Cicer super-pangenome provides insights into species evolution and agronomic trait loci for crop improvement in chickpea.

Chickpea (Cicer arietinum L.)-an important legume crop cultivated in arid and semiarid regions-has limited genetic diversity. Efforts are being undertaken to broaden its diversity by utilizing its wild relatives, which remain largely unexplored. Here, we present the Cicer super-pangenome based on the de novo genome assemblies of eight annual Cicer wild species. We identified 24,827 gene families, including 14,748 core, 2,958 softcore, 6,212 dispensable and 909 species-specific gene families. The dispensable genome was enriched for genes related to key agronomic traits. Structural variations between cultivated and wild genomes were used to construct a graph-based genome, revealing variations in genes affecting traits such as flowering time, vernalization and disease resistance. These variations will facilitate the transfer of valuable traits from wild Cicer species into elite chickpea varieties through marker-assisted selection or gene-editing. This study offers valuable insights into the genetic diversity and potential avenues for crop improvement in chickpea.

Comparative gene co-expression networks show enrichment of brassinosteroid and vitamin B processes in a seagrass under simulated ocean warming and extreme climatic events.

Introduction: Ocean warming combined with extreme climatic events, such as marine heatwaves and flash flooding events, threaten seagrasses globally. How seagrasses cope with these challenges is uncertain, particularly for range-edge populations of species such as Posidonia australis in Shark Bay, Western Australia. Analyzing gene expression while manipulating multiple stressors provides insight into the genetic response and resilience of seagrasses to climate change. We conducted a gene expression study on a polyploid clone of P. australis during an 18-week mesocosm experiment to assess the responses to single and combined future climate change-associated stressors. Methods: Plants were exposed to (1) future ocean warming temperature (baseline +1.5°C) followed by a simulated marine heat wave (baseline +5.5°C), (2) light deprivation simulating observed marine heatwave driven turbidity (95% shade) at baseline temperatures, or (3) both stressors simultaneously. Basal leaf meristems were sampled for gene expression analysis using RNA-seq at four time points during the experiment. Weighted gene co-expression network analysis, GO term enrichment, and KEGG pathway enrichment analyses were used to identify stress responses. Results: Shaded plants showed specific gene enrichment for shade avoidance (programmed cell death) after three weeks of stress, and before any heated tanks showed a specific heat response. Shaded plants were positively correlated with programmed cell death and stress-related processes at the end of the experiment. Once ocean warming temperatures (+1.5°C) were in effect, gene enrichment for heat stress (e.g., ROS scavenging and polyamine metabolism) was present. Vitamin B processes, RNA polymerase II processes. and light-related meristematic phase changes were expressed with the addition of simulated MHW. Heated plants showed meristematic growth signatures as well as trehalose and salicylic acid metabolism. Brassinosteroid-related processes were significantly enriched in all stressor treatments at all time points, except for the isolated heat-stressed plants three weeks after stressor initiation. Discussion: Gene expression responses to the interaction between heat waves and turbidity-induced light reduction support the observed geographical scale mortality in seagrasses observed for P. australis in Shark Bay, suggesting that even this giant polyploid clone will be negatively impacted by more extreme climate change projections.

Toward genome assemblies for all marine vertebrates: current landscape and challenges.

Marine vertebrate biodiversity is fundamental to ocean ecosystem health but is threatened by climate change, overharvesting, and habitat degradation. High-quality reference genomes are valuable foundational scientific resources that can inform conservation efforts. Consequently, global consortia are striving to produce reference genomes for representatives of all life. Here, we summarize the current landscape of available marine vertebrate reference genomes, including their phylogenetic diversity and geographic hotspots of production. We discuss key logistical and technical challenges that remain to be overcome if we are to realize the vision of a comprehensive reference genome library of all marine vertebrates.

DNABERT-based explainable lncRNA identification in plant genome assemblies.

Long non-coding ribonucleic acids (lncRNAs) have been shown to play an important role in plant gene regulation, involving both epigenetic and transcript regulation. LncRNAs are transcripts longer than 200 nucleotides that are not translated into functional proteins but can be translated into small peptides. Machine learning models have predominantly used transcriptome data with manually defined features to detect lncRNAs, however, they often underrepresent the abundance of lncRNAs and can be biased in their detection. Here we present a study using Natural Language Processing (NLP) models to identify plant lncRNAs from genomic sequences rather than transcriptomic data. The NLP models were trained to predict lncRNAs for seven model and crop species (Zea mays, Arabidopsis thaliana, Brassica napus, Brassica oleracea, Brassica rapa, Glycine max and Oryza sativa) using publicly available genomic references. We demonstrated that lncRNAs can be accurately predicted from genomic sequences with the highest accuracy of 83.4% for Z. mays and the lowest accuracy of 57.9% for B. rapa, revealing that genome assembly quality might affect the accuracy of lncRNA identification. Furthermore, we demonstrated the potential of using NLP models for cross-species prediction with an average of 63.1% accuracy using target species not previously seen by the model. As more species are incorporated into the training datasets, we expect the accuracy to increase, becoming a more reliable tool for uncovering novel lncRNAs. Finally, we show that the models can be interpreted using explainable artificial intelligence to identify motifs important to lncRNA prediction and that these motifs frequently flanked the lncRNA sequence.

Blueberry and cranberry pangenomes as a resource for future genetic studies and breeding efforts.

Domestication of cranberry and blueberry began in the United States in the early 1800s and 1900s, respectively, and in part owing to their flavors and health-promoting benefits are now cultivated and consumed worldwide. The industry continues to face a wide variety of production challenges (e.g. disease pressures), as well as a demand for higher-yielding cultivars with improved fruit quality characteristics. Unfortunately, molecular tools to help guide breeding efforts for these species have been relatively limited compared with those for other high-value crops. Here, we describe the construction and analysis of the first pangenome for both blueberry and cranberry. Our analysis of these pangenomes revealed both crops exhibit great genetic diversity, including the presence-absence variation of 48.4% genes in highbush blueberry and 47.0% genes in cranberry. Auxiliary genes, those not shared by all cultivars, are significantly enriched with molecular functions associated with disease resistance and the biosynthesis of specialized metabolites, including compounds previously associated with improving fruit quality traits. The discovery of thousands of genes, not present in the previous reference genomes for blueberry and cranberry, will serve as the basis of future research and as potential targets for future breeding efforts. The pangenome, as a multiple-sequence alignment, as well as individual annotated genomes, are publicly available for analysis on the Genome Database for Vaccinium-a curated and integrated web-based relational database. Lastly, the core-gene predictions from the pangenomes will serve useful to develop a community genotyping platform to guide future molecular breeding efforts across the family.

Approaches to increase the validity of gene family identification using manual homology search tools.

Identifying homologs is an important process in the analysis of genetic patterns underlying traits and evolutionary relationships among species. Analysis of gene families is often used to form and support hypotheses on genetic patterns such as gene presence, absence, or functional divergence which underlie traits examined in functional studies. These analyses often require precise identification of all members in a targeted gene family. Manual pipelines where homology search and orthology assignment tools are used separately are the most common approach for identifying small gene families where accurate identification of all members is important. The ability to curate sequences between steps in manual pipelines allows for simple and precise identification of all possible gene family members. However, the validity of such manual pipeline analyses is often decreased by inappropriate approaches to homology searches including too relaxed or stringent statistical thresholds, inappropriate query sequences, homology classification based on sequence similarity alone, and low-quality proteome or genome sequences. In this article, we propose several approaches to mitigate these issues and allow for precise identification of gene family members and support for hypotheses linking genetic patterns to functional traits.

Investigating Pangenome Graphs Using Wheat Panache.

Pangenome graphs quickly become the central data structure representing the diversity of variation we see across related genomes. Pangenome graphs have been published for some species, including plants of agronomic interest. However, visualizing these graphs is not easy as the graphs are large, and variants within these graphs are complex. Tools are needed to visualize graph data structures. Here, we present a workflow to search and visualize a wheat pangenome graph using Wheat Panache. The approach presented assists researchers interested in wheat genomics.

Seafood label quality and mislabelling rates hamper consumer choices for sustainability in Australia.

Seafood mislabelling and species substitution, compounded by a convoluted seafood supply chain with significant traceability challenges, hinder efforts towards more sustainable, responsible, and ethical fishing and business practices. We conducted the largest evaluation of the quality and accuracy of labels for 672 seafood products sold in Australia, assessing six seafood groups (i.e., hoki, prawns, sharks and rays, snapper, squid and cuttlefish, and tuna) from fishmongers, restaurants, and supermarkets, including domestically caught and imported products. DNA barcoding revealed 11.8% of seafood tested did not match their label with sharks and rays, and snappers, having the highest mislabelling rate. Moreover, only 25.5% of products were labelled at a species-level, while most labels used vague common names or umbrella terms such as 'flake' and 'snapper'. These poor-quality labels had higher rates of mislabelling than species-specific labels and concealed the sale of threatened or overfished taxa, as well as products with lower nutritional quality, reduced economic value, or potential health risks. Our results highlight Australia's weak seafood labelling regulations and ambiguous non-mandatory naming conventions, which impede consumer choice for accurately represented, sustainable, and responsibly sourced seafood. We recommend strengthening labelling regulations to mitigate seafood mislabelling and substitution, ultimately improving consumer confidence when purchasing seafood.

crosshap: R package for local haplotype visualization for trait association analysis.

SUMMARY: Genome-wide association studies (GWAS) excels at harnessing dense genomic variant datasets to identify candidate regions responsible for producing a given phenotype. However, GWAS and traditional fine-mapping methods do not provide insight into the complex local landscape of linkage that contains and has been shaped by the causal variant(s). Here, we present crosshap, an R package that performs robust density-based clustering of variants based on their linkage profiles to capture haplotype structures in a local genomic region of interest. Following this, crosshap is equipped with visualization tools for choosing optimal clustering parameters (ɛ) before producing an intuitive figure that provides an overview of the complex relationships between linked variants, haplotype combinations, phenotype, and metadata traits. AVAILABILITY AND IMPLEMENTATION: The crosshap package is freely available under the MIT license and can be downloaded directly from CRAN with R >4.0.0. The development version is available on GitHub alongside issue support (https://github.com/jacobimarsh/crosshap). Tutorial vignettes and documentation are available (https://jacobimarsh.github.io/crosshap/).

The conservation of gene models can support genome annotation.

Many genome annotations include false-positive gene models, leading to errors in phylogenetic and comparative studies. Here, we propose a method to support gene model prediction based on evolutionary conservation and use it to identify potentially erroneous annotations. Using this method, we developed a set of 15,345 representative gene models from 12 legume assemblies that can be used to support genome annotations for other legumes.

Blueberry and cranberry pangenomes as a resource for future genetic studies and breeding efforts.

Domestication of cranberry and blueberry began in the United States in the early 1800s and 1900s, respectively, and in part owing to their flavors and health-promoting benefits are now cultivated and consumed worldwide. The industry continues to face a wide variety of production challenges (e.g. disease pressures) as well as a demand for higher-yielding cultivars with improved fruit quality characteristics. Unfortunately, molecular tools to help guide breeding efforts for these species have been relatively limited compared with those for other high-value crops. Here, we describe the construction and analysis of the first pangenome for both blueberry and cranberry. Our analysis of these pangenomes revealed both crops exhibit great genetic diversity, including the presence-absence variation of 48.4% genes in highbush blueberry and 47.0% genes in cranberry. Auxiliary genes, those not shared by all cultivars, are significantly enriched with molecular functions associated with disease resistance and the biosynthesis of specialized metabolites, including compounds previously associated with improving fruit quality traits. The discovery of thousands of genes, not present in the previous reference genomes for blueberry and cranberry, will serve as the basis of future research and as potential targets for future breeding efforts. The pangenome, as a multiple-sequence alignment, as well as individual annotated genomes, are publicly available for analysis on the Genome Database for Vaccinium - a curated and integrated web-based relational database. Lastly, the core-gene predictions from the pangenomes will serve useful to develop a community genotyping platform to guide future molecular breeding efforts across the family.

Comparative pangenome analyses provide insights into the evolution of Brassica rapa resistance gene analogues (RGAs).

Brassica rapa is grown worldwide as economically important vegetable and oilseed crop. However, its production is challenged by yield-limiting pathogens. The sustainable control of these pathogens mainly relies on the deployment of genetic resistance primarily driven by resistance gene analogues (RGAs). While several studies have identified RGAs in B. rapa, these were mainly based on a single genome reference and do not represent the full range of RGA diversity in B. rapa. In this study, we utilized the B. rapa pangenome, constructed from 71 lines encompassing 12 morphotypes, to describe a comprehensive repertoire of RGAs in B. rapa. We show that 309 RGAs were affected by presence-absence variation (PAV) and 223 RGAs were missing from the reference genome. The transmembrane leucine-rich repeat (TM-LRR) RGA class had more core gene types than variable genes, while the opposite was observed for nucleotide-binding site leucine-rich repeats (NLRs). Comparative analysis with the B. napus pangenome revealed significant RGA conservation (93%) between the two species. We identified 138 candidate RGAs located within known B. rapa disease resistance QTL, of which the majority were under negative selection. Using blackleg gene homologues, we demonstrated how these genes in B. napus were derived from B. rapa. This further clarifies the genetic relationship of these loci, which may be useful in narrowing-down candidate blackleg resistance genes. This study provides a novel genomic resource towards the identification of candidate genes for breeding disease resistance in B. rapa and its relatives.

Diversity of fatty acid biosynthesis genes across the soybean pangenome.

Soybean (Glycine max) is a major crop that contributes more than half of global oilseed production. Much research has been directed towards improvement of the fatty acid profile of soybean seeds through marker assisted breeding. Recently published soybean pangenomes, based on thousands of soybean lines, provide an opportunity to identify new alleles that may be involved in fatty acid biosynthesis. In this study, we identify fatty acid biosynthesis genes in soybean pangenomes based on sequence identity with known genes and examine their sequence diversity across diverse soybean collections. We find three possible instances of a gene missing in wild soybean, including FAD8 and FAD2-2D, which may be involved in oleic and linoleic acid desaturation, respectively, although we recommend follow-up research to verify the absence of these genes. More than half of the 53 fatty acid biosynthesis genes identified contained missense variants, including one linked with a previously identified QTL for oil quality. These variants were present in multiple studies based on either short read mappings or alignment of reference grade genomes. Missense variants were found in previously characterized genes including FAD2-1A and FAD2-1B, both of which are involved in desaturation of oleic acid, as well as uncharacterized candidate fatty acid biosynthesis genes. We find that the frequency of missense alleles in fatty acid biosynthesis genes has been reduced significantly more than the global average frequency of missense mutations during domestication, and missense variation in some genes is near absent in modern cultivars. This could be due to the selection for fatty acid profiles in seed, though future work should be conducted towards understanding the phenotypic impacts of these variants.

Identification of candidate genes for LepR1 resistance against Leptosphaeria maculans in Brassica napus.

Utilising resistance (R) genes, such as LepR1, against Leptosphaeria maculans, the causal agent of blackleg in canola (Brassica napus), could help manage the disease in the field and increase crop yield. Here we present a genome wide association study (GWAS) in B. napus to identify LepR1 candidate genes. Disease phenotyping of 104 B. napus genotypes revealed 30 resistant and 74 susceptible lines. Whole genome re-sequencing of these cultivars yielded over 3 million high quality single nucleotide polymorphisms (SNPs). GWAS in mixed linear model (MLM) revealed a total of 2,166 significant SNPs associated with LepR1 resistance. Of these SNPs, 2108 (97%) were found on chromosome A02 of B. napus cv. Darmor bzh v9 with a delineated LepR1_mlm1 QTL at 15.11-26.08 Mb. In LepR1_mlm1, there are 30 resistance gene analogs (RGAs) (13 nucleotide-binding site-leucine rich repeats (NLRs), 12 receptor-like kinases (RLKs), and 5 transmembrane-coiled-coil (TM-CCs)). Sequence analysis of alleles in resistant and susceptible lines was undertaken to identify candidate genes. This research provides insights into blackleg resistance in B. napus and assists identification of the functional LepR1 blackleg resistance gene.

Legume-wide comparative analysis of pod shatter locus PDH1 reveals phaseoloid specificity, high cowpea expression, and stress responsive genomic context.

Pod dehiscence is a major source of yield loss in legumes, which is exacerbated by aridity. Disruptive mutations in "Pod indehiscent 1" (PDH1), a pod sclerenchyma-specific lignin biosynthesis gene, has been linked to significant reductions in dehiscence in several legume species. We compared syntenic PDH1 regions across 12 legumes and two outgroups to uncover key historical evolutionary trends at this important locus. Our results clarified the extent to which PDH1 orthologs are present in legumes, showing the typical genomic context surrounding PDH1 has only arisen relatively recently in certain phaseoloid species (Vigna, Phaseolus, Glycine). The notable absence of PDH1 in Cajanus cajan may be a major contributor to its indehiscent phenotype compared with other phaseoloids. In addition, we identified a novel PDH1 ortholog in Vigna angularis and detected remarkable increases in PDH1 transcript abundance during Vigna unguiculata pod development. Investigation of the shared genomic context of PDH1 revealed it lies in a hotspot of transcription factors and signaling gene families that respond to abscisic acid and drought stress, which we hypothesize may be an additional factor influencing expression of PDH1 under specific environmental conditions. Our findings provide key insights into the evolutionary history of PDH1 and lay the foundation for optimizing the pod dehiscence role of PDH1 in major and understudied legume species.

Predicting Cloned Disease Resistance Gene Homologs (CDRHs) in Radish, Underutilised Oilseeds, and Wild Brassicaceae Species.

Brassicaceae crops, including Brassica, Camelina and Raphanus species, are among the most economically important crops globally; however, their production is affected by several diseases. To predict cloned disease resistance (R) gene homologs (CDRHs), we used the protein sequences of 49 cloned R genes against fungal and bacterial diseases in Brassicaceae species. In this study, using 20 Brassicaceae genomes (17 wild and 3 domesticated species), 3172 resistance gene analogs (RGAs) (2062 nucleotide binding-site leucine-rich repeats (NLRs), 497 receptor-like protein kinases (RLKs) and 613 receptor-like proteins (RLPs)) were identified. CDRH clusters were also observed in Arabis alpina, Camelina sativa and Cardamine hirsuta with assigned chromosomes, consisting of 62 homogeneous (38 NLR, 17 RLK and 7 RLP clusters) and 10 heterogeneous RGA clusters. This study highlights the prevalence of CDRHs in the wild relatives of the Brassicaceae family, which may lay the foundation for rapid identification of functional genes and genomics-assisted breeding to develop improved disease-resistant Brassicaceae crop cultivars.

Copy Number Variation among Resistance Genes Analogues in Brassica napus.

Copy number variations (CNVs) are defined as deletions, duplications and insertions among individuals of a species. There is growing evidence that CNV is a major factor underlining various autoimmune disorders and diseases in humans; however, in plants, especially oilseed crops, the role of CNVs in disease resistance is not well studied. Here, we investigate the genome-wide diversity and genetic properties of CNVs in resistance gene analogues (RGAs) across eight Brassica napus lines. A total of 1137 CNV events (704 deletions and 433 duplications) were detected across 563 RGAs. The results show CNVs are more likely to occur across clustered RGAs compared to singletons. In addition, 112 RGAs were linked to a blackleg resistance QTL, of which 25 were affected by CNV. Overall, we show that the presence and abundance of CNVs differ between lines, suggesting that in B. napus, the distribution of CNVs depends on genetic background. Our findings advance the understanding of CNV as an important type of genomic structural variation in B. napus and provide a resource to support breeding of advanced canola lines.

Isolation and Sequencing of Chromosome Arm 7RS of Rye, Secale cereale.

Rye (Secale cereale) is a climate-resilient cereal grown extensively as grain or forage crop in Northern and Eastern Europe. In addition to being an important crop, it has been used to improve wheat through introgression of genomic regions for improved yield and disease resistance. Understanding the genomic diversity of rye will assist both the improvement of this crop and facilitate the introgression of more valuable traits into wheat. Here, we isolated and sequenced the short arm of rye chromosome 7 (7RS) from Triticale 380SD using flow cytometry and compared it to the public Lo7 rye whole genome reference assembly. We identify 2747 Lo7 genes present on the isolated chromosome arm and two clusters containing seven and sixty-five genes that are present on Triticale 380SD 7RS, but absent from Lo7 7RS. We identified 29 genes that are not assigned to chromosomal locations in the Lo7 assembly but are present on Triticale 380SD 7RS, suggesting a chromosome arm location for these genes. Our study supports the Lo7 reference assembly and provides a repertoire of genes on Triticale 7RS.

Pangenomics and Crop Genome Adaptation in a Changing Climate.

During crop domestication and breeding, wild plant species have been shaped into modern high-yield crops and adapted to the main agro-ecological regions. However, climate change will impact crop productivity in these regions, and agriculture needs to adapt to support future food production. On a global scale, crop wild relatives grow in more diverse environments than crop species, and so may host genes that could support the adaptation of crops to new and variable environments. Through identification of individuals with increased climate resilience we may gain a greater understanding of the genomic basis for this resilience and transfer this to crops. Pangenome analysis can help to identify the genes underlying stress responses in individuals harbouring untapped genomic diversity in crop wild relatives. The information gained from the analysis of these pangenomes can then be applied towards breeding climate resilience into existing crops or to re-domesticating crops, combining environmental adaptation traits with crop productivity.