Genome-wide analysis and expression profiling of glyoxalase gene families in soybean (Glycine max) indicate their development and abiotic stress specific response

Ghosh, Ajit; Islam, Tahmina

doi:10.1186/s12870-016-0773-9

Cited by 71 publications

(124 citation statements)

References 61 publications

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“…In G. max , out of the 13 possible active GLYIs, five are predicted to be chloroplastic and four code for cytosolic proteins. In addition, the authors predict three GLYI enzymes to be nucleus-localized [30]. In agreement, as we have reported in our recent studies, the characterization of nuclear localized GLYI proteins from both rice and Arabidopsis [15].…”

Section: Subcellular Localization Properties Of Glyoxalasessupporting

confidence: 90%

“…The genome studies further revealed that among the multiple GLYI present in rice and Arabidopsis , it is the Ni 2+ -dependent form that is coded for by multiple genes, while only single gene codes for Zn 2+ -dependent GLYI [26,29]. Recently, a genome-wide study carried out in Glycine max further confirms the presence of glyoxalases as multi-gene family in plants, with G. max genome possessing 24 GLYI and 12 GLYII genes, of which three genes possibly code for Zn 2+ -dependent GLYI and eight code for Ni 2+ -dependent forms [30]. The presence of multiple Zn 2+ -dependent forms in G. max is probably the result of whole-genome duplication events in G. max .…”

Section: Presence Of Glyoxalase Isoforms In Biological Systemsmentioning

confidence: 99%

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Characteristic Variations and Similarities in Biochemical, Molecular, and Functional Properties of Glyoxalases across Prokaryotes and Eukaryotes

Kaur

Sharma

Hasan

et al. 2017

IJMS

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The glyoxalase system is the ubiquitous pathway for the detoxification of methylglyoxal (MG) in the biological systems. It comprises two enzymes, glyoxalase I (GLYI) and glyoxalase II (GLYII), which act sequentially to convert MG into d-lactate, thereby helping living systems get rid of this otherwise cytotoxic byproduct of metabolism. In addition, a glutathione-independent GLYIII enzyme activity also exists in the biological systems that can directly convert MG to d-lactate. Humans and Escherichia coli possess a single copy of GLYI (encoding either the Ni- or Zn-dependent form) and GLYII genes, which through MG detoxification provide protection against various pathological and disease conditions. By contrast, the plant genome possesses multiple GLYI and GLYII genes with a role in abiotic stress tolerance. Plants possess both Ni2+- and Zn2+-dependent forms of GLYI, and studies on plant glyoxalases reveal the various unique features of these enzymes distinguishing them from prokaryotic and other eukaryotic glyoxalases. Through this review, we provide an overview of the plant glyoxalase family along with a comparative analysis of glyoxalases across various species, highlighting similarities as well as differences in the biochemical, molecular, and physiological properties of these enzymes. We believe that the evolution of multiple glyoxalases isoforms in plants is an important component of their robust defense strategies.

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Section: Subcellular Localization Properties Of Glyoxalasessupporting

confidence: 90%

Section: Presence Of Glyoxalase Isoforms In Biological Systemsmentioning

confidence: 99%

Characteristic Variations and Similarities in Biochemical, Molecular, and Functional Properties of Glyoxalases across Prokaryotes and Eukaryotes

Kaur

Sharma

Hasan

et al. 2017

IJMS

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“…Recently, highthroughput RNA sequencing (RNA-seq) technology has been widely used for gene expression studies in a variety of applications, such as expression profiling of mRNAs or non-coding RNA [1][2][3], de novo assembly and characterization of transcriptomes [4,5], and the identification of novel alternatively spliced transcript [6,7]. These novel transcripts or differentially expressed genes (DEGs) identified from RNA-seq data may serve as human disease biomarkers or gene signatures for the clinical diagnosis [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Inference and Sample Size Calculations Based on Statistical Tests in a Negative Binomial Distribution for Differential Gene Expression in RNAseq Data

Li¹,

Cooper²,

Shyr³

et al. 2017

J Biom Biostat

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The high throughput RNA sequencing (RNA-seq) technology has become the popular method of choice for transcriptomics and the detection of differentially expressed genes. Sample size calculations for RNA-seq experimental design are an important consideration in biological research and clinical trials. Currently, the sample size formulas derived from the Wald and the likelihood ratio statistical tests with a Poisson distribution to model RNA-seq data have been developed. However, since the mean read counts in the real RNA-seq data are not equal to the variance, an extended method to calculate sample sizes based on a negative binomial distribution using an exact test statistic was proposed by . In this study, we alternatively derive five sample size calculation methods based on the negative binomial distribution using the Wald test, the log-transformed Wald test and the log-likelihood ratio test statistics. A comparison of our five methods and an existing method was performed by calculating the sample sizes and the simulated power in different scenarios. We first calculated the sample sizes for testing a single gene using the six methods given a nominal significance level α at 0.05 and 80% power. Then, we calculated the sample sizes for testing multiple genes given a false discovery rate (FDR) at 0.05 and 0.10. The empirical power and true prognostic genes for differential gene expression analysis corresponding to the estimated sample sizes from the six methods are also estimated via the simulation studies. Using the sample size formulas derived from log-transformed and Wald-based tests, we observed smaller sample properties while maintaining the nominal power close to or higher than 80% in all the settings compared to other methods. Moreover, the Wald test based sample size calculation method is easier to compute and faster in an RNA-seq experimental design. Later, several sample size calculation methods that were derived from the score statistic and the log-likelihood ratio test (LRT) statistic using the Poisson distribution were proposed [16]. However, the assumption of a Poisson distribution that the expected mean and variance are equal usually does not hold for RNA-seq studies, where the variance is typically greater than the mean of the read counts [17]. Therefore, a negative binomial distribution with a dispersion parameter is used to model RNA-seq data by the existing software packages such as DESeq [17] and edgeR [18], in which an exact test is used to test DEGs between conditions. Subsequently, a sample size calculation method based on an exact test statistic with the aid of the edgeR package [18] was proposed [19]. However, sample size methods derived from other test statistics such as the Wald test, the LRT and an extension of Wald test via log-transformation using negative binomial distribution to model the RNA-seq data have not yet been explored.

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“…We set m = 10,000, 1 Then, we calculated the sample size by substituting * and power into the equations (15)(16)(17)(18) and the published method using the exact test. For each designed setting, we also conducted 5000 simulations from an independent negative binomial distribution.…”

Section: Sample Size Calculation Based On Testing Multiple Genes Via mentioning

confidence: 99%

“…Recently, high-throughput RNA sequencing (RNAseq) technology has been widely used for gene expression studies in a variety of applications, such as expression profiling of mRNAs or non-coding RNA [1][2][3], de novo assembly and characterization of transcriptomes [4,5], and the identification of novel alternatively spliced transcripts [6,7]. These novel transcripts or differentially expressed genes (DEGs) identified from RNA-seq data may serve as human disease biomarkers or gene signatures for the clinical diagnosis [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Sample size calculations and normalization methods for RNA-seq data.

Li¹

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Genome-wide analysis and expression profiling of glyoxalase gene families in soybean (Glycine max) indicate their development and abiotic stress specific response

Cited by 71 publications

References 61 publications

Characteristic Variations and Similarities in Biochemical, Molecular, and Functional Properties of Glyoxalases across Prokaryotes and Eukaryotes

Characteristic Variations and Similarities in Biochemical, Molecular, and Functional Properties of Glyoxalases across Prokaryotes and Eukaryotes

Inference and Sample Size Calculations Based on Statistical Tests in a Negative Binomial Distribution for Differential Gene Expression in RNAseq Data

Sample size calculations and normalization methods for RNA-seq data.

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