An incoherent feedforward loop facilitates adaptive tuning of gene expression

  1. Jungeui Hong
  2. Nathan Brandt
  3. Farah Abdul-Rahman
  4. Ally Yang
  5. Tim Hughes
  6. David Gresham  Is a corresponding author
  1. New York University, United States
  2. Memorial Sloan Kettering Cancer Center, United States
  3. University of Toronto, Canada
4 figures and 3 additional files

Figures

Figure 1 with 1 supplement
Repeated LTEE in ammonium-limited chemostats selects for adaptive mutations in GAT1.

(a) LTEE design. Three chemostat vessels w ere founded with a clonal ancestral population and maintained under constant dilution with defined media containing 0.4 mM (NH 4) 2 SO 4 , (0.8mM …

https://doi.org/10.7554/eLife.32323.002
Figure 1—figure supplement 1
Model of NCR regulon.

The NCR regulon is controlled by four GATA-binding transcription factors: two activators (GLN3 and GAT1) and two repressors (DAL80 and GZF3). Under nitrogen rich conditions, GLN3, and any GAT1, are …

https://doi.org/10.7554/eLife.32323.003
Figure 2 with 1 supplement
Adaptive GAT1 mutations underlie increased fitness and confer antagonistic pleiotropy.

(a) 3D structure model of GAT1 DNA binding domain. The 3D structure is based on available GATA factor DNA binding domain structures in the modbase database (https://modbase.compbio.ucsf.edu/). …

https://doi.org/10.7554/eLife.32323.004
Figure 2—figure supplement 1
GAT1 variant fitness effects are due to its effect on MEP2.

(a) Fitness effect of MEP2 KO in the background of the ancestral strain (FY4), gat1-1 and gat1-3 in ammonium-limited chemostats. All conditions and methods are identical to those used in Figure 2b. …

https://doi.org/10.7554/eLife.32323.005
Figure 3 with 2 supplements
Adaptive GAT1 variants exhibit differential effects on target gene expression.

(a) Gene expression profile of NCR genes in different GAT1 variant lineages. ‘Blue’ gene names indicate permease-encoding genes for different nitrogen sources. Clustered gene expression values …

https://doi.org/10.7554/eLife.32323.006
Figure 3—figure supplement 1
Construction and validation of transcriptional reporters.

(a) Construction of transcriptional reporters for GAP1, MEP2, GZF3 and DAL80 promoters. The promoters of five different NCR target genes were amplified via PCR to include partial overlap with GFP …

https://doi.org/10.7554/eLife.32323.007
Figure 3—figure supplement 2
GFP reporter assay for MEP2 and DAL80 in the background of GLN3 KO.

GLN3 is an a transcriptional activator of NCR genes. A GLN3 KO in the background of either the gat1-1 or gat1-3 alleles shows no expression from the MEP2 and DAL80 promoters.

https://doi.org/10.7554/eLife.32323.008
Figure 4 with 2 supplements
Adaptive GAT1 variants alter DNA binding affinities in a promoter-specific manner.

(a) Design of protein-DNA binding assays. We purified a 151-amino acid long protein fragments of GAT1 containing the DNA binding domain (DBD) (positions 310–349) for the ancestral GAT1 and two …

https://doi.org/10.7554/eLife.32323.009
Figure 4—figure supplement 1
Increasing GAT1 expression results in increased MEP2 expression.

We studied the effect of increased GAT1 expression across the same parameter space investigated in Figure 4f. (a) We find that when GAT1 expression is increased by a factor of two, the effect of …

https://doi.org/10.7554/eLife.32323.010
Figure 4—figure supplement 2
Purification of GST-tagged DNA binding domains of GAT1 variants.

Representative SDS-PAGE gel showing purified products used for EMSA experiments. Purified DNA binding domains for GAT1-wt (DGP269), gat1-1 (DGP270), and gat1-3 (DGP271) do not run as a single band, …

https://doi.org/10.7554/eLife.32323.011

Additional files

Supplementary file 1

(A) SNPs identified in each LTEE. Variants identified in each LTEE at four different time points throughout the evolution experiments and present at an allele frequency of at least 1% are listed. (B) Global gene expression analysis. Mean expression values for each gene for three GAT1 variants relative to the ancestral genotype as determined by RNAseq are presented as log2 values along with an estimate of statistical significance. Gene expression of lineages and the population studied in (Hong and Gresham, 2014) are shown for reference. (C) Quantification of promoter activity using transcriptional reporters. The median fluorescence for each transcriptional reporter in each of the four GAT1 genotypes relative to the ancestral strain was determined in different conditions. (D) Quantification of transcription factors binding affinity. Parameters estimated from fitting a Michaelis-Menten function to EMSA data. Fr_max is the maximal of bound over total DNA. K_x is the concentration of protein required for half-maximal binding. Non-significant parameter values are in grey italics. (E) Strains used in this study. (F) Plasmids used in this study.

https://doi.org/10.7554/eLife.32323.012
Supplementary file 2

(A) Determination of GAT1 variants binding specificities by Protein Binding Microarray. Each purified protein was assayed on two different microarrays containing different probe sequences (ME or HK). E-scores for the top 8-mer bound by the protein on each microarray were calculated. A PWM was generated for each protein variant based on an alignment of the top ten 8-mers. (B) GAT1 protein fragment sequences used in PBM and EMSA assays. The DNA binding domain is in blue. Flanking sequences included in the expressed protein fragment are in green. (C) Oligonucleotides used to generate duplex DNA for EMSAs. Bold and underlined sequences are GATA sequences bound by GAT1. The minimum match score was set as 0.8 using ‘matchPWM()’ function in ‘Biostrings’ library in R. Negative motifs differ from target motifs only at the underline GATA by replacement with random sequence.

https://doi.org/10.7554/eLife.32323.013
Transparent reporting form
https://doi.org/10.7554/eLife.32323.014

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