The genomes and transposable elements in plants: are they friends or foes?

Machine learning in photosynthesis: Prospects on sustainable crop development

Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.

A review of strategies used to identify transposition events in plant genomes

Transposable elements (TEs) were initially considered redundant and dubbed 'junk DNA'. However, more recently they were recognized as an essential element of genome plasticity. In nature, they frequently become active upon exposition of the host to stress conditions. Even though most transposition events are neutral or even deleterious, occasionally they may happen to be beneficial, resulting in genetic novelty providing better fitness to the host. Hence, TE mobilization may promote adaptability and, in the long run, act as a significant evolutionary force. There are many examples of TE insertions resulting in increased tolerance to stresses or in novel features of crops which are appealing to the consumer. Possibly, TE-driven de novo variability could be utilized for crop improvement. However, in order to systematically study the mechanisms of TE/host interactions, it is necessary to have suitable tools to globally monitor any ongoing TE mobilization. With the development of novel potent technologies, new high-throughput strategies for studying TE dynamics are emerging. Here, we present currently available methods applied to monitor the activity of TEs in plants. We divide them on the basis of their operational principles, the position of target molecules in the process of transposition and their ability to capture real cases of actively transposing elements. Their possible theoretical and practical drawbacks are also discussed. Finally, conceivable strategies and combinations of methods resulting in an improved performance are proposed.

Evolution of the human pathogenic lifestyle in fungi

Fungal pathogens cause more than a billion human infections every year, resulting in more than 1.6 million deaths annually. Understanding the natural history and evolutionary ecology of fungi is helping us understand how disease-relevant traits have repeatedly evolved. Different types and mechanisms of genetic variation have contributed to the evolution of fungal pathogenicity and specific genetic differences distinguish pathogens from non-pathogens. Insights into the traits, genetic elements, and genetic and ecological mechanisms that contribute to the evolution of fungal pathogenicity are crucial for developing strategies to both predict emergence of fungal pathogens and develop drugs to combat them.

InpactorDB: A Classified Lineage-Level Plant LTR Retrotransposon Reference Library for Free-Alignment Methods Based on Machine Learning

Long terminal repeat (LTR) retrotransposons are mobile elements that constitute the major fraction of most plant genomes. The identification and annotation of these elements via bioinformatics approaches represent a major challenge in the era of massive plant genome sequencing. In addition to their involvement in genome size variation, LTR retrotransposons are also associated with the function and structure of different chromosomal regions and can alter the function of coding regions, among others. Several sequence databases of plant LTR retrotransposons are available for public access, such as PGSB and RepetDB, or restricted access such as Repbase. Although these databases are useful to identify LTR-RTs in new genomes by similarity, the elements of these databases are not fully classified to the lineage (also called family) level. Here, we present InpactorDB, a semi-curated dataset composed of 130,439 elements from 195 plant genomes (belonging to 108 plant species) classified to the lineage level. This dataset has been used to train two deep neural networks (i.e., one fully connected and one convolutional) for the rapid classification of these elements. In lineage-level classification approaches, we obtain up to 98% performance, indicated by the F1-score, precision and recall scores.

Evolutionary dynamics of transposable elements and satellite DNAs in polyploid Spartina species

Repeated sequences and polyploidy play a central role in plant genome dynamics. Here, we analyze the evolutionary dynamics of repeats in tetraploid and hexaploid Spartina species that diverged during the last 10 million years within the Chloridoideae, one of the poorest investigated grass lineages. From high-throughput genome sequencing, we annotated Spartina repeats and determined what sequence types account for the genome size variation among species. We examined whether differential genome size evolution correlated with ploidy levels and phylogenetic relationships. We also examined the tempo of repeat sequence dynamics associated with allopatric speciation over the last 3-6 million years between hexaploid species that diverged on the American and European Atlantic coasts and tetraploid species from North and South America. The tetraploid S. spartinae, whose phylogenetic placement has been debated, exhibits a similar repeat content as hexaploid species, suggesting common ancestry. Genome expansion or contraction resulting from repeat dynamics seems to be explained mostly by the contrasting divergence times between species, rather than by genome changes triggered by ploidy level change per se. One 370 bp satellite may be exhibiting 'meiotic drive' and driving chromosome evolution in S. alterniflora. Our results provide crucial insights for investigating the genetic and epigenetic consequences of such differential repeat dynamics on the ecology and distribution of the meso- and neopolyploid Spartina species.

The chromosome-scale reference genome of black pepper provides insight into piperine biosynthesis

Black pepper (Piper nigrum), dubbed the ‘King of Spices’ and ‘Black Gold’, is one of the most widely used spices. Here, we present its reference genome assembly by integrating PacBio, 10x Chromium, BioNano DLS optical mapping, and Hi-C mapping technologies. The 761.2 Mb sequences (45 scaffolds with an N50 of 29.8 Mb) are assembled into 26 pseudochromosomes. A phylogenomic analysis of representative plant genomes places magnoliids as sister to the monocots-eudicots clade and indicates that black pepper has diverged from the shared Laurales-Magnoliales lineage approximately 180 million years ago. Comparative genomic analyses reveal specific gene expansions in the glycosyltransferase, cytochrome P450, shikimate hydroxycinnamoyl transferase, lysine decarboxylase, and acyltransferase gene families. Comparative transcriptomic analyses disclose berry-specific upregulated expression in representative genes in each of these gene families. These data provide an evolutionary perspective and shed light on the metabolic processes relevant to the molecular basis of species-specific piperine biosynthesis.

Black pepper (Piper nigrum) belongs to the long-isolated lineage of basal angiosperm and its fruit has been used for food spice and phytomedicines for thousands of years. Here, the authors assemble the reference genome of this species and analyze gene families associated with piperine biosynthesis.

Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning

Transposable elements (TEs) are genomic units able to move within the genome of virtually all organisms. Due to their natural repetitive numbers and their high structural diversity, the identification and classification of TEs remain a challenge in sequenced genomes. Although TEs were initially regarded as "junk DNA", it has been demonstrated that they play key roles in chromosome structures, gene expression, and regulation, as well as adaptation and evolution. A highly reliable annotation of these elements is, therefore, crucial to better understand genome functions and their evolution. To date, much bioinformatics software has been developed to address TE detection and classification processes, but many problematic aspects remain, such as the reliability, precision, and speed of the analyses. Machine learning and deep learning are algorithms that can make automatic predictions and decisions in a wide variety of scientific applications. They have been tested in bioinformatics and, more specifically for TEs, classification with encouraging results. In this review, we will discuss important aspects of TEs, such as their structure, importance in the evolution and architecture of the host, and their current classifications and nomenclatures. We will also address current methods and their limitations in identifying and classifying TEs.

Helitron distribution in Brassicaceae and whole Genome Helitron density as a character for distinguishing plant species

BACKGROUND

Helitron is a rolling-circle DNA transposon; it plays an important role in plant evolution. However, Helitron distribution and contribution to evolution at the family level have not been previously investigated.

RESULTS

We developed the software easy-to-annotate Helitron (EAHelitron), a Unix-like command line, and used it to identify Helitrons in a wide range of 53 plant genomes (including 13 Brassicaceae species). We determined Helitron density (abundance/Mb) and visualized and examined Helitron distribution patterns. We identified more than 104,653 Helitrons, including many new Helitrons not predicted by other software. Whole genome Helitron density is independent from genome size and shows stability at the species level. Using linear discriminant analysis, de novo genomes (next-generation sequencing) were successfully classified into Arabidopsis thaliana groups. For most Brassicaceae species, Helitron density negatively correlated with gene density, and Helitron distribution patterns were similar to those of A. thaliana. They preferentially inserted into sequence around the centromere and intergenic region. We also associated 13 Helitron polymorphism loci with flowering-time phenotypes in 18 A. thaliana ecotypes.

CONCLUSION

EAHelitron is a fast and efficient tool to identify new Helitrons. Whole genome Helitron density can be an informative character for plant classification. Helitron insertion polymorphism could be used in association analysis.

Novel Insights into Plant Genome Evolution and Adaptation as Revealed through Transposable Elements and Non-Coding RNAs in Conifers

Plant genomes are punctuated by repeated bouts of proliferation of transposable elements (TEs), and these mobile bursts are followed by silencing and decay of most of the newly inserted elements. As such, plant genomes reflect TE-related genome expansion and shrinkage. In general, these genome activities involve two mechanisms: small RNA-mediated epigenetic repression and long-term mutational decay and deletion, that is, genome-purging. Furthermore, the spatial relationships between TE insertions and genes are an important force in shaping gene regulatory networks, their downstream metabolic and physiological outputs, and thus their phenotypes. Such cascading regulations finally set up a fitness differential among individuals. This brief review demonstrates factual evidence that unifies most updated conceptual frameworks covering genome size, architecture, epigenetic reprogramming, and gene expression. It aims to give an overview of the impact that TEs may have on genome and adaptive evolution and to provide novel insights into addressing possible causes and consequences of intimidating genome sizes (20⁻30 Gb) in a taxonomic group, conifers.

The genome structure of Arachis hypogaea (Linnaeus, 1753) and an induced Arachis allotetraploid revealed by molecular cytogenetics

Peanut, Arachis hypogaea (Linnaeus, 1753) is an allotetraploid cultivated plant with two subgenomes derived from the hybridization between two diploid wild species, A. duranensis (Krapovickas & W. C. Gregory, 1994) and A. ipaensis (Krapovickas & W. C. Gregory, 1994), followed by spontaneous chromosomal duplication. To understand genome changes following polyploidy, the chromosomes of A. hypogaea, IpaDur1, an induced allotetraploid (A. ipaensis × A. duranensis)^4x and the diploid progenitor species were cytogenetically compared. The karyotypes of the allotetraploids share the number and general morphology of chromosomes; DAPI⁺ bands pattern and number of 5S rDNA loci. However, one 5S rDNA locus presents a heteromorphic FISH signal in both allotetraploids, relative to corresponding progenitor. Whilst for A. hypogaea the number of 45S rDNA loci was equivalent to the sum of those present in the diploid species, in IpaDur1, two loci have not been detected. Overall distribution of repetitive DNA sequences was similar in both allotetraploids, although A. hypogaea had additional CMA₃⁺ bands and few slight differences in the LTR-retrotransposons distribution compared to IpaDur1. GISH showed that the chromosomes of both allotetraploids had preferential hybridization to their corresponding diploid genomes. Nevertheless, at least one pair of IpaDur1 chromosomes had a clear mosaic hybridization pattern indicating recombination between the subgenomes, clear evidence that the genome of IpaDur1 shows some instability comparing to the genome of A. hypogaea that shows no mosaic of subgenomes, although both allotetraploids derive from the same progenitor species. For some reasons, the chromosome structure of A. hypogaea is inherently more stable, or, it has been at least, partially stabilized through genetic changes and selection.