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The Ct (the number of cycles to reach threshold) results from qPCR were then analyzed by calculating the fold-change using the ∆∆Ct method: ∆Ct (treated1) = 3.245, ∆Ct (treated2) = 5.54, ∆Ct (treated3) = 1.21, ∆Ct (control1) = -0.21, ∆Ct (control2) = -5.715, ∆Ct (control3) = -1.75. The control ∆Ct’s had a mean of -2.558333 and standard deviation of 2.840125 while the treatment ∆Ct’s had a mean of 3.331667 and standard deviation of 2.166301. A box plot of these ∆Ct was created on R (Figure 8).

The ∆Ct values allowed us to calculate the ∆∆Ct to be 5.89, so the fold-change between the control and treated samples was 0.016863, indicating a sharp decrease in expression of the Bradi1g72430 gene in treated samples. A T-test performed on R revealed the p-value to be 0.04993, meaning there is a significant statistical difference in the fold-change between control and treated samples. The expression of Bradi1g72430 in roots decreases in response to IAA treatment and subsequent increased growth.

Bradi1g72430 was hypothesized to play a role in cell wall biogenesis, so it was expected to be more highly expressed with increase tissue growth. We hypothesized that if Brachypodium distachyon plants were treated with indole-3-acetic acid (IAA), which stimulates root growth, we would see increased gene expression due to the increased root growth and subsequent formation of cell walls.

    We set up two 30 mL agar plates, one with 1.5 µL of ethanol (control) and one with 1.5 µL of 0.0001M IAA for a final concentration of 5 nM IAA (treated). Once set, four B. distachyon seeds were planted on each plate, and the plates were placed in the incubator for seven days, covered by a blue-minus filter, before the entire root of each plant was removed for RNA extraction. The four control plants were labeled as samples 1-4 while the four treated plants were labeled as samples 5-8. The extracted RNA was quantified with NanoDrop (Table 3) and visualized via agarose gel electrophoresis (Figure 7).

On R, the leaf, root, and stem gene expression data were plotted (Figure 5), and a T-test was performed for both the leaf, root, and stem relative expressions and stress response gene expression data. The T-test resulted in p-values of 0.00408 for root versus stem, 0.0008745 for root versus leaf, and 8.741x10-6 for leaf versus stem. All of these p-values were lower than the Bonferroni correction p-value cut-off of 0.01667, meaning there was a significant statistical difference in all three comparisons of gene expression between the tissues (Figure 5). The T-test on the stress response expression resulted in p-values of 0.1951 for CS versus HS, 0.03036 for CS versus DS, 0.8509 for CS versus SS, 0.0069 for HS versus DS, 0.16 for HS versus SS, and 0.04488 for DS versus SS. Only the p-value of HS versus DS was lower than the Bonferroni correction p-value cut-off of 0.008333, meaning there was only a significant statistical difference between the gene expression levels under heat stress and drought stress (Figure 4).

The activity of regulatory enzymes is controlled by internal signals that reflect the conditions in a particular cell. Internal signals include substrate availability, cofactor availability, activators/inhibitors, and feedback inhibition. On the other hand, the activity of regulatory enzymes are also controlled by external signals that provide information about conditions in the organism. External signals include activators/inhibitors and hormones, which usually mediate the phosphorylation or dephosphorylation of pathway components. Hormones are chemical signals produced in response to specific conditions and distributed throughout an organism via the bloodstream. They are the primary method of regulation used to control metabolic reactions. Hormones interact with target cell receptors in order to alter the behavior of the cell. Only cells with receptors for a hormone response, and receptor proteins are specific, interacting with one or a few hormones. The same hormone can cause different responses in different cells, even if it interacts with the same receptor. Hormones binding to cell membrane receptor proteins function via a amplification mechanism.

Muscle cells use a variety of fuel sources: fatty acids at rest and glucose during exertion, at least initially. Muscle cells also vary widely in their energy demands and use glycogen stores only for themselves, not sharing with other cells. Glycogen breakdown overly exceeds glycogen synthesis by 300-fold, and they do not respond to glucagon. Muscle cells also do not perform gluconeogenesis, fatty acid synthesis, or ketogenesis. On the other hand, liver cells are very important for fatty acid homeostasis, performing triacylglycerol formation and fatty acid synthesis. They are also the primary site of ketone body synthesis and directly regulate blood glucose levels in response to hormones. Liver cells are also important storage sites for glycogen with equal rates of synthesis and breakdown of glycogen. Like muscle cells, liver cells use a variety of fuel sources that change depending on conditions.

Metabolic pathways must be regulated to release energy when required, to store extra energy, and to synthesize molecules when needed. Specific reactions or enzymes in a mechanism serve as key regulatory steps, and usually they are those with largely negative changes in free energy. These are irreversible reactions that cannot be reversed through the manipulation of cellular conditions. If a chemical reaction in one direction is irreversible, then the opposing pathway must use a different chemical reaction and different regulatory enzyme. Different regulatory enzymes for opposing pathways allows for independent regulation based on cellular conditions, also known as fine-tuning. This is essential for regulating pathways. Regulatory enzymes are often as the beginning or end of a pathway, and the step that commits the pathway to a certain response is also highly regulated.

Thermogenin is a protein found in the inner mitochondrial membrane in the adipose tissue of some animals. It allows protons to flow from the intermembrane space to the mitochondrial matrix. If thermogenin is present in large quantities, it will decrease the rate of ATP synthesis but increase the rate of oxygen consumption, which is a measure of electron transport chain activity. This is because the proton gradient cannot form if protons are allowed to flow back to the matrix and cannot be built up in the intermembrane space, and this proton gradient is what powers the synthesis of ATP. On the contrary, oxygen consumption will increase since the electron transport chain will continue to pump protons into the intermembrane space in an attempt to form a gradient. Thermogenin allows for heat production without the production of ATP, so it is present in human infants and hibernating bears, animals that require heat but not much ATP.

In the phylogenetic tree created on the MAFFT program, there were 37 redundant entries removed, and about four natural groupings could be identified. There were no clear outliers on the tree (Figure 6). The Bradi1g72430 gene was found to be most closely related to a Oryza sativa gene, OSNPB_030183800. Within its natural grouping, the gene was most closely related to O. sativa and Brachypodium distachyon, then Arabidopsis thaliana genes (Figure 6).

Cross-sectioning of wildtype and mutant plant stems revealed mutant stems to have a smaller diameter than wild type, and based on the dyed sections, mutant plants seem to have thicker polysaccharide and lignin walls with smaller openings in the center of the stem.

The four mutant plant sequences obtained through Sanger sequencing were labeled S31-34. S31’s first quality base was the 50th base, while the last quality base was the 615th base. S32 had no quality bases. S33 and S34’s first quality bases were also the 50th base, while the last quality bases were the 600th and 620th bases respectively. The NCBI pairwise BLAST between the Sanger and reference genome sequences revealed one misalignment at the beginning of the sequence, but in between the beginning and end, the Sanger and reference sequences were perfectly aligned except at the 133rd position. This was confirmed to be the position of the NaN1898_Bd1_70884553_Het mutation, and it was located on the 133rd position of the chromatogram (Figure 5). On the chromatogram, there were two peaks, G and A, at the 133rd position, although the algorithm read it as A. This indicated a heterozygous mutation with a wild type G base and mutant A base, which is consistent with the NaN1898_Bd1_70884553_Het mutation. This mutation was at position 715 in the whole genome sequence.

The NaN1898_Bd1_70884553_Het mutation was located on the Bradi72430 gene as a point mutation converting a guanine to adenine. Through Primer 3, primers for PCR of the mutation site were identified: GGGGTTCTTGTCTGCGCTCTGGT (left) and GCACACGAGAGGAAAACGACCGC (right). The primers were for a product of 928 bases, and the mutation was 134 bases away from the left primer (Figure 1).

DNA was successfully extracted from six mutant plants and two wildtype plants. PCR of this DNA was performed with an annealing temperature of 66°C. The resulting PCR products were visualized via gel electrophoresis. Bands on the gel of PCR products diluted double distilled H2O (Figure 2) were much more prominent, indicating higher DNA content than PCR products diluted with T10E1 (Figure 3). Bands were present in lanes 2-5 and 7-8 of both gels about 3.5 cm from the wells. There were no bands in lanes 6 and 9 of both gels. Bands in lanes 2-5 (mutants 1-4) of gel 2 were the brightest and least smeared overall, so these were used for extraction and purification. Once extracted and purified, the DNA of mutants 1 proved to have the highest DNA content (Table 1). The A260/A280 ratios revealed the DNA of mutants 1 and 4 to be relatively pure, with ratios close to 2.0. DNA from mutants 2 and 3 were more contaminated. These four samples of extracted and purified DNA were sent for Sanger sequencing.