7 research outputs found

    Factors affecting yield and gelling properties of agar

    No full text
    Agar, a gelatinous polysaccharide in the cell wall of many red algal species, is widely used as a gelling, thickening and stabilizing agent. The commercial value of seaweed is judged by their agar content and gel quality. Seaweed materials with higher agar yield and better gelling properties are desired due to the growing demand for agar in the global market. Agar biosynthesis in seaweeds is affected by genetic variations, developmental stages and environmental conditions, while different agar extraction techniques can also affect the yield and quality of agar. In this paper, the effects of different physiological states of seaweed, abiotic and biotic factors, seaweed storage and agar extraction techniques on the agar yield and gelling characteristics, are reviewed. This information is important as a guide for marine aquaculture of potential agarophytes and the possible effects of climate change on the stock of this natural resource

    Pathway Analysis Integrating Genome-Wide and Functional Data Identifies PLCG2 as a Candidate Gene for Age-Related Macular Degeneration

    Get PDF
    Purpose: Age-related macular degeneration (AMD) is the worldwide leading cause of blindness among the elderly. Although genome-wide association studies (GWAS) have identified AMD risk variants, their roles in disease etiology are not well-characterized, and they only explain a portion of AMD heritability. Methods: We performed pathway analyses using summary statistics from the International AMD Genomics Consortium's 2016 GWAS and multiple pathway databases to identify biological pathways wherein genetic association signals for AMD may be aggregating. We determined which genes contributed most to significant pathway signals across the databases. We characterized these genes by constructing protein-protein interaction networks and performing motif analysis. Results: We determined that eight genes (C2, C3, LIPC, MICA, NOTCH4, PLCG2, PPARA, and RAD51B) "drive" the statistical signals observed across pathways curated in the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, and Gene Ontology (GO) databases. We further refined our definition of statistical driver gene to identify PLCG2 as a candidate gene for AMD due to its significant gene-level signals (P < 0.0001) across KEGG, Reactome, GO, and NetPath pathways. Conclusions: We performed pathway analyses on the largest available collection of advanced AMD cases and controls in the world. Eight genes strongly contributed to significant pathways from the three larger databases, and one gene (PLCG2) was central to significant pathways from all four databases. This is, to our knowledge, the first study to identify PLCG2 as a candidate gene for AMD based solely on genetic burden. Our findings reinforce the utility of integrating in silico genetic and biological pathway data to investigate the genetic architecture of AMD
    corecore