Leaf Senescence

The Macrophenomics pipeline consists of hardware and software components. A specialized robotic system (Macrobot) implements the image acquisition part of the Macrophenomics pipeline. The Macrobot autonomously acquires images of detached leaf segments mounted on standard size microtiter plates (MTPs) (Figure 1).
Typically, the wheat and barley plants are grown in 24-well trays in a greenhouse. The samples are taken at the 2-leaf stage from the middle part of the second leaf. The leaf fragments are mounted on standard 4-well MTPs with 1% water agar (Phyto agar, Duchefa, Haarlem, the Netherlands) supplemented with 20 mg L⁻¹ benzimidazole as a leaf senescence inhibitor. For achieving regular inoculation of all leaves, the plates without lids are placed in a rotating table inside an inoculation tower and are inoculated by blowing in conidiospores from sporulating material. Inoculated plates are incubated in environmentally controlled plant growth chambers (20°C, 60% RH constant; 16 h light, 15 μE m⁻² s⁻¹) for 6 days until the disease symptoms are visible. The infected plates are loaded into the Macrobot system for automated imaging. The acquired images are transferred to the image analysis server for quantification of the disease symptoms.

A total of 25 MARS populations were initiated in 2008 and 2009. Quality control (QC) genotyping [35 (link)] of F₁s and their parents with 100 SNP markers identified all F₁s with true-to-type parental alleles for ≥ 95% of the polymorphic SNPs for advancement either to F_2:3 or BC₁F₃, while those with >5% non-parental alleles were discarded. Seven of the 25 MARS populations either failed to pass the quality control genotyping criteria or had broad sense heritability < 0.10 and/or < 0.20 in the combined analyses of all the stressed and optimum environments, respectively, and were excluded from analyses. Phenotypic evaluations were performed on testcrosses derived by crossing either the F_2:3 or BC₁F₃ families with one single cross tester from opposite heterotic group. The parents crossed with the same tester, and selected commercial checks were included in each of the trials. Each population was planted using an alpha lattice design, with 2 replications per location, and evaluated in 2-4 managed water stressed and 3-4 well watered locations (Table 3). Each entry was planted in a 5 m long row with spacing of 0.75 m between rows and 0.25 m between plants. In maize, it is well known that grain yield is often reduced 2-3 times more when water deficits coincide with flowering, compared with other growth stages [36 (link)]. Therefore, water stress evaluation was conducted during the dry (rain free) season in Kenya, Zimbabwe and Zambia by withdrawing irrigation two weeks before flowering. Irrigation was resumed at the end of the flowering stage, corresponding to the end of silk emergence, and maintained until harvest to allow grain filling. Evaluation under optimum conditions in the 3 countries was carried out during the long rainy season.
Each population was evaluated for 12-17 different traits, including grain yield, anthesis date, number of ears per plant, and leaf senescence, which are commonly associated with drought tolerance. Only grain yield and ASI were selected as the main target traits in the present study. ASI was computed as the difference between days to silking and anthesis. Each trial was harvested when all leaves had senesced. Ears were dried and shelled, grain was weighed, and grain moisture determined by a capacitance meter. SAS program v9.2 was used for phenotypic data analyses, including calculating Best Linear Unbiased Predictor (BLUP), variance components and heritability under stressed and optimum environments.

Approximately 150 panicles showing grain husk color variation in C14-8 landraces were collected from diverse fields in Andaman districts at the time of maturity in the month of January 2012. During the subsequent years (2012–2014), all these panicle-to-row progenies were grown and evaluated for both qualitative and quantitative traits. Based on grain yield and grain husk color, 20 lines were chosen from these panicle-to-row progenies so that five representative lines from each of the four grain husk color types were selected for further investigation. The 20 lines were classified into four groups, such as Group I: brown husk with white apiculus, Group II: yellow husk with black apiculus, Group III: yellow husk with yellowish apiculus, and Group IV: golden furrows on brown husk with black apiculus (Table 1; Figures 2B,C). These four groups, henceforth, will be referred to as basic classification groups. During 2012 and 2014, these 20 selections of C14-8 were characterized and evaluated in a randomized block design (RBD) with three replications at Bloomsdale Farm, ICAR-CIARI, and Port Blair for morphological markers, such as basal leaf sheath color, leaf sheath anthocyanin, stem length, anthocyanin pigment on nodes, apiculus color, grain husk color, panicle secondary branching, leaf senescence, and decorticated grain color, and the data on the recorded traits were averaged across 2 years, which is more representative. The lines were also evaluated for quantitative traits such as plant height (PH, cm), days to 50% flowering (DF), ear bearing tillers per plant (EBT), panicle length (PL, cm), 1,000-grain weight (TGW, gm), grain length (GL, mm), grain width (GW, mm), grain yield (GY, t/ha), brown rice recovery (BRR, %), milled rice recovery (MRR, %), and head rice recovery (HRR, %), as mentioned in Table 2. The chemical test to confirm whether C14-8 belongs to indica or japonica sub-specific group revealed C14-8 to be japonica type due to yellow grain husk color retention as per the method of Sanni et al. (35 (link)).
The classification for stem length was carried out as per National Guidelines for the conduct of tests for distinctness, uniformity, and stability (DUS) published by ICAR-Indian Institute of Rice Research (IIRR), Hyderabad, India. Therefore, the following scale was followed for stem length: very short (<91 cm), short (91–110 cm), medium (111–130 cm), long (131–150 cm), and very long (>150 cm). Next, when panicles have relatively more secondary branches, it is classified as “strong.” When panicles have a greater number of tertiary branches, it is classified as “clustered.” Furthermore, leaf senescence is classified as “light” (observed only in two selections) and medium (18 selections) as per leaf color appearance at maturity according to Shobha et al. (41 ).

The expression values of the 34 genes during the leaf senescence process were extracted from A. thaliana [49 (link)] and Populus tomentosa [50 (link)]. The A. thaliana data included 14 time points for the leaf development process, tracked from the growth-to-maturation stage (G-to-M; 4–18 d) to the maturation-to-senescence stage (M-to-S; 16–30 d). The P. tomentosa data contains 16 time points for autumn leaf senescence process, tracked over the mature stage (M; L1-10), early senescence stage (ES; L11-L13) and later senescence stage (LS; L14-L16). Furthermore, the raw RNA-Seq data of A. thaliana and P. tomentosa was downloaded from NCBI’s Sequence Read Archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra/ (accessed on 5 September 2022)) [51 (link)], with the SRA study accession number PRJNA186843 for A. thaliana [49 (link)] and PRJNA561520 for P. tomentosa [50 (link)], and the detailed information was displayed in Supplemental Dataset 1. The ‘SRR’ format data downloaded using SRAToolkit package tool was transformed to ‘fastq’ format via ‘fastq-dump’ command. For data quality control and reads cleaning, the adapter in the reads were removed, and then the low-quality reads (reads with Q_phred <= 20 bases account for more than 50% of the entire read) and reads with a ratio of N (N means that the base information cannot be determined) greater than 10% were also removed. Then, the clean reads were aligned to the Arabidopsis genome [52 (link)] and Populus v3 genome [43 (link)] using HISAT2 algorithm, respectively [53 (link)]. Transcripts Per Million (TPM) [54 (link)] was used to measure gene expression levels and log-transformed values for visualization. The heatmaps were generated by using the ‘pheatmap’ package [55 ] in R v4.1.2 program. The red and blue color represent the high and low expression levels, respectively.

Data on Striga emergence count, ear rot, stalk and root lodging were transformed as [log (counts+1)] to reduce the heterogeneity of variances. The ANOVA for the 150 hybrids generated using the NCD II pooled over sets for each research condition [15 (link)] and across the stress conditions was carried out using the version 9.4 of SAS [33 ]. The genotypic component of the source of variation was partitioned into the variation due to males (sets), females (sets), and female × male (sets) interaction. The F-tests for male (sets), female (sets) and male × female (sets) mean squares were performed using male (sets) × environment, female (sets) × environment and male × female (sets) × environment mean squares, respectively. The mean squares attributable to environment × female × male (sets) were tested using the pooled error mean squares.
The following general linear model was used for the NCD II mating design:
$${\mathbf{X}}_{\mathbf{i}\mathbf{j}\mathbf{k}\mathbf{l}}=\mathbf{\mu }+{\mathbf{m}}_{\mathbf{i}}+{\mathbf{f}}_{\mathbf{i}}+{\left(\mathbf{m}\mathbf{f}\right)}_{\mathbf{i}\mathbf{j}}+{\mathbf{p}}_{\mathbf{i}\mathbf{j}\mathbf{k}}+{\mathbf{I}}_{\mathbf{l}}+{\mathbf{\epsilon }}_{\mathbf{i}\mathbf{j}\mathbf{k}\mathbf{l}}$$
where X_ijkl = the observed value of the progeny of the i^th male crossed with j^th female in the k^th replication; μ = the overall population mean; m_i = effect of the i^th female; f_j = the effect of the j^th male mated to the i^th female; (mf)_ij = the interaction effect between the i^thfemale and the j^th male; p_ijk = the effect of the k^th progeny from the cross between i^th female and j^th male; r_l = the effect of the l^th replication; ε_ijkl = the experimental error. The general combining ability (GCA) effects for male and female within sets (GCA_m and GCA_f) and specific combining ability (SCA) for each trait were estimated according to Kearsey and Pooni [34 ] as shown below:
$$\mathrm{G}\mathrm{C}{\mathrm{A}}_{\mathrm{m}}={\mathrm{X}}_{\mathrm{m}}-\mathrm{\mu }$$
$$\mathrm{G}\mathrm{C}{\mathrm{A}}_{\mathrm{f}}={\mathrm{X}}_{\mathrm{f}}-\mathrm{\mu }$$
where, GCA_m and GCA_f = General combining ability effects of male and female parents respectively; X_m and X_f = Average performance of a line when used as a male and female in crosses, respectively and μ = Overall mean of crosses in the set.
Standard errors (SE) for testing significance of GCA_m and GCA_f estimates, for traits of genotype, were computed from the mean squares of GCA_m × environment and GCA_f × environment, respectively as follows:
$$\mathrm{S}\mathrm{E}\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{G}\mathrm{C}{\mathrm{A}}_{\mathrm{m}}=\surd \mathrm{M}\mathrm{S}\mathrm{m}\times \mathrm{e}/\left(\mathrm{f}\times \mathrm{e}\times \mathrm{r}\right)$$
$$\mathrm{S}\mathrm{E}\mathrm{f}\mathrm{o}\mathrm{r}\mathrm{G}\mathrm{C}{\mathrm{A}}_{\mathrm{f}}=\surd \mathrm{M}\mathrm{S}\mathrm{f}\times \mathrm{e}/\left(\mathrm{m}\times \mathrm{e}\times \mathrm{r}\right)$$
where, MSm × e and MSf × e were the mean squares of the interaction between male and environment as well as female × environment, respectively; f, m, r, and e were the number of females, males, replicates, and environments, respectively.
A multiple trait base index (MI) that integrated grain yield with the number of emerged Striga plants, Striga damage rating, plant and ear aspects, delayed leaf senescence, anthesis-silking interval and number of ears per plant was used to select the best performing hybrids across optimal, Striga and low-N conditions [5 (link)]. The means, adjusted for block effects of each genotype for each measured variable was standardized to minimize the effects of the different scales. A positive multiple trait base index value therefore indicated tolerance/resistance of the genotype to both Striga and low-N, while negative values indicated susceptibility to the stresses. The multiple trait base index was computed as follows:
MI = (2 × YLD) + EPP–EASP–PASP—STGR–RAT1 –RAT2 –(0.5 × C01)–(0.5 × C02)
On the other hand, the base indices for Striga and Low-N were computed as STRBI = 2.0 YLD + 1.0 EPP–(RAT1 + RAT2)– 0.5 (C01 + C02) and LNBI = 2.0 YLD + EPP–STGR–ASI—PASP–EASP, respectively to select superior hybrids under the respective stress conditions.
Where: MI = Multiple trait base index
STRBI = Base index for StrigaLNBI = Base index for Low-N
YLD = grain yield across research conditions
EPP = number of ears per plant across research conditions
EASP = Ear aspect across research conditions
PASP = Plant aspect across low-N and optimal conditions
STGR = Stay green characteristic across low-N conditions
RAT1 and RAT2 = Striga damage rating at 8 and 10 WAP across Striga infested conditions
C01 and C02 = Number of emerged Striga plants at 8 and 10 WAP across Striga -infested conditions.