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aBIOTECH https://doi.org/10.1007/s42994-022-00090-5 aBIOTECH REVIEW Weed genomics: yielding insights into the genetics of weedy traits for crop improvement 1 1 2 2 3 Yujie Huang , Dongya Wu , Zhaofeng Huang , Xiangyu Li , Aldo Merotto Jr , 4& 1& Lianyang Bai , Longjiang Fan Institute of Crop Science and Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China Department of Crop Sciences, Agricultural School Federal University of Rio Grande do Sul, Porto Alegre 91540-000, Brazil Hunan Weed Science Key Laboratory, Hunan Academy of Agriculture Sciences, Changshang 410125, China Received: 27 October 2022 / Accepted: 6 December 2022 Abstract Weeds cause tremendous economic and ecological damage worldwide. The number of genomes established for weed species has sharply increased during the recent decade, with some 26 weed species having been sequenced and de novo genomes assembled. These genomes range from 270 Mb (Barbarea vulgaris) to almost 4.4 Gb (Aegilops tauschii). Importantly, chromosome-level assemblies are now available for 17 of these 26 species, and genomic investigations on weed populations have been conducted in at least 12 species. The resulting genomic data have greatly facilitated studies of weed management and biology, especially origin and evolution. Available weed genomes have indeed revealed valuable weed-derived genetic materials for crop improvement. In this review, we summarize the recent progress made in weed genomics and provide a perspective for further exploitation in this emerging ﬁeld. Keywords Weeds, Genome sequencing, Population genomics, Adaptive traits, Evolution INTRODUCTION populations also offers accurate and plentiful molecular markers from which to infer and reconstruct the com- The Arabidopsis thaliana genome sequence was plex evolutionary histories of plant species, particularly released in 2000 and represented a hallmark in plant for crop species. research as the ﬁrst sequenced and assembled plant Crops are not the only plants that grow in ﬁelds, genome (The Arabidopsis Genome Initiative 2000). however, weeds—deﬁned here as non-crop plants Driven by the rapid development of sequencing tech- growing within crop ﬁelds—can have competitive nologies and bioinformatics methods, hundreds of plant advantages over crop plants and cause yield loss (Basu genomes have since been sequenced and assembled et al. 2004). To date, 2847 plant species belonging to (Sun et al. 2022a). High-quality reference genomes have 177 families and 1118 genera have been designated as provided vital resources for molecular genetics and weeds (Weed Science Society of America database, have accelerated and improved precision crop breeding. http://www.wssa.net). Notably, weeds are the main Whole-genome genetic information for entire contributors to yield loss for ﬁeld crops, compared to pests and pathogens, and on average result in a 30% annual yield loss across the major crops (Oerke 2006). & Correspondence: firstname.lastname@example.org (L. Bai), email@example.com Although agricultural production is substantially (L. Fan) The Author(s) 2023 aBIOTECH affected by weeds, until recently, weed studies have not also been sequenced. For example, chromosome-level been given sufﬁcient attention, in terms of both tradi- genomes of highly heterozygous Amaranthus species (A. tional molecular biology and genome analyses. Recent tuberculatus, A. hybridus, and A. palmeri) have been comparative genomics and population genomics analy- developed (Montgomery et al. 2020). Of the 26 weed ses have revealed the effect of weeds on crop agronomic species, 16 are dicots from six different families, with traits and the mechanisms underlying weediness, such the remaining nine species being monocots from only as in barnyard grass (Echinochloa crus-galli), tall one family (Poaceae) (Fig. 1). Polyploid species usually waterhemp (Amaranthus tuberculatus), and weedy rice exhibit more dominant advantages in their adaptation (Oryza sativa f. spontanea) (Guo et al. 2017; Kreiner (te Beest et al. 2012), and the genomes of four polyploid et al. 2018, 2019; Gaines et al. 2020; Qiu et al. 2020). In weed species, comprising three tetraploid (L. chinensis, addition, the complex relationships among crops, weeds, E. oryzicola, and Capsella bursa-pastoris) and one hex- humans, and abiotic environments in agricultural aploid (E. crus-galli) species, have been sequenced. ecosystems, provide an ideal model for the study of Notably, several weeds are very closely related to crop biological interactions. Considering the potential of species (i.e., they represent different subspecies or weed biology, recently, the weed research community accessions of the same species), and the corresponding endeavored to initiate genome sequencing of global crop genome can therefore be used as a reference weed species (Ravet et al. 2018). genome for weeds. For example, the available barnyard In this review, we summarize genome sequencing of millet (E. colona var. frumentacea) genome provided an weed species over the past decade and explore future important reference for barnyard grass (E. colona var. directions and potential applications in agricultural colona) (Wu et al. 2022b), as did the crop sorghum production. (Sorghum bicolor) for Johnsongrass (Sorghum hale- pense), cultivated pearl millet (Pennisetum glaucum) for wild pearl millet (Pennisetum violaceum), rye (Secale WEED GENOME SEQUENCING AND DE NOVO cereale) for weedy rye (S. cereale subsp. segetale), sugar ASSEMBLY beet (Beta vulgaris) for sea beet (Beta vulgaris ssp. maritima), and rice for weedy rice. In recent years, the number of genomes released for weed species has sharply increased (Table 1), with genomes for at least 26 weed species being sequenced. WHOLE-GENOME SEQUENCING OF WEED Their genome sizes range from 270 Mb (Barbarea vul- POPULATIONS garis) to 4360 Mb (Aegilops tauschii); 17 of these gen- omes have been assembled to the chromosome level, Whole-genome sequencing, which provides excellent based on long-read sequencing technologies. Mean- tools for mining genetic mechanisms and evolutionary while, a signiﬁcant improvement in sequence quality for studies, has been widely used in crop genomics (Jia et al. weed genomes was achieved along with the develop- 2021). Since 2017, this method has also been applied to ment of new sequencing technologies. For instance, the a limited number of weed species, mainly for paddy genomes of the barnyard grass species, E. crus-galli and weeds, such as weedy rice and barnyard grass (Table 2). E. oryzicola, which grow in paddy ﬁelds and compete Weedy rice was the ﬁrst weed species to be used for with rice, were assembled into draft genomes and later genomic investigation, via whole-population sequenc- anchored to chromosomes by incorporating data from ing. As weedy rice can be considered a wild-like rice chromosome conformation capture (Hi-C) (Guo et al. ecotype, the genome of cultivated rice provides a good 2017; Ye et al. 2020; Wu et al. 2022b). The genome of reference for calling single-nucleotide polymorphisms weedy rice (Oryza sativa f. spontanea) was also (SNPs) in individuals. Over 650 accessions of weedy rice sequenced and assembled, at the chromosome level, in have been sequenced, being derived from global rice 2019 (Sun et al. 2019). In addition, the genomes of production areas, which has deepened our under- tetraploid Chinese sprangletop (Leptochloa chinensis) standing of weedy rice origins and adaptation strategies were assembled (Wang et al. 2022). An invasive weed in (Li et al. 2017; Qiu et al. 2017, 2020; Imaizumi et al. wheat ﬁelds, ﬁeld pennycress (Thlaspi arvense), had its 2021; Wedger et al. 2022). genome assembled in 2015 and independently Other weeds affecting paddy ﬁelds have also been anchored to chromosomes in 2021 and 2022 (Dorn studied, at the genomic level. For barnyard grass, the et al. 2015; Geng et al. 2021; Nunn et al. 2022). The release of its genome (Guo et al. 2017) heralded the genomes for other agronomically important weeds have beginning of population genomics in this species, with The Author(s) 2023 aBIOTECH Table 1 Progress of de novo sequencing and assembly of weed genomes in the past decade Year Common Scientiﬁc name Ploidy Genome Assembly Contig Main crop References released name size (Mb) level N50 (kb) 2013 Tausch’s Aegilops tauschii Diploid 4244 Scaffold 4 Wheat Jia et al. (2013) goatgrass 2014 Horseweed Conyza canadensis Diploid 326 Scaffold 21 Cotton, corn Peng et al. and soybean (2014) 2015 Field Thlaspi arvense Diploid 343 Scaffold 20 Wheat Dorn et al. pennycress (2015) 2017 Barnyard Echinochloa crus-galli Hexaploid 1340 Scaffold 1800 Rice Guo et al. grass (2017) Tausch’s Aegilops tauschii Diploid 4225 Scaffold 93 Wheat Luo et al. goatgrass (2017) Tausch’s Aegilops tauschii Diploid 4310 Chromosome 113 Wheat Zhao et al. goatgrass (2017) Shepherd’s Capsella bursa-pastoris Tetraploid 252 Scaffold 37 Wheat Kasianov et al. purse (2017) Yellow rocket Barbarea vulgaris Diploid 168 Scaffold 14 Lawn Byrne et al. (2017) 2018 Australian Cuscuta australis / 265 Scaffold 3630 Fabaceae Sun et al. dodder (2018) Dodder Cuscuta campestris / 477 Scaffold 16 Fabaceae Vogel et al. (2018) Wild Saccharum spontaneum Haploid 2560 Chromosome 45 Poaceae Zhang et al. sugarcane (2018) / Leersia perrieri Diploid 267 Chromosome 50 Rice Stein et al. (2018) 2019 Kochia Bassia scoparia Diploid 711 Scaffold 61 Wheat Patterson et al. (2019a) Goose grass Eleusine indica Diploid 584 Scaffold 4 Fabaceae Zhang et al. (2019) Weedy rice Oryza sativa f. spontanea Diploid 373 Chromosome 6090 Rice Sun et al. (2019) Tall Amaranthus tuberculatus Diploid 664 Chromosome 1740 Cotton, corn Kreiner et al. waterhemp and soybean (2019) Witchweed Striga asiatica Diploid 472 Scaffold 16 Poaceae Kreiner et al. (2019) 2020 Horseweed Conyza canadensis Diploid 426 Chromosome 1676 Poaceae Lu et al. (2020) Bitter vine Mikania micrantha / 1350 Chromosome 1790 Cocoa, citrus Liu et al. and bananas (2020) Palmer Amaranthus palmeri Diploid 408 Chromosome 2540 Cotton, corn Montgomery amaranth and soybean et al. (2020) Tall Amaranthus tuberculatus Diploid 573 Chromosome 2580 Cotton, corn Montgomery waterhemp and soybean et al. (2020) Smooth Amaranthus hybridus Diploid 403 Chromosome 2260 Corn and Montgomery pigweed soybean et al. (2020) Green foxtail Setaria viridis Diploid 395 Chromosome 11,200 Poaceae Mamidi et al. (2020) Green foxtail Setaria viridis Diploid 397 Chromosome 19,521 Poaceae Thielen et al. (2020) Barnyard Echinochloa crus-galli Hexaploid 1340 Scaffold 1570 Rice Ye et al. (2020) grass Barnyard Echinochloa oryzicola Tetraploid 946 Scaffold 1870 Rice Ye et al. (2020) grass The Author(s) 2023 aBIOTECH Table 1 continued Year Common Scientiﬁc name Ploidy Genome Assembly Contig Main crop References released name size (Mb) level N50 (kb) 2021 Tausch’s Aegilops tauschii Diploid 4290 Chromosome 1720 Wheat Wang et al. goatgrass (2021b) Tausch’s Aegilops tauschii Diploid 4075 Chromosome 2200 Wheat Zhou et al. goatgrass (2021) Wild radish Raphanus raphanistrum Diploid 421 Chromosome 7764 Wheat Zhang et al. ssp. raphanistrum (2021) Wild radish Raphanus raphanistrum Diploid 418 Chromosome 4068 Wheat Zhang et al. ssp. landra (2021) Field Thlaspi arvense Diploid 527 Chromosome 4180 Wheat Geng et al. pennycress (2021) 2022 Field Thlaspi arvense Diploid 526 Chromosome 13,300 Wheat Nunn et al. pennycress (2022) Barnyard Echinochloa crus-galli Hexaploid 1340 Chromosome 1570 Rice Wu et al. grass (2022b) Barnyard Echinochloa oryzicola Tetraploid 946 Chromosome 1870 Rice Wu et al. grass (2022b) Chinese Leptochloa chinensis Tetraploid 416 Chromosome 8500 Rice Wang et al. sprangletop (2022) Common Ambrosia artemisiifolia Diploid 1258 Scaffold 271 Tomato, lettuce Bieker et al. ragweed and maize (2022) Sunﬂower Orobanche cumana / 1418 Chromosome 13,334 Sunﬂower Xu et al. (2022) broomrape Egyptian Phelipanche aegyptiaca / 3877 Scaffold 9973 Cucurbitaceae Xu et al. (2022) broomrape Ryegrass Lolium rigidum Diploid 2440 Chromosome 361,790 Wheat Paril et al. (2022) Scaffold N50 size (kb) /Data missing over 700 genomes of accessions collected from all over GENOMIC INSIGHTS INTO WEED BIOLOGY the world being re-sequenced for studies on evolu- tionary history and typical weed adaptation syndromes Environmental adaptation (Ye et al. 2019, 2020; Wu et al. 2022b). Similarly, gen- ome resequencing of 89 Chinese accessions revealed Weeds have great potential as model systems in which that sprangletop originated from a local population in to understand plant responses to biotic and abiotic tropical areas of South Asia and Southeast Asia and that stresses (Vigueira et al. 2013). They can survive in the geographical range of individuals with herbicide disrupted environments and persist under multiple resistance genes expanded, likely due to ﬁeld manage- challenges, in particular escaping from control measures ment practices (Wang et al. 2022). in the ﬁeld, including targeted tillage practices, herbi- In recent years, signiﬁcant efforts have been made to cide use, and hand-weeding (Sharma et al. 2021; Neve explore the adaptation and evolutionary dynamics of and Caicedo 2022). In addition, weeds are not dis- ﬁeld pennycress. For example, 40 ﬁeld pennycress lines tributed in limited ecological niches, but rather, they from different altitude regions were re-sequenced, often exhibit a widespread distribution, even among resulting in the identiﬁcation of one SNP responsible for areas with distinct conditions, exemplifying their strong the adaptation to latitude, via constructing ultra-high- environmental plasticity (Sharma et al. 2021). density linkage maps (Geng et al. 2021). In another Genomic studies have signiﬁcantly improved our example, a genomic region located on scaffold 6 was understanding of weed environmental adaptations to identiﬁed as causing the seedling color phenotype in biotic and abiotic stresses. For example, T. arvense is an annual weed from the Brassicaceae family that lives at ﬁeld pennycress by bulk-sequencing of DNA pools from 20 wild-type and 20 pale plants (Nunn et al. 2022). different altitudes, ranging from sea level to 4500 m above sea level. Genomic analyses of populations from different ecological conditions identiﬁed a SNP that led The Author(s) 2023 aBIOTECH Fig. 1 List of weed species sequenced and their phylogenetic relationships. For detailed information about all genome sequencing results, please see Table 1 Table 2 Summary of recent investigations on weed populations by genome resequencing Common name Scientiﬁc name Population size Sequencing depth Region References Weedy rice O. sativa f. spontanea 38 19 9 USA Li et al. (2017) 155 18 9 China Qiu et al. (2017) 30 10 9 Korea He et al. (2017) 331 19 9 Global Qiu et al. (2020) 50 23 9 Japan Imaizumi et al. (2021) 48 40 9 USA Wedger et al. (2022) Barnyard grass E. crus-galli 328 15 9 China Ye et al. (2019) 578 15 9 Global Wu et al. (2022b) Barnyard grass E. walteri 15 15 9 USA Wu et al. (2022b) Barnyard grass E. colona var. colona 20 15 9 Global Wu et al. (2022b) Barnyard grass E. oryzicola 85 15 9 Global Wu et al. (2022b) Tall waterhemp A. tuberculates 173 10 9 USA Kreiner et al. (2019) Green foxtail S. viridis 598 43 9 Global Mamidi et al. (2020) Fonio millet D. longiﬂora 17 20 9 Africa Abrouk et al. (2020) Field pennycress T. arvense 40 19 9 China Geng et al. (2021) 40 15 9 USA Nunn et al. (2022) Chinese sprangletop L. chinensis 89 19 9 China Wang et al. (2022) Weedy rye S. cereale subsp. Segetale 30 10 9 Global Sun et al. (2022a) Common ragweed A. artemisiifolia 655 24 9 USA Bieker et al. (2022) The Author(s) 2023 aBIOTECH to a loss-of-function allele in FLOWERING LOCUS C on barnyard grass (at least in E. crus-galli and E. oryzicola), chromosome 1, which contributed to the early ﬂowering an unintentional human selection (UHS) resulting from trait that was key to the success of high-elevation pop- human action (Fig. 2). ulations (Geng et al. 2021). Crop mimicry describes the adaptation of a weed Another conspicuous trait related to environmental through its acquiring some of the morphological char- adaptation in weeds is herbicide resistance (Hawkins acteristics of neighboring domesticated crops, at a et al. 2019; Gaines et al. 2020). Comparative genomics speciﬁc stage of their life history, to escape their between herbicide-susceptible and -resistant individu- removal by hand-weeding (Barrett 1983; Ye et al. 2019). als, from the same species, and between species, can The preadapted plants, or wild species that were ﬁrst to offer glimpses into innovations in herbicide resistance colonize in cultivated ﬁelds, during the early agricultural pathways (Kreiner et al. 2018). Waterhemp (A. tuber- stage (so-called ancient weeds), gradually became culatus), which is troublesome in maize (Zea mays) and mimic weeds under strong artiﬁcial (weeding) selection. soybean (Glycine max) ﬁelds, is notorious for exhibiting Genomic signatures of human selection on crop mimicry multiple herbicide-resistant (MHR) traits. Recently, a were elucidated by comparing the genomes of mimetic reduction–dehydration–glutathione (GSH) conjugation and non-mimicry lines of barnyard grass collected from system was discovered as a possible pathway for MHR paddy ﬁelds in the Yangtz River basin, China (Ye et al. (Concepcion et al. 2021). In palmer amaranth 2019). Several genes underlying plant architecture (e.g., (Amaranthus palmeri), genomic analysis helped deter- tiller angle) were identiﬁed, including LAZY1, a gene mine that herbicide resistance is conferred by an responsible for plant tiller angles, which was also under extrachromosomal circular DNA (eccDNA) of about selection during rice domestication. The genomic study 400 kb in length that harbors 5-ENOYLPYRUVYL- of mimicry of rice seedlings, by barnyard grass, is an SHIKIMATE-3-PHOSPHATE SYNTHASE (EPSPS), which example of how weeds can adapt to disturbed envi- encodes the enzyme targeted by the herbicide glypho- ronments with selective pressure from human beings, sate (Gaines et al. 2010; Molin et al. 2020). Although the via a genomic approach. ampliﬁcation of genes and gene clusters, via eccDNAs or Allelopathic secondary metabolites also are a repre- other structures, is a common stress-avoidance mecha- sentative response of weeds to biotic stress. Benzox- nism in plants (Nandula et al. 2014; Singh et al. 2020), it azinoids, which acted against microbial pathogens and is usually transient and not stably inherited (Lanciano neighboring plants, were identiﬁed in a multitude of et al. 2017; Gaines et al. 2019). species of the family Poaceae, such as maize, wheat As the most dominant weed in rice ﬁelds, barnyard (Triticum aestivum), and barnyardgrass (Frey et al. grass has also evolved global resistance to major her- 2009;Wuetal. 2022a). As a predominant representa- bicides. Genome resequencing of barnyard grass indi- tive of benzoxazinoids in plants, DIMBOA is present in viduals from Brazil, Italy, and China revealed four barnyard grass with multiple copies and inhibits plant mutations in the gene encoding aceto-lactate synthase height and fresh weight of neighboring rice (Guo et al. (ALS), which conferred herbicide resistance, namely 2017). Another example is momilactone A, which has Ala-122-Thr, Trp-574-Leu, Ser-653-Asn, and a Gly-654- similar functions to Benzoxazinoids in rice. Based on the Cys substitution identiﬁed for the ﬁrst time, with a momilactone A biosynthesis genes of rice, a syntenic tendency to occur in sub-genome A (barnyard grass is a gene cluster was identiﬁed in barnyard grass. Up-regu- hexaploid). Moreover, after comparing the genomes of lated expression of MAS and KSL4, within this cluster, resistant and susceptible individuals from Brazil, an under fungal infection indicated its contribution to Arg-86-Gln mutation in the conserved degron tail region resistance to blast infection in the paddy environment of Echinochloa AUXIN-INDUCED (AUX)/INDOLE-3- (Guo et al. 2017). ACETIC ACID INDUCIBLE 12 (IAA12) was identiﬁed, which has since been conﬁrmed to confer resistance to Origins of weeds other auxin-like herbicides (LeClere et al. 2018; Fig- ueiredo et al. 2021;Wuetal. 2022b). Understanding the origin of agricultural weeds is crucial Great progress has also been made in understanding to their proper management. Weed origins can be via the responses of weeds to biotic stresses. Before her- several routes. Preadapted plants or wild species can bicides were used in agriculture, the direct interaction colonize cultivated ﬁelds in human-made ecological between weeds and human beings was through hand- niches (Larson et al. 2014). With the expansion of cul- weeding, which placed high pressure on weed mor- tivated ﬁelds, the emergence and diversiﬁcation of phology, especially plant architecture. One example is weeds may have resulted from hybridization between the Vavilovian mimicry or crop mimicry seen in The Author(s) 2023 aBIOTECH Fig. 2 Possible origination routes for three notorious paddy weeds in rice ﬁelds, as supported by recent genomic studies. Wild progenitors include wild Oryza, Echinochloa, and Leptochloa species in the grass family. HUS, human unintentional selection crop and wild groups, along with other routes (Iriondo encoding seed storage proteins (Li et al. 2017; Qiu et al. et al. 2018; Janzen et al. 2019). 2020). Recent genomic studies focused on paddy weeds A similar process was also described for the origins revealed many interesting insights about their possible of E. crus-galli var. oryzoides, which is currently regar- origin(s) and evolution (Fig. 2). Weedy rice (Oryza ded as a paddy weed (Fig. 2). The signiﬁcantly lower sativa f. spontanea) has attracted much attention for its nucleotide diversity, longer linkage disequilibrium origin of de-domestication, i.e., the conversion of a decay, more immune response genes, larger grains, and domesticated form to a wild-like form (Wu et al. 2021). non-shattering spikelets in this species, compared to Weedy rice mimics rice cultivars, at the seedling stage, weed populations, indicate that var. oryzoides is an while retaining wild phenotypes, such as strong seed abandoned crop (Wu et al. 2022b). dormancy and shattering. De-domestication from culti- vated rice (including cultivars and landraces) is the main route for rice feralization, along with introgres- PERSPECTIVES IN WEED GENOMICS sions from wild rice, which is commonly seen in Southeast Asia and South China, where wild rice is We need complete, contiguous, and accurate genome distributed, as well as inter-subspecies hybridization assemblies for many more weed species. Indeed, in (Stewart 2017; Sun et al. 2019; Qiu et al. 2020;Wuetal. notable contrast to the massive increase in sequenced 2021). Genomic mining, aided by comparisons between crop genomes, only 26 weeds have been decoded thanks the genomes of weedy, wild, and cultivated rice popu- to the sequencing and assembly of their genomes. The lations, has revealed distinct differentiation regions on enormous gap between crops and weeds underscores chromosomes during de-domestication compared to how much weeds are currently being overlooked. For those resulting from domestication, with the identiﬁca- example, Commelinales, with about 750 extant species, tion of a genomic island possibly underlying feralization including pickerel weed (Monochoria vaginalis), are traits on chromosome 7. This genomic region harbors important weeds growing in paddy ﬁelds. Likewise, Rc, controlling red pericarp and seed dormancy (Swee- common water hyacinth (Eichhornia crassip) is the most ney et al. 2006), and several tandem-duplicated genes common invasive plant according to a survey by the The Author(s) 2023 aBIOTECH Weed Science Society of America database (WSSA, information (Devine and Shukla 2000; Yuan et al. 2007; http://www.wssa.net). Yet, these two species still lack a De´lye 2013; Kreiner et al. 2018). For example, the representative genome. Several sedges (e.g., Cyperus, availability of the barnyard grass genome made it pos- Scirpus, and Fimbristilis) are found worldwide and sible to identify, for the ﬁrst time, a signiﬁcant increase exhibit particular weediness traits, but very little in copy number for cytochrome P450 genes in the weed genomic information is currently available. genomes, as well as a Gly-654-Cys substitution, with We even lack a thorough understanding and charac- both strategies contributing to ALS resistance. Another terization of notorious weeds affecting croplands, such example resulting from the comparative analysis of as hairy crabgrass (Digitaria sanguinalis), a typical waterhemp genomes was the report of a possible upland weed growing in maize and soybean ﬁelds. pathway for MHR, via reduction–dehydration–glu- Moreover, a higher-quality genome of weeds is required tathione. We anticipate that, along with the develop- to shed light on related biological topics. The gap-free ment of weed genomics, additional discoveries about genomes of many plants, such as Arabidopsis, rice, and gene functions and their interactions will be watermelon (Citrullus lanatus), have recently been forthcoming. assembled, providing the ﬁrst complete genome struc- More valuable genetic resource of weeds will be ture of any plant (Song et al. 2021; Wang et al. 2021a; revealed with the sequencing of more weed genomes, Deng et al. 2022). With the incorporation of sequences which will have beneﬁts for the genetic improvement of from highly repetitive regions and centromeres into crops and even their de novo domestication. Crops, genome assemblies at the chromosome scale, a greater particular orphan crops, are genetically very closely understanding of the global pattern of weed polymor- related to weeds. For example, orphan crops usually phisms and the genetic basis of their weedy traits and have a notorious weed species in the same genus (Ye high adaptability is ﬁnally within reach, but only if more and Fan 2021). Given their strong environmental plas- genomes are sequenced or improved upon. These issues ticity and high level of genetic variation, weeds are an were also noted by the International Weed Science untapped genetic resource for domestication. For Consortium, which has designated Plantae (www.plan example, mutating the orthologs for qSH1 (Shattering tae.org) as a platform for community collaboration QTL 1) and Sh4 (Shattering 4) genes in weeping rice efforts and has developed a weed genomics website grass (Microlaena stipoides), an Australian wild relative (www.weedgenomics.org) (Ravet et al. 2018). of rice, caused the loss of shattering in this species We expect and anticipate more studies exploring the (Shapter et al. 2013). Historically, some weeds have population genomics of weeds, which will be helpful for been domesticated into crops, such as rye (Rye secale) the understanding of their evolutionary strategies and (Sun et al. 2022b). Presently, de novo domestication of evolutionary ecology, while offering more options for new crops is an option being considered to mitigate the weed management. Current evolutionary patterns tend effects of climate change on global crop production. We to highlight pressure imposed by the natural environ- propose that some weeds, in particular those mimicking ment, perhaps neglecting the role that human activities crops, are ideal targets for de novo domestication. play in a novel ecosystem labeled with speciﬁc species In addition to crop improvement, weed management assemblages and environmental factors. Studying weed will also beneﬁt from the advances in weed genomes. populations with complex evolutionary trajectories of Gene silencing techniques are offer a promising traits will enhance our ability to decode their distinct approach to manipulate the expression level of weed evolutionary strategies under different conditions. In traits genes to reduce their impact with improved addition, a better understanding of the evolution of understanding of characteristic regulated pathways agricultural weeds will be crucial to weed management. (Neve 2018). For example, if genomics can identify the Given the increasing number of rapid weed adaptations, basis of allelopathy, weeds could be modiﬁed with low such as herbicide resistance, ongoing selection for other levels of allelopathic compounds, thereby reducing their weedy traits should be a driving force to adjust all weed competitive ability in paddy ﬁelds. However, major management practices to mitigate the spread and suc- challenges remain to be overcome; e.g., the designation cess of weeds. of highly speciﬁc gene silencing triggers with high her- With the advantage of more available genomes, weed itability (Patterson et al. 2019b). functional genomics will step to the front stage. Our Post-transcriptional silencing, using exogenous understanding of the mechanisms by which multiple application of RNA, known as spray-induced gene weed species acquire herbicide resistance (particularly silencing (SIGS), is a promising technology that may non-target resistance) to the same class of herbicide has revolutionize weed control. Several limitations and considerably improved with released genomic opportunities are associated with the development of The Author(s) 2023 aBIOTECH Concepcion JCT, Kaundun SS, Morris JA, Hutchings S-J, Strom SA, this technology. The main requirement for SIGS is Lygin AV et al (2021) Resistance to a nonselective 4-hydrox- selective gene silencing in weeds and the absence of yphenylpyruvate dioxygenase-inhibiting herbicide via novel effects on crops and non-target organisms. Therefore, reduction–dehydration–glutathione conjugation in Amaran- the development of this non-transgenic, and environ- thus tuberculatus. 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Published: Mar 1, 2023
Keywords: Weeds; Genome sequencing; Population genomics; Adaptive traits; Evolution
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