Transcription Factors that Strongly Regulate the Genes They Bind

You probably recall the lac operon model of gene regulation in prokaryotes. It was easy…mutation of a regulator alters the output of the genes that are bound by the regulator. However, having tinkered with both transcription factor (TF) binding data and transcriptome data, you may also have found that the two forms of data fail to overlap, at least when higher eukaryotes are involved.  If the prokaryotic model holds, after all, shouldn’t transcripts that are repressed by a TF be strongly upregulated when the TF is knocked-out?

We searched over 3,000,000 combinations of TF binding data and TF alteration (knockout/down, overexpression, mutation) data for cases where TFs actually do strongly and directly regulate their bound genes. We found that such cases are indeed quite rare, but they do exist. Take a look.

GENE	study	type	log (P)	effect
xbp1	GSE44949	oe	-65.92	A
irf1	GSE26817	oe	-63.22	A
rest	GSE34625	ko	-50.11	I
rest	28178232	kd	-41.63	I
rest	GSE43033	kd	-37.18	I
xbp1	GSE78070	mut vs wt	-34.74
rest	GSE98871	kd	-27.35	I
hmgxb3	35688146	kd	-25.41	A
srebp2	GSE51736	oe	-24.21	A
znf335	35688146	kd	-22.93	A
rest	GSE51461	kd	-19.1	I
atf4	GSE39194	mut vs wt	-18.32	A
cdkn1b	GSE98546	mut vs wt	-17.68	A
kdm5a	GSE147435	ko	-17.2	I
cic	GSE80359	ko	-16.36	I
hif1a	GSE27415	kd	-15.53	I
irf2	GSE132178	ko	-14.72	A
esr1	GSE18431	kd	-14.18	A
ar	GSE151429	mut vs wt	-14.15
nfkb2	GSE64234	kd	-13.9	A
yap	GSE60579	oe	-13.28	A
tead	GSE156912	drug	-12.36	A
rela	GSE65040	oe	-11.86	A
grhl1	GSE47407	oe	-10.78	A
p53	GSE98727	ko	-10.75	A
prdm10	GSE135021	ko	-10.71	A
gata1	GSE43356	mut vs wt	-10.31	A
twist1	GSE43495	oe	-9.15	A
twist1	GSE62739	oe	-8.95	A
tfeb	GSE108384	oe	-8.65	A
ring1	29874594	ko	-8.54	I
ezh2	GSE84243	ko	-8.39	I
smad4	GSE70940	kd	-8.38	I
atf4	GSE72658	ko	-8.2	A
e2f1	GSE16454	ko	-7.63	I
taf1	35688146	kd	-7.51	A
tp73	GSE162860	oe	-7.15	A
ar	GSE66722	kd	-7	A
myod	GSE168703	oe	-6.97	A
nrf4a1	GSE79490	oe	-6.79	A
otx2	GSE21900	ko	-6.5	A
spdef	GSE48928	kd	-6.33	A
six2	GSE79024	cell type	-6.03	A
stat1	GSE49519	ko	-5.85	A
taf2	35688146	kd	-5.62	A
ronin	GSE120008	ko	-5.43	A
tal1	GSE46970	ko	-5.26	A
e2f6	35688146	kd	-5.15	I
hoxc9	GSE34420	oe	-4.36	I
znf750	GSE32685	kd	-3.92	I

 

The binding data against which the above studies were compared was primarily retrieved from Chip-Atlas. Whenever possible, we provide the GSE-identifier of the data we used, so you can tinker with the underlying data yourself.* In other cases, the PMID ID is provided. Regarding the type of TF alteration (e.g. knockout or “ko”), splicing alterations are listed as mutations. The effect of the alteration may be A or I (activation or inhibition). In two experiments where the TF isoform was altered (against the dominant form), we refrain from the A/I designation. It’s not difficult to assign the A/I designation. If, for example, a TF is knocked down, and its gene products are upregulated, it’s obvious that the TF is predominantly an inhibitor, at least in the context of the experiment. The P-values are not adjusted; an adjusted P=.05 cutoff occurs around -8.5 above. Some results are included by virtue of the fact that, despite unimpressive P-values, the study was the single-best “aligner” with the TF binding data.

Despite the fact that all the TF-binding data was derived from human cells, 12 of the 50 studies above are mouse studies. This seems like a high number. Unlike human studies, mouse studies are usually conducted in vivo; is it possible that we'd see more TF-binding/transcriptome correspondence if human studies could be conducted in vivo? (No, I'm not bemoaning this state of affairs!)

You can see some repeat offenders, as well as some impressive P-values above. REST is seen five times, while ATF4, AR, TWIST1, and XBP1 are seen twice. Perhaps it’s not surprising that a number of these genes are involved in stress, hormone, or antiviral responses; these are actions that should be taken immediately, without dithering about waiting for confirmatory signals.

Chip-seq binding data can align strongly with studies that don’t seem to directly involve the transcription factor in question. For example, an MDM2 antagonist strongly downregulates LIN9 chip-seq targets (log(P)= -87, see GSE189152). TCF2 kd downregulates STAT2 chip-seq targets (-83, GSE48367). PRDM10 ko strongly downregulates BRCA2 chip-seq targets. Needless to say, there may be therapeutic implications there (-65, GSE135021). Along this line, the BRAF V600E mutation seems to downregulate SREBF2 targets. Sometimes, related TFs seem to regulate each other; SNAI1 oe downregulates SNAIL2 Chip-seq targets (GSE169735). In general, the transcripts perturbed on PRDM10, PRDM1, PRDM6, ATF6, and EZH2 alterations seem to do a fine job of overlapping the binding sites of numerous TFs.

 

 

*You can, of course, tinker with the data at our website. Just enter the gene name or GSE-id in the keyword box in the “Relevant Studies” app, and you’ll find the relevant database ID, which you can use for further investigations (e.g. plug the database ID into the Fisher app).

8/2023: Having greatly expanded our database, here's another TF that strongly activates the genes it binds: znf335.

whatismygene.com 

Become a Patron!