The genomic basis underlying the selection for environmental adaptation and yield-related traits in maize remains poorly understood. Here we carried out genome-wide profiling of the small RNA (sRNA) transcriptome (sRNAome) and transcriptome landscapes of a global maize diversity panel under dry and wet conditions and uncover dozens of environment-specific regulatory hotspots. Transgenic and molecular studies of Drought-Related Environment-specific Super eQTL Hotspot on chromosome 8 (DRESH8) and ZmMYBR38, a target of DRESH8-derived small interfering RNAs, revealed a transposable element-mediated inverted repeats (TE-IR)-derived sRNA- and gene-regulatory network that balances plant drought tolerance with yield-related traits. A genome-wide scan revealed that TE-IRs associate with drought response and yield-related traits that were positively selected and expanded during maize domestication. These results indicate that TE-IR-mediated posttranscriptional regulation is a key molecular mechanism underlying the tradeoff between crop environmental adaptation and yield-related traits, providing potential genomic targets for the breeding of crops with greater stress tolerance but uncompromised yield.
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The sRNA and mRNA sequencing data of different maize genotypes have been deposited in National Genomics Data Center (https://bigd.big.ac.cn/) with a GSA accession number CRA003871, which will be reached via the link https://ngdc.cncb.ac.cn/gsa/browse/CRA003871. Maize genome sequence and gene model files (version 4.32) were download from ensemble plants (http://ftp.ensemblgenomes.org/pub/plants/). Sequence information of snRNA, snoRNA, tRNA and rRNA were downloaded from GtRNAdb (http://lowelab.ucsc.edu/GtRNAdb/Zmays5/), silva (http://www.arb-silva.de/), Rfam (ftp://ftp.ebi.ac.uk/pub/databases/Rfam/12.0/), the plant snoRNA database (http://bioinf.scri.sari.ac.uk/cgi-bin/plant_snorna/ home/) and silva (http://www.arb-silva.de/). sRNA data for sRNA-related genes were downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/) with accession numbers GSE66986 (dcl1 dcl4), GSE12173 (mop1) and GSE70487 (rmr6). sRNA data for maize plants exposed to various abiotic stresses were downloaded at GEO with accession number GSE33211. All GEO datasets can be download by SraToolkit (https://trace.ncbi.nlm.nih.gov/Traces/sra/). Hapmap3 data used for nucleotide diversity calculation were downloaded from maizeGDB (https://www.maizegdb.org/diversity). All data that support the findings of this study are available to the public. Source data are provided with this paper.
The custom scripts/codes used in this study are available to the public at https://github.com/SalieriTitor/Project_sRNA_2016/.
Food and Agriculture Organization of the United Nations Agriculture Databases; https://www.fao.org/statistics/databases/en/
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We thank E. Buckler (Cornell University), J. Ross-Ibarra (University of California, Davis), Z. Fei (Cornell University), J. Hua (Cornell University), Y. Qi (Tsinghua University) and J. Lai (China Agricultural University) for valuable comments and suggestions on the data analyses and the manuscript; F. Tian (China Agricultural University) for providing DNA of landraces and X. Liu (Jilin Academy of Agricultural Sciences) for nursing the maize transgenic plants. This study was supported by grants from the National Science Foundation of China (32061143031 to M.D., 91940301 to F.L. and 92035302 to L.L.), the National Key Research and Development Program of China (2016YFD0100600 to M.D.), the Fundamental Research Funds for the Central Universities of China (2662020SKY009 to M.D. and 2662014PY008 to F.L.), the major Program of Hubei Hongshan laboratory (2021hszd008 to L.L.) and the Baichuan Project at the College of Life Science and Technology, Huazhong Agricultural University (to X.S.).
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a, Diagrammatic representation of the growth pools used for maize growth and treatments. b, c, Overview of 338 inbred accessions (b) and representative maize accessions (c) grown under WW and DS conditions in the growth pools. d, Overlapping drought-responsive sRNAs and drought-responsive sRNA clusters detected by paired t-tests, biomarker GWAS, and regression (between sRNA expression and survival rates after drought). e, Representative miR827 and miR156 Northern blot experiments to detect DE sRNAs in maize inbred lines grown under WW and DS conditions. Sizes of the small RNA and U6 RNA are indicated to the right. The experiments were repeated independently for three times and similar results were obtained each time. f, g. Distribution of total, upregulated, and downregulated sRNAs among drought-responsive sRNAs (f) and drought-responsive sRNA clusters (g). The various sizes of sRNAs in drought-responsive sRNA clusters are shown. Among all drought-responsive s-traits, most 21-nt (75%) and 22-nt (73.4%) sRNAs tended to be upregulated, while most 24-nt (61.4%) s-traits tended to be downregulated (f). Of the upregulated sc-traits, most had a main sRNA size of 21-nt (95.6%) or 22-nt (77.9%), while of the downregulated sc-traits, most had a main sRNA size of 24 nt (72.9%) (g).
a, Coexpression networks between drought-responsive sRNAs (triangles) and putative sRNA target genes. drought-responsive sRNAs with putative target genes involved in abiotic stimuli are shown as magenta triangles. sRNA target genes were grouped into various GO terms (circles) and KEGG pathways (hexagons). The size of each circle or hexagon is proportional to the number of enriched genes: larger sizes indicate more genes. b-e, Correlations between the expression of miRNAs and their target genes. Four known representative miRNA / target pairs, miR168 / AGO1c (b), miR167 / ARF6 (c), miR169 / NF-YA8 (d) and miR156 / SPL2 (e), are shown. The P values were calculated by test of correlation coefficient (two-sided Student’s t-test). f, New coexpression relationships detected between sRNAs / sRNA clusters and genes (indicated in red color) involved in steroid biosynthesis.
Extended Data Fig. 3 Effects of DRESH8 on sRNA and local gene expression, and ZmMYBR38 on plant drought tolerance.
a, Mapping of 21-, 22-, and 24-nt sRNA reads to DRESH8 and mRNA reads to DRESH8, ZmPP2C16, and ZmWRKY51. DRESH8 interrupts ZmPP2C16. mRNA and sRNA reads both map to the DRESH8 locus, but only mRNA reads map to ZmPP2C16 and ZmWRKY51. DT2-DT4 indicate various drought stress levels, as reported previously51. b, Genomic structures of the ZmPP2C16 and ZmWRKY51 loci. Lines ending with arrows indicate the direction of transcription. Arrowheads indicate the positions of the primers used to detect ZmPP2C16 (in red) and ZmWRKY51 (in blue) in PCR analyses. c, d, PCR results using genomic DNA and cDNA templates for ZmPP2C16 (c) and ZmWRKY51 (d). Primer annealing sites are indicated in (b). e, f, DRESH8 does not affect the expression of ZmPP2C16 (e) or ZmWRKY51 (f) under either WW or DS conditions. Values (dots) indicate qRT-PCR data for maize inbred accessions with (+, N = 28) and without (-, N = 63) DRESH8 grown under WW and DS conditions. n.s., not significant, as determined by two-sided Student’s t-test. g,h, Overexpression of ZmMYBR38 in transgenic maize (g) and Arabidopsis (h) plants. WT, maize KN5585 used for transformation; Col-0, Arabidopsis WT accession used for transformation. i, Transgenic Arabidopsis plants overexpressing ZmMYBR38 (OX1 and OX4) are more drought tolerant. Values shown on the right represent means ± SD from three replicates (N = 292 plants for OX1 per replicate, 192 plants for OX4 per replicate) of the percentage of plants that survived. The P values were calculated by two-sided Student’s t-test. For c, d, g, h, the experiments were repeated independently for three times and similar results were obtained each time.
Fst values between maize and teosinte (upper) or between tropical/subtropical (TST) and temperate (Temp) maize (middle) at DRESH8 locus with 0.5 Mb flanking regions, the left Y axis indicates the Fst value of each SNP and the right Y axis indicates the average Fst value with a 10 kb window size. Bottom panel, selection signals (XP-CLR scores) detected between TST and Temp maize at DRESH8 locus with 0.5 Mb flanking regions. The horizontal grey dashed line represents a genome-wide top 5% cutoff that defines statistical significance.
Extended Data Fig. 5 Association of DRESH8 with kernel length of the maize panel and the expression levels of four negative regulators of seed development.
a. Comparison of kernel length (KL) between maize inbred lines with DRESH8 (DRESH8+/+) and those without DRESH8 (DRESH8−/−) under well-watered conditions. Values indicate means ± s.d. and the P values were calculated by two-sided Student’s t-test from ears with DRESH8+/+ (N = 57) and DRESH8−/− (N = 318) in the association panel used in the comparison. b–e. Expression of ZmARF2 (b), ZmRPT2A (c), ZmDA2 (d) and ZmHK4 (e), which were involved in kernel development, in maize inbred accessions with (+, N = 37) or without (−, N = 160) DRESH8. The genes ZmARF2, ZmRPT2A, ZmDA2, and ZmHK4 were previously reported to play negative roles during seed development16. The expression data are from RNA-seq of 197 maize inbred accessions grown under WW or DS conditions. Box middle line: median; box edges: 25th and 75th percentiles; whiskers: values that do not exceed ± IQR (interquartile range) × 1.5; further outliers are marked individually. The P values were calculated by two-sided Student’s t-test. f–i. Expression of ZmARF2 (f), ZmRPT2A (g), ZmDA2 (h) and ZmHK4 (i) in transgenic dDRESH8 plants and the nontransgenic B104 plants. Values indicate means ± s.d. and the P values were calculated by two-sided Student’s t-test from different technical repeats in f (N = 6 for dDRESH8, 5 for B104), g (N = 3 for dDRESH8, 5 for B104), h (N = 8 for dDRESH8, 6 for B104), i (N = 5 for both dDRESH8 and B104). The experiments were repeated three times and similar results were obtained.
Extended Data Fig. 6 Comparison of the yield-related traits between wild-type B104 and dDRESH8 plants.
a, c. Comparison of the ears between B104 (a) and dDRESH8 (c) plants. b, c. Comparison of the ear diameters between B104 (b) and dDRESH8 (d) plants. e. Diagram of the maize kernel length, width and thickness. f-h. Comparison of the kernel length (f), width (g) and thickness (h) between B104 and dDRESH8 plants. Bars = 1 cm.
Extended Data Fig. 7 IR-mediated regulation of sRNA production and the involvement of IR-derived sRNAs in other stress responses.
a–c. The chromosome-wide view of IR2976 (a), IR3718 (b) and IR5413 (c) loci. d–f. Mapping of 21-, 22-, and 24-nt sRNA reads to IR2976 (d), IR3718 (e) and IR5413 (f). The sizes of these IRs are indicated below the IRs. The sizes of two InDels near IR3718 and IR5413 are indicated. g–i. Effects of IR2976 variation, the InDels nearby IR3718 and IR5413 on the expression of s / sc-traits, as indicated by the representative examples of sRNA177329 (g), cluster175431 (h) and sRNA001735 (i), respectively. −, absence; +, presence. P values were calculated by two-sided Student’s t-test (N = 138 for IR2976-, 167 for IR2976 + in g; N = 250 for InDel-, 35 for InDel+ in h; N = 196 for InDel−, 116 for InDel+ in i). j, IR sRNAs that were up- or downregulated in maize after exposure to cold or salt stress.
Statistic results suggested a general tradeoff between kernel length (KL) and stress tolerance (SR) shown in almost all SNPs adjacent to IRs, including SNPs located inside the IRs (100%, or all 29) or in the regions 1 kb (100%, or all 143), 2 kb (100%, or all 247), 5 kb (99.8%, or 581 out of 582), 10 kb (99.6%, or 1123 out of 1127), or 20 kb (99.6%, or 2549 out of 2559) on either side of each IR that were significantly associated with both kernel lengths and survival rates. The x-axis indicates the difference of kernel length between the two genotypes (alleles) of each SNP, and the y-axis indicates the difference of survival rate between the two genotypes of each SNP. In most cases, the two genotypes of each SNP had opposite effects on kernel length and survival rate.
Extended Data Fig. 9 Enrichment of sRNA target genes in gene groups regulating different agronomic traits.
Of the target genes, the genes regulating ear row number (ERN), kernel length (KL) and kernel number per row (KNR), but not the genes regulating hundred-kernel weight (HKW), kernel thickness (KT), kernel width (KW) or plant height (PH) showed significant enrichment. n.s. not significant. P values were calculated by chi-squared test.
Extended Data Fig. 10 Enrichment of genes associated with or regulating yield-related traits in GY genes.
a. Enrichment of genes associated with yield-related traits in GY genes. Permutation analyses were conducted 100 times for the randomly selected genes, and the distribution of the overlapping genes between the randomly selected genes (Random) or genes significantly associated with yield-related traits (from GWAS)25,27,28,29 (Observed) and the GY genes (shown in Fig. 4i) were compared. The comparison showed that the genes with SNPs significantly associated with yield-related traits were significantly enriched in the GY genes. The distances of the SNPs to the genes was shown on the top of each chart: Inside, SNPs located in the genes; 1-, 2-, 5-, 10- or 20 kb flanking, SNPs located in the regions 1, 2, 5, 10 or 20 kb on either side of the gene body. b. Enrichment of genes regulating yield-related traits in GY genes. Permutation analyses were conducted 100 times for the randomly selected genes, and the distribution of the overlapping genes between the randomly selected genes (Random) or genes regulating maize grain-yield-related traits published in the last 25 years (Observed) and the GY genes (shown in Fig. 4i) were compared. The comparison showed that the genes regulating grain-yield-related traits were significantly enriched in the GY genes. P values in a and b were calculated by chi-squared test.
Supplementary Table 1. Summary of sRNA sequencing of 338 maize inbred accessions. Supplementary Table 2. Summary of RNA sequencing of 197 maize inbred accessions. Supplementary Table 3. Expression levels of all unique sRNAs under WW and DS conditions. Supplementary Table 4. Expression levels of all sRNA clusters under WW and DS conditions. Supplementary Table 5. Expression regression of sRNAs and their target genes under WW conditions. Supplementary Table 6. Expression regression of sRNAs and their target genes under DS conditions. Supplementary Table 7. Expression regression of sRNA clusters and their nearby genes under WW conditions. Supplementary Table 8. Expression regression of sRNA clusters and their nearby genes under DS conditions. Supplementary Table 9. seQTLs associated with expression of drought-responsive sRNAs under WW conditions. Supplementary Table 10. seQTLs associated with expression of drought-responsive sRNAs under DS conditions. Supplementary Table 11. seQTLs associated with expression of drought-responsive sRNA clusters under WW conditions. Supplementary Table 12. seQTLs associated with expression of drought-responsive sRNA clusters under DS conditions. Supplementary Table 13. meQTLs associated with expression of mRNAs under WW conditions. Supplementary Table 14. meQTLs associated with expression of mRNAs under DS conditions. Supplementary Table 15. Co-localizing eQTLs that were associated with expression of both drought-responsive s-/sc-traits and genes (sm-eQTLs). Supplementary Table 16. DRESH8 genotypes of 338 maize inbred accessions. Supplementary Table 17. Expression of DRESH8-derived sRNA targets. Supplementary Table 18. DRESH8 genotypes of 121 teosinte accessions. Supplementary Table 19. DRESH8 genotypes of 751 landraces and their associated monthly precipitations. Supplementary Table 20. Genome-wide IRs in the B73 reference genome. Supplementary Table 21. Transposons that formed IR structures. Supplementary Table 22. Primers used in this study.
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Sun, X., Xiang, Y., Dou, N. et al. The role of transposon inverted repeats in balancing drought tolerance and yield-related traits in maize. Nat Biotechnol 41, 120–127 (2023). https://doi.org/10.1038/s41587-022-01470-4