Human activity is an important driver of ecological and evolutionary change on our planet. In particular, domestication and biological introductions have important and long-lasting effects on species’ genomic architecture and diversity. However, genome-wide analysis of independent domestication and introduction events within a single species has not previously been performed. The Pacific kelp Undaria pinnatifida provides such an opportunity because it has been cultivated in its native range in Northeast Asia but also introduced to four other continents in the past 50 years. Here we present the results of a genome-wide analysis of natural, cultivated and introduced populations of U. pinnatifida to elucidate human-driven evolutionary change. We demonstrate that these three categories of origin can be distinguished at the genome level, reflecting the combined influence of neutral (demography and migration) and non-neutral (selection) processes.

Summer mortality, caused by thermal conditions, is the biggest threat to abalone aquaculture production industries. Various measures have been taken to mitigate this issue by adjusting the environment; however, the cellular processes of Pacific abalone (Haliotis discus hannai) have been overlooked due to the paucity of genetic information. The draft genome of H. discus hannai has recently been reported, prompting exploration of the genes responsible for thermal regulation in Pacific abalone. In this study, 413 proteins were systematically annotated as members of the heat shock protein (HSP) super families, and among them 26 HSP genes from four Pacific abalone tissues (hemocytes, gill, mantle, and muscle) were differentially expressed under cold and heat stress conditions. The co-expression network revealed that HSP expression patterns were tissue-specific and similar to those of other shellfish inhabiting intertidal zones. Finally, representative HSPs were selected at random and their expression patterns were identified by RNA sequencing and validated by qRT-PCR to assess expression significance. The HSPs expressed in hemocytes were highly similar in both analyses, suggesting that hemocytes could be more reliable samples for validating thermal condition markers compared to other tissues.

Nut weight is one of the most important traits that can affect a chestnut grower’s returns. Due to the long juvenile phase of chestnut trees, the selection of desired characteristics at early developmental stages represents a major challenge for chestnut breeding. In this study, we identified single nucleotide polymorphisms (SNPs) in transcriptomic regions, which were significantly associated with nut weight in chestnuts (Castanea crenata), using a genome-wide association study (GWAS). RNA-sequencing (RNA-seq) data were generated from large and small nut-bearing trees, using an Illumina HiSeq. 2000 system, and 3,271,142 SNPs were identified. A total of 21 putative SNPs were significantly associated with chestnut weight (false discovery rate [FDR] < 10⁻⁵), based on further analyses. We also applied five machine learning (ML) algorithms, support vector machine (SVM), C5.0,k-nearest neighbour (k-NN), partial least squares (PLS), and random forest (RF), using the 21 SNPs to predict the nut weights of a second population. The average accuracy of the ML algorithms for the prediction of chestnut weights was greater than 68%. Taken together, we suggest that these SNPs have the potential to be used during marker-assisted selection to facilitate the breeding of large chestnut-bearing varieties.

The Papaver spp. (Papaver rhoeas (Corn poppy) and Papaver nudicaule (Iceland poppy)) genera are ornamental and medicinal plants that are used for the isolation of alkaloid drugs. In this study, we generated 700 Mb of transcriptome sequences with the PacBio platform. They were assembled into 120,926 contigs, and 1185 (82.2%) of the benchmarking universal single-copy orthologs (BUSCO) core genes were completely present in our assembled transcriptome. Furthermore, using 128 Gb of Illumina sequences, the transcript expression was assessed at three stages of Papaver plant development (30, 60, and 90 days), from which we identified 137 differentially expressed transcripts. Furthermore, three co-occurrence heat maps are generated from 51 different plant genomes along with the Papaver transcriptome, i.e., secondary metabolite biosynthesis, isoquinoline alkaloid biosynthesis (BIA) pathway, and cytochrome. Sixty-nine transcripts in the BIA pathway along with 22 different alkaloids (quantified with LC-QTOF-MS/MS) were mapped into the BIA KEGG map (map00950). Finally, we identified 39 full-length cytochrome transcripts and compared them with other genomes. Collectively, this transcriptome data, along with the expression and quantitative metabolite profiles, provides an initial recording of secondary metabolites and their expression related to Papaver plant development. Moreover, these profiles could help to further detail the functional characterization of the various secondary metabolite biosynthesis and Papaver plant development associated problems.

Poppies are well-known plants in the family Papaveraceae that are rich in alkaloids. This family contains 61 species, and in this study we sequenced the transcriptomes of four species’ (Papaver rhoeas, Papaver nudicaule, Papaver fauriei, and Papaver somniferum) leaves. These transcripts were systematically assessed for the expression of secondary metabolite biosynthesis (SMB) genes and cytochromes, and their expression profiles were assessed for use in bioinformatics analyses. This study contributed 265 Gb (13 libraries with three biological replicates) of leaf transcriptome data from three Papaver plant developmental stages. Sequenced transcripts were assembled into 815 Mb of contigs, including 226 Mb of full-length transcripts. The transcripts for 53 KEGG pathways, 55 cytochrome superfamilies, and benzylisoquinoline alkaloid biosynthesis (BIA) were identified and compared to four other alkaloid-rich genomes. Additionally, 22 different alkaloids and their relative expression profiles in three developmental stages of Papaver species were assessed by targeted metabolomics using LC-QTOF-MS/MS. Collectively, the results are given in co-occurrence heat-maps to help researchers obtain an overview of the transcripts and their differential expression in the Papaver development life cycle, particularly in leaves. Moreover, this dataset will be a valuable resource to derive hypotheses to mitigate an array of Papaver developmental and secondary metabolite biosynthesis issues in the future.