Molecular mechanisms of fiber differential development between G. barbadense and G. hirsutum revealed by genetical genomics

Cotton fiber qualities including length, strength and fineness are known to be controlled by genes affecting cell elongation and secondary cell wall (SCW) biosynthesis, but the molecular mechanisms that govern development of fiber traits are largely unknown. Here, we evaluated an interspecific backcrossed population from G. barbadense cv. Hai7124 and G. hirsutum acc. TM-1 for fiber characteristics in four-year environments under field conditions, and detected 12 quantitative trait loci (QTL) and QTL-by-environment interactions by multi-QTL joint analysis. Further analysis of fiber growth and gene expression between TM-1 and Hai7124 showed greater differences at 10 and 25 days post-anthesis (DPA). In this two period important for fiber performances, we integrated genome-wide expression profiling with linkage analysis using the same genetic materials and identified in total 916 expression QTL (eQTL) significantly (P<0.05) affecting the expression of 394 differential genes. Many positional cis-/trans-acting eQTL and eQTL hotspots were detected across the genome. By comparative mapping of eQTL and fiber QTL, a dataset of candidate genes affecting fiber qualities was generated. Real-time quantitative RT-PCR (qRT-PCR) analysis confirmed the major differential genes regulating fiber cell elongation or SCW synthesis. These data collectively support molecular mechanism for G. hirsutum and G. barbadense through differential gene regulation causing difference of fiber qualities. The down-regulated expression of abscisic acid (ABA) and ethylene signaling pathway genes and high-level and long-term expression of positive regulators including auxin and cell wall enzyme genes for fiber cell elongation at the fiber developmental transition stage may account for superior fiber qualities.     

An integrative analysis of four CESA isoforms specific for fiber cellulose production between Gossypium hirsutum and Gossypium barbadense

Cotton fiber is an excellent model system of cellulose biosynthesis; however, it has not been widely studied due to the lack of information about the cellulose synthase (CESA) family of genes in cotton. In this study, we initially identified six full-length CESA genes designated as GhCESA5–GhCESA10. Phylogenetic analysis and gene co-expression profiling revealed that CESA1, CESA2, CESA7, and CESA8 were the major isoforms for secondary cell wall biosynthesis, whereas CESA3, CESA5, CESA6, CESA9, and CESA10 should involve in primary cell wall formation for cotton fiber initiation and elongation. Using integrative analysis of gene expression patterns, CESA protein levels, and cellulose biosynthesis in vivo, we detected that CESA8 could play an enhancing role for rapid and massive cellulose accumulation in Gossypium hirsutum and Gossypium barbadense. We found that CESA2 displayed a major expression in non-fiber tissues and that CESA1, a housekeeping gene like, was predominantly expressed in all tissues. Further, a dynamic alteration was observed in cell wall composition and a significant discrepancy was observed between the cotton species during fiber elongation, suggesting that pectin accumulation and xyloglucan reduction might contribute to cell wall transition. In addition, we discussed that callose synthesis might be regulated in vivo for massive cellulose production during active secondary cell wall biosynthesis in cotton fibers.     

Genome-Wide Analysis of the Sus Gene Family in Cotton

Sucrose synthase (Sus) is a key enzyme in plant sucrose metabolism. In cotton, Sus (EC 2.4.1.13) is the main enzyme that degrades sucrose imported into cotton fibers from the phloem of the seed coat. This study demonstrated that the genomes of Gossypium arboreum L., G. raimondii Ulbr., and G. hirsutum L., contained 8, 8, and 15 Sus genes, respectively. Their structural organizations, phylogenetic relationships, and expression profiles were characterized. Comparisons of genomic and coding sequences identified multiple introns, the number and positions of which were highly conserved between diploid and allotetraploid cotton species. Most of the phylogenetic clades contained sequences from all three species, suggesting that the Sus genes of tetraploid G. hirsutum derived from those of its diploid ancestors. One Sus group (Sus I) underwent expansion during cotton evolution. Expression analyses indicated that most Sus genes were differentially expressed in various tissues and had development-dependent expression profiles in cotton fiber cells. Members of the same orthologous group had very similar expression patterns in all three species. These results provide new insights into the evolution of the cotton Sus gene family, and insight into its members' physiological functions during fiber growth and development.     

棉纤维发育及其相关基因表达调控研究进展

棉纤维的强度和长度是评价棉花品质优劣的重要标准。棉纤维发育是一个高度程序化的调控过程。在纤维发育的各个时期,均有大量基因参与纤维细胞发育的调控。本文介绍棉纤维细胞发育各个时期的形态结构和生理生化特征以及一些纤维特异性基因表达调控等方面的研究概况和最新进展,以期能够在细胞和分子水平上了解棉纤维发育的基本生物学过程及其调控机制。     

棉纤维发育及其相关基因表达调控研究进展

棉纤维的强度和长度是评价棉花品质优劣的重要标准。棉纤维发育是一个高度程序化的调控过程。在纤维发育的各个时期, 均有大量基因参与纤维细胞发育的调控。本文介绍棉纤维细胞发育各个时期的形态结构和生理生化特征以及一些纤维特异性基因表达调控等方面的研究概况和最新进展, 以期能够在细胞和分子水平上了解棉纤维发育的基本生物学过程及其调控机制。     

Bayesian hierarchical modeling for time course microarray experiments

Time course microarray experiments designed to characterize the dynamic regulation of gene expression in biological systems are becoming increasingly important. One critical issue that arises when examining time course microarray data is the identification of genes that show different temporal expression patterns among biological conditions. Here we propose a Bayesian hierarchical model to incorporate important experimental factors and to account for correlated gene expression measurements over time and over different genes. A new gene selection algorithm is also presented with the model to simultaneously identify genes that show changes in expression among biological conditions, in response to time and other experimental factors of interest. The algorithm performs well in terms of the false positive and false negative rates in simulation studies. The methodology is applied to a mouse model time course experiment to correlate temporal changes in azoxymethane-induced gene expression profiles with colorectal cancer susceptibility.