High-throughput multiplex microsatellite marker assay for detection and quantification of adulteration in Basmati rice (Oryza sativa)

Basmati rice is a very special type of aromatic rice known world-wide for its extra long grains and pleasant and distinct aroma. Traditional Basmati rice cultivars, confined to Indo-Gangetic regions of the Indian subcontinent, are often reported to be adulterated with crossbred Basmati varieties and long-grain non-Basmati varieties in the export market. At present, there is no commercial scale technology to reliably detect adulteration. We report here a CE-based multiplex microsatellite marker assay for detection as well as quantification of adulteration in Basmati rice samples. The single-tube assay multiplexes eight microsatellite loci to generate variety-specific allele profiles that can detect adulteration from 1% upwards. The protocol also incorporates a quantitative-competitive PCR-based analysis for quantification of adulteration. Accuracy of quantification has been shown to be ±1.5%. The experiments used to develop and validate the methodology are described

InSatDb: a microsatellite database of fully sequenced insect genomes

InSatDb presents an interactive interface to query information regarding microsatellite characteristics per se of five fully sequenced insect genomes (fruit-fly, honeybee, malarial mosquito, red-flour beetle and silkworm). InSatDb allows users to obtain microsatellites annotated with size (in base pairs and repeat units); genomic location (exon, intron, up-stream or transposon); nature (perfect or imperfect); and sequence composition (repeat motif and GC%). One can access microsatellite cluster (compound repeats) information and a list of microsatellites with conserved flanking sequences (microsatellite family or paralogs). InSatDb is complete with the insects information, web links to find details, methodology and a tutorial. A separate ‘Analysis’ section illustrates the comparative genomic analysis that can be carried out using the output. InSatDb is available at

Genetic analyses of casuarinas using ISSR and FISSR markers

Inter simple sequence repeat polymerase chain reaction (ISSR-PCR) was used for the genetic analysis of the six species of Allocasuarina, five species of Casuarina and 12 superior performing selections of C. equisetifolia L. We also fingerprinted C. equisetifolia L. selections using Fluorescent-ISSR-PCR (FISSR-PCR), an improvised ISSR-PCR assay. The ISSR analysis provided information on the frequency of various simple sequence repeats in the casuarina genome. The di-nucleotide repeats were more common, among which (CA)n and its complementary nucleotide (GT)n repeat motifs amplified relatively higher number of bands with an average of 6.0 ± 3.5 and 6.3 ± 1.8 respectively. Eleven species of casuarinas were amplified with 10 primers anchored either at 5' or 3' end. A total of 253 PCR products were obtained and all were polymorphic, out of which 48 were specific to Allocasuarina and 36 were specific to Casuarina genus. Genetic similarity among the species was 0.251. A UPGMA dendrogram grouped all the Casuarina species together. The 12 superior performing selections of C. equisetifolia L. produced 57 polymorphic ISSR markers while the FISSR assay revealed 105 polymorphic markers. The primer CRR(ATT)4 distinguished all the selections. DNA profiles obtained with ISSR and FISSR assays would serve as a reference library for the establishment of clonal identity in casuarinas

Molecular and morphometric analyses reveal discrete grouping of pomegranate (Punica granatum) genebank accessions away from cultivars

Management of germplasm in the field genebanks is greatly assisted by genetic analysis. Estimation of genetic diversity and assessment of genetic relationships among 45 accessions of pomegranate (Punica granatum L.) that included cultivars and germplasm collections were carried out using nine morphometric, 241 ISSR and six SSR markers. The average genetic distance values based on ANOVA sum of squares (Ward) were 3.94 and 5.10 for morphometric and DNA markers respectively. Hierarchical clustering based on genetic distances grouped the accessions into at least three distinct clusters; the two-way clustering showing the contribution of individual markers in genetic grouping. Discrete grouping of field genebank accessions (with IC numbers) away from cultivars was evident based both on quantitative traits data as well as DNA marker data. The findings suggested the possibility of broadening genetic base of cultivated varieties by augmenting the breeding programmes in India with diverse as well as trait- specific pomegranate germplasm

Proceedings and recommendations of the National Webinar on Implementation of Access to Plant Genetic Resources and Benefit Sharing

A National Webinar on “Implementation of Access to Plant Genetic Resources and Benefit Sharing (ABS)” was held on August 27, 2020. The meeting was co-organized by UN Environment Implemented GEF Project, Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT), Delhi Office, India and the Indian Society of Plant Genetic Resources (ISPGR) and was attended by 200 stakeholders from academia, policy, management, farmers and private sector. This document provides briefly the deliberations held during the webinar and the major recommendations which emerged

Evaluation and Multivariate Analysis of Cowpea [Vigna unguiculata (L.) Walp] Germplasm for Selected Nutrients—Mining for Nutri-Dense Accessions

A total of 120 highly diverse cowpea [Vigna unguiculata (L.) Walp] genotypes, including indigenous and exotic lines, were evaluated for different biochemical traits using AOAC official methods of analysis and other standard methods. The results exhibited wide variability in the content of proteins (ranging from 19.4 to 27.9%), starch (from 27.5 to 42.7 g 100 g−1), amylose (from 9.65 to 21.7 g 100 g−1), TDF (from 13.7 to 21.1 g 100 g−1), and TSS (from 1.30 to 8.73 g 100 g−1). The concentration of anti-nutritional compounds like phenols and phytic acid ranged from 0.026 to 0.832 g 100 g−1 and 0.690 to 1.88 g 100 g−1, respectively. The correlation coefficient between the traits was calculated to understand the inter-trait relationship. Multivariate analysis (PCA and HCA) was performed to identify the major traits contributing to variability and group accessions with a similar profile. The first three principal components, i.e., PC1, PC2, and PC3, contributed to 62.7% of the variation, where maximum loadings were from starch, followed by protein, phytic acid, and dietary fiber. HCA formed six distinct clusters at a squared Euclidean distance of 5. Accessions in cluster I had high TDF and low TSS content, while cluster II was characterized by low amylose content. Accessions in cluster III had high starch, low protein, and phytic acid, whereas accessions in cluster IV contained high TSS, phenol, and low phytic acid. Cluster V was characterized by high protein, phytic acid, TSS, and phenol content and low starch content, and cluster VI had a high amount of amylose and low phenol content. Some nutri-dense accessions were identified from the above-mentioned clusters, such as EC169879 and IC201086 with high protein (>27%), TSS, amylose, and TDF content. These compositions are promising to provide practical support for developing high-value food and feed varieties using effective breeding strategies with a higher economic value

Computational identification of microRNAs and their target genes in sesame (Sesamum indicum L.)

Not AvailableMicroRNAs (miRNAs) are non-coding small RNAs, which play a crucial function in specific biological and metabolic processes of plants and animals. MicroRNAs are very small sequences of 18 to 26 nucleotides length, which regulate gene expression through either inhibition of mRNA translation or mRNA cleavage by binding to target mRNAs. EST-based homology search, a powerful and comparative computational approach, is applied to detect conserved miRNAs in sesame. A homology search of previously identified miRNAs of Viridiplantae was performed against 16548 transcripts of sesame so as to detect novel miRNAs of sesame crop. In the present study, 4 potential miRNAs were detected through various stringent filtering criteria. Using psRNATarget server, 143 potential target genes were predicted which corresponds to the predicted miRNAs in sesame. The target genes tend primarily to encode DNA-binding proteins, transcription factors, stress-responsive genes and genes that regulate signal transduction. Results from the current study will hasten the way for understanding some mechanisms of post-transcriptional gene silencing and other functions of miRNAs in sesame