28 research outputs found

    A SNP and SSR Based Genetic Map of Asparagus Bean (Vigna. unguiculata ssp. sesquipedialis) and Comparison with the Broader Species

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    Asparagus bean (Vigna. unguiculata ssp. sesquipedialis) is a distinctive subspecies of cowpea [Vigna. unguiculata (L.) Walp.] that apparently originated in East Asia and is characterized by extremely long and thin pods and an aggressive climbing growth habit. The crop is widely cultivated throughout Asia for the production of immature pods known as ‘long beans’ or ‘asparagus beans’. While the genome of cowpea ssp. unguiculata has been characterized recently by high-density genetic mapping and partial sequencing, little is known about the genome of asparagus bean. We report here the first genetic map of asparagus bean based on SNP and SSR markers. The current map consists of 375 loci mapped onto 11 linkage groups (LGs), with 191 loci detected by SNP markers and 184 loci by SSR markers. The overall map length is 745 cM, with an average marker distance of 1.98 cM. There are four high marker-density blocks distributed on three LGs and three regions of segregation distortion (SDRs) identified on two other LGs, two of which co-locate in chromosomal regions syntenic to SDRs in soybean. Synteny between asparagus bean and the model legume Lotus. japonica was also established. This work provides the basis for mapping and functional analysis of genes/QTLs of particular interest in asparagus bean, as well as for comparative genomics study of cowpea at the subspecies level

    The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance.

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    Investment in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing in Africa over the past year has led to a major increase in the number of sequences that have been generated and used to track the pandemic on the continent, a number that now exceeds 100,000 genomes. Our results show an increase in the number of African countries that are able to sequence domestically and highlight that local sequencing enables faster turnaround times and more-regular routine surveillance. Despite limitations of low testing proportions, findings from this genomic surveillance study underscore the heterogeneous nature of the pandemic and illuminate the distinct dispersal dynamics of variants of concern-particularly Alpha, Beta, Delta, and Omicron-on the continent. Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve while the continent faces many emerging and reemerging infectious disease threats. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

    The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

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    INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

    Identification and comparative analysis of drought-associated microRNAs in two cowpea genotypes

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    <p>Abstract</p> <p>Background</p> <p>Cowpea (<it>Vigna unguiculata</it>) is an important crop in arid and semi-arid regions and is a good model for studying drought tolerance. MicroRNAs (miRNAs) are known to play critical roles in plant stress responses, but drought-associated miRNAs have not been identified in cowpea. In addition, it is not understood how miRNAs might contribute to different capacities of drought tolerance in different cowpea genotypes.</p> <p>Results</p> <p>We generated deep sequencing small RNA reads from two cowpea genotypes (CB46, drought-sensitive, and IT93K503-1, drought-tolerant) that grew under well-watered and drought stress conditions. We mapped small RNA reads to cowpea genomic sequences and identified 157 miRNA genes that belong to 89 families. Among 44 drought-associated miRNAs, 30 were upregulated in drought condition and 14 were downregulated. Although miRNA expression was in general consistent in two genotypes, we found that nine miRNAs were predominantly or exclusively expressed in one of the two genotypes and that 11 miRNAs were drought-regulated in only one genotype, but not the other.</p> <p>Conclusions</p> <p>These results suggest that miRNAs may play important roles in drought tolerance in cowpea and may be a key factor in determining the level of drought tolerance in different cowpea genotypes.</p

    Genetic Architecture of Delayed Senescence, Biomass, and Grain Yield under Drought Stress in Cowpea

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    <div><p>The stay-green phenomenon is a key plant trait with wide usage in managing crop production under limited water conditions. This trait enhances delayed senescence, biomass, and grain yield under drought stress. In this study we sought to identify QTLs in cowpea (<i>Vigna unguiculata</i>) consistent across experiments conducted in Burkina Faso, Nigeria, Senegal, and the United States of America under limited water conditions. A panel of 383 diverse cowpea accessions and a recombinant inbred line population (RIL) were SNP genotyped using an Illumina 1536 GoldenGate assay. Phenotypic data from thirteen experiments conducted across the four countries were used to identify SNP-trait associations based on linkage disequilibrium association mapping, with bi-parental QTL mapping as a complementary strategy. We identified seven loci, five of which exhibited evidence suggesting pleiotropic effects (stay-green) between delayed senescence, biomass, and grain yield. Further, we provide evidence suggesting the existence of positive pleiotropy in cowpea based on positively correlated mean phenotypic values (0.34< r <0.87) and allele effects (0.07< r <0.86) for delayed senescence and grain yield across three African environments. Three of the five putative stay-green QTLs, <i>Dro-1</i>, <i>3</i>, and <i>7</i> were identified in both RILs and diverse germplasm with resolutions of 3.2 cM or less for each of the three loci, suggesting that these may be valuable targets for marker-assisted breeding in cowpea. Also, the co-location of early vegetative delayed senescence with biomass and grain yield QTLs suggests the possibility of using delayed senescence at the seedling stage as a rapid screening tool for post-flowering drought tolerance in cowpea breeding. BLAST analysis using EST sequences harboring SNPs with the highest associations provided a genomic context for loci identified in this study in closely related common bean (<i>Phaseolus vulgaris</i>) and soybean (<i>Glycine max</i>) reference genomes.</p></div

    Grain and biomass yield QTLs identified in the cowpea recombinant inbred line population IT93K-503-1 × CB46.

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    a<p>Linkage groups (LG) in brackets correspond to the AFLP-only map (Muchero et al. 2009a); LGs in bold correspond to the SNP-only map (Muchero et a l. 2009b) and the cM range of the delayed senescence QTL remapped on the SNP-only map.</p>b<p>GY = Grain yield; BY = Biomass yield.</p
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