• SHIHUA ZHANG

      Articles written in Journal of Biosciences

    • Sequence periodic pattern of HERV LTRs: A matrix simulation algorithm

      Shihua Zhang Jing Xu Chaoling Wei

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      Flanking regulatory long terminal repeats (LTRs) in Human endogenous retrovirus (HERV) is a kind of typical DNA repeat that is widespread in the human genome. Currently, many algorithms have been developed to detect the latent periodicity of a wide range of DNA repeats. However, no such attempt was made for HERV LTRs. The present study focused on the investigation of the possible sequence periodic patterns in the HERV LTRs and their regulatory mechanisms. We calculated the sequence periods of 5′, 3′ and combined LTRs in HERVs with our devised matrix simulation algorithm. It is interesting that 5′ and 3′ LTRs have the same period of 7, and combined LTRs have a period of 9. These results indicated that HERV LTRs have predominant periodic patterns. Based on the obtained sequence periodicity, we constructed periodic consensus sequences of 5′, 3′ and combined LTRs. As to 5′ and 3′ LTRs with the same period – 7, we manually scanned the nucleotide bases in the corresponding positions of their periodic consensus sequences, and found some positions have the nucleotide base unchanged, such as the 1st, 5th and 7th positions. These conservative nucleotide base positions represent critical binding sites of regulatory LTRs, and may be indicative of conserved regulatory mechanisms in LRT-participating regulatory networks.

    • A predicted protein functional network aids in novel gene mining for characteristic secondary metabolites in tea plant (Camellia sinensis)

      SHIHUA ZHANG YONG MA RUI ZHANG XIAOLONG HE YING CHEN JINGKE DU CHI-TANG HO YOUHUA ZHANG GUOMIN HAN XIAOYI HU

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      Modeling a protein functional network in concerned species is an efficient approach for identifying novel genesin certain biological pathways. Tea plant (Camellia sinensis) is an important commercial crop abundant innumerous characteristic secondary metabolites (e.g., polyphenols, alkaloids, alkaloids) that confer tea qualityand health benefits. Decoding novel genes responsible for tea characteristic components is an important basisfor applied genetic improvement and metabolic engineering. Herein, a high-quality protein functional networkfor tea plant (TeaPoN) was predicted using cross-species protein functional associations transferring andintegration combined with a stringent biological network criterion control. TeaPoN contained 31,273 nonredundantfunctional interactions among 6,634 tea proteins (or genes), with general network topologicalproperties such as scale-free and small-world. We revealed the modular organization of genes related to themajor three tea characteristic components (theanine, caffeine, catechin) in TeaPoN, which served as strongevidence for the utility of TeaPoN in novel gene mining. Importantly, several case studies regarding geneidentification for tea characteristic components were presented. To aid in the use of TeaPoN, a concise webinterface for data deposit and novel gene screening was developed (http://teapon.wchoda.com). We believe thatTeaPoN will serve as a useful platform for functional genomics studies associated with characteristic secondarymetabolites in tea plant.

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