High expression of the MADS-box geneVRT2increases the number of rudimentary basal spikelets in wheat

Abstract

Spikelets are the fundamental building blocks of Poaceae inflorescences, and their development and branching patterns determine the various inflorescence architectures and grain yield of grasses. In wheat (Triticum aestivum), the central spikelets produce the most and largest grains, while spikelet size gradually decreases acro- and basipetally, giving rise to the characteristic lanceolate shape of wheat spikes. The acropetal gradient corresponds with the developmental age of spikelets; however, the basal spikelets are developed first, and the cause of their small size and rudimentary development is unclear. Here, we adapted G&T-seq, a low-input transcriptomics approach, to characterize gene expression profiles within spatial sections of individual spikes before and after the establishment of the lanceolate shape. We observed larger differences in gene expression profiles between the apical, central, and basal sections of a single spike than between any section belonging to consecutive developmental timepoints. We found that SVP (SHORT VEGETATIVE PHASE) MADS-box transcription factors, including VRT-A2 (VEGETATIVE TO REPRODUCTIVE TRANSITION 2), are expressed highest in the basal section of the wheat spike and display the opposite expression gradient to flowering E-class SEP1 (SEPALLATA1) genes. Based on multi-year field trials and transgenic lines, we show that higher expression of VRT-A2 in the basal sections of the spike is associated with increased numbers of rudimentary basal spikelets. Our results, supported by computational modelling, suggest that the delayed transition of basal spikelets from vegetative to floral developmental programs results in the lanceolate shape of wheat spikes. This study highlights the value of spatially resolved transcriptomics to gain insights into developmental genetics pathways of grass inflorescences.

Transcriptional signatures of wheat inflorescence development

“…Consensus and gene regulatory networks provide the means to analyze temporal transcriptomic datasets as a complementary approach to characterize functional pathways underlying wheat inflorescence development. The incorporation of higher resolution datasets at both the spatial and temporal levels within meristem tissues will build on these findings 29 . Although reverse genetics will be required to validate the hypotheses generated from in silico network analyses, the integration of functional datasets from wheat and related species facilitates the identification of critical regulators 61 .…”

“…Genes upregulated across these three timepoints were most significantly enriched for "floral organ identity" (Supplementary data 6). There are fewer developmental changes between W3.25 and W3.5, relative to changes between W1 and W3.0, which may be due in part to basal and apical spikelets being at similar developmental stages between the latter timepoints 29 .…”

Transcriptional signatures of wheat inflorescence development

“…Genes upregulated across these 3 timepoints were most significantly enriched for “floral organ identity” (GO:0048437) and included PISTILLA-like MADS-box genes ( TraesCS1A02G264300 , TraesCS1B02G275000 ) (Supplementary Data 6 ). There are fewer developmental changes between W3.25 and W3.5, relative to changes between W1 and W3.0, which may be due in part to basal and apical spikelets being at similar developmental stages between the latter timepoints 29 .…”

“…Consensus and gene regulatory networks provide the means to analyze temporal transcriptomic datasets as a complementary approach to characterize functional pathways underlying wheat inflorescence development. The incorporation of higher resolution datasets at both the spatial and temporal levels within meristem tissues will build on these findings 29 . Although reverse genetics will be required to validate the hypotheses generated from in silico network analyses, the integration of functional datasets from wheat and related species facilitates the identification of critical regulators 65 .…”

Unifying comprehensive genomics and transcriptomics in individual cells to illuminate oncogenic and drug resistance mechanisms

“…Indeed, multiple displacement amplification (MDA) 6 drives the genomic amplification of G&T-seq and DR-Seq has genomic amplification uniformity comparable to that of MALBAC 7 , both of which are outperformed by PTA 1 in terms of genomic coverage, allelic balance and SNV calling metrics. Definition of clonal evolution at the SNV/CNV level in a primary patient sample has been accomplished utilizing G&T-seq, yet was limited to a candidate gene survey of exome-level data whereby clusters where defined by 59 oncogenes 8 and another studying employing G&T-seq limited their analysis to the RNA workflow of the method to take advantage of the low input requirement, without assessment of genomic level data 9 . Thus, we address here an unmet need to add genome-wide, high sensitivity and high precision SNV calling capability to a joint DNA/RNA single-cell methodology.…”