Assessing small RNA profiles in potato diploid hybrid and its resynthesized allopolyploid reveals conserved abundance with distinct genomic distribution.

KEY MESSAGE: The shock produced by the allopolyploidization process on a potato interspecific diploid hybrid displays a non-random remobilization of the small RNAs profile on a variety of genomic features. Allopolyploidy, a complex process involving interspecific hybridization and whole genome duplication, significantly impacts plant evolution, leading to the emergence of novel phenotypes. Polyploids often present phenotypic nuances that enhance adaptability, enabling them to compete better and occasionally to colonize new habitats. Whole-genome duplication represents a genomic "shock" that can trigger genetic and epigenetic changes that yield novel expression patterns. In this work, we investigate the polyploidization effect on a diploid interspecific hybrid obtained through the cross between the cultivated potato Solanum tuberosum and the wild potato Solanum kurtzianum, by assessing the small RNAs (sRNAs) profile of the parental diploid hybrid and its derived allopolyploid. Small RNAs are key components of the epigenetic mechanisms involved in silencing by RNA-directed DNA Methylation (RdDM). A sRNA sequencing (sRNA-Seq) analysis was performed to individually profile the 21 to 22 nucleotide (21 to 22-nt) and 24-nt sRNA size classes due to their unique mechanism of biogenesis and mode of function. The composition and distribution of different genomic features and differentially accumulated (DA) sRNAs were evaluated throughout the potato genome. We selected a subset of genes associated with DA sRNAs for messenger RNA (mRNA) expression analysis to assess potential impacts on the transcriptome. Interestingly, we noted that 24-nt DA sRNAs that exclusively mapped to exons were correlated with differentially expressed mRNAs between genotypes, while this behavior was not observed when 24-nt DA sRNAs were mapped to intronic regions. These findings collectively emphasize the nonstochastic nature of sRNA remobilization in response to the genomic shock induced by allopolyploidization.

Interpreting machine learning models to investigate circadian regulation and facilitate exploration of clock function.

The circadian clock is an important adaptation to life on Earth. Here, we use machine learning to predict complex, temporal, and circadian gene expression patterns in Arabidopsis Most significantly, we classify circadian genes using DNA sequence features generated de novo from public, genomic resources, facilitating downstream application of our methods with no experimental work or prior knowledge needed. We use local model explanation that is transcript specific to rank DNA sequence features, providing a detailed profile of the potential circadian regulatory mechanisms for each transcript. Furthermore, we can discriminate the temporal phase of transcript expression using the local, explanation-derived, and ranked DNA sequence features, revealing hidden subclasses within the circadian class. Model interpretation/explanation provides the backbone of our methodological advances, giving insight into biological processes and experimental design. Next, we use model interpretation to optimize sampling strategies when we predict circadian transcripts using reduced numbers of transcriptomic timepoints. Finally, we predict the circadian time from a single, transcriptomic timepoint, deriving marker transcripts that are most impactful for accurate prediction; this could facilitate the identification of altered clock function from existing datasets.

Genomic re-assessment of the transposable element landscape of the potato genome.

KEY MESSAGE: We provide a comprehensive and reliable potato TE landscape, based on a wide variety of identification tools and integrative approaches, producing clear and ready-to-use outputs for the scientific community. Transposable elements (TEs) are DNA sequences with the ability to autoreplicate and move throughout the host genome. TEs are major drivers in stress response and genome evolution. Given their significance, the development of clear and efficient TE annotation pipelines has become essential for many species. The latest de novo TE discovery tools, along with available TEs from Repbase and sRNA-seq data, allowed us to perform a reliable potato TEs detection, classification and annotation through an open-source and freely available pipeline ( https://github.com/DiegoZavallo/TE_Discovery ). Using a variety of tools, approaches and rules, we were able to provide a clearly annotated of characterized TEs landscape. Additionally, we described the distribution of the different types of TEs across the genome, where LTRs and MITEs present a clear clustering pattern in pericentromeric and subtelomeric/telomeric regions respectively. Finally, we analyzed the insertion age and distribution of LTR retrotransposon families which display a distinct pattern between the two major superfamilies. While older Gypsy elements concentrated around heterochromatic regions, younger Copia elements located predominantly on euchromatic regions. Overall, we delivered not only a reliable, ready-to-use potato TE annotation files, but also all the necessary steps to perform de novo detection for other species.

Haplotype block analysis of an Argentinean hexaploid wheat collection and GWAS for yield components and adaptation.

BACKGROUND: Increasing wheat (Triticum aestivum L.) production is required to feed a growing human population. In order to accomplish this task a deeper understanding of the genetic structure of cultivated wheats and the detection of genomic regions significantly associated with the regulation of important agronomic traits are necessary steps. To better understand the genetic basis and relationships of adaptation and yield related traits, we used a collection of 102 Argentinean hexaploid wheat cultivars genotyped with the 35k SNPs array, grown from two to six years in three different locations. Based on SNPs data and gene-related molecular markers, we performed a haplotype block characterization of the germplasm and a genome-wide association study (GWAS). RESULTS: The genetic structure of the collection revealed four subpopulations, reflecting the origin of the germplasm used by the main breeding programs in Argentina. The haplotype block characterization showed 1268 blocks of different sizes spread along the genome, including highly conserved regions like the 1BS chromosome arm where the 1BL/1RS wheat/rye translocation is located. Based on GWAS we identified ninety-seven chromosome regions associated with heading date, plant height, thousand grain weight, grain number per spike and fruiting efficiency at harvest (FEh). In particular FEh stands out as a promising trait to raise yield potential in Argentinean wheats; we detected fifteen haplotypes/markers associated with increased FEh values, eleven of which showed significant effects in all three evaluated locations. In the case of adaptation, the Ppd-D1 gene is consolidated as the main determinant of the life cycle of Argentinean wheat cultivars. CONCLUSION: This work reveals the genetic structure of the Argentinean hexaploid wheat germplasm using a wide set of molecular markers anchored to the Ref Seq v1.0. Additionally GWAS detects chromosomal regions (haplotypes) associated with important yield and adaptation components that will allow improvement of these traits through marker-assisted selection.

MITE Tracker: an accurate approach to identify miniature inverted-repeat transposable elements in large genomes.

BACKGROUND: Miniature inverted-repeat transposable elements (MITEs) are short, non-autonomous class II transposable elements present in a high number of conserved copies in eukaryote genomes. An accurate identification of these elements can help to shed light on the mechanisms controlling genome evolution and gene regulation. The structure and distribution of these elements are well-defined and therefore computational approaches can be used to identify MITEs sequences. RESULTS: Here we describe MITE Tracker, a novel, open source software program that finds and classifies MITEs using an efficient alignment strategy to retrieve nearby inverted-repeat sequences from large genomes. This program groups them into high sequence homology families using a fast clustering algorithm and finally filters only those elements that were likely transposed from different genomic locations because of their low scoring flanking sequence alignment. CONCLUSIONS: Many programs have been proposed to find MITEs hidden in genomes. However, none of them are able to process large-scale genomes such as that of bread wheat. Furthermore, in many cases the existing methods perform high false-positive rates (or miss rates). The rice genome was used as reference to compare MITE Tracker against known tools. Our method turned out to be the most reliable in our tests. Indeed, it revealed more known elements, presented the lowest false-positive number and was the only program able to run with the bread wheat genome as input. In wheat, MITE Tracker discovered 6013 MITE families and allowed the first structural exploration of MITEs in the complete bread wheat genome.