Reversing Disease Signatures

Here, we discuss the use of WIMG tools to search for drugs or treatments or gene perturbations that may reverse various  disease signatures. Perhaps I'm jumping the gun a bit here...it would first be nice to show that reversing a disease signature can actually reverse a disease. I may provide concrete examples that both confirm and contradict the possibility in the future. Based on the experience of scouring tens of thousands of studies, however, it is fairly obvious that reversing a disease signature can often, if not always, effectively treat a disease. When examining cancer signatures, for example, MEK inhibitors, commonly used in cancer treatment, often do a fine job of downregulating transcripts that are upregulated in cancer, and upregulating transcripts that are downregulated in cancer. We will ignore complicating factors such as resistance. We also assume that readers are educated/experienced enough to understand that most treatments involve tradeoffs...self-experimentation is not recommended.

We've accumulated a number of gene lists involving "canonical" disease signatures. They are listed at the bottom of this page. Additional details, such as the number of studies examined in accumulating the data, are omitted for simplicity. If your disease of interest is found in the list below, you can perform several actions to search for studies in which the signature is reversed. For example, you could open up the "Fisher" app, enter the DBASE ID for "WIMG up-regulated in bald skin" in the "Enter identifers or database ID" box, and simply hit "submit." If you are only interested in reversing the signature, select "downregulated" in the "Regulation" box. Then again, if you're interested in searching for factors that could encourage balding, you could choose "upregulated." You can also enter both portions of a study (upregulated and downregulated) into the "Match Studies" tool; to reverse the two be sure to select "Inverse Correlations." It is advisable to try both apps, if possible: "Match Studies" will give you individual studies ranked according their potencies in reversing both the up- and down-regulated portions of a study. It's possible that the most potent treatment for disease reversal would involve separately altering the up- and downregulated portions (i.e. two drugs), in which case you'd want to stick with the Fisher app.

If you're only interested in drug-based treatments, you can choose "drug" in the "Experiment" box. Choose "treatment" for non-small-molecule approaches (antibody-based therapy, etc). "Environment/behavior" might also be worth examining. 

One nice, very unique WIMG option is the "natural" option in the "Cell Type" box*. Choose it, and you will only receive "do-it-yourself" types of treatments as output...fitness programs, diets, vitamins, Chinese medicine, and stuff you might find in a "health-food" store. Again, I will assume readers are mature enough to be cautious here.

It is possible that the upregulated (or downregulated) portion of a disease signature would best be reversed by two or more treatments. Here, you might consider using the "Third Set" tool. Enter, say, the upregulated portion of disease transcripts and the downregulated portion of a signature involving a drug that you know to be effective. It's important that "Set1" be the upregulated disease signature. The tool will spit out a list of studies that intersect with the disease signature, but not the known drug signature.Again, you will probably wish to select "downregulated" in the "Regulation" box, and something like "drug" in the "Experiment" box. If you're insane and wish to find three non-overlapping drug treatments, you'll need to know all the transcripts that are considered to be downregulated in the two drug studies above...WIMG doesn't provide you with this, so you could contact us or dig up that data yourself in the studies of interest. Find the union of those two sets and discard one copy of any genes that appear twice. Use this new "dual" drug signature in the "Match Studies" tool, along with the upregulated disease signature.

Looking below, you will see that we have a fairly limited selection of canonical disease signatures to choose from. That's because we usually create these lists when there's a substantial selection of studies from which we can draw repeatedly perturbed genes. If you wish to reverse a disease signature that doesn't have a "WIMG list", you can create one yourself using whatever studies you can find. In the case of a rare disease, there may be only one study that is relevant. It's possible that no studies exist for a disease of interest, in which case you would have to find a signature for a similar disease. You could ask us to try to dig up the studies...don't worry, we're neurotic about hoarding and analyzing data.

DBASE ID STUDY

118765101 WIMG canonical up in cancer vs. adjacent 

118766101 WIMG canonical down in cancer vs. adjacent 

118767101 WIMG canonical up-regulated in metastasis vs. primary 

118768101 WIMG canonical down-regulated in metastasis vs. primary 

118771101 WIMG new canonical cytokine storm up 

118772101 WIMG new canonical cytokine storm down 

123049121 WIMG canonical up in human Alzheimer's brain 

123050121 WIMG canonical down in human Alzheimer's brain 

123069121 WIMG canonically upregulated in blood of Alzheimer's patients 

123070121 WIMG canonically downregulated in blood of Alzheimer's patients 

124415121 WIMG canonically up in Parkinson's brain 

124416121 WIMG canonically down in Parkinson's brain 

124416122 WIMG canonically down in alcoholic brain 

124416131 WIMG canonically up in alcoholic brain 

124417121 WIMG canonically up in schizophrenia brain 

124418121 WIMG canonically down in schizophrenia brain 

124419121 WIMG canonically up in depression/bipolar brain 

124419122 WIMG canonically down in depression/bipolar brain 

124420121 WIMG canonically up in autism brain 

124421121 WIMG canonically down in autism brain 

125583121 WIMG canonical up-regulated in aging brain 

125584121 WIMG canonical down-regulated in aging brain 

137716203 WIMG canonically up-regulated in lung squamous cell carcinoma vs lung adenocarcinoma 

137717203 WIMG canonically down-regulated in lung squamous cell carcinoma vs lung adenocarcinoma 

141048203 WIMG canonically up-regulated in lung cancer 

141049203 WIMG canonically down-regulated in lung cancer 

141259203 WIMG up-regulated in liver cancer vs adjacent 

141260203 WIMG down-regulated in liver cancer vs adjacent 

142124203 WIMG canonically up-regulated in colorectal cancer vs adjacent/normal 

142125203 WIMG canonically down-regulated in colorectal cancer vs adjacent/normal 

142928203 WIMG up-regulated in cervical cancer 

142929203 WIMG down-regulated in cervical cancer 

143176203 WIMG transcripts rarely perturbed in human cancer 

143177203 WIMG transcripts most commonly perturbed in human cancer 

143178203 WIMG transcripts most rarely up-regulated in human cancer 

143179203 WIMG transcripts most commonly up-regulated in human cancer 

143180203 WIMG transcripts most rarely down-regulated in human cancer 

143181203 WIMG transcripts most commonly down-regulated in human cancer 

146502203 WIMG genes that are rarely down-regulated in cancer vs adjacent studies 

146503204 WIMG genes that are rarely up-regulated in cancer vs adjacent studies 

146503205 WIMG genes that are never down-regulated in our cancer vs adjacent studies 

146504206 WIMG genes that are never up-regulated in our cancer vs adjacent studies 

160517531 WIMG up-regulated in aging 

160518531 WIMG down-regulated in aging 

160519531 WIMG up-regulated in HUMAN aging 

160520531 WIMG down-regulated in HUMAN aging 

164739532 WIMG up-regulated in bald skin 

164740532 WIMG down-regulated in bald skin 

165959532 WIMG up-regulated on cancer recurrence 

165960532 WIMG down-regulated on cancer recurrence 

165961532 WIMG up-regulated in high vs low-grade cancer 

165962532 WIMG down-regulated in high vs low-grade cancer 

176813532 WIMG up-regulated in blood of systemic sclerosis patients 

176814532 WIMG down-regulated in blood of systemic sclerosis patients 

180119532 WIMG up-regulated in inflammatory disease 

180120532 WIMG down-regulated in inflammatory disease 

*Why is the "natural" option found in the "Cell Type" box? It's unintuitive, but it was easy to program. We can fix that in the future.

whatismygene.com 

Become a Patron!

Genes beginning with "LOC"

Our database contains more than 27,000 genes that begin with the "LOC" designation (meaning "locus"). In total, they make 390,000 appearances in the database. Most of these genes are poorly characterized; one indication of that is the fact that all but about 2000 are lacking ENSG identifiers. Nevertheless, a couple of these LOCs appear more than 1,000 times in the database, and 869 appear at least 100 times. Most, but not all of these, are non-coding. 

Before proceeding, we should note that "poorly characterized" can also mean "unsure about their existence as separate species." LOC102724852, noted below, is associated with chromosome 11, but apparently hasn't been pinpointed to a location. It is also co-expressed with other chromosome 11 genes, which is a bit odd.

Using the "Cell Type" app on our website, let's plug in some of the most common LOCs in our database and get a feeling for what they do.

LOC102724852: Appears 1013 times in the database. Found significantly more often in female tissue than male, despite being found on chromosome 11. Perhaps amazingly, 15 studies in our database list this gene as the top ranking perturbation. Using the co-expression tool, we also see that it is very commonly found in association with H19 (H19 Imprinted Maternally Expressed Transcript) and, to a lesser extent, mir675, both of which are also found on chromosome 11. Hmmmm.

LOC112268238: Appears 920 times in the database. Again, more common in female studies. Significantly associated with results involving bromodomain targeting (drugs, knockout, etc). Also associated with degron experiments, which seems odd until you realize that degron experiments often target bromodomain proteins. Co-expressed genes are hugely overrepresented by histones. BRD2, a bromodomain gene, is also strongly associated.

LOC112268430: Appears 897 times in the database, but isn't strongly associated with any of our key words.

LOC107986126: 830 times. Slight association with leukemia.

LOC112268313: 810 times. Again, associated with degron experiments.

LOC100044068: 786 times. A mouse gene, oddly associated with knockout experiments (log(p) = -22). Also associated with the brain, particularly the hippocampus.

LOC105374985: 735 times. Associated with prostate studies.

LOC100419583: 717 times. Strongly associated with innate immune response keywords (ifn, cytokine, virus, infection, etc).

LOC112267876: 689 times. Associated with stem cell studies.

LOC107984316: 685 times. No strong associations.

LOC112268267: 668 times. Associated with studies involving ifn-gamma.

LOC101928841: 661 times. Associated with studies involving the HELA cell line.

LOC101929185: 660 times. Strongly associated with HEK293 studies.

LOC112268284: 658 times. Associated with studies involving fungi (i.e. fungal infections).

LOC112268155: 654 times. Commonly found in MCF7 studies (female breast line), but also LNCAP (prostate).

LOC107986762: 643 times. Weak association with macrophage studies.

LOC105369370: 643 times. Associated with carcinoma.

LOC107987206: 636 times. No significant associations.

LOC105378936: 597 times. Slight association with leukemia.

LOC112268109: 535 times. Associated with studies involving huvecs.

LOC112268426: 534 times. Associated with endothelial cells.

LOC112268447: 504 times. Strong association with fibroblast studies.

Just for the fun of it, we lumped together the top 250 of these LOCs and ran the list through our Fisher app. Somehow, it seems that a large number of these LOCs found themselves in a list of genes upregulated in "glioblastoma tissue after g207 innoculation" (unadjusted log(p) = -16). They are also "downregulated in high-grade T1 micropapillary bladder cancer w/micropapillarity = 1 vs 0", and "upregulated in caco2 line on 12h vs 7h SARS-CoV-2 infection." The most common keyword associated with the list was "line", meaning the LOCs are overrepresented in cell line experiments (particularly HEK293) vs in vivo studies. The bias toward female studies is also retained. However, this bias may relate to a disproportionate number of female cell lines, as the bias disappears when cell lines are eliminated from consideration. In fact, when only in vivo tissue is examined, the association with the keyword "disease" is surprisingly significant (log(p) = -44). Other keywords of interest include "resistance" (as in drug resistance) and "virus".

whatismygene.com 

Become a Patron!