, other amino acids are encoded by two to six synonymous codons. Even though synonymous codons encode the same amino acids, coding sequences in DNA do not use synonymous codons with equal frequencies within and between organisms (Grantham et al. 1980). It is well known that synonymous codon bias is non- random and species-speciﬁc (Grantham et al.1981). Analysis of the codon usage bias will provide helpful information for understanding the molecular evolution and adaptation of living organisms. A large number of studies have been reported on the synonymous codon usage bias of the nuclear genomes of many plant species (Jia and Xue 2009;Leet al.2011).

However, the signiﬁcance of the synonymous codon usage bias of organellar genomes like chloroplast and mitochondria have not been well understood (Wang et al.2011). Chloroplasts are plant-speciﬁc organelles that conduct photosynthesis to supply essential energy for both plants and algae. They are self-replicating and are inherited maternally. Generally, the chloroplast genome has a highly conserved organisation within land plants and is composed of a circular DNA molecule encoding ~80 protein genes with a quadripartite structure that includes a pair of inverted repeats separated by a large and a small single-copy region (Howe et al.2003). Due to a highly conservative nature and slow evolutionary rate, several sequences from the chloroplast genome are considered to be the most promising loci for plant barcoding (Gielly and Taberlet 1994; Shaw et al.2005; Hollingsworth et al.2011).

The chloroplast is considered to have originated from cyanobacteria through endosymbiosis ~1–1.5 billion years ago, which results in the assumption that the translation mechanism and patterns of codon usage in chloroplasts are similar to those of organisms like Escherichia coli (Sugiura 1992). Poaceae (also known as Gramineae) is a large and nearly ubiquitous family of monocot ﬂowering plants representing CSIRO PUBLISHING Australian Journal of Botany, 2012, 60, 461–470 Journal compilation CSIRO 2012 11 461 species belonging to 777 genera ( org/browse/A/Poaceae/, accessed 6 June 2012). It is considered to be the most economically important family, which includes the staple food grains and cereal crops (such as wheat, rice, barley and rye) grown all over the world. Analysis of the codon usage pattern of Poaceae chloroplast genomes will improve the understanding of the mechanism of codon distribution, variation and evolution in this family (Sablok et al.2011).

To investigate the codon usage variation and to identify the factors affecting the codon usage bias, we systematically analysed the synonymous codon usage patterns in 23 Poaceae chloroplast genomes representing ﬁve subfamilies (Anomochlooideae, Panicoideae, Pooideae, Bambusoideae, and Ehrhartoideae) in this study. The complete chloroplast genome sequences of 23 grass plants belonging to ﬁve different subfamilies of Poaceae (Table 1) were obtained from GenBank ( accessed 6 June 2012). All the protein-coding sequences in each chloroplast genome were examined for being full-length and for the presence of proper start and stop codons. To avoid sampling bias and short sequence length-associated estimation errors (Rosenberg et al.2003), Coding sequences (CDS) shorter than 300 bp in length were excluded in codon usage calculations (Wright 1990). For each genome, the hypothetical, duplicated and non-native genes were precluded.

All remaining functional coding sequences in each chloroplast genome were concatenated to calculate the overall GC content of coding regions (GCc), of each codon position (GC 1 and GC content of whole genomes (GCg). GC 3S the frequency of G + C at the third synonymously variable coding position (excluding Met, Trp, and three stop codons) was also calculated. The codon usage pattern across genes was examined by the ENc-plot analysis. A plot of ENc versus GC 3S general strategy to investigate the patterns of synonymous codon usage, following the criterion that a gene whose codon usage is only constrained by a G +C mutation bias will lie just below the curve of the predicted values, while the gene would fall on the continuous curve if GC is the only determination of the codon usage variation of that gene (Wright 1990). The ENc values will be between 20 (when only one codon is effectively used for each amino acid) and 61 (when codons are used randomly) (Wright 1990).

The Codon Adaptation Index (CAI) is a very simple and effective measure of the extent of synonymous codon usage bias speciﬁcally in the direction of the bias seen in highly expressed genes. It uses a reference set of highly expressed genes from a species to assess the relative merits of each codon and identiﬁes the role of selective pressure in modelling the patterns of codon usage (Sharp and Li 1987). This CAI index ranges from zero to one, being one if a gene always uses the most frequently used synonymous codons in a reference set of genes. Relative synonymous codon usage (RSCU) is the observed frequency of a codon divided by the frequency expected, if all synonyms for that amino acid were used equally (Hou and Yang 2000; Gupta et al.2004). Thus, RSCU values close to 1.0 indicates lack of bias whereas values more than 1 indicates that a codon was used more frequently than expected, while the converse is true for RSCU values less than 1. The RSCU values of all the genes were analysed for correspondence analysis (COA).

An optimal or preferred codon is deﬁned as the codon that occur more abundantly (relative to their synonyms) in highly expressed genes than in genes with low expression (estimated from CAI) (Ikemura 1985), which were identiﬁed using a 2 (rows) 2 (columns) matrix (also called contingency table) with one degree of freedom (McInerney 1998). To determine preferred codons, we used 5% of the total genes with extremely high- and low-CAI values that were considered as the high- and low-expression genes datasets, respectively. Chi-square contingency test was used to compare the codon usage of these two groups, and the codons whose frequency of usage were signiﬁcantly higher (P<0.01) in the highly expressed genes than in genes with low level of expression were considered as the optimal codons (Liu 2006). To identify the intra-genomic variation of amino acid compositions, COA is performed to explore the variation in the RSCU values for each of the 61 sense codons, other than the unique methionine and tryptophan codons.

This analysis partitions the variation along 59 orthogonal axes, with 41 degrees of freedom. The ﬁrst axis is the one that captures most of the variation in codon usage, with each subsequent axis explaining a decreasing amount of the variance. The dispersion of the genes on the two main axes identiﬁes the trend that explains the major variation (McInerney 1998). Statistical analyses tools Indices of codon usage were calculated using CodonW (http:// codonw.sourceforge.net, accessed 6 June 2012). The correlation between codon usage variations among genes was analysed using the non-parametric Spearman’s rank correlation analysis method wrapped in the multi-analysis software SPSS version 18.0. ( new-advanced-features/). In addition, Pearson’s Chi-square test was employed to examine the signiﬁcance of codon usage difference between two datasets. For each of the 59 sense codons, the Chi-square test involves a 2 2 (contingency) table that yields one degree of freedom; the signiﬁcance was examined at 5% level (Chi-square value of 3.841).

Codon usage pattern in chloroplast genomes of Poaceae family GC content index including GCg, GCc, GC 1 CAI NC Basal lineage Anomochlooideae Anomochloa marantoidea 138 412 NC_014062 85 19 853 0.387 0.391 0.472 0.396 0.305 0.275 0.168 49.94 PACMAD clade Panicoideae Coix lacryma-jobi 140 745 NC_013273 104 20 911 0.385 0.394 0.470 0.398 0.313 0.284 0.166 50.60 Saccharum hybrid cultivar NCo310 141 182 NC_006084 117 22 818 0.384 

0.394 0.467 0.397 0.319 0.290 0.163 51.00 Saccharum ofﬁcinarum hybrid SP-80-3280 141 182 NC_005878 97 18 960 0.384 0.396 0.473 0.400 0.315 0.285 0.167 50.68 Sorghum bicolor 140 754 NC_008602 84 19 938 0.385 0.391 0.474 0.396 0.304 0.274 0.167 49.90 Zea mays 140 384 NC_001666 111 22 521 0.385 0.395 

0.469 0.398 0.317 0.288 0.165 50.83 BEP clade Pooideae Agrostis stolonifera 136 584 NC_008591 85 19 995 0.385 0.390 0.473 0.397 0.300 0.271 0.168 49.72 Brachypodium distachyon 135 199 NC_011032 81 18 329 0.386 0.392 0.472 0.400 0.303 0.273 0.168 49.71 Festuca arundinacea 136 048 NC_011713 80 19 417 0.384 0.389 0.471

 0.396 0.301 0.271 0.167 49.85 Hordeum vulgare subsp.

vulgare 136 462 NC_008590 83 19 877 0.383 0.389 0.472 0.397 0.298 0.268 0.167 49.51 Lolium perenne 135 282 NC_009950 84 19 887 0.383 0.388 0.471 0.396 0.299 0.268 0.166 49.63 Triticum aestivum 134 545 NC_002762 83 19 982 0.383 0.389 0.472 0.396 0.299 0.270 0.167 49.67 Ehrhartoideae Oryza nivara 134 494 NC

_005973 119 22 866 0.390 0.397 0.471 0.397 0.323 0.293 0.168 51.22 Oryza sativa cv.

93–11 134 496 NC_008155 64 16 327 0.390 0.394 0.476 0.397 0.309 0.278 0.172 50.34 Oryza sativa cv.

Nipponbare 134 525 NC_001320 108 22 148 0.390 0.396 0.471 0.397 0.320 0.290 0.169 50.91 Bambusoideae Bambuseae Bambusa emeiensis 139 493 NC_015830 84 20 086 0.389 0.395 0.475 0.398 0.311 0.282 0.166 50.49 Bambusa oldhamii 139 350 NC_012927 82 19 332 0.389 0.395 0.476 0.399 0.309 0.280 0.167 50.33 Dendrocalamus latiﬂ

orus 139 394 NC_013088 85 2005 0.389 0.393 0.474 0.397 0.309 0.279 0.167 50.35 Arundinarieae Acidosasa purpurea 139 697 NC_015820 82 19 803 0.389 0.393 0.475 0.397 0.308 0.278 0.167 50.52 Ferrocalamus rimosivaginus 139 467 NC_015831 84 19 952 0.389 0.395 0.475 0.399 0.310 0.281 0.166 50.39 Indocalamus longiauritus 139 668

 NC_015803 82 19 799 0.389 0.394 0.475 0.397 0.308 0.279 0.167 50.29 Phyllostachys edulis 139679 NC_015817 84 20 049 0.389 0.395 0.476 0.398 0.311 0.282 0.166 50.48 Phyllostachys nigra var.

henonis 139 839 NC_015826 84 20 048 0.389 0.395 0.476 0.398 0.311 0.282 0.166 50.49 Codon usage patterns of the Poaceae chloroplast genomes Australian Journal of Botany 463 three codon positions, neutrality plots (GC 3 . Signiﬁcant positive correlations in neutrality plots of the chloroplast genomes in all species (P<0.01) indicated that the effect on GC contents by the intragenomic GC mutation bias was similar at all the three codon positions (Fig. 1and Fig. S1). We further investigated the relationship between nucleotide content and codon usage by the ENc-plot (ENc v. GC 3S 23 chloroplast genomes analysed showed similar patterns of ENc-plots and a majority of genes follow the parabolic line of trajectory (Fig. 2and Fig.

S2), suggesting that the nucleotide composition at the third codon position is the major determinant force of the effective number of codons. Although a few genes lay on the expected curve, a majority of points with low ENc values were lying below the expected curve, which indicated that in addition to compositional constraints, some other factors were also involved in driving codon usage variations among these genes. The putative optimal codons in all the 23 Poaceae plastomes that were identiﬁed using a two-way Chi-square contingency test at the signiﬁcance level of 0.05 are presented in Table 2. Most of these preferred codons ended in either T or A, which is consistent with the AT richness of the third codon position in the plastomes of Poaceae species. These results suggested that natural translational selection has not played an important role in shaping the codon usage in Poaceae chloroplast genomes. Furthermore, codons with T in the third position were detected more frequently than A, indicating that CDS with highly biased codon usage did not use A and T equally.

To investigate whether these biased codon choices are restricted in highly biased CDS, the association between purines (A and G) and pyrimidines (C and T) content in four-codon amino acid families was analysed by Parity Rule 2 bias plot analysis (Sueoka 1999). Our results showed that G and C were used proportionally whereas A and T were not. T was used more frequently than A in all the 23 chloroplast genomes (Fig. 3and Fig. S3). Correspondence analyses The COA of RSCU generated a series of orthogonal axes, which reﬂects the trends responsible for the variation in codon usage. The coordinate of each gene on the ﬁrst two COA axes is presented in Fig. 4. The ﬁrst axis (COA/RSCU) contributes ~10% to the total amount of variation in 23 grass species, with 0.6 (b) Fig. 1.

Neutrality plots (GC 12 3 ) of the ﬁve representative taxa Panicoideae, Ehrhartoideae, Bambusoideae, Pooideae and Anomochlooideae are shown. The Neutrality plots of the rest of the species are available in Fig. S1. (a)Anomochloa marantoidea, the regression line is y= 0.33x+ 0.3341; (b)Brachypodium distachyon, the regression line is y= 0.0698x+ 0.413; (c)Dendrocalamus latiﬂorus, the regression line is y= 0.2084x+ 0.3699; (d)Oryza nivara, the regression line is y= 0.1318x+ 0.3964; ( e)Sorghum bicolor, the regression line is y= 0.2173x+ 0.3707. 464 Australian Journal of Botany Y. Zhang et al.

each subsequent axis explaining a decreasing amount of the variation. The origin represented the average RSCU for all genes, with respect to the two axes. The distance between genes on this plot with respect to the ﬁrst two axes was a reﬂection of their dissimilarity in RSCU. Since the ﬁrst axis explained only a partial amount of variation of codon usage among the genes in chloroplast genomes of Poaceae species in this study, it was clear that besides natural selection, several other factors also affected codon usage variation. The corresponding distribution of synonymous codons showed separation of C/G- ending codons and A/U-ending codons along the primary axis. This indicates that the variations in synonymous codon usage among the genes are based on the nucleotide content of the genes. Effect of other factors on codon usage To understand whether the gene expression level affects the codon usage bias in grass chloroplast genomes, we carried out the correlation analysis to identify the correlation between CAI against the positions of genes along the ﬁrst major axis, nucleotide composition and ENc.

In all Arundinarieae species, we observed that the Axis 1 coordinates were signiﬁcantly correlated with the gene expression level as assessed by CAI values and GC 3S content (P<0.01; P<0.05), and were also signiﬁcantly correlated with ENc values (P<0.05) (Table 3). Furthermore, correlation analysis also found that a signiﬁcant correlation existed between CAI and GC (P<0.01), but signiﬁcantly negative correlation between CAI values and ENc (P<0.01). It indicated that gene expression level was the major factor in shaping codon usage in the Arundinarieae chloroplast genome. The same relationships were also identiﬁed in Hordeum vulgare,Triticum aestivum and Oryza nivara chloroplast genomes but were not found in other species (Table 3and Table S2).

These results indicated that the involvement of the gene expression level in affecting the codon usage was only limited to a few grass chloroplast genomes, which suggested that although all the grass chloroplast genomes appeared to have similar patterns of codon usage bias, some speciﬁc evolutionary features were also existed within the genus and subfamily level. In this study, we further investigated the relationship between gene length and codon bias in grass chloroplast genomes. Genes longer than 300 bp were used and divided into three groups: 300–999, 1000–1999 and more than 2000 bp. The ENc, GC 3S CAI were used as codon bias measures. Results indicated each of these species showed very distinct correlations between codon usage bias and gene length. It was evident that the genes with length ranging from 1000 to 1999 bp had lower ENc and higher CAI values, indicating they had the highest expression level and most biased codon usage, with a preferred A or T at the third position of codon (Table S1).

Our result showed that the high expression genes in grass chloroplast genomes appeared to have more biased codon usage, probably because of the use of optimal codons. Base composition of a genome is the result of appropriate balance between G + C nucleotide pairs and mutational pressure, which is one of the most pervasive effects on codon usage (Sueoka 1962). It has been demonstrated that various biological factors determine the codon usage bias, such as tRNA abundance, strand- speciﬁc mutational bias, gene expression level, gene length, GC composition and so on (Sueoka and Kawanishi 2000; Blake et al.2003; Wan et al.2004;Luet al.2005). However, neutral mutations and selective constraint are the two major factors that have been widely accepted accounting for both inter-speciﬁc 70 Z. mays A. marantoidea O. nivara T. aestivum B. oldhamii F.

rimosivaginus 60 00.2 0.4 0.6 0.8 1.0 1.2 0 0.2 0.4 GC3s 0.6 0.8 1.0 1.2 0 0.2 0.4 0.6 0.8 1.0 1.2 Fig. 2. Relationship between GC 3S and ENc (ENc-plot). ENc was plotted against GC content at the third codon position. In Poaceae, all 23 species had their chloroplast genes distributed around the expected curve.

Nc-GC 3S plots of ﬁve representative taxa, Anomochloa marantoidea (subfamily Anomochlooideae), Zea mays (subfamily Panicoideae), Triticum aestivum (subfamily Pooideae), Bambusa oldhamii (subfamily Bambusoideae, tribe Bambuseae), Oryza nivara (subfamily Ehrhartoideae) and Ferrocalamus rimosivaginus (subfamily Bambusoideae, tribe Arundinarieae) are shown here. The Nc-GC 3S species are available in the Supplementary material. Codon usage patterns of the Poaceae chloroplast genomes Australian Journal of Botany 465 Table 2. Putative optimal codons in chloroplast genomes of Poaceae family Comparison of codon usage frequencies between highly and lowly expressed sequences, as discriminated by the Codon Adaptation Index. The codons marked with an * are signiﬁcantly more frequent among the highly expressed genes (P<0.01) according to a Chi-square contingency test.

AP, Acidosasa purpurea; FR, Ferrocalamus rimosivaginus; PE, Phyllostachys edulis; PN, P. nigra var. henonis; IL, Indocalamus longiauritus; AM, Anomochloa marantoidea; CL, Coix lacryma-jobi;SH, Saccharum ofﬁcinarum hybrid cultivar NCo310 and hybrid SP-80–3280; SB, Sorghum bicolor;ZM,Zea mays; AS, Agrostis stolonifera; BD, Brachypodium distachyon; FA, Festuca arundinacea; HV, Hordeum vulgare subsp. vulgare; LP, Lolium perenne; TA, Triticum aestivum; ON, Oryza nivara; OI, O. sativa cv. 93–11; OJ, O. sativa cv. Nipponbare; BE, Bambusa emeiensis; BO, B.

oldhamii; DL, Dendrocalamus latiﬂorus Amino acid Codon AP FR PE PN IL AM CL SH SB ZM AS BD FA HV LP TA ON OI OJ BE BO DL Amino acid Codon AP FR PE PN IL AM CL SH SB ZM AS BD FA HV LP TA ON OI OJ BE BO DL Phe TTT –––––– –––– – – – – –– ––––– – Tyr TAT –––––––––––––––––––––– 466 Australian Journal of Botany Y. Zhang et al. and intra-genomic codon usage variation (Sueoka 1988;Luet al. 2005). Although all genes are subjected to some degree of selection, selection is strong enough to constitute the dominant evolutionary force only among the most highly expressed genes (Gouy and Gautier 1982; Sharp et al.2010).

It has been postulated that if natural selection is the underlying cause of synonymous codon usage bias, then the bias should be more pronounced in the highly expressed genes than in the rest of the genome (Xia 1998; Lynn et al.2002). In our analysis, we observed a regression line with a higher slope in the 23 genomes, and also found the signiﬁcant correlations between GC that mutation may play a larger role in codon usage bias in chloroplast genomes of the Poaceae species. At the same time, the GC content could be one of the most important factors in the evolution of genomic structures (Bellgard et al.2001; Liu and Xue 2005). We found that GC content was highly conserved in almost all the investigated Poaceae chloroplast genomes, suggesting that these genomes have achieved evolutionary equilibrium. In this study, we have analysed 23 Poaceae chloroplast genomes, compared with previous studies of Liu and Xue (2005) (four genomes) and Sablok et al.(2011) (three genomes), respectively.

A relatively wider GC 3 was observed in this study, revealing a more comprehensive codon usage pattern of the grass chloroplast genome. It has been shown that GC in the nuclear genomes of grasses follows bimodal distribution, with two classes of genes differing in codon usage: high-GC gene and low-GC gene (Carels and Bernardi 2000; Lescot et al.2008; Tatarinova et al.2010). It appears that Poaceae chloroplast genomes are more conservative in GC content and more homogeneous in their organisation than their nuclear counterpart, suggesting that chloroplast and nuclear genomes were subjected to different evolutionary constraints and mutational pressures, resulting in different feature of codon usage. Previous studies have reported that codons ending in A or T at the third position were preferred in dicot nuclear genomic genes while preference for C or G has been observed in monocot genes (Campbell and Gowri 1990; Kawabe and Miyashita 2003; Tatarinova et al.2010).

In plant chloroplast genomes, A- and T-ending codons were detected as preferred codons and T was generally determined more frequently as the optimal codon (Liu and Xue 2005). Codon usage of Poaceae chloroplast genes were found to be biased towards a high representation of NNA and NNT codons, which unravel a general bias towards A or U at the third position. These ﬁndings suggested that mutational bias towards A or T nucleotides would be expected to increase RSCU values for synonymous A + T-encoding codons. Optimal codons of all the 23 chloroplast genomes were also identiﬁed to ﬁnd that codons ending with A or T are preferred. The frequency of these preferred codons in a gene seems to be directly proportional to gene expression level and could be a useful indicator to detect the expression level of the putative genes or open reading frames. Furthermore, these preferred codons could be useful in designing degenerate primers, introduction of point mutation, and also in evolutionary studies.

Signiﬁcant heterogeneity of codon usage among genes has been reported in most of the previously analysed species (Ikemura 1985; Sharp et al.1988; Wright 1990; Sueoka 1999; Liu 2006). Thus, sophisticated extensive multivariate techniques are necessary to understand the trends as well as sources of variation among genes. COA is widely used to identify the major sources of variation in the synonymous codon usage among genes. In the present study, the COA (RSCU) of 1.0 3 )] of ﬁve representative taxa. (a)Anomochloa marantoidea;(b)Bambusa oldhamii;(c)Brachypodium distachyon;(d)Oryza nivara;(e)Sorghum bicolor. The PR2-bias plots of the other species are available in the Supplementary material.

Codon usage patterns of the Poaceae chloroplast genomes Australian Journal of Botany 467 Poaceae chloroplast genomes showed that the ﬁrst axis explained only a partial amount of variation of codon usage among the genes, suggesting that together with mutational bias and translational selection, several other factors, such as gene expression level and gene length, could be partially accounting for the codon usage variation. Previous studies have shown that gene length is positively correlated with synonymous codon usage bias in E. coli and Streptococcus pneumoniae (Hou and Yang 2000). In contrast, negative correlation was observed in Caenorhabditis elegans,Drosophila and Arabidopsis (Duret and Mouchiroud 1999). Furthermore, the gene expression level which has a signiﬁcant relationship with codon usage bias was also found to strongly depend on the gene length (Castillo-Davis et al.2002), suggesting that the gene length should exert some inﬂuence in shaping the codon usage bias.

In our study, the relationship between the length of the coding region and the codon usage bias was supported as the length ranging from 1000 to 1999 bp showed the highest expression and most biased codon usage in all the 23 grass chloroplast genomes, which suggested that gene length was involved in the codon usage bias of Poaceae chloroplast genomes. Furthermore, correlation analysis conﬁrmed that genes with higher expression level exhibit greater degree of codon usage bias in O. nivara,T. aestivum, H. vulgare and other ﬁve species in the genus Arundinarieae, which shows that gene expression level also played a role in shaping codon usage in the chloroplast genome of some Poaceae species. The gene length effect, genes encoding short proteins possess stronger codon usage bias than that for the long ones, has been observed both in plant and metazoan species, but not in E. coli, where codon usage bias increases with the gene length (Eyre-Walker 1996; Duret and Mouchiroud 1999).

Preference for short gene length with highly expressed genes is found in some nuclear genomes (Castillo-Davis et al.2002). In the grass chloroplast genomes analysed in our study, the genes with moderate length (1000–1999 bp) exhibited the highest expression level and most biased codon usage, an interesting feature not observed before, suggesting a novel mechanism underlying codon usage and its relationship with gene length as well as gene expression levels in grass chloroplast genomes. To conclude, we have systematically analysed the codon usage pattern and identiﬁed the factors responsible for the codon usage bias in 23 Poaceae chloroplast genomes. It provides some useful information about the codon distribution and variation in Poaceae chloroplast genomes, which will 0.6 –0.8 –0.4 Fig. 4. Correspondence analysis (COA) of the relative synonymous codon usage (RSCU) values in chloroplast genes of the Poaceae family. Each point on the plot corresponds to the coordinates on the ﬁrst- and second-principal axes produced by the COA.

In 23 species, the variation of ~10% was not remarkably high for relative inertia explained by Axis 1. COA/RSCU of ﬁve representative taxa, Zea mays,Triticum aestivum,Oryza nivara,Bambusa emeiensis and Anomochloa marantoidea, are shown. 468 Australian Journal of Botany Y. Zhang et al. facilitate the phylogenetic and molecular evolutionary studies within this family and beyond. Supplementary material Supplementary material showing neutrality plots, ENc-plot, PR2-bias plots, the summary of correlation between the codon usage indices (CAI, Enc, GC 3S , GC) and the correlation analysis between gene length and codon bias of Poaceae chloroplast genomes is available at the Journal’s website. Acknowledgements The authors would like to thank the reviewers for their valuable comments and suggestions. This research was mainly funded by the Cultivation Fund of the Key Scientiﬁc and Technical Innovation Project, Chinese Ministry of Education (707054) and partial ﬁnancial support was obtained from 948 Program (2010-S1), Chinese Ministry of Agriculture.

References Bellgard M, Schibeci D, Trifonov E, Gojobori T (2001) Early detection of G + C differences in bacterial species inferred from the comparative analysis of the two completely sequenced Helicobacter pylori strains. Journal of Molecular Evolution 53, 465–468. doi:10.1007/s002390010236 Blake WJ, Kaern M, Cantor CR, Collins JJ (2003) Noise in eukaryotic gene expression. Nature 422, 633–637. doi:10.1038/nature01546 Campbell WH, Gowri G (1990) Codon usage in higher plants, green algae, and cyanobacteria. Plant Physiology 92,1–11. doi:10.1104/pp.92.1.1 Carels N, Bernardi G (2000) Two classes of genes in plants. Genetics 154, 1819–1825.

Castillo-Davis CI, Mekhedov SL, Hartl DL, Koonin EV, Kondrashov FA (2002) Selection for short introns in highly expressed genes. Nature Genetics 31, 415–418. Duret L, Mouchiroud D (1999) Expression pattern and, surprisingly, gene length shape codon usage in Caenorhabditis,Drosophila, and Arabidopsis. Proceedings of the National Academy of Sciences of the United States of America 96, 4482–4487. doi:10.1073/pnas.96.8.4482 Eyre-Walker A (1996) Synonymous codon bias is related to gene length in Escherichia coli: selection for translational accuracy? Molecular Biology and Evolution 13, 864–872.

doi:10.1093/oxfordjournals.molbev.a025646 Gielly L, Taberlet P (1994) The use of chloroplast DNA to resolve plant phylogeny: noncoding versus rbcL sequences. Molecular Biology and Evolution 11, 769–777. Gouy M, Gautier C (1982) Codon usage in bacteria: correlation with gene expressivity. Nucleic Acids Research 10, 7055–7074. doi:10.1093/nar/10.22.7055 Grantham R, Gautier C, Gouy M (1980) Codon frequencies in 119 individual genes conﬁrm consistent choices of degenerate bases according to genome type. Nucleic Acids Research 8, 1893–1912.

doi:10.1093/nar/8.9.1893 Grantham R, Gautier C, Gouy M, Jacobzone M, Mercier R (1981) Codon catalog usage is a genome strategy modulated for gene expressivity. Nucleic Acids Research 9, 213. doi:10.1093/nar/9.1.213-b Gupta SK, Bhattacharyya TK, Ghosh TC (2004) Synonymous codon usage in Lactococcus lactis: mutational bias versus translational selection. Journal of Biomolecular Structure & Dynamics 21, 527–535. doi:10.1080/07391102.2004.10506946 Hollingsworth PM, Graham SW, Little DP (2011) Choosing and using a plant DNA barcode. PLoS ONE 6, e19254.

doi:10.1371/journal.pone.0019254 Hou ZC, Yang N (2000) Analysis of factors shaping S. pneumoniae codon usage. Acta Genetica Sinica 29, 747–752. Table 3. Summary of correlation between the codon usage indices in eight grass chloroplast genomes AP, Acidosasa purpurea; FR, Ferrocalamus rimosivaginus; IL, Indocalamus longiauritus; PE, Phyllostachys edulis; PN, Phyllostachys nigra var. henonis; ON, Oryza sativa cv. Nipponbare; TA, Triticum aestivum; HV, Hordeum vulgare subsp. vulgare. Signiﬁcance at: *P<0.05; **P<0.01 No.

of axis Codon Adaptation Index ENc AP FR IL PE PN ON TA HV AP FR IL PE PN ON TA HV Axis 1 –0.39** –0.42** 0.37** 0.41** –0.40** –0.33* –0.46** 0.40** 0.30* 0.29* –0.35** –0.30* 0.30* 0.48** 0.46** –0.52** Axis 2 –0.55** 0.35* –0.54** –0.31* –0.32* 0.02 –0.34* 0.48** –0.09 –0.15 –0.08 0.18 0.17 –0.07 –0.13 0.2 Axis 3 0.05 0.49** 0.01 0.44** 0.43** –0.13 0.23 0

.07 0.02 0.27 –0.03 0.09 0.1 –0.09 0.21 –0.06 Axis 4 –0.14 –0.13 0.25 0.22 0.23 0.32* –0.13 –0.13 –0.23 –0.34* 0.23 0.32* 0.29* 0.45** –0.09 –0.27 No.

of axis GC 3S GC AP FR IL PE PN ON TA HV AP FR IL PE PN ON TA HV Axis 1 0.48** 0.48** –0.04* –0.46** 0.46** 0.24 0.23 –0.26 0.17 0.17 –0.08 –0.19 0.2 0.12 0.04 –0.08 Axis 2 0.18 0.18 –0.18 –0.18 –0.2 –0.51** –0.30* 0.34* –0.02 –0.02 –0.24 0.01 –0.01 –0.13 –0.25 0.28* Axis 3 0.33* 0.33* 0.02 0.15 0.12 –0.23 0.12 0.01 0.29* 0.29* 0.

19 0.19 0.18 –0.12 –0.11 0.26 Axis 4 –0.44** –0.44** 0.26 0.35* 0.31* 0.45** –0.1 –0.32* –0.09 –0.09 0.12 0.2 0.18 0.2 0.2 0.04 Codon usage patterns of the Poaceae chloroplast genomes Australian Journal of Botany 469 Howe CJ, Barbrook AC, Koumandou VL, Nisbet RE, Symington HA (2003) Evolution of the chloroplast genome.

Philosophical Transactions of the Royal Society B Biological Sciences 358,99–107. doi:10.1098/rstb.2002.1176 Ikemura T (1985) Codon usage and tRNA content in unicellular and multicellular organisms. Molecular Biology and Evolution 2,13–34. Jia J, Xue Q (2009) Codon usage biases of transposable elements and host nuclear genes in Arabidopsis thaliana and Oryza sativa. Genomics, Proteomics & Bioinformatics 7, 175–184. doi:10.1016/S1672-0229(08)60047-9 Kawabe A, Miyashita NT (2003) Patterns of codon usage bias in three dicot and four monocot plant species. Genes & Genetic Systems 78, 343–352.

doi:10.1266/ggs.78.343 Le Z, Jin LG, Luo L, Wang YP, Dong ZM, Sun SH, Qiu LJ (2011) Analysis of nuclear gene codon bias on soybean genome and transcriptome. Acta Agronomica Sinica 37, 965–974. doi:10.3724/SP.J.1006.2011.00965 Lescot M, Piffanelli P, Ciampi AY, Ruiz M, Blanc G, Leebens-Mack J, da Silva FR, Santos MR, D’Hont A, Garsmeur O, Vilarinhos AD, Kanamori H, Matsumoto T, Ronning CM, Cheung F, Haas BJ, Althoff R, Arbogast T, Hine E, Pappas GJ, Sasaki T, Souza MT, Miller R, Glaszmann JC, Town CD (2008) Insights into the Musa genome: syntenic relationships to rice and between Musa species.

BMC Genomics 9, 58. doi:10.1186/1471-2164-9-58 Liu QP (2006) Analysis of codon usage pattern in the radioresistant bacterium Deinococcus radiodurans. Bio Systems 85,99–106. doi:10.1016/j.biosystems.2005.12.003 Liu QP, Xue QZ (2005) Comparative studies on codon usage pattern of chloroplasts and their host nuclear genes in four plant species. Journal of Genetics 84,55–62. doi:10.1007/BF02715890 Lu H, Zhao WM, Zheng Y, Wang H, Qi M, Yu XP (2005) Analysis of synonymous codon usage bias in Chlamydia. Acta Biochimica et Biophysica Sinica 37,1–10.

doi:10.1093/abbs/37.1.1 Lynn DJ, Singer GA, Hickey DA (2002) Synonymous codon usage is subject to selection in thermophilic bacteria. Nucleic Acids Research USA 30, 4272–4277. doi:10.1093/nar/gkf546 McInerney JO (1998) Replicational and transcriptional selection on codon usage in Borrelia burgdorferi. Proceedings of the National Academy of Sciences of the United States of America 95, 10698–10703. doi:10.1073/pnas.95.18.10698 Rosenberg MS, Subramanian S, Kumar S (2003) Patterns of transitional mutation biases within and among mammalian genomes. Molecular Biology and Evolution 20, 988–993.

doi:10.1093/molbev/msg113 Sablok G, Nayak KC, Vazquez F, Tatarinova TV (2011) Synonymous codon usage, GC3, and evolutionary patterns across plastomes of three pooid model species: emerging grass genome models for monocots. Molecular Biotechnology 49, 116–128. doi:10.1007/s12033-011-9383-9 Sharp PM, Li WH (1987) The codon adaptation index –a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Research 15, 1281–1295.

doi:10.1093/nar/15.3.1281 Sharp PM, Cowe E, Higgins DG, Shields DC, Wolfe KH, Wright F (1988) Codon usage in Escherichia coli,Bacillus subtilis,Saccharomyces cerevisiae,Schizosaccharomyces pombe,Drosophila melanogaster and Homo sapiens; a review of the considerable within-species diversity. Nucleic Acids Research 16, 8207–8211. doi:10.1093/nar/16.17.8207 Sharp PM, Emery LR, Zeng K (2010) Forces that inﬂuence the evolution of codon bias. Philosophical Transactions of the Royal Society Biological Sciences 365, 1203–1212.

doi:10.1098/rstb.2009.0305 Shaw J, Lickey EB, Beck JT, Farmer SS, Liu W, Miller J, Chaw SK, Winder CT, Schilling EE, Small RL (2005) The tortoise and the hare: relative utility of 21 noncoding chloroplast DNA sequences for phylogenetic analysis. American Journal of Botany 92, 142–166. doi:10.3732/ajb.92.1.142 Sueoka N (1962) On the genetic basis of variation and heterogeneity of DNA base composition. Proceedings of the National Academy of Sciences of the United States of America 48, 582–592. doi:10.1073/pnas.48.4.582 Sueoka N (1988) Directional mutation pressure and neutral molecular evolution. Proceedings of the National Academy of Sciences of the United States of America 85, 2653–2657.

doi:10.1073/pnas.85.8.2653 Sueoka N (1999) Translation-coupled violation of parity rule human genes is not the case of heterogeneity of the DNA G + C content of third codon position. Gene 238,53–58. doi:10.1016/S0378-1119(99)00320-0 Sueoka N, Kawanishi Y (2000) DNA G + C content of the third codon position and codon usage biases of human genes. Gene 261,53–62. doi:10.1016/S0378-1119(00)00480-7 Sugiura M (1992) The chloroplast genome. Plant Molecular Biology 19, 149–168.

doi:10.1007/BF00015612 Tatarinova TV, Alexandrov NN, Bouck JB, Feldmann KA (2010) GC 3 biology in corn, rice, sorghum and other grasses. BMC Genomics 11, 308. doi:10.1186/1471-2164-11-308 Wan XF, Xu D, Kleinhofs A, Zhou JZ (2004) Quantitative relationship between synonymous codon usage bias and GC composition across unicellular genomes. BMC Evolutionary Biology 4, 19. doi:10.1186/1471-2148-4-19 Wang B, Yuan J, Liu J, Jin L, Chen JQ (2011) Codon usage bias and determining forces in green plant mitochondrial genomes. Journal of Integrative Plant Biology 53, 324–334.

doi:10.1111/j.1744-7909.2011.01033.x Wright F (1990) The ‘effective number of codons’used in a gene. Gene 87, 23–29. doi:10.1016/0378-1119(90)90491-9 Xia X (1998) How optimized is the translational machinery in Escherichia coli,Salmonella typhimurium and Saccharomyces cerevisiae?Genetics 149,37–44. 470 Australian Journal of Botany Y. Zhang et al. CitationsCitations3 ReferencesReferences48 True Grasses - Poaceae - Details - Encyclopedia of Life Diagram of a typical lawn grass plant. Grasses generally have the following characteristics (the image gallery can be used for reference): Poaceae have hollow stems called culms plugged at intervals by solid leaf-bearing nodes. Grass leaves are nearly always alternate and distichous (in one plane), and have parallel veins.

Each leaf is differentiated into a lower sheath hugging the stem and a blade with entire (i.e., smooth) margins. The leaf blades of many grasses are hardened with silica phytoliths , which discourage grazing animals; some, such as sword grass , are sharp enough to cut human skin. A membranous appendage or fringe of hairs called the ligule lies at the junction between sheath and blade, preventing water or insects from penetrating into the sheath. Parts of a spikelet Flowers of Poaceae are characteristically arranged in spikelets , each spikelet having one or more florets. The spikelets are further grouped into panicles or spikes . A spikelet consists of two (or sometimes fewer) bracts at the base, called glumes , followed by one or more florets. A floret consists of the flower surrounded by two bracts, one external—the lemma —and one internal—the palea . The flowers are usually hermaphroditic — maize being an important exception—and anemophilous or wind-pollinated. The perianth is reduced to two scales, called lodicules , that expand and contract to spread the lemma and palea; these are generally interpreted to be modified sepals.

This complex structure can be seen in the image on the right, portraying a wheat (Triticum aestivum) spikelet. The fruit of Poaceae is a caryopsis , in which the seed coat is fused to the fruit wall. A tiller is a leafy shoot other than the first shoot produced from the seed. Grass flowers Growth and development[ edit ] Grass blades grow at the base of the blade and not from elongated stem tips. This low growth point evolved in response to grazing animals and allows grasses to be grazed or mown regularly without severe damage to the plant. [4] Three general classifications of growth habit present in grasses: bunch-type (also called caespitose), stoloniferous , and rhizomatous . The success of the grasses lies in part in their morphology and growth processes, and in part in their physiological diversity. Most of the grasses divide into two physiological groups, using the C3 and C4 photosynthetic pathways for carbon fixation. The C4 grasses have a photosynthetic pathway, linked to specialized Kranz leaf anatomy, which allows for increased water use efficiency , rendering them better adapted to hot, arid environments and those lacking in carbon dioxide .

The C3 grasses are referred to as "cool-season" grasses, while the C4 plants are considered "warm-season" grasses; they may be either annual or perennial . Ecology[ edit ] Grass-dominated biomes are called grasslands . If only large, contiguous areas of grasslands are counted, these biomes cover 31% of the planet's land. [5] Grasslands include pampas , steppes , and prairies . Grasses provide food to many grazing mammals—such as livestock, deer, and elephants—as well as to many species of butterflies and moths . The evolution of large grazing animals in the Cenozoic contributed to the spread of grasses. Without large grazers, fire-cleared areas are quickly colonized by grasses, and with enough rain, tree seedlings. Trees eventually shade out and kill most grasses. Trampling grazers kill seedling trees but not grasses.

[6] Etymology[ edit ] The Poaceae name was given by John Hendley Barnhart in 1895, [14] based on the tribe Poeae (described in 1814 by Robert Brown), and the type genus Poa (described in 1753 by Linnaeus ). The term is derived from the Ancient Greek term for grass. Uses[ edit ] Grasses are, in human terms, perhaps the most economically important plant family. Grasses' economic importance stems from several areas, including food production, industry, and lawns. Food production[ edit ] Agricultural grasses grown for their edible seeds are called cereals or grains (although the latter term, agriculturally, refers to both cereals and legumes ). Three cereals – rice , wheat , and maize (corn) – provide more than half of all calories eaten by humans. [15] Of all crops, 70% are grasses. [16] Cereals constitute the major source of carbohydrates for humans and perhaps the major source of protein, and include rice in southern and eastern Asia , maize in Central and South America , and wheat and barley in Europe , northern Asia and the Americas .

Sugarcane is the major source of sugar production. Many other grasses are grown for forage and fodder for animal feed, particularly for sheep and cattle , thereby indirectly providing more human calories. Industry[ edit ] Grasses are used for construction. Scaffolding made from bamboo is able to withstand typhoon-force winds that would break steel scaffolding. [5] Larger bamboos and Arundo donax have stout culms that can be used in a manner similar to timber, and grass roots stabilize the sod of sod houses . Arundo is used to make reeds for woodwind instruments , and bamboo is used for innumerable implements. Grass fiber can be used for making paper , and for biofuel production. Phragmites australis (common reed) is important in water treatment, wetland habitat preservation and land reclamation in Afro-Eurasia . Lawn and ornamental grasses[ edit ] Grasses are the primary plant used in lawns , which themselves derive from grazed grasslands in Europe. They also provide an important means of erosion control (e.g., along roadsides), especially on sloping land.

Although supplanted by artificial turf in some games, grasses are still an important covering of playing surfaces in many sports, including football (soccer) , tennis , golf , cricket , softball and baseball . Ornamental grasses , such as perennial bunch grasses , are used in many styles of garden design for their foliage, inflorescences, seed heads, and slope stabilization. They are often used in natural landscaping , xeriscaping , contemporary or modern landscaping, wildlife gardening , and native plant gardening . Economically important grasses[ edit ] Grasses and society[ edit ] Grass-covered house in Iceland Grasses have long had significance in human society. They have been cultivated as feed for domesticated animals for up to 10,000 years, and have been used to make paper since the second century AD.[ citation needed ] Also, the primary ingredient of beer is usually barley or wheat, both of which have been used for this purpose for over 4,000 years.[ citation needed ] Some common aphorisms involve grass. For example: "The grass is always greener on the other side" suggests an alternate state of affairs will always seem preferable to one's own.

"Don't let the grass grow under your feet" tells someone to get moving. "A snake in the grass" means dangers that are hidden. "When elephants fight, it is the grass which suffers" tells of bystanders caught in the crossfire. A folk myth about grass is that it refuses to grow where any violent death has occurred. [17] This article is about cereal grains in general. For breakfast cereal, see Breakfast cereal . For food grains in general, see Food grain . For other uses, see Cereal (disambiguation) . Not to be confused with pseudocereal . A cereal is a grass , a member of the monocot family Poaceae , [1] cultivated for the edible components of its grain (botanically, a type of fruit called a caryopsis ), composed of the endosperm , germ , and bran . Cereal grains are grown in greater quantities and provide more food energy worldwide than any other type of crop;[ citation needed ] they are therefore staple crops . In their natural form (as in whole grain ), they are a rich source of vitamins , minerals , carbohydrates , fats , oils, and protein . When refined by the removal of the bran and germ, the remaining endosperm is mostly carbohydrate .

In some developing nations , grain in the form of rice , wheat , millet , or maize constitutes a majority of daily sustenance. In developed nations , cereal consumption is moderate and varied but still substantial. The word cereal derives from Ceres , the name of the Roman goddess of harvest and agriculture. Contents This section does not cite any references or sources . Please help improve this section by adding citations to reliable sources . Unsourced material may be challenged and removed . (October 2012) The first cereal grains were domesticated about 8,000 years ago by ancient farming communities in the Fertile Crescent region. Emmer wheat , einkorn wheat , and barley were three of the so-called Neolithic founder crops in the development of agriculture . Around the same time, millets and rices were starting to become domesticated in east Asia. Sorghum and millets were also being domesticated in sub-Saharan West Africa. Production[ edit ] The following table shows the annual production of cereals in 1961, [2] 2010, 2011, 2012, and 2013 ranked by 2013 production.

[3] All but buckwheat and quinoa are true grasses (these two are pseudocereals ). Grain 0.03 Pseudocereal, traditional to the Andes , but increasingly popular elsewhere. Maize, wheat, and rice together accounted for 89% of all cereal production worldwide in 2012, and 43% of all food calories in 2009, [3] while the production of oats and triticale have drastically fallen from their 1960s levels. Other grains that are important in some places, but that have little production globally (and are not included in FAO statistics), include: Teff , an ancient grain that is a staple in Ethiopia . It is high in fiber and protein. Its flour is often used to make injera . It can also be eaten as a warm breakfast cereal similar to farina with a chocolate or nutty flavor. Its flour and whole grain products can usually be found in natural foods stores. Wild rice , grown in small amounts in North America. Amaranth , an ancient pseudocereal, formerly a staple crop of the Aztec Empire and now widely grown in Africa.

Kañiwa , close relative of quinoa . Several other species of wheat have also been domesticated, some very early in the history of agriculture: Spelt , a close relative of common wheat. Einkorn , a wheat species with a single grain. Emmer , one of the first crops domesticated in the Fertile Crescent . Durum , the only tetraploid species of wheat currently cultivated, used to make semolina . Kamut , an ancient relative of durum with an unknown history. In 2013 global cereal production reached a record 2,521 million tonnes. A slight dip to 2,498 million tonnes was forecast for 2014 by the FAO in July 2014. Farming[ edit ] A wheat field in Dorset , England While each individual species has its own peculiarities, the cultivation of all cereal crops is similar. Most are annual plants ; consequently one planting yields one harvest.

Wheat, rye, triticale, oats, barley, and spelt are the "cool-season" cereals.[ citation needed ] These are hardy plants that grow well in moderate weather and cease to grow in hot weather (approximately 30 °C, but this varies by species and variety). The "warm-season" cereals are tender and prefer hot weather. Barley and rye are the hardiest cereals, able to overwinter in the subarctic and Siberia . Many cool-season cereals are grown in the tropics. However, some are only grown in cooler highlands, where it may be possible to grow multiple crops in a year. For a few decades, there has also been increasing interest in perennial grain plants. This interest developed due to advantages in erosion control, reduced need of fertiliser, and potential lowered costs to the farmer. Though research is still in early stages, The Land Institute in Salina, Kansas has been able to create a few cultivars that produce a fairly good crop yield. [6] Cereal grain seeds from left to right: wheat , spelt , barley , oat . The warm-season cereals are grown in tropical lowlands year-round and in temperate climates during the frost-free season.

Rice is commonly grown in flooded fields, though some strains are grown on dry land. Other warm climate cereals, such as sorghum , are adapted to arid conditions. Cool-season cereals are well-adapted to temperate climates. Most varieties of a particular species are either winter or spring types. Winter varieties are sown in the autumn, germinate and grow vegetatively, then become dormant during winter. They resume growing in the springtime and mature in late spring or early summer. This cultivation system makes optimal use of water and frees the land for another crop early in the growing season. Winter varieties do not flower until springtime because they require vernalization : exposure to low temperatures for a genetically determined length of time. Where winters are too warm for vernalization or exceed the hardiness of the crop (which varies by species and variety), farmers grow spring varieties. Spring cereals are planted in early springtime and mature later that same summer, without vernalization. Spring cereals typically require more irrigation and yield less than winter cereals. Period[ edit ] Threshing; Tacuinum Sanitatis, 14th century Once the cereal plants have grown their seeds, they have completed their life cycle .

The plants die and become brown and dry. As soon as the parent plants and their seed kernels are reasonably dry, harvest can begin. In developed countries, cereal crops are universally machine-harvested, typically using a combine harvester , which cuts, threshes , and winnows the grain during a single pass across the field. In developing countries, a variety of harvesting methods are in use, depending on the cost of labor, from combines to hand tools such as the scythe or cradle . If a crop is harvested during wet weather, the grain may not dry adequately in the field to prevent spoilage during its storage. In this case, the grain is sent to a dehydrating facility, where artificial heat dries it. In North America, farmers commonly deliver their newly harvested grain to a grain elevator , a large storage facility that consolidates the crops of many farmers. The farmer may sell the grain at the time of delivery or maintain ownership of a share of grain in the pool for later sale. Storage facilities should be protected from small grain pests, rodents and birds. Nutritional facts[ edit ] Some grains are deficient in the essential amino acid lysine .

That is why many vegetarian cultures, in order to get a balanced diet, combine their diet of grains with legumes . Many legumes, on the other hand, are deficient in the essential amino acid methionine , which grains contain. Thus, a combination of legumes with grains forms a well-balanced diet for vegetarians. Common examples of such combinations are dal (lentils) with rice by South Indians and Bengalis , dal with wheat in Pakistan and North India , and beans with corn tortillas, tofu with rice, and peanut butter with wheat bread (as sandwiches) in several other cultures, including Americans. [7] The amount of crude protein found in grain is measured as the grain crude protein concentration. [8] Standardization[ edit ] The ISO has published a series of standards regarding cereal products which are covered by ICS 67.060. [9]

Question: The Poaceae or Gramineae family of organisms is better known as?
Answer: Grasses