Pyropia yezoensis is the most important commercial edible red algae in China, carrying a variety of resident microbes at its surface. To understand microbiome diversity, community structure, interactions and functions with hosts in this regard, thalli and seawater sampleswere collected from Yantai and Rizhao cultivation farms in the Yellow Sea. The thalli and seawater samples (n = 12) were collected and studied using an Illumina NovaSeq 6000 platform and 16S ribosomal RNA (rRNA) gene sequencing, along with the consideration of environmental factors. Bacterial communities in association with P. yezoensis and surrounding seawater were predominated by Cyanobacteria, Proteobacteria, and Bacteroidetes. The variability of bacterial communities related to P. yezoensis and seawater were predominantly shaped by nitrate (NO₃), ammonium (NH₄), and temperature. Cluster analysis revealed a close relationship between thalli (RTH and YTH) and seawater (RSW and YSW) in terms of the residing bacterial communities, respectively. PICRUSt analysis revealed the presence of genes associated with amino acid transportation and metabolism, which explained the bacterial dependence on algal-provided nutrients. This study reveals that the diversity of microbiota for P. yezoensis is greatly influenced by abiotic factors and algal organic exudates which trigger chemical signaling and transportation responses from the bacterial community, which in turn activates genes to metabolize subsequent substrates.

Contamination from cytosolic DNA (plastid and mitochondrion) and epiphytic bacteria is challenging the efficiency and accuracy of genome-wide analysis of nori-producing marine seaweed Pyropia yezoensis. Unlike bacteria and organellar DNA, Pyropia nuclear DNA is closely associated with histone proteins. In this study, we applied Chromatin Immunoprecipitation (ChIP) of histone H3 to isolate nuclear DNA, followed by high-throughput sequencing. More than 99.41% of ChIP-sequencing data were successfully aligned to the reference nuclear genome; this was remarkably higher than those from direct extraction and direct extraction data, in which 40.96% to 42.95% are from plastids. The proportion of data that were mapped to the bacterial database when using ChIP extraction was very low. Additionally, ChIP data can cover up to 89.00% of the nuclear genome, higher than direct extraction data at equal data size and comparable to the latter at equal sequencing depth. The uncovered regions from the three methods are mostly overlapping, suggesting that incomplete sequencing accounts for the missing data, rather than failed chromatin-antibody binding in the ChIP extraction method. This ChIP extraction method can successfully separate nuclear DNA from cytosolic DNA and bacterial DNA, thus overwhelmingly reducing the sequencing cost in a genome resequencing project and providing strictly purified reference data for genome assembly. The method’s applicability to other macroalgae makes it a valuable contribution to the algal research community.

Click on the article title to read more.

Chromosome rearrangements play an important role in the speciation of plants and animals, and the recognition of chromosome rearrangement patterns is helpful to elucidate the mechanism of species differentiation at the chromosome level. However, the existing chromosome rearrangement recognition methods have some major limitations, such as low quality, barriers to parental selection, and inability to identify specific rearrangement patterns. Based on the whole genome protein sequences, we constructed the combined figure according to the slope of the collinear fragment, the number of homologous genes, the coordinates in the top left and bottom right of the collinear fragment. The standardized combination figure is compared with the four standard pattern figures, and then combined with the information entropy analysis strategy to automatically classify the chromosome images and identify the chromosome rearrangement pattern. This paper proposes an automatic karyotype analysis method EntroCR (intelligent recognition method of chromosome rearrangement based on information entropy), which integrates rearrangement pattern recognition, result recommendation and related chromosome determination, so as to infer the evolution process of ancestral chromosomes to the existing chromosomes. Validation experiments were conducted using whole-genome data of Gossypium raimondii and Gossypium arboreum, Oryza sativa and Sorghum bicolor. The conclusions were consistent with previous results. EntroCR provides a reference for researchers in species evolution and molecular marker assisted breeding as well as new methods for analyzing karyotype evolution in other species.