We haven't posted for nearly 3 months. That's not for lack of activity; we're writing a paper! That means we're doubling our efforts to load the database with studies. Right now we have about 52,000 gene lists from 25,000 studies. The gene list total jumps to 92,000 if we include a massive perturb-seq study.
We note an abundance of studies involving HDAC inhibition. There are 11 different histone deacetylases in the human proteome, falling into 4 classes. Particular HDAC inhibitors, often used in cancer treatment, can inhibit a single HDAC (e.g. HDAC1), several, or all. Despite these variations, we note that the transcriptomic effects of HDAC inhibition seem to be somewhat stereotyped. We thus clumped the results of 59 inhibition studies into "WIMG up-regulated on hdac inhibition" and "WIMG down-regulated on hdac inhibition" lists with database IDs 150844207 and 150845207. Bearing in mind that our gene lists tend to contain about 200 IDs, the stereotyped nature of HDAC inhibition is seen in the fact that, despite an array of about 15 different inhibitors, the gene DHRS2 was upregulated in 28 studies, while LANCL2, a glutathione transferase, was downregulated in 32. Neither of these genes has been targeted in big-data fashion.
More recognizable names upregulated on HDAC inhibition include HSPA2, MAPT, KIF5C, and FOS. On the downregulation side, we have KEAP1, ATF5, CREBBP, U2AF2, SMARCB1, FOXM1, MCM7, and XBP1. All of these appeared in at least 13 of the 59 studies. Here, "recognizability" is not scientifically defined...it's about me eyeballing the gene lists.
Using our "Match Studies" app, we can say the following:
*Emulating HDAC inhibition via knockdown is accomplished by targeting rfwd3, followed by fis1 and grem1. The best way to reverse hdac inhibition via knockdown would be by targeting foxd3. It's interesting to note that specific HDAC knockdown studies aren't prominent in the list of knockdown studies that parallel HDAC inhibition. This is possibly because nobody performs pan-HDAC knockdowns (at least 11 siRNAs required). Another answer is simply that inhibition and knockdown are different experiments; in the case of inhibition, HDAC levels may remain unperturbed, interacting with various protein partners as usual; that's not the case with knockdown.
*Utilizing knockout, pom121 ko does a fine job of acting as an HDAC inhibitor. We don't see any knockout studies that strongly reverse the HDAC inhibition signature. Again, HDAC knockout studies don't strongly mimic HDAC inhibition.
*Perhaps oddly, the overexpression study that best emulates HDAC inhibition involved overexpression of noncoding retrotransposon line-1 sequences. The underlying study makes no mention of hdac inhibition.
*HDAC-inhibition, or reversal thereof, does not seem to be a phenomenon associated with infection; of more than a thousand infection studies in our database, none showed the signature of HDAC inhibition (or reversal). In fact, these inhibitors neither paralleled or reversed any diseases in particular. We do note, however, that disease signatures weakly reversed by HDAC inhibition were definitely enriched in cancer studies...melanoma, neuroectodermal tumors, cervical carcinoma, etc.
*HDAC inhibition is associated with cell stages. For example, a study conducted with sh-sy5y cells showed the HDAC inhibition signature upon induction of differentiation.
Moving to the "Fisher" app, which doesn't require input of both up- and down-regulated portions of a study, we see that...
*Of our short catalog (about 200 entries) of GO lists, none of them fare well in capturing HDAC inhibition. On the side of up-regulation, GOBP_SISTER_CHROMATID_SEGREGATION seems to fare best, with an unadjusted -log(P) = 4.5. On the down-side, WP_G1_TO_S_CELL_CYCLE_CONTROL comes in with -log(P) = 3.9. Bear in mind that we apply "background-adjustment" to these lists...the P-values would certainly be higher otherwise. The above GOBP_SISTER_CHROMATID_SEGREGATION list placed second (-log(P) =3.6) on the down-side, pointing out another weakness of many (not all) of these sorts of gene lists.
*Resveratrol does a decent job of mimicking HDAC inhibition.
*Genes upregulated on HDAC inhibition overlap nicely with our "WIMG transcripts most rarely up-regulated in human cancer" list (-log(P) = 7.9), while genes downregulated on HDAC inhibition overlap with "WIMG transcripts most commonly up-regulated in human cancer" (-log(P) = 9.5), making a nice case for the use of HDAC inhibitors in cancer.
The "Third Set" app also allows users to input lists of genes. However, the two lists must intersect to some extent, which is not the case here.
Other WIMG apps require the input of only one or two gene names, as opposed to lists, meaning that entry of the above two lists would not be fruitful. We can, however, make guesses as to the functions of the aforementioned two genes, DHRS2 and LANCL2, with the co-expression app. This way, we see that histone genes like HIST1H2BJ and H1F0 are strongly associated with DHRS2. Interestingly, quite a few lncrnas (e.g. linc00624) associate with DHRS2. Regarding LANCL2, DUS3L, a trna modification enzyme, is most strongly associated (-log(P) = 26). Going a step further, we grab the full lists of genes co-expressed with DHRS2 and LANCL2, and enter them into the Fisher app. This way, we see that DHRS2 associates with genes that are commonly perturbed in epithelial cells, genes upregulated following radiation treatment, genes upregulated on ERG knockdown and, not surprisingly, genes upregulated in a variety of studies involving HDAC inhibition. LANCL2 associates even more strongly with genes involved (on the side of downregulation) in HDAC inhibition. Ignoring these sorts of studies, we again see involvement with radiotherapy.
The "Cell Type" app informs us the DHRS2 is often perturbed in adenocarcinoma studies (which could involve either cell lines or actual cancer tissue; you could filter out the cell lines, if desired). The gene is rarely perturbed in blood or brain. LANCL2 is far less significantly associated with particular cell types. One possible reason for this is simply that LANCL2 only appears 200 times in the database, making it difficult to show that this gene is strongly associated with particular cell types. The trend is for the gene to be perturbed in the brain, and unperturbed in cancer tissue.
We should point out that the above "experiments" can be performed by any WIMG user using the 150844207 and 150845207 database IDs. The level of detail, of course, will far exceed the above summary of the effects of HDAC inhibition.
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