T-cell Exhaustion

"T-Cell Exhaustion" is associated with an inability of the immune system to fight off cancer and other diseases. We grabbed 7 markers of exhausted t-cells (pd-1, ctla4, tigit, lag3, tim3, cd244 and cd160) and searched our database for studies in which these markers were strongly perturbed. In only one of 91,000 gene lists were all 7 of these markers perturbed: Hematopoietic Progenitor Kinase1 (HPK1) Mediates T Cell Dysfunction and Is a Druggable Target for T Cell-Based Immunotherapies, wherein knockout of map4k1 downregulated all of these markers.

Grabbing all gene lists in which at least three of the markers were perturbed gave us 307 lists. Retaining the markers, we generated a frequency table of genes most commonly found in these lists. The markers lag3, pd-1, and tim3 topped the list. The fourth most frequent gene in our list was not one of the 7 markers: gzmb. After ctla4 and tigit we have ccl5, cst7, ccl4, gzma, and ccl3. Cd244 and cd160 occupied the 21st and 27th positions on the list. Our final list of genes associated with t-cell exhaustion contains 188 genes, with all genes required to be found at least 60 times over the 307 lists.

Presumably, we'd like to downregulate these genes aggressively in cancer, allowing the immune system and immunotherapies to go to work. Sticking with known drug/treatment regimens (as opposed to, say, knockouts which may be difficult to implement for the time being) in lymphocytes, the single best treatment would be the presence (versus absence) of zinc in mouse drinking water: Interleukin-10 induces interferon-γ-dependent emergency myelopoiesis. Next is a dca (16-didehydro-cortistatin A) regimen: The Cyclin-Dependent Kinase 8 (CDK8) Inhibitor DCA Promotes a Tolerogenic Chemical Immunophenotype in CD4+ T Cells via a Novel CDK8-GATA3-FOXP3 Pathway. This is followed by mouse studies involving leukocyte costimulatory blockade antibody treatment, Short-term Immunosuppression Promotes Engraftment of Embryonic and Induced Pluripotent Stem Cells, and NAC treatment, Impaired mitochondrial oxidative phosphorylation limits the self-renewal of T cells exposed to persistent antigen. A mouse study involving ricolinostat, an hdac6 inhibitor, follows, but we note that this drug also upregulated a significant number of genes in our t-cell exhaustion list. Such is biology.

The first human study wherein a treatment downregulates genes in the t-cell exhaustion list is this: TNFR2 Costimulation Differentially Impacts Regulatory and Conventional CD4+ T-Cell Metabolism. The study involves application of a tnfr2 agonist antibody to cd4 t-cells. The next human study involves treatment with a cd45 fragment: The soluble cytoplasmic tail of CD45 (ct‐CD45) in human plasma contributes to keep T cells in a quiescent state.

Ignoring solutions that might be relatively practical in 2024, we see a study in which a foxp3 k18r mutation results in exhaustion gene downregulation (Foxp3 Reprograms T Cell Metabolism to Function in Low-Glucose, High-Lactate Environments), followed by the aforementioned map4k1 ko, batf3 oe, tbx21 ko, tak1 ko, tfam ko, regnase-1 ko, rbx1 ko, and en2 ko.

In terms of disease-related studies, we see these exhaustion genes downregulated in responding vs non-responding leukemia patients in Reversal of in situ T-cell exhaustion during effective human antileukemia responses to donor lymphocyte infusion. This is not surprising, but it's nice to see validation of the standard dogma regarding t-cell exhaustion. Then again, the next disease study on the list might surprise: In Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma, t-cells associated with relapse tended to be depleted of exhaustion genes. Upregulated exhaustion genes were not only seen in cancers: see lymphocytic genes in Metallothioneins as dynamic markers for brain disease in lysosomal disorders and  Hypomethylation and Overexpression of Th17-Associated Genes is a Hallmark of Intestinal CD4+ Lymphocytes in Crohn's Disease. HIV progression vs control is associated with upregulation of exhaustion genes in Transcriptional analysis of HIV-specific CD8+ T cells shows that PD-1 inhibits T cell function by upregulating BATF. In DUSP4-mediated accelerated T-cell senescence in idiopathic CD4 lymphopenia, mouse t-regs show an upregulated exhaustion signature in the diseased state.

Unfortunately, there aren't any "DIY" sorts of treatments that downregulate exhaustion genes with high significance (we set P = 10^-15 as a cutoff). Zinc supplementation is interesting, but we wish the study were conducted in humans. We will upload the exhaustion list to our database in the next week or two and post the database ID just below when we do*. Then you can search for all treatments, diseases, knockouts, etc. that up- or down-regulate the exhaustion signature. It is possible that strong alteration of the exhaustion signature could be accomplished with a cocktail of treatments, each without astounding efficacy alone; to test such hypothesise, be sure to check out our "Third Set" tool to examine this possibility.

*The dbase ID is 188419856 .

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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 

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 

The Best Gene Names

NIPSNAP3B

COBL (cordon-bleu WH2 repeat protein)

ASS1

Any RING protein (Really Interesting New Gene)

MINDY (I'm not Dead Yet)

SMAD (Mothers Against Decapentaplegic Homolog)

PYGO (pygopus)

MTHFR (Methylenetetrahydrofolate Reductase, but it reminds me of a nasty word)

SVIL (supervillin)

Bonus: Best drug name

Eltrombopag

whatismygene.com 

A Preprint

It has been a while since we posted. That's largely because of the effort put into generating a paper. Check it out on BioRxiv.

This is actually my first experience publishing a preprint. After the preprint became public, my e-mail was bombarded with solicitations for publication, entirely by journals I'd never heard of. Only one was relevant to bioinformatics. With a couple exceptions, impact factors were quite low. One journal boasted a respectable 9.0 factor, but I found it odd that only a couple years prior the factor was sub-2.0. Presumably, one or two heavily cited articles changed the fate of this journal.

For the time being, I'm happy to leave the paper in an unpublished state. I was hoping for all-important constructive criticism, but have received nary a peep, either positive or negative. As the paper sits unpublished, I have, however, generated a lengthy list of self-critiques. When the paper satisfies me, that'll be the time to think seriously about the tedious task of submission for peer-review.

More blog posts forthcoming soon!

whatismygene.com 

Reversing Aging

Combining results from 331 studies, we've created lists of the genes most commonly up- and down-regulated in aging. We eliminated studies involving embryonic and early-life aging, as our goal in creating the lists is this: try to determine how to not get old. The up- and down-regulation lists have dbase IDs 160517531 and 160518531. Those two incorporate data from a variety of animal species. We also created human-only lists, derived from 115 studies: 160519531 and 160520531. A plurality of studies involves blood, particularly in the case of human aging; we made no attempts to reach a balance between all tissue types in compiling these lists.

What gene is most commonly up-regulated in aging (i.e. when comparing old folks to young)? The answer was surprisingly clear: serpina3, upregulated in 21% of the 331 studies. The gene has indeed been recognized as aging-related, particularly with respect to Alzheimer's disease and neurological conditions. Other upregulated genes included CD74, LYZ, HLA-DQA1, LCN2, C4B, and more. It's interesting that CD74 and HLA-DQA1 both relate to HLA Class II processes, while LYZ and LCN2 relate to anti-bacterial defense. Examining the human-only list, IGFBP3, an insulin regulator, ranks first, being found in 14% of all studies. IGFBP3 is followed by CD74, HLA-DQA1, FKBP5, CLU, and more. Serpina3 is ranked 20th in this list.

On the downregulation side, we have NREP (Neuronal Regeneration Related Protein!), found in 17% of aging studies. NREP is followed by COL3A1, COL1A1, COL1A2, and SPARC; a lot of involvement with collagen there. The human-only list is led by LRRN3, followed by ABLIM1, NELL2, BCL11A, and the above NREP.

Let's not waste time in attempting to answer the question of the moment: how do we reverse aging? Specifically, what treatments both down-regulate entities that are up-regulated in aging, and up-regulate entities that are down-regulated in aging? To answer the question, we simply load the above WIMG IDs into the "Match Studies" app, select "inverse correlations", and submit. We were pleased with the #1 ranked result, as it doesn't involve insanely expensive drugs, gene knockouts, or regimens that would be difficult to repeat in the real world: alpha-keto-glutarate (aKG) supplementation. The mouse study in question is here. Specifically, gene signatures in MSCs were examined. The aKG levels used in the study (.25-.75% in drinking water) do seem a bit difficult to replicate at home, but 1) we're talking about mice with short life spans and 2) there's no indication of a lower limit of aKG effectiveness in the study. In addition to reversing aging signatures in MSCs, aKG supplementation had a number of clear, positive effects on mouse morphology; in particular, attenuation of aging-related bone loss.

The aKG study was followed by studies that don't fall into the "try this at home" category: vegfa overexpression, and mysm ko. Abatacept, a common rheumatoid arthritis treatment, reversed the aging signature in arthritic synovium. Wonderfully, the next study in the list involves human muscle and a workout regimen: the popular HIIT training system. A little googling shows that aKG levels are indeed raised following a workout.

The list of aging-reversers did include some counter-intuitive results. In one case, macrophages from obese vs lean mice showed the reverse-aging signature. The experiment involved 24 hours gingivalis exposure; perhaps a strong short-term immune response overlaps with aging, and obese mice show a weaker immune response. Nicotine reversed the aging signature in mouse lungs. 

How about treatments that are often considered as anti-aging? Resveratrol weakly trended toward downregulating transcripts that are upregulated in aging. Metformin showed no anti-aging trends whatsoever. 

Examining the human-only lists, anti-retroviral treatment reversed the aging signature in infected human pbmcs. That rings a bell...we previously examined the possibility that anti-retroviral treatment could reverse Alzheimer's. Again, though, we have to concede that the anti-aging effect most likely corresponds with the killing of viruses and an accompanying decrease in inflammation; could somebody please perform some anti-retroviral studies in non-infected tissues?* The mouse aKG study still strongly overlapped with with our composite lists of human-only aging studies. Fish oil treatment and vitamin D supplementation are found on the list.  Fantastically, a twins study, Differences in muscle and adipose tissue gene expression and cardio-metabolic risk factors in the members of physical activity discordant twin pairs, showed that high activity (measured over a period of 30 years!) twins evinced an anti-aging signature in adipose tissue relative to their low-activity counterparts. There are a number of studies that show somewhat counterintuitive results: e.g. cancer studies where the higher-stage tissue appears younger than the lower-stage grade tissue, a study in which tissue from dementia patients is "young" relative to healthy patients, and a study in which B-cells and dendritic cells from severe Covid-19 infection patients out-younged such cells from healthy individuals.

Let's untick the "inverse correlations" box and see what conditions might actually accelerate aging. The list is led by a study involving raver2 knockout in mouse epithelial cells...not exactly something you could inadvertently perform at home. Scanning the list for at-home aging accelerators, we see cholesterol loading in mouse hearts, a variety of EAE and viral/bacterial infection studies, DHT exposure, ifn-g treatment, and a long list of other inflammatory stimuli.

Applying the same exercise with the human-only WIMG lists, we again see a fairly un-surprising litany of inflammatory stimuli inducing an aging signature. Chloroquine (remember?) treatment appears to induce aging. Hypertension patients evince a greater aging signature in pbmcs vs healthy individuals. Dexamethasone, raloxifene, and testosterone (again) enhance the aging signature. We note rosuvastatin treatment (which lowers cholesterol levels) and a lycopene-enriched diet as examples of treatments that may have effects that are counterintuitively pro-aging.

Progeria and lmna mutations are commonly associated with aging. No studies involving progeria/lmna overlapped with our aging lists, with one exception: genes upregulated in mouse heart on an lmna D300N mutation actually tended to align with genes that are canonically downregulated in aging! Hmmmm.

Biology is complex. Looking at the human up/down-regulation lists, 14 genes were actually found in both lists! These are: HLA-DRB4, THBS1, IFI44L, SPP1, RPS4Y1, IFIT1, VCAN, SNCA, S100A8, DSC2, ANXA3, IFI6, HLA-DRB1, and CD14. In the case of the HLA-related genes, we wonder if various polymorphisms are relevant to aging. The list is also loaded w/cell matrix genes and inflammation genes. Since we mixed aging-related genes regardless of tissue type, it may be possible that up-regulation of a particular gene manifests as aging-relevant in one tissue, but down-regulation manifests as aging-relevant in another.

Finally, we note that while the WIMG aging lists matched up nicely with WIMG lists involving mouse EAE, mouse Alzheimer's models, and ifn-g treatment and other inflammatory stimuli, there was no significant overlap between our aging lists and our human Alzheimer's lists, once again suggesting that Alzheimers is not merely a state of hyperaging and/or hyperinflammation.

*8/2024: Somebody did it. Look here.

whatismygene.com 

Another Data Dump

Here's a massive new mass-spec-based screen of drug effects: Proteome-Wide Atlas of Drug Mechanism of Action. 875 drugs were tested in the hct116 (colon cancer) line, and there wasn't any compromising on sensitivity in the name of efficiency or budget; about 7700 proteins were detected for each of the 875 perturbations. 

Another nice feature of the study is the fact that most of the 875 drugs were chosen to have very specific, as opposed to broad, targets. Thus, you can ask questions like "How does BACE1 inhibition compare to BACE1 knockdown?"

Needless to say, we entered all the results in our database. 875 tests, each with up- and down-regulated portions, adds 1750 gene lists to the database. Data was entered simply on the basis of fold-change. Each list contains 100 up- or down-regulated proteins. If, for some reason, you wish to search these 1750 lists exclusively, just paste "proteome-wide atlas of drug mechanism of action" (no quotes) into the "keyword" box in the WIMG tool of your choice. Using the "relevant studies" tool, for example, we found 10 drugs that altered CDK4 protein levels (5 up, and 5 down, coincidentally) in the study.

These sorts of data dumps make our tools all the more powerful. There's more chance that some insight will be generated when you search against an outrageous variety of studies. Unfortunately, the speed of output decreases as the database grows larger. Hopefully, processing power on our server will increase in step with the size of the database. If you find yourself getting antsy while waiting for output, we suggest you use our filters liberally. In particular, the "Restrict IDs" and "Emphasize Internal Significance" filters can decrease processing time substantially.

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