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Comprehensive Phylogenetic Analysis of Root-knot Nematodes Predicts Emerging Virulent Species
Kamrul Islam,
Mohammad Jakir Hosen,
Sourav Chakraborty,
Auditi Purkaystha,
Mahmudul Hasan,
Bonhi Elora
Issue:
Volume 8, Issue 1, June 2020
Pages:
1-8
Received:
17 September 2019
Accepted:
30 December 2019
Published:
3 February 2020
Abstract: Among the root-knot nematodes three Meloidogyne species namely Meloidogyne incognita, M. javanica, and M. arenaria are emerging as an important pest of many cultivated plants, and recognized as the most economically destructive plant parasitic nematodes species of all over the world. Although other root-knot nematodes may virulent for plant but limited information is available. Thus, a comprehensive bioinformatics analysis including sequence acquisition, multiple sequence alignment and the phylogenetic tree construction for well-known Meloidogyne species was employed to predict the emerging virulent species. About eighty seven (87) 18S rRNA sequences of three damaging Meloidogyne species (M. javanica, M. arenaria and M. incognita) were retrieved from NCBI database, and allowed to construct phylogenetic trees using both NJ and ME methods of Molecular Evolution Genetic Analysis (MEGA) tools. Phylogeny analysis revealed that M. enterolobii_1, M. sp._Mi_c3a, M. sp_Mj_c1a and M._sp._Mj_c3a are genetically as well as evolutionally related to existing well recognized virulent nematodes. Moreover, evolutionally emerging strains of existing virulent species of M. javanica, M. arenaria and M. incognita along with the predicted virulence nematodes could become a great challenge to agriculture. The study could initiate the further analysis for novel insights in the pathogenesis of emerging virulence species of Meloidogyne that must be needed for future crop management strategies.
Abstract: Among the root-knot nematodes three Meloidogyne species namely Meloidogyne incognita, M. javanica, and M. arenaria are emerging as an important pest of many cultivated plants, and recognized as the most economically destructive plant parasitic nematodes species of all over the world. Although other root-knot nematodes may virulent for plant but lim...
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Discover the Dehydration Response Genes in Boea hygrometrica Transcriptome Using Bayesian Network Approach
Mengmeng Zhang,
Lu Wang,
Ping Wan
Issue:
Volume 8, Issue 1, June 2020
Pages:
9-14
Received:
3 March 2020
Accepted:
18 March 2020
Published:
23 March 2020
Abstract: “Drying without dying” is an amazing feature in land plant evolution. Boea hygrometrica is an important resurrection plant model. The current genome and transcriptome analysis have revealed that some biological processes may contribute to its dehydration tolerance, but genes play pivotal roles in the dehydration response remains unclear. Bayesian network approach is a powerful tool for transcriptome data analysis and biological network reconstruction. In this work, by using the Bayesian network approach, we first reconstruct a gene regulation network with the B. hygrometrica transcriptome data. The network contains 1292 genes. Next, we defined the hub node genes in the network and focus on their functions in order to understand the response B. hygrometrica carried out under the dehydration stress. Finally, by an association analysis, we deduce the function of the unknown gene Bhs126_021 which has a degree of 84 in the network. The data-driven strategy we applied in this work not only finds out the knowledge from the knowledge-driven strategy analysis, but also provides novel findings from the B. hygrometrica transcriptome. Our findings give insight of control genes in land plant under the dehydration stress. The data-driven strategy applied in this work can also efficiently analyze other similar transcriptome data sets.
Abstract: “Drying without dying” is an amazing feature in land plant evolution. Boea hygrometrica is an important resurrection plant model. The current genome and transcriptome analysis have revealed that some biological processes may contribute to its dehydration tolerance, but genes play pivotal roles in the dehydration response remains unclear. Bayesian n...
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Discovering Escherichia coli K-12 Promoter Features Using Convolutional Neural Network
Mengmeng Zhang,
Lu Wang,
Ping Wan
Issue:
Volume 8, Issue 1, June 2020
Pages:
15-19
Received:
24 May 2020
Accepted:
8 June 2020
Published:
20 June 2020
Abstract: The mechanism of prokaryotic gene expression remains incompletely understood. Promoters are regions in genome that locating upstream to genes and regulate of gene expressions. Despite more and more E. coli K-12 promoter sequences have been obtained experimentally, and some regions such as -10 region and -30 region have been described, the features in promoter sequences are far from explicitly characterized. Here, we address this challenge using an approach based on the deep convolutional neural network (CNN). We collected six classes of E. coli K-12 promoter sequences which are all annotated as with strong evidence and belong to only one promoter class in RegulonDB database. Then, we applied the CNN model to recognize the six classes of promoters. The CNN model achieved an accuracy of above 97% for all six classes of promoters. Next, we extracted the weight matrix of the last convolution layer in CNN with the Grad-Cam algorithm, and convert the weight matrix to an information content matrix. Finally, we visualized the information content matrix as promoter logos using the logomaker tool and discover the promoter features in six classes of promoters. Our approach could not only find the previous described promoter feature regions, but could also discover promoter features with better sensitivity and accuracy. We provide a novel computational approach to discover features in biological sequences.
Abstract: The mechanism of prokaryotic gene expression remains incompletely understood. Promoters are regions in genome that locating upstream to genes and regulate of gene expressions. Despite more and more E. coli K-12 promoter sequences have been obtained experimentally, and some regions such as -10 region and -30 region have been described, the features ...
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The Effect of the Infection Rate on Oncolytic Virotherapy
Dongwook Kim,
Haeyoung Kim,
Hui Wu,
Dong-Hoon Shin
Issue:
Volume 8, Issue 1, June 2020
Pages:
20-28
Received:
10 June 2020
Accepted:
23 June 2020
Published:
4 July 2020
Abstract: Oncolytic viruses have become a novel therapeutic tool for various cancer treatments. Several naturally occurring oncolytic viruses and engineered oncolytic viruses are developed for oncolytic virotherapies. Although we have a good understanding on molecular mechanisms of viral replication and virus-induced cell lysis at the cellular level, it is unclear how oncolytic viruses and cancer cells interact as a population. Several mathematical models of oncolytic virotherapy have been developed to advance the understanding of dynamic interaction between oncolytic viruses and cancer cells. Many authors investigated the effect of the virus replication on dynamics of cancer cell population and proposed that the bursting rate of viruses is an important factor for successful oncolytic virotherapy. In this study, we investigate the effect of infection rate of oncolytic viruses on an oncolytic virotherapy model. Particularly, we focused on studying the relationship between two control parameters, bursting rate and infection rate of the virus, to generate the patterns from equilibrium steady state to periodic solutions. Based on the model, the interaction between cancer cells and oncolytic viruses shows an intriguing two-dimensional bifurcation, showing three parameter regions (equilibrium steady state, damped oscillations and oscillations). Our result suggests that both infection rate and bursting rate are crucial properties of oncolytic viruses to design a successful oncolytic virotherapy.
Abstract: Oncolytic viruses have become a novel therapeutic tool for various cancer treatments. Several naturally occurring oncolytic viruses and engineered oncolytic viruses are developed for oncolytic virotherapies. Although we have a good understanding on molecular mechanisms of viral replication and virus-induced cell lysis at the cellular level, it is u...
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