Exploring the Genetic Diversity among Weedy Rice Accessions Differing in Herbicide Tolerance and Allelopathic Potential

Increasing agricultural productivity is indispensable to meet future food demand. Crop improvement programs rely heavily on genetic diversity. The success of weeds in the ecosystem can be attributed to genetic diversity and plasticity. Weedy rice, a major weed of rice, has diverse morphology and phenology, implying wide genetic diversity. Study was conducted to genotype weedy rice accessions (n = 54) previously phenotyped for herbicide tolerance and allelopathic potential using 30 SSR markers. Cultivated rice (CL163, REX) and allelopathic rice (RONDO, PI312777, PI338047) were also included in the study. Nei’s genetic diversity among weedy rice (0.45) was found to be higher than cultivated rice (0.24) but less than allelopathic rice (0.56). The genetic relationship and population structure based on herbicide tolerance and allelopathic potential were evaluated. Herbicide-tolerant and susceptible accessions formed distinct clusters in the dendrogram, indicating their genetic variation, whereas no distinction was observed between allelopathic and non-allelopathic weedy rice accessions. Weedy rice accession B2, which was previously reported to have high allelopathy and herbicide tolerance, was genetically distinct from other weedy rice. Results from the study will help leverage weedy rice for rice improvement programs as both rice and weedy rice are closely related, thus having a low breeding barrier.

Exploring the Genetic Diversity Among Weedy Rice Accessions Differing in Herbicide Tolerance and Allelopathic Potential

Increasing agricultural productivity is indispensable to meet future food demand. Crop im-provement programs rely heavily on genetic diversity. The success of weeds in the ecosystem can be attributed to genetic diversity and plasticity. Weedy rice, a major weed of rice, has diverse morphology and phenology, implying wide genetic diversity. Study was conducted to genotype weedy rice accessions (n =54) previously phenotyped for herbicide tolerance and allelopathic potential using 30 SSR markers. Cultivated rice (CL163, REX) and allelopathic rice (RONDO, PI312777, PI338047) were also included in the study. Nei’s genetic diversity among weedy rice (0.45) was found to be higher than cultivated rice (0.24) but less than allelopathic rice (0.56). The genetic relationship and population structure based on herbicide tolerance and allelopathic po-tential were evaluated. Herbicide-tolerant and susceptible accessions formed distinct clusters in the dendrogram, indicating their genetic variation, whereas no distinction was observed between allelopathic and non-allelopathic weedy rice accessions. Weedy rice accession B2, which was previously reported to have high allelopathy and herbicide tolerance, was genetically distinct from other weedy rice. Results from the study will help leverage weedy rice for rice improvement programs as both rice and weedy rice are closely related, thus having a low breeding barrier.

Variability and genetic diversity study based on agro-morphological traits in a diverse set of Indian mustard [Brassica juncea (L.) Czern. & Coss.] germplasm

Aim: The present investigation was designed to evaluate 150 diverse germplasm of Indian mustard along with five checks for seed yield and attributing traits. Methodology: A total of 150 genotypes of Indian mustard were evaluated in Augmented Block Design along with five check cultivars (Kranti, Maya, Pusa Bold, NPJ-112, RGN 73) in five blocks. Data were collected for 16 different quantitative traits on each genotype. Results: ANOVA revealed significant differences between genotypes for all traits studied. Seed yield plant-1 had significant and positive correlation with most of the yield contributing traits. For seed yield/plant, 15 accessions (DRMR-1722, BDJ-I-538, DRMR-4, JMG-113, DRMR-681, DRMR-1849, DRMR-1697, DRMR-1610, DRMR-1996, DRMR-107, DRMR-1588, DRMR-1379, DRMR-1995, IB-26, DRMR-2116) were significantly superior over best check. Interpretation: The present study was found useful for identification of diverse genotypes of Indian mustard for different yield attributing traits. The promising lines will be utilized in Indian mustard breeding for improving seed yield and oil content.

Phylogenetic Analysis and Genetic Diversity of Colletotrichum falcatum Isolates Causing Sugarcane Red Rot Disease in Bangladesh

Colletotrichum falcatum Went causes red rot disease in sugarcane farming in the tropical and sub-tropical regions. This disease causes significant economic loss to the sugarcane production industry. Successful disease management strategies depend on understanding the evolutionary relationship between pathogens, genetic diversity, and population structure, particularly at the intra-specific level. Forty-one isolates of C. falcatum were collected from different sugarcane farms across Bangladesh for molecular identification, phylogeny and genetic diversity study. The four genes namely, ITS-rDNA, β-tubulin, Actin and GAPDH sequences were conducted. All the 41 C. falcatum isolates showed a 99–100% similarity index to the conserved gene sequences in the GenBank database. The phylogram of the four genes revealed that C. falcatum isolates of Bangladesh clustered in the same clade and no distinct geographical structuring were evident within the clade. The four gene sequences revealed that C. falcatum isolates from Bangladesh differed from other countries´ isolates because of nucleotides substitution at different loci. The genetic structure of C. falcatum isolates were determined using ISSR marker generated 404 polymorphic loci from 10 selected markers. The percentage of polymorphic loci was 99.01. The genetic variability at species level was slightly higher than at population level. Total mean gene diversity at the species level was 0.1732 whereas at population level it was 0.1521. The cluster analysis divided 41 isolates into four main genetic groups and the principal component analysis was consistent with cluster analysis. To the best of our knowledge, this is the first finding on characterizing C. falcatum isolates infesting sugarcane in Bangladesh. The results of this present study provide important baseline information vis a vis C. falcatum phylogeny analysis and genetic diversity study.

Single-Strand Conformation Polymorphism Fingerprint Method for Dictyostelids

Dictyostelid social amoebae are a highly diverse group of eukaryotic soil microbes that are valuable resources for biological research. Genetic diversity study of these organisms solely relies on molecular phylogenetics of the SSU rDNA gene, which is not ideal for large-scale genetic diversity study. Here, we designed a set of PCR–single-strand conformation polymorphism (SSCP) primers and optimized the SSCP fingerprint method for the screening of dictyostelids. The optimized SSCP condition required gel purification of the SSCP amplicons followed by electrophoresis using a 9% polyacrylamide gel under 4°C. We also tested the optimized SSCP procedure with 73 Thai isolates of dictyostelid that had the SSU rDNA gene sequences published. The SSCP fingerprint patterns were related to the genus-level taxonomy of dictyostelids, but the fingerprint dendrogram did not reflect the deep phylogeny. This method is rapid, cost-effective, and suitable for large-scale sample screening as compared with the phylogenetic analysis of the SSU rDNA gene sequences.

Genetic Diversity Study of African Rice (Oryza glaberrima) and its Wild Relatives using Microsatellites Markers

The Africa Rice Center Gene Bank hold about 2,500 accessions of Oryza glaberrima. To understand well the genetic diversity in O. glaberrima and its wild species, the use of molecular tools is prominent. The sample consisted of 217 accessions of O. glaberrima, 46 of O. barthii and 7 of O. sativa (checks) was genotyped with 21 polymorphic microsatellites markers. A total of 245 alleles were detected with average 11.67 alleles per locus. Number of alleles was ranged from 2 (RM124) to 20 (RM536). The polymorphic information content value was 0.49 while the heterozygosity was 0.091. The result showed that the sample can be clustered into four genotypic groups. Two groups among them were homogeneous. The first one consisted of O. barthii accessions with 82 alleles in total with average 3.90 alleles per locus. However, the second one consisted of only O. glaberrima accessions with 122 alleles with average 5.80 alleles per locus. O. glaberrima accessions were analyzed using model-based population structure. Results revealed two groups among O. glaberrima accessions. At the end, the identified core collection has 26 accessions consisted of 16 O. glaberrima and 10 O. barthii based on 21 microsatellites markers.

PPI-GA: A Novel Clustering Algorithm to Identify Protein Complexes within Protein-Protein Interaction Networks Using Genetic Algorithm

Comprehensive analysis of proteins to evaluate their genetic diversity, study their differences, and respond to the tensions is the main subject of an interdisciplinary field of study called proteomics. The main objective of the proteomics is to detect and quantify proteins and study their post-translational modifications and interactions using protein chemistry, bioinformatics, and biology. Any disturbance in proteins interactive network can act as a source for biological disorders and various diseases such as Alzheimer and cancer. Most current computational methods for discovering protein complexes are usually based on specific topological characteristics of protein-protein networks (PPI). To identify the protein complexes, in this paper, we, first, present a new encoding method to represent solutions; we then propose a new clustering algorithm based on the genetic algorithm, named PPI-GA, employing a new multiobjective quality function. The proposed algorithm is evaluated on two gold standard and real-world datasets. The result achieved demonstrates that the proposed algorithm can detect important protein complexes, and it provides more accurate results compared with state-of-the-art protein complex identification algorithms.