S. M. Shahinul Islam

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

Heterotrimeric G-proteins (α, β 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 β 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.

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.

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.

Production of Abiotic Stress Tolerant Fertile Transgenic Plants using Androgenesis and Genetic Transformation Methods in Cereal Crops

The in vitro production of doubled haploid plants through androgenesis (anther and microspore culture) is an efficient system for the production of fully homozygous plants rapidly. To date, anther and microspore cultures are commonly used to accelerate breeding in a number of cereals and other crop species. Traditionally, plant breeders achieve homozygosity by using self-fertilization or backcrossing, which is a time consuming process. Significant advantage is that the system not only speeds up the process to obtain homozygosity, but also increases the selection efficiency. Doubled haploid plants are genetically normal and phenotypically stable. Abiotic stresses showed adverse effects on the growth of plants and the productivity of crops, thus resulting in significant economic losses worldwide. Conventional plant breeding is being employed to develop varieties resistant to abiotic stresses, but progress has been slow. There is a great need to exploit all genetic variabilities that can be used in breeding in adverse environmental conditions. Use of unconventional techniques, such as doubled haploid (DH) breeding through androgenesis will become more useful to speed up the application of conventional plant breeding methods. Genetic transformation is a novel approach for plant molecular genetics and breeding. This system offers an attractive alternative to conventional breeding programs because it can allow specific traits to be transferred into selected genotypes without adversely affecting their desirable genetic background. Till now, there are some reports using desirable genes to obtain stress tolerance transgenic plants with various transformation systems. There are some reports using explants directly to isolated microspores, protoplast isolation for microspore-derived suspension culture, anther and microspore-derived embryos for rapid production of transgenic fertile plants. However, this report mainly highlights the need to develop abiotic stress tolerance fertile transgenic plants, especially in cereal crops through androgenesis and genetic transformation system.

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.

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 β-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.

Robust Significance Analysis of Microarrays by Minimum β-Divergence Method

Identification of differentially expressed (DE) genes with two or more conditions is an important task for discovery of few biomarker genes. Significance Analysis of Microarrays (SAM) is a popular statistical approach for identification of DE genes for both small- and large-sample cases. However, it is sensitive to outlying gene expressions and produces low power in presence of outliers. Therefore, in this paper, an attempt is made to robustify the SAM approach using the minimum β-divergence estimators instead of the maximum likelihood estimators of the parameters. We demonstrated the performance of the proposed method in a comparison of some other popular statistical methods such as ANOVA, SAM, LIMMA, KW, EBarrays, GaGa, and BRIDGE using both simulated and real gene expression datasets. We observe that all methods show good and almost equal performance in absence of outliers for the large-sample cases, while in the small-sample cases only three methods (SAM, LIMMA, and proposed) show almost equal and better performance than others with two or more conditions. However, in the presence of outliers, on an average, only the proposed method performs better than others for both small- and large-sample cases with each condition.

Embryogenic Callus Induction and Efficient Plant Regeneration in Three Varieties of Soybean (Glycine max)

An efficient callus induction and plant regeneration system has been developed using three local soybean ( Glycine max L.) varieties. All the varieties showed good callusing (78.30 ‐ 88.80%) from shoot tip (ST) in MS + 3.0 mg/l 2,4‐D + 1.0 mg/l BAP. Best callus induction and embryo formation were recorded in T 5 for BS‐6 as 88.80 and 81.20%, respectively from ST. In case of cotyledonary node (CN) the maximum callus induction (82.40%) and embryo formation (74.20%) were recorded also in T 5 for BS‐6. Highest frequency (79.40%) of plant regeneration was recorded where ST were used and cultured in MS supplemented with 2.5 mg/l BAP + 1.0 mg/l NAA as well as 76.30% from CN in BS‐6. The length of shoot was observed 5.32 and 4.62 cm, respectively from ST and CN for BS‐6 with the same medium composition. It was observed that half strength of MS + 2.0 mg/l IBA showed best rooting (9.04). Among the genotypes BS‐6 proved to be best explants than ST that exhibited better performance on callus induction and green plant regeneration for all parameters. Plant Tissue Cult. & Biotech. 27(1): 41-50, 2017 (June)

Enhancement of Somatic Embryogenesis by Mature and Immature Seeds in Wheat (Triticum aestivum L.)

A suitable plant regeneration system has been established using 3-4 weeks old calli derived from immature and mature seeds of four wheat varieties viz . Pavon 76, Akbar, Barkat, and Kanchan. As plant growth regulators, various auxins (2,4-D, BAP and IAA) either single or in combination were used in MS medium. The variety Pavon 76 showed maximum (72.25%) callus induction and Akbar exhibited the lowest (37.78%) from calli derived from immature seeds. Hormonal effects on callus induction were evaluated and significant results were found in case of genotypes at P <0.01. Out of four genotypes, the highest frequency of plant regeneration was recorded in Pavon 76 (67.00%) and lowest in Kanchan (43.10%) when 1.5 mg/l BAP and 0.5 mg/l IAA was added in the medium. It was observed that Pavon 76 produced highest number of green plants than others. For mature seeds all of the mentioned genotypes showed significant difference with maximum frequency of callusing in Pavon 76 (69.57%) in MS + 2.5 mg/l 2,4-D followed by Kanchan (60.84%), Barkat (52.73%), and Akbar (47.19%). For plant regeneration, Pavon 76 also showed best performance (64.36%) in MS + 2.0 BAP + 1.0 mg/l IAA, using calli derived from mature seeds. The other genotypes Barkat, Kanchan and Akbar exhibited 59.44, 52.71 and 52.32% regeneration in the same medium respectively. Here, the lowest regeneration (40.63%) was found in Akbar. In this case, it was aimed to establish a suitable protocol for in vitro callus induction and regeneration for advance biotechnological research on wheat in Bangladesh.

Effect of silver nitrate and carbon sources on callus induction and regeneration in maize (Zea mays L.)

To evaluate the efficacy of silver nitrate and various carbon sources on callus induction and regeneration, four maize varieties of Bangladesh viz., ‘Barnali’, ‘Mohar’, ‘Shuvra’ and ‘Khoi bhutta’ were evaluated. Four carbon sources either singly or in combination forming seven treatments were tested. The highest callusing (67.1%) was recorded in treatment wherein 2.0% sucrose was used for ‘Mohar’. Study on the impact of seven different doses of AgNO3 revealed approximately 1.5 fold higher callus induction in ‘Mohar’ when 9 mg L−1 AgNO3 was added to N6 medium. For regeneration, 3–4 weeks old calli were transferred to plant regeneration medium where IAA 0.5 mg L−1 + BAP 1.0 mg L−1 were used. Of the four genotypes, ‘Mohar’ showed highest regeneration (62.1%) in MS medium. Maximum shoot elongation and well-developed root formation were observed in ‘Mohar’ (15.75 cm) and ‘Khoi bhutta’ (82.2%), respectively. Higher doses of AgNO3 were found effective in callus induction. Analysis of variance showed significant differences in genotypes with respect to callusing and regeneration efficiency.