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    Assimilation, Cryptic variability (Lauter 2002), Dolores’s previous paper (Piperno 2014).


    Growth Chamber Experiment

    RNA sequencing

    For the RNAseq, the total RNA was isolated from the leaf tissue of the plants with RNeasy mini Kit (Qiagen) following the manufacturer’s protocol. Under 400ppm and 265ppm of CO2, respectively 12 and 11 biological replicates were included. RNA quality and concentration was verified using a Bioanalyzer (Agilent RNA Nano). Sequencing library preparation was performed as previously describe (Zhong et al., 2011). Briefly, the mRNA was extracted with Dynabeads oligo(dt)25 (Ambion). After chemical shearing with divalent cations, the First strand synthesis was performed with Random Hexamers Primers (Invitrogen) and SuperScript III (Invitrogen). The second strand cDNA was synthetized with DNApol1 (Thermo Scientific) and RNaseH (New England Biolabs). The cDNA fragments were prepared for Illumina sequencing. First, the cDNA fragments were repaired with the End-Repair enzyme mix (New England Biolab). An deoxyadenosine triphosphate was added at each 3’ ends with the Klenow fragment (New England Biolab). Illumina Trueseq adapters (Affymetrix) were added with the Quick ligase kit (New England Biolab). Between each enzymatic step the cDNA was washed with AMpure beads (Beckman Coulter). Finally the The 23 samples were multiplexed and sequenced in one lane of Hiseq 2500 (UCDavis genome center sequencing facility) for 50 bases single-end reads with an insert size of approximately 300 bases. After demultiplexing, 3.8-8.8 million reads were generated for each sample (Table \ref{tab:readmoms}).


    Sample Reads Maize-like mom
    265_4B.1.txt 3814875
    400_1A.1.txt 4070011 X
    265_3C.1.txt 4139946 X
    265_3A.1.txt 4399187
    265_4C.1.txt 4746629 X
    400_3C.1.txt 5029499 X
    265_1B.1.txt 5031069
    265_2B.1.txt 5063618 X
    400_3B.1.txt 5433424
    265_3B.1.txt 5564170
    400_2B.1.txt 5812095 X
    400_4A.1.txt 5857687
    400_1A_2.1.txt 5989185
    265_2B_2.1.txt 6467938
    265_2A.1.txt 6732943
    265_4A.1.txt 7427625
    400_2B_2.1.txt 7455893
    400_1B.1.txt 7570271
    265_1A_2.1.txt 7648528
    400_2A.1.txt 7882249
    400_3A.1.txt 8630267
    265_1A.1.txt 8643790 X
    400_4C.1.tx 8836575 X

    Data Analysis

    Low qaulity (base quality <33) bases were trimmed using fastx toolkit v. 0.0.13 ((, and adapters were subsequently removed using scythe (( Trimmed reads were mapped to the AGPv3.21 of the maize genome using STAR version 2.3.0 (Dobin 2013) with default parameters. Read counting was performed with HTseq (Anders 2014) using a modified version of the ENSEMBL Zea_mays.AGPv3.21.gff3 annotation file . Only reads with mapping quality 30 or higher were included in subsequent analyses.

    Expression analysis was performed using the EdgeR package (Robinson2013).


    General Expression Results



    Genes for which assimilation is important should show:

    • Differential expression (DE) between 400 and 265 (this study)

    • DE between maize and teosinte (Swanson-Wagner2012, Hufford2012) (also cite Lemmon)

    • Evidence of selection during domestication (Hufford2012)

    • Canalization (lack of plastic response) in maize (Dolores’s test this summer)

    Cryptic variation

    Genes for which cryptic variation is important should show:

    • Decreased coefficient of variation (CoV) among teosinte populations/lines (Swanson-Wagner2012, Hufford2012)

    • Increased CoV of expression in new conditions (this study)

    • Lower CoV in maize than in teosinte in new conditions (Dolores’s test this summer)

    We want to know:

    • Which genes are in the above category? Any interesting biology there?

    • GO term analysis

    • What proportion of all genes showing DE between maize and teosinte and selection during domestication? Can this tell us something about relative importance?

    We whould also compare:

    • To lists of stress response genes.


    Points we will have to discuss:

    • Adaptation: teosinte has adapted in the intervening 10K years. What we see may not be reflective.

    • Stress: Does this say anything about climate, or only stress-response genes?

    • Timing: How does plasticity work if climate change is slow relative to population genetic processes?