Ecological Genetics and Genomics最新文献

The study investigates the distribution and diversity of the old-world moth genus Theretra from the family Sphingidae in the Indian subcontinent. Through extensive data collection and molecular analysis from the Northernmost Western Ghats (Nashik District), seven species of Theretra were identified: T. alecto, T. castanea, T. clotho, T. gnoma, T. nessus, T. oldenlandiae, and T. sumatrensis.

Molecular clustering identifies genetically similar specimens, which further helps to recognise similar ecological niches and the associated ecological drivers regulating the distribution pattern of similar specimens. A dataset of 196 published records from Barcode of Life Data Systems (BOLD), including outgroup and the sequences generated in the present study for the Indian species of Theretra, were compiled in a dataset ‘THEREIND’. The crucial role of monsoon and elevation in the diversity and distribution of these moths was comprehended using DNA barcoding and sequence clustering on BOLD. The comparisons suggested a strong correlation with either monsoon or elevation or both.

In the purview of the sixth mass extinction and the first true extinction of insects, adequate information on the diversity and the factors affecting it would provide fundamental information to insinuate conservation strategies required for coping with continuous climatic changes.

‘Bhat’ is the soybean variety highly valued in the traditional food and therapeutic system of Uttarakhand state in the Indian Himalayan region. It is available in diverse colors, shapes, and sizes, often linked to their nutritional parameters. The present investigation was conducted to assess the diversity and genetic relatedness in ‘bhat’ genotype indigenous to the Garhwal division of Uttarakhand. 25 polymorphic simple sequence repeats (SSR) markers were used to determine the genetic diversity and varietal identification among the 45 genotypes collected from the different villages of Uttarakhand. Out of the different markers assessed only 13 primers showed amplification with the maximum of 2–4 alleles obtained with each primer with the molecular weight ranging between 80 and 400 kb. The highest band amplification was observed in Satt 257 and Satt 197. The allelic frequencies of amplified primers ranged from 0.5 to 0.833 with a mean value of 0.645 and the mean gene diversity and PIC value was found to be 0.43 and 0.33. Satt 183, Satt 288, and Satt 389 showed the highest polymorphism, while Satt257 and Satt245 exhibited the presence of unique alleles in some samples. The phylogenetic analysis grouped the genotypes into 4 major clusters having visually distinct phenotypes in each group, indicating the mixing of population and loss of authenticity also confirming that phenotypic attributes are not indicative of genetic relatedness among the genotype under study. The results indicate the need for the development of novel ‘bhat’ specific markers for more accurate genetic identification of the nutritionally rich indigenous soybean variety.

Identification of miRNAs and their target proteins infer their functions to understand the biological processes of miRNAs and their involvement in plant growth and development. The homology-based approach (BLAST suite) was used for the identification of miRNA related to Zn and Cu deficiency in the bread wheat genome. Calculated the coding potential for the precursor miRNA and then predicted their secondary structure through RNAfold. PmiREN online server identified the miRNA target wheat protein. Further, STRING database predicted the biological relevance of the target protein. This in-silico study has identified the 3 miRNAs of the respective family of miR528, miR397, and miR168 of Triticum aestivum related to Cu and Zn deficiency. Out of the 42 targets for tae-miR397c; one of the targets is MFS domain-containing protein that contributes to “electron transfer” between photosystem700 and the “cytochrome b6-f complex” in photosystem and rest of the targets are laccase protein; involved in cell wall ligning deposition. Tae-miR528c has 6 targets; four are uncategorized proteins and the remaining two targets viz. GRF-type domain-containing protein and phytocyanin domain-containing protein are responsible for Zn ion binding and participate in electron transfer activity. The protein-protein interactions (PPIs) have found the various proteins that are associated with these identified miRNAs (tae-miR397c and tae-miR528c) target the protein that could be annotated further for their role in plant growth and development. The current computational hypothesis has developed a fast and robust pipeline to identify plant miRNAs and their targets compared to other used approaches.

One of the most devastating diseases impacting wheat (Triticum aestivum L.) worldwide is stripe rust, which is propagated by Puccinia striiformis f. Sp. Tritici (Pst). The development and utilization of resistant cultivars offer an effective and eco-friendly approach to manage this disease. However, the emergence of new virulent strains of Pst, driven by continuous mutations within the pathogen, rapidly undermines the effectiveness of existing resistance genes. This necessitates the ongoing identification and incorporation of new resistance genes to breed wheat varieties that can maintain resistance against evolving strains of the pathogen. A genome-wide association study (GWAS) and genomic prediction (GP) were conducted using yellow rust data from the seedling stage under controlled conditions, involving 200 elite bread wheat genotypes and 13,151 SNP markers. GWAS analysis identified fourteen SNP markers significantly associated with yellow rust resistance, using a general linear model (GLM). The markers (wsnp Ex c1085 2078944, wsnp Ku c3682 6,786,230) on chromosome 1D and (wsnp Ex c8240 13,914,674) on chromosome 3A were notably correlated with seedling-level resistance to yellow rust. Additionally, the marker 'AX-94703603′ on chromosome 3A, which recorded the highest -Log10(p) value, was linked to the gene 'TraesCS3A02G335300' encoding the protein kinase domain. These markers, after validation, could be utilized for gene pyramiding in wheat breeding programs to enhance rust resistance through marker-assisted selection.

Genetic relatedness analysis is the first step toward efficient conservation, maintenance, and improvement of the existing genetic diversity. Recently, DNA-based markers have been widely used due to their reliability and technical advantages. In the present study, twenty Simple Sequence Repeats (SSR) primers were used to evaluate genetic relatedness among ten sapota genotypes. The SSR analysis produced a total of 59 alleles with a range of 88 to 239bp. The maximum numbers of alleles (4) were recorded in SapSSR-15, SapSSR-5, SapSSR-21, SapSSR-39, and SapSSR-59. The Highest Nei genetic diversity was recorded in SapSSR-37 (0.500), whereas the lowest was in SapSSR-23 (0.320). The highest Shannon's information index (I) was observed for SapSSR-37 (0.693), while SapSSR-23 showed the lowest I value (0.499). The PIC value was in the range of 0.71–0.89 among the different primers, where the highest was observed for SapSSR-54. Based on marker analysis, SapSSR-4, SapSSR-21, SapSSR-36, and SapSSR-54 were found to be most effective for the genetic diversity analysis of sapota. The wide range of Jaccard's similarity coefficients (0.29–0.86) reported a moderate to high level of diversity among the studied sapota genotypes. Further, the dendrogram analysis showed clustering of genotypes based on geographical origin. The PCA analysis reported the evenly distribution of all genotypes across the four coordinates. The variety-specific alleles reported in this work can be exploitable for molecular fingerprinting purposes. The genetic relatedness revealed in this study can be useful for both varietal identification and sapota improvement programs.

Populations of highly mobile species that undertake long distance migrations are typically considered to be panmictic. Nonetheless, mechanisms related to behavior or local environmental conditions promote genetic isolation in the absence of physical barriers. Highly migratory shark species exhibit varying levels of fidelity to specific regions, shaping the genetic architecture of different populations and resulting in geographically based genetic variation with potential adaptive value. An understanding of the genetic variation of highly migratory species is needed to develop effective conservation strategies. This study aimed to assess the neutral and adaptive variation of the smooth hammerhead shark (Sphyrna zygaena) in the northern Mexican Pacific (NMP) via single nucleotide polymorphisms (SNPs). We analyzed 1480 SNPs in 92 individuals from four geographic regions in the NMP, of which 1469 SNPs were neutral loci (n-SNP), and 11 were putatively under selection (o-SNP) using four genoma scan methods. Genetic diversity was geographically similar among regions (Ho = 0.275). The neutral variation showed panmixia (n-SNPs; F_ST = 0.0012, p = 0.44), which may be associated with the high dispersal capacity of S. zygaena. A pattern of adaptive variation between individuals from the Gulf of California and Pacific coast was revealed using o-SNPs F_ST-based methods (24 oSNPs; F_ST = 0.061, p < 0.001), which may be promoted by individual preferences based on physiological limitations. The estimated effective population size (Ne) of S. zygaena was 1390 individuals, which is theoretically optimal for the population to persist over time.

The study examined the impact of 166 barley genotypes on yield performance in Tigray, revealing that year, environmental, and genotype factors significantly influence grain yield per plant (GYP). The analysis used AMMI and GGE biplot models, revealing environment as the dominant factor (95.3%), followed by genotypes (2.8%). The genotypes G126, G60, G108, G64, G52, G12, G62, G104, G47, G10, G83, G66, G39, and G30 were found to be highly productive genotypes showing low interaction with environments (genotypes centered near the origin) for the AMMI2 biplot for the IPCA1 and IPCA2 in GEI. The GGE biplot analysis also showed that top-performing genotypes outperformed in grain yield per plant, while Saesa and Himblil parental varieties fell below the top genotypes with yield scores of 15.34 gm/plant and 16.55 gm/plant, respectively. The IPCA1 and average environment coordination (AEC) scores at Mekelle_2018/19 (E3 & E7), Aleasa_2019 (E6), and Habes_2018/19 (E4 & E8) revealed the most stable environments. Though unstable and distant from AEC, Ayba_2018/19 (E1 and E5) significantly contributed to genotype-environment interaction. GGE-biplot of the "which-won-where" showed the 8 environments grouped into 4 mega-environments, with the winning genotypes of each environment being G112 for Ayba_2018, G82 for Aleasa_2018, G25 for Mekelle_2018, G61 for Habes_2018, and G4 for Ayba_2019. Similarly, AMMI biplot analysis revealed high average yields across test locations, with RIL genotypes G36, G72, G25, G118, and G112 showing genetic advancements and potential for future breeding initiatives.

The selection of high-yielding genotypes with high micronutrient and protein contents can play a major role in reducing protein-energy and micronutrient malnutrition. In this study, 20 bread wheat genotypes were examined under normal irrigation and end-season water deficit stress conditions. The grain yield (GY), thousand-kernel weight (TKW), protein (PR), iron (Fe), and zinc (Zn) contents were determined. The data were analyzed using the genotype by yield*trait (GYT) biplot method, and the superiority index was calculated from the integration of all yield-trait combinations. According to the results, the polygon view of GYT biplot under normal irrigation conditions showed that G20 was the best genotype in combining GY with TKW and PR contents. The G19 was the best genotype in combining GY with Fe and Zn contents. Under end-season water deficit stress, G20 was the best genotype in combining GY with TKW and Fe content. In addition, the G6 was the best genotype in combining GY with PR and Zn. The average tester coordinate (ATC) view of GYT biplot showed that there is not any genotype that was higher than the average yield-trait combination. However, G20 and G19 were relatively superior to other genotypes in this study. According to the superiority index, G20 and G19 genotypes were superior. Based on our results, G20, G19, and G6 genotypes were the best genotypes in combining GY with all or some evaluated traits, in two conditions. Therefore, they can be considered in genetic biofortification programs or variety introduction.

The study investigated the genetic diversity and population structure of Candidatus P. aurantifolia using tuf gene sequences sourced from GenBank™. A total of 29 sequences were analyzed based on location availability, unveiling 8 distinct haplotypes. Haplotype 5, encompassing sequences from Egypt, Australia, and Thailand, emerged as the most prevalent. Phylogenetic analysis revealed two different major clades with a significant genetic divergence within species. Notably, calculated haplotype diversity ranged from 0.0 to 0.5909, with India exhibiting the highest diversity. Moreover, haplotype diversity (Hd) was absent in several regions, including Egypt, Australia, and Burkina Faso. Furthermore, nucleotide diversity varied across regions, with Oman displaying the highest value (0.78182) and Thailand showing the lowest (0.09422). A high raggedness index value in the populations of Oman, and Thailand and a low value in India. Substantial genetic divergence in the populations between India-Egypt with limited gene flow was evident by high values of DeltaSt, GammaSt and Fst. Fu Fs values, indicative of demographic expansions or selection events, were notably high in India (34.035). Significant Tajima D values (2.035) in India further support deviations from neutral evolution. Our findings provide unprecedented insights into the population genetics and haplotype network of Candidatus P. aurantifolia, shedding light on its genetic diversity and geographic distribution in different countries. The study results will aid in the prediction of bacterium transmission and the implementation of effective quarantine measures. Moreover, the identification of regions having high genetic divergence and unique haplotypes may help in developing disease management strategies for targeted areas.

The performance of oat genotypes usually varies across environments due to variations in growing environments and the existence of genotype by environment interaction (GEI) complicates the selection process. In this study, twenty-four oat genotypes were assessed for grain yield and yield components in nine environments using randomized complete bock design (RCBD) with three replications in 2020/2021 cropping season. Hence, a GEI study was performed using additive main effects and multiplicative interactions (AMMI) analysis model to identify high grain yielding and stable genotypes. The AMMI analysis of variance for grain yield showed significant variation for genotype, environment and GEI effects and the environment's main effect was a predominant source of variation (44.62%) followed by genotype (28.84%) and their interactions (26.54%). The first two interaction principal component axes of AMMI were significant and cumulatively explained 63.96% of the total GEI variance. The environments located far from the biplot origin based on the AMMI-1 and AMMI-2 analyses were E2, E6, E5, E3, and E7 indicating these environments had high discriminating power and more contribution to GEI compared to other environments. Among the studied genotypes, G8, G17, G12, G19, G5, G14, G11, G22, G16, and G4 had mean grain yield above the grand mean. The result of stability analysis obtained from the AMMI-2 analysis was more accurate than the AMMI-1. Accordingly, genotypes which had mean grain yield above the grand mean and relatively stable performance were G4, G11, G12, G22, G14, G8, and G17. However, G4, G11, G12, and G14 were released varieties while G8, G17, and G22 have not been yet released. Therefore, G8 and G17 were selected for verification and commercial production in oat growing areas of Ethiopia.