Identification of abiotic stress responsive genes: A genome wide analysis of the cytokinin response regulator gene family in rice.

Response regulators (RRs), which are implicated in various developmental processes as well as environmental responses by acting as either positive or negative regulators, are crucial components of cytokinin signaling in plants. We characterized 36 RRs using in silico and computational analyses of publicly available data. A comprehensive analysis of OsRR family members was performed covering their physicochemical properties, chromosomal distribution, subcellular localization, phylogeny, gene structure, distribution of conserved motifs and domains, and gene duplication events. Gene Ontology analysis results indicate that 22 OsRR genes contribute mainly to the cytokinin-response and signal transduction. Predicted cis-elements in RRs promoter sequences related to phytohormones and abiotic stresses indicate that RRs are involved in hormonal and environmental responses as described in previous studies. MicroRNA (miRNA) target analysis showed that 148 miRNAs target 29 OsRR genes. In some cases, those RRs are targets of the same miRNA group, and may be controlled by common stimulus responses. Based on the analysis of publicly available gene expression data, OsRR4, OsRR6, OsRR9, OsRR10, OsRR22, OsPRR73, and OsPRR95 were found to be involved in response to abiotic stresses. Using quantitative reverse transcription polymerase chain reaction (qPCR) we confirmed that those RRs, namely OsRR4, OsRR6, OsRR9, OsRR10, OsRR22, and OsPRR73, are involved in the response to salinity, osmotic, alkaline and wounding stresses, and can potentially be used as models to understand molecular mechanisms underlying stress responsiveness.

Application of Three Compounds Extracted from Cynodon dactylon against Streptococcus mutans Biofilm Formation to Prevent Oral Diseases.

Streptococcus mutans bacteria form a biofilm called plaque that causes oral diseases, including tooth decay. Therefore, inhibition of biofilm formation is essential to maintaining good oral health. The health and nutritional benefits of Cynodon dactylon are well documented, but very little is known about its use to treat against oral diseases. The aim of this study was to detect the adhesion strength of the S. mutans bacterial biofilm in 100 cases in the Rajshahi region and evaluate the inhibitory activity of different compound extracts of C. dactylon on the S. mutans bacterial biofilm by determining the composition of isolated compounds using phytochemical analysis. Nuclear magnetic resonance (NMR) spectroscopy confirmed that three specific compounds from C. dactylon were discovered in this study: 3,7,11,15 tetramethyl hexadec-2-4dien 1-o1, compound 3,7,11,15 tetramethylhexadec-2-en-1-o1 from phytol derivatives, and stigmasterol. Results indicated that the compound of 3,7,11,15-tetramethyl-hexadec-2-en-1-ol exhibited higher antibiofilm activities on S. mutans than those of the other compound extracts. A lower level of minimum inhibitory concentration was exposed by 3, 7, 11,15 tetramethyl hexadeca-2-en-1-o1 (T2) on S. mutans at 12.5 mL. In this case, the compound of 3,7,11,15 tetramethyl hexadec 2en-1-o1 was used, and patients showed a mean value and standard error reduced from 3.42 ± 0.21 to 0.33 ± 0.06 nm. The maximum inhibition was (80.10%) in the case of patient no. 17, with a value of p < 0.05 found for S. mutans to which 12.5 µL/mL ethyl acetate extract was applied. From these findings, it may be concluded that C. dactylon extracts can be incorporated into various oral preparations to prevent tooth decay.

Robust regression based genome-wide multi-trait QTL analysis.

In genome-wide quantitative trait locus (QTL) mapping studies, multiple quantitative traits are often measured along with the marker genotypes. Multi-trait QTL (MtQTL) analysis, which includes multiple quantitative traits together in a single model, is an efficient technique to increase the power of QTL identification. The two most widely used classical approaches for MtQTL mapping are Gaussian Mixture Model-based MtQTL (GMM-MtQTL) and Linear Regression Model-based MtQTL (LRM-MtQTL) analyses. There are two types of LRM-MtQTL approach known as least squares-based LRM-MtQTL (LS-LRM-MtQTL) and maximum likelihood-based LRM-MtQTL (ML-LRM-MtQTL). These three classical approaches are equivalent alternatives for QTL detection, but ML-LRM-MtQTL is computationally faster than GMM-MtQTL and LS-LRM-MtQTL. However, one major limitation common to all the above classical approaches is that they are very sensitive to outliers, which leads to misleading results. Therefore, in this study, we developed an LRM-based robust MtQTL approach, called LRM-RobMtQTL, for the backcross population based on the robust estimation of regression parameters by maximizing the ß-likelihood function induced from the ß-divergence with multivariate normal distribution. When ß = 0, the proposed LRM-RobMtQTL method reduces to the classical ML-LRM-MtQTL approach. Simulation studies showed that both ML-LRM-MtQTL and LRM-RobMtQTL methods identified the same QTL positions in the absence of outliers. However, in the presence of outliers, only the proposed method was able to identify all the true QTL positions. Real data analysis results revealed that in the presence of outliers only our LRM-RobMtQTL approach can identify all the QTL positions as those identified in the absence of outliers by both methods. We conclude that our proposed LRM-RobMtQTL analysis approach outperforms the classical MtQTL analysis methods.

Effect of silver nitrate and growth regulators to enhance anther culture response in wheat (Triticum aestivum L.).

Application of chemical substances as stress pre-treatment factors may positively influence androgenetic responses in cereal and other crops. AgNO3 is an anti-ethylene compounds that played a significant role in combination with other chemicals for anther culture responses in cereal and other crop plants. For this study two local wheat cultivars viz. Kheri and Akbar were considered to evaluate the effect of AgNO3 and to optimize the suitable doses of plant growth regulators, amino acids and sucrose that supplemented in MS medium. Data were recorded on the basis of embryoids induction, regenerated green and albino plants. The results clearly stated that anther culture responses and its major outcomes on regeneration significantly increased with suitable dosages of chemicals. The most noteworthy increases embryo like structures and regenerated green plants accomplished by utilizing the combined effect of AgNO3 (50 mg/l) and as plant growth regulators IAA (1.0 mg/l) + kinetin (0.5 mg/l). Best embryo like structures (79.17%) and green plants (33.33%) were recorded in Kheri. The results clearly stated that reducing albinism and increasing embryos induction and green plants 50-75 mg/l silver nitrate along with optimum doses of IAA and kinetin showed very effective results in wheat.

Regression based fast multi-trait genome-wide QTL analysis.

Multivariate simple interval mapping (SIM) is one of the most popular approaches for multiple quantitative trait locus (QTL) analysis. Both maximum likelihood (ML) and least squares (LS) multivariate regression (MVR) are widely used methods for multi-trait SIM. ML-based MVR (MVR-ML) is an expectation maximization (EM) algorithm based iterative and complex time-consuming approach. Although the LS-based MVR (MVR-LS) approach is not an iterative process, the calculation of likelihood ratio (LR) statistic in MVR-LS is also a time-consuming complex process. We have introduced a new approach (called FastMtQTL) for multi-trait QTL analysis based on the assumption of multivariate normal distribution of phenotypic observations. Our proposed method can identify almost the same QTL positions as those identified by the existing methods. Moreover, the proposed method takes comparatively less computation time because of the simplicity in the calculation of LR statistic by this method. In the proposed method, LR statistic is calculated only using the sample variance-covariance matrix of phenotypes and the conditional probability of QTL genotype given the marker genotypes. This improvement in computation time is advantageous when the numbers of phenotypes and individuals are larger, and the markers are very dense resulting in a QTL mapping with a bigger dataset.

Robust identification of differentially expressed genes from RNA-seq data.

BACKGROUND: Identification of differentially expressed genes (DEGs) under two or more experimental conditions is an important task for elucidating the molecular basis of phenotypic variation. In the recent years, next generation sequencing (RNA-seq) has become very attractive and competitive alternative to the microarrays because of reducing the cost of sequencing and limitations of microarrays. A number of methods have been developed for detecting the DEGs from RNA-seq data. Most of these methods are based on either Poisson distribution or negative binomial (NB) distribution. However, identification of DEGs based on read count data using skewed distribution is inflexible and complicated of in presence of outliers or extreme values. RESULTS: Most of the existing DEGs selection methods produce lower accuracies and higher false discoveries in presence of outliers. There are some robust approaches such as edgeR_robust and DEseq2 perform well in presence of outliers for large sample case. But they show weak performance for small-sample case, in presence of outliers. To address this issues an alternative approach has emerged by transforming the RNA-seq data into microarray like data. Among various transformation methods voom using limma pipeline is proven better for RNA-seq data. However, limma by voom transformation is sensitive to outliers for small-sample case. Therefore, in this paper, we robustify the voom approach using the minimum ß-divergence method. We demonstrate the performance of the proposed method in a comparison of seven popular biomarkers selection methods: DEseq, DEseq2, SAMseq, Bayseq, limma (voom), edgeR and edgeR_robust using both simulated and real dataset. Both types of experimental results show that the performance of the proposed method improve over the competing methods, in presence of outliers and in absence of outliers it keeps almost equal performance with these methods. CONCLUSION: We observe the improved performance of the proposed method from simulation and real RNA-seq count data analysis for both small-and large-sample cases, in presence of outliers. Therefore, our proposal is to use the proposed method instead of existing methods to obtain the better performance for selecting the DEGs.

A Robust Approach for Identification of Cancer Biomarkers and Candidate Drugs.

Background and objectives: Identification of cancer biomarkers that are differentially expressed (DE) between two biological conditions is an important task in many microarray studies. There exist several methods in the literature in this regards and most of these methods designed especially for unpaired samples, those are not suitable for paired samples. Furthermore, the traditional methods use p-values or fold change (FC) values to detect the DE genes. However, sometimes, p-value based results do not comply with FC based results due to the smaller pooled variance of gene expressions, which occurs when variance of each individual condition becomes smaller. There are some methods that combine both p-values and FC values to solve this problem. But, those methods also show weak performance for small sample cases in the presence of outlying expressions. To overcome this problem, in this paper, an attempt is made to propose a hybrid robust SAM-FC approach by combining rank of FC values and rank of p-values computed by SAM statistic using minimum ß-divergence method, which is designed for paired samples. Materials and Methods: The proposed method introduces a weight function known as ß-weight function. This weight function produces larger weights corresponding to usual and smaller weights for unusual expressions. The ß-weight function plays the significant role on the performance of the proposed method. The proposed method uses ß-weight function as a measure of outlier detection by setting ß = 0.2. We unify both classical and robust estimates using ß-weight function, such that maximum likelihood estimators (MLEs) are used in absence of outliers and minimum ß-divergence estimators are used in presence of outliers to obtain reasonable p-values and FC values in the proposed method. Results: We examined the performance of proposed method in a comparison of some popular methods (t-test, SAM, LIMMA, Wilcoxon, WAD, RP, and FCROS) using both simulated and real gene expression profiles for both small and large sample cases. From the simulation and a real spike in data analysis results, we observed that the proposed method outperforms other methods for small sample cases in the presence of outliers and it keeps almost equal performance with other robust methods (Wilcoxon, RP, and FCROS) otherwise. From the head and neck cancer (HNC) gene expression dataset, the proposed method identified two additional genes (CYP3A4 and NOVA1) that are significantly enriched in linoleic acid metabolism, drug metabolism, steroid hormone biosynthesis and metabolic pathways. The survival analysis through Kaplan-Meier curve revealed that combined effect of these two genes has prognostic capability and they might be promising biomarker of HNC. Moreover, we retrieved the 12 candidate drugs based on gene interaction from glad4u and drug bank literature based gene associations. Conclusions: Using pathway analysis, disease association study, protein-protein interactions and survival analysis we found that our proposed two additional genes might be involved in the critical pathways of cancer. Furthermore, the identified drugs showed statistical significance which indicates that proteins associated with these genes might be therapeutic target in cancer.

Robust Feature Selection Approach for Patient Classification using Gene Expression Data.

Patient classification through feature selection (FS) based on gene expression data (GED) has already become popular to the research communities. T-test is the well-known statistical FS method in GED analysis. However, it produces higher false positives and lower accuracies for small sample sizes or in presence of outliers. To get rid from the shortcomings of t-test with small sample sizes, SAM has been applied in GED. But, it is highly sensitive to outliers. Recently, robust SAM using the minimum ß-divergence estimators has overcome all the problems of classical t-test & SAM and it has been successfully applied for identification of differentially expressed (DE) genes. But, it was not applied in classification. Therefore, in this paper, we employ robust SAM as a feature selection approach along with classifiers for patient classification. We demonstrate the performance of the robust SAM in a comparison of classical t-test and SAM along with four popular classifiers (LDA, KNN, SVM and naive Bayes) using both simulated and real gene expression datasets. The results obtained from simulation and real data analysis confirm that the performance of the four classifiers improve with robust SAM than the classical t-test and SAM. From a real Colon cancer dataset we identified 21 additional DE genes using robust SAM that were not identified by the classical t-test or SAM. To reveal the biological functions and pathways of these 21 genes, we perform KEGG pathway enrichment analysis and found that these genes are involved in some important pathways related to cancer disease.

Serum and Plasma Metabolomic Biomarkers for Lung Cancer.

In drug invention and early disease prediction of lung cancer, metabolomic biomarker detection is very important. Mortality rate can be decreased, if cancer is predicted at the earlier stage. Recent diagnostic techniques for lung cancer are not prognosis diagnostic techniques. However, if we know the name of the metabolites, whose intensity levels are considerably changing between cancer subject and control subject, then it will be easy to early diagnosis the disease as well as to discover the drug. Therefore, in this paper we have identified the influential plasma and serum blood sample metabolites for lung cancer and also identified the biomarkers that will be helpful for early disease prediction as well as for drug invention. To identify the influential metabolites, we considered a parametric and a nonparametric test namely student׳s t-test as parametric and Kruskal-Wallis test as non-parametric test. We also categorized the up-regulated and down-regulated metabolites by the heatmap plot and identified the biomarkers by support vector machine (SVM) classifier and pathway analysis. From our analysis, we got 27 influential (p-value<0.05) metabolites from plasma sample and 13 influential (p-value<0.05) metabolites from serum sample. According to the importance plot through SVM classifier, pathway analysis and correlation network analysis, we declared 4 metabolites (taurine, aspertic acid, glutamine and pyruvic acid) as plasma biomarker and 3 metabolites (aspartic acid, taurine and inosine) as serum biomarker.

Effects of salt and heat pre-treatment factors on efficient regeneration in barley (Hordeum vulgare L.).

An efficient callus induction and plant regeneration system has been developed using salt and heat as pre-treatment factors for three barley genotypes viz. BB-3, BB-6 and BHL-18. Different concentrations of NaCl (1.5, 2.5, 3.5, 4.5, 5.5 and 6.5 g/L) were used and its effects were determined on the basis of the viability of callus (CV), plant regeneration (PR), relative growth rate (RGR) and tolerance index (TI). The BB-6 showed highest performance on tolerance based on CV (14.72%), PR (7.69%), RGR (0.91%) and TI (0.42%) at 6.5 g/L NaCl. Various NaCl concentrations displayed significantly differences at P < 0.01 level as compared with the control. Plant regeneration capability was recorded after heat pre-treatment using calli at 30, 35 and 40 °C. In this study, BHL-18 produced highest callus induction (59.71%) after desiccated at 40 °C for BB-6. Highest regeneration was recorded around 41.66% when 4 weeks old calli were pre-treated at 35 °C. Furthermore, heat pre-treatment factors were very effective for enhancing plant regeneration (25-41.66%) which was 1.8-2.14 fold higher compared to the control (13.88-19.44%). Hence, heat treated calli displayed higher tolerance level to survive in NaCl-induced treatment for determining abiotic stress and increased regeneration rate at 35 °C temperature in BB-6 barley genotype.

Metabolomic Biomarker Identification in Presence of Outliers and Missing Values.

Metabolomics is the sophisticated and high-throughput technology based on the entire set of metabolites which is known as the connector between genotypes and phenotypes. For any phenotypic changes, potential metabolite (biomarker) identification is very important because it provides diagnostic as well as prognostic markers and can help to develop new biomolecular therapy. Biomarker identification from metabolomics data analysis is hampered by the use of high-throughput technology that provides high dimensional data matrix which contains missing values as well as outliers. However, missing value imputation and outliers handling techniques play important role in identifying biomarker correctly. Although several missing value imputation techniques are available, outliers deteriorate the accuracy of imputation as well as the accuracy of biomarker identification. Therefore, in this paper we have proposed a new biomarker identification technique combining the groupwise robust singular value decomposition, t-test, and fold-change approach that can identify biomarkers more correctly from metabolomics dataset. We have also compared the performance of the proposed technique with those of other traditional techniques for biomarker identification using both simulated and real data analysis in absence and presence of outliers. Using our proposed method in hepatocellular carcinoma (HCC) dataset, we have also identified the four upregulated and two downregulated metabolites as potential metabolomic biomarkers for HCC disease.

Effects of Melatonin on Colchicine-Treated PLBs of Dendrobium sonia-28 Orchid.

Dendrobium hybrid orchid is popular in orchid commercial industry due to its short life cycle and ability to produce various types of flower colours. This study was conducted to identify the morphological, biochemical and scanning electron microscopy (SEM) analysis in the Dendrobium sonia-28 orchid plants. In this study, 0.05 and 0.075 % of colchicine-treated Dendrobium sonia-28 (4-week-old culture) protocorm-like bodies (PLBs) were treated in different concentrations of melatonin (MEL) posttreatments (0, 0.05, 0.1, 0.5, 1, 5 and 10 µM). Morphological parameters such as number of shoots, growth index and number of PLBs were determined. In the 0.05 and 0.075 % of colchicine-treated PLBs which were posttreated with 0.05 µM MEL resulted in the highest value of the morphological parameters tested based on the number of shoots (84.5 and 96.67), growth index (16.94 and 12.15) and number of PLBs (126.5 and 162.33), respectively. SEM analysis of the 0.05 µM MEL posttreatment on both the colchicine-treated regenerated PLBs showed irregular cell lineages, and some damages occurred on the stomata. This condition might be due to the effect of plasmolyzing occurred in the cell causing irregular cell lineages.

Effect of air desiccation and salt stress factors on in vitro regeneration of rice (Oryza sativa L.).

Enhancement of callus induction and its regeneration efficiency through in vitro techniques has been optimized for 2 abiotic stresses (salt and air desiccation) using 3 rice genotypes viz. BR10, BRRI dhan32 and BRRI dhan47. The highest frequency of callus induction was obtained for BRRI dhan32 (64.44%) in MS medium supplemented with 2, 4-D (2.5 mgL(-1)) and Kin (1.0 mgL(-1)). Different concentrations of NaCl (2.9, 5.9, 8.8 and 11.7 gL(-1)) were used and its effect was recorded on the basis of viability of calli (VC), relative growth rate (RGR), tolerance index (TI) and relative water content (RWC). It was observed that in all cases BRRI dhan47 showed highest performance on tolerance to VC (45.33%), RGR (1.03%), TI (0.20%) and RWC (10.23%) with 11.7 gL(-1) NaCl. Plant regeneration capability was recorded after partial air desiccation pretreatment to calli for 15, 30, 45 and 60 h. In this case BRRI dhan32 gave maximum number of regeneration (76.19%) when 4 weeks old calli were desiccated for 45 h. It was observed that air desiccation was 2-3 folds more effective for enhancing green plantlet regeneration compared to controls. Furthermore, desiccated calli also showed the better capability to survive in NaCl induced abiotic stress; and gave 1.9 fold (88.80%) increased regeneration in 11.7 gL(-1) salt level for BRRI dhan47. Analysis of variance (ANOVA) showed that the genotypes, air desiccation and NaCl had significant effect on plant regeneration at P < 0.01.

Rice heterotrimeric G-protein gamma subunits (RGG1 and RGG2) are differentially regulated under abiotic stress.

Heterotrimeric G-proteins (α, ß and γ subunits) are primarily involved in diverse signaling processes by transducing signals from an activated transmembrane G-protein coupled receptor (GPCR) to appropriate downstream effectors within cells. The role of α and ß G-protein subunits in salinity and heat stress has been reported but the regulation of γ subunit of plant G-proteins in response to abiotic stress has not heretofore been described. In the present study we report the isolation of full-length cDNAs of two isoforms of Gγ [RGG1(I), 282 bp and RGG2(I), 453 bp] from rice (Oryza sativa cv Indica group Swarna) and described their transcript regulation in response to abiotic stresses. Protein sequence alignment and pairwise comparison of γ subunits of Indica rice [RGG(I)] with other known plant G-protein γ subunits demonstrated high homology to barley (HvGs) while soybean (GmG2) and Arabidopsis (AGG1) were least related. The numbers of the exons and introns were found to be similar between RGG1(I) and RGG2(I), but their sizes were different. Analyses of promoter sequences of RGG(I) confirmed the presence of stress-related cis-regulatory signature motifs suggesting their active and possible independent roles in abiotic stress signaling. The transcript levels of RGG1(I) and RGG2(I) were upregulated following NaCl, cold, heat and ABA treatments. However, in drought stress only RGG1(I) was upregulated. Strong support by transcript profiling suggests that γ subunits play a critical role via cross talk in signaling pathways. These findings provide first direct evidence for roles of Gγ subunits of rice G-proteins in regulation of abiotic stresses. These findings suggest the possible exploitation of γ subunits of G-protein machinery for promoting stress tolerance in plants.

Enhancement of androgenesis by abiotic stress and other pretreatments in major crop species.

Rapid production of doubled haploids (DHs) through androgenesis is an important and promising method for genetic improvement of crop plants. Through androgenesis complete homozygous plants can be produced within a year compared to long inbreeding methods that may take several years and costly. Significant advantage of androgenesis is that it not only speeds up the process to achieve homozygosity, but also increases the selection efficiency. Though success in androgenesis has been achieved in many crop plants, yet there are certain limitations especially, low frequency of embryogenesis and regeneration in few species. In fact in many cereals, induction of embryos and regeneration of green plants is still a hurdle that one needs to overcome to improve the efficiency of androgenesis. Efficient androgenesis is usually induced by the successful application of different stress pretreatment. Since so many stress factors can trigger the reprogramming of microspores and that have been co-related to change the ultrastuctural changes of cells to embryos and finally haploid plants. It has been shown that certain pretreatment such as (i) physical stresses as cold, heat shock, starvation, drought stress, osmotic pressure, gamma irradiation, oxidative stress, reduced atmospheric pressure, and (ii) chemical treatments such as colchicine, heavy metal, ABA, CGA, AEC, Azetidine, 2-NHA, either individual or combined effect of more than one stress factors may positively influence androgenetic efficiency. This review highlights the recent and past work on uses of various abiotic stresses and pretreatments and their impact on enhancing the efficiency of androgenesis on some major crop species for the development of doubled haploid plants.