PubMed · 14931862
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
A A TSIMERINOV, S M RAFALOVICH. [Microspora].. https://pubmed.ncbi.nlm.nih.gov/14931862/
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
We present a comprehensive synthesis of recent developments in methodological approaches to combine experimental and genomic studies of picophytoplankton-virus interactions. This synthesis will not only enhance our understanding of these relationships but also stimulate new hypotheses for the interpretation of metagenomic data and enrich current modeling efforts. Marine picophytoplankton are significant primary producers despite their low contribution to biomass. Pan-oceanic metagenomic studies have revealed an astounding genetic diversity within these communities and the astronomical abundance of viruses that infect them. Despite recent advances in understanding the genetic and genomic diversity of picophytoplankton and viruses, the interpretation of these data relies heavily on ecological and physiological insights. Specifically, our understanding of the mechanisms and dynamics governing host-virus interactions is limited because the current wealth of sequence data has not yet been matched with corresponding life-history traits. Linking phenotypes to genotypes in picophytoplankton-virus systems is also essential for accurately modeling their interactions and their impact on the global carbon cycle. Ultimately, the synergy of the three domains allows for a mechanistic interpretation of environmental data.
Microalgae are gaining attention as sustainable resources for the production of valuable compounds, including biofuels, pigments, and bioactive metabolites. To support metabolic engineering and genome editing approaches aimed at enhancing these traits, high-quality genome assemblies are essential; however, genomic information remains limited for many microalgal lineages. Tetraselmis sp. NKG400013 is a green alga known for high glycosphingolipid accumulation with distinctive structural features. Here, we report a draft genome assembly of this strain generated using PacBio HiFi sequencing and transcriptome-supported annotation. The assembled genome spans 423.7 Mbp, with 74.5% repetitive sequences and 15,322 predicted protein-coding genes. Comparative analyses across 11 green algal species revealed a positive correlation between genome sizes and repeat contents, indicating that transposable element expansion, particularly long terminal repeat retrotransposons, has substantially contributed to genome enlargement in Tetraselmis. Genome-wide functional annotation and ortholog inference identified core enzymes required for glycosylceramide biosynthesis. Both sphingolipid Δ4 and Δ8 desaturases were identified in Tetraselmis and their coexistence suggests an expanded capacity for long-chain base modification that may underlie its distinctive glycosphingolipid profile. These results establish a genomic framework for understanding the high glycosphingolipid-producing capacity of NKG400013 and provide insights into the evolutionary diversification of sphingolipid metabolism in green algae.
Algal bioassays and pesticide analysis were joined in Tuscany regional monitoring of surface and ground waters. Results were compared. Waters affected algal growth in any of the following ways: stimulation, inhibition, stimulation at low concentrations and inhibition at higher concentrations. Most of ground waters inhibited algal growth whereas only a little percentage were contaminated with pesticide. Most of surface waters stimulated algal growth and a higher percentage were contaminated with pesticides. In waters with inhibitory effect toxicological parameters (EC50, EC25, EC10 and NOEC) were measured and compared; EC25 values were very close to NOEC values. Hazard to aquatic primary productivity for each pesticide found during chemical monitoring was evaluated; for this aim the margin of safety (MOS) was calculated by using algal toxicity and the pesticide concentration measured.