DIY Reduction of Ace2 Levels?

Here’s a title for a GEO dataset we espied today: BRD2 inhibition blocks SARS-CoV-2 infection by reducing transcription of the host cell receptor ACE2. ACE2 is indeed reduced by BRD2 knockdown in the Calu-3 cells used in this unpublished study. We’ll add the data to our database shortly.

What other drugs/treatments might reduce ACE2 levels? It’s easy to use our “Relevant Studies” tool to get some clues: type “ACE2” in the identifiers box, choose “downregulated” under “Regulation”, and choose “lung” as “Cell type.”

In terms of “do it at home” approaches, it appears that increased selenium might do it. You’ll have to check out the fine details in the underlying study to see if the levels used in mice would be at all “reasonable” in humans: Dietary Selenium Levels Affect Selenoprotein Expression and Support the Interferon-γ and IL-6 Immune Response Pathways in Mice. That’s about it. The three other approaches suggested by the Relevant Studies app are not exactly home-friendly: knockouts of TLR3, RNase L, or transfection of mir-138.

Perhaps bizarrely, there’s actually a study that links inadequate selenium to Covid-19 infection. The sample size was fairly small (30 patients, 30 controls), but an impressive P-value was derived (.0003): An exploratory study of selenium status in healthy individuals and in patients with COVID-19 in a south Indian population: The case for adequate selenium status.

We can eliminate the requirement that the downregulation occur in the lung. In that case, we get 102 approaches! In terms of drugs, there’s cabergoline, GSK3 inhibitors, dimethyl maleate, and a long list of other drugs. Even after eliminating the “lung” requirement, selenium is still the only potential DIY approach.

Of course, transcriptomic results don’t always translate to proteomic results. In terms of proteomic studies, only the aforementioned RNaseL KO is found in the database.

What might increase ACE2 levels? With the “lung” requirement, we see only two results, neither of which suggest that common drugs could lead to inadvertant ACE2 over-expression. Without the requirement, we get 115 results. Interestingly, interferon-alpha treatment might not be recommended as a Covid-19 preventative. Trametinib, metformin, resveratrol, ifn-kappa, antibiotics, dexamethasone, cholesterol, low riboflavin (vitamin B2) levels, and gold ion could also be contra-indicated for folks who are keen to avoid Covid-19. Cholesterol in particular catches our eyes, as many viruses seem to require it to carve out their little lipid-encased niches in or around the host endoplasmic reticulum. Naturally, though, it’s questionable whether dietary cholesterol alterations would have any effect on ACE2 levels in the human lung (mouse hippocampi were examined in the study in question).

Hopefully, we needn’t point out that everything above should be validated in the lab/field. It’s also worth remembering that Covid-19 prevention and treatment are very, very different subjects; potentially diametrically opposed! Both dexamethasone and interferons have indeed been used as treatments for infected patients.

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Stem Cells as Culprits in Alzheimer's Disease

Previously, we used the “Match Studies” app to search for knockouts, drugs, whatever that mimic or counter the Alzheimer’s transcriptomic fingerprint. We could use the app because our own canonical Alzheimer’s signatures, as well as the signatures in Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets, contained both upregulated and downregulated sets. Many datasets do not contain both. For example, cell-type clustering sets usually do not contain upregulated and downregulated portions. They may simply contain “cluster 1”, “cluster 2”, “cluster 3”, etc.

Here, we wish to look at Alzheimer’s disease from the point of view of cell types that may either be predominant or depleted. Again, a caveat under which we proceed is that fact the our canonical Alzheimer’s sets are derived from multiple studies that examined a variety of brain regions. However, we’ll also look at the five clusters described in the previous post, which focus entirely on the parahippocampal gyrus (PHG).

First, we grab a set that is upregulated in Alzheimer’s…our “canonical” set (dbase ID 123049121). We paste the ID into the “Fisher” app, select “cell type” for “Experiment/Data Type”, and “Brain” under “Cell Type”, and submit. We then examine the output, and proceed with entry of 11 more datasets (the canonically downregulated set and the five PHG signatures, both of which have up- and downregulated portions). Below, in a crude form, is what we found:

Dataset	Cell Types that Mimic the Dataset
Canonically up in Alzheimer’s	upregulated in glioblastoma stem-cell like tissue (GSCs) vs GSC culture-spheres, human embryo ventral midbrain radial glia IIa cluster, 60% human embryo ventral midbrain pericyte cluster, mouse astrocyte cluster 3
Canonically down in Alzheimer’s	human embryo ventral midbrain serotonergic cluster, human embryo ventral midbrain dopaminergic 2 cluster, human embryo ventral midbrain dopaminergic 1 cluster, human embryo ventral midbrain NBgabaergic cluster, fetal retina cluster 8
Up in Signature A	mouse olfactory bulb neuron cluster 7, human embryo ventral midbrain dopaminergic 2 cluster, dominant (at least 95% of all counts) in microglia "ambiguous" cluster 13, human embryo ventral midbrain serotonergic cluster
Down in Signature A	fetal retina cluster 11 (fibroblast)
Up in Signature B1	Nothing of significance
Down in Signature B1	upregulated in adult nscs vs glioma nscs, dominant (at least 50% of all counts) in microglia cluster 10, upregulated in glioblastoma stem-cell like tissue (GSCs) vs GSC culture-spheres
Up in Signature B2	Nothing of significance
Down in Signature B2	upregulated in adult nscs vs glioma nscs
Up in Signature C1	astrocytes cluster 4, upregulated in glioblastoma stem-cell like tissue (GSCs) vs GSC culture-spheres, mouse astrocyte module "lightcyan"
Down in Signature C1	mouse olfactory bulb neuron cluster 7
Up in Signature C2	astrocytes cluster 4
Down in Signature C2	human embryo ventral midbrain serotonergic cluster, human embryo ventral midbrain mature neuron cluster, human embryo ventral midbrain dopaminergic 1 cluster

Results that appear more than once are colored. Pardon the gaudiness. As always, if you want the finer details (p-values, study names/IDs, etc), you can go through the exercise yourself. It doesn’t take long. Some of the above studies overlap quite significantly with the Alzheimer’s sets, with P-values as high as 10^-25.

Here’s our prime observation: it’s all about stem and embryonic cells. We have plenty of studies involving adult brain tissues, but none of these came to the fore. Thus we see results involving upregulation in stem cells vs. more mature cells, multiple embryonic brain clusters, and adult neural stem cells vs. glioma stem cells. Note that the olfactory bulb (along with the subventricular zone of the brain) is a hotbed for neural stem cells (NSCs). Note also that some of the knockouts/mutations mentioned in our previous post as mimicking/countering Alzheimer's would appear relevant to stem cell biology...arx, hoxa5, sufu, etc.

Unfortunately, as might be expected given the opposing five signatures in Molecular subtyping of Alzheimer’s disease, it’s unlikely that there’s an NSC enriching/depleting strategy for all forms of Alzheimer’s. For example, the serotonergic cluster that is downregulated in both our canonical set and signature C2 is upregulated in signature A.

A quick Google Scholar search reveals a myriad of studies/reviews relating to stem cell therapy for neurodegeneration, but a paucity of works suggesting stem cells as culprits. Given the low abundances of these cell types and the necessity of working with post-mortem human brains, it’s not the easiest sort of study to conduct. Here’s a review that does offer a perspective and relevant citations: Adult Hippocampal Neurogenesis in Major Depressive Disorder and Alzheimer’s Disease.

To be clear, the above data doesn't absolutely point a finger at brain stem cells. After all, there aren't many of them kicking around in the brain, let alone the aged brain. More likely, we're looking at a signature that is associated with stem cells, spread over more abundant classes of cells. Actual stem cells could be playing a role in initiation of these signatures. 

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Alzheimer's is not One Disease!

What % of academic papers make serious headway in resolving a question? In my opinion, not many. 1% is optimistic. Here's a paper that falls into that 1%: Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets. There's plenty of tricky bioinformatics within, but the basic task isn't hard to grok: get a load of Alzheimer's data from a particular region of the brain, cluster it, and use every tool you can find to link the clusters to patient types, cell types, mouse studies, whatever. 

The big takeaway is that Alzheimer's isn't a single entity. In fact, that's an understatement. The same genes that are upregulated relative to controls in Cluster A, for example, may be downregulated in Cluster C. These are not subtle differences between clusters...they're massive. 

There were 5 clusters in the study (A, B1, B2, C1, C2), each with characteristic up/downregulated transcripts. We took those 5 pairs of transcripts, passed them through our "Match Studies" app and, in a format that would never pass peer review, summarize the results below. We also passed the WhatIsMyGene canonical Alzheimer's regulation lists (up/down) through the app.

A
tau neighborhood genes, tau protein binding, up-regulation of gaba/glutaminergic, dendritic synaptic pathways, downregulation of immune response, increase of neuronal regulation genes,tauP301L model
mimics	counters
arx ko	C2
B2	C1
high activity	alcoholism
B1	autism
brains lacking b/NK cells	some alzheimer studies
	hras g12s mut
	rosmarinic acid
	hoxa5 gain
	IAV infection
	methylphenidate
	tyr-trp dipeptide
	GBM stem cells w/gpr56 ko
	arx mutant
	huntington's mouse
	med23 ko
B1
tau neighborhood genes, up-regulation of gaba/glutaminergic, glycinergic, dendritic synaptic pathways, downregulation of immune response, downregulation of oligodendrocytic genes, APOJ/CD2AP/BIN1 mouse models
mimics	counters
B2	C1
A	some alzheimer studies
B2
tau neighborhood genes, up-regulation of gaba/glutaminergic, glycinergic, dendritic synaptic pathways, immune pathways, APOE2 dosage, downregulation of oligodendrocytic genes, APOJ/CD2AP/BIN1 mouse models
mimics	counters
B1	some alzheimer studies
C1	idh1 expression in NSCs
C2
A
C1
AB binding, fiber clearance, down-regulation of gaba/glutaminergic, glycinergic, dendritic synaptic pathways, immune pathways, APOE4 dosage, 5XFAD/APP Dutch/APP Swedish mice
mimics	counters
C2	A
autism	B1
nasu hakola brain	antiretrovirals
alcoholics	high activity
HIV patients (w/o antiretrovirals)	schizophrenia
MS lesions	zinc restriction
cocaine addiction	DHA treatment
B2	apoe4 organoids
ALS motor cortex	high protein diet
Creutzfeldt-Jakob
Down's syndrome
JEV/WNV infection
wig1 kd
mog (neuroinflammation)
hoxa5 gain
HIV
depression
C2
down-regulation of gaba/glutaminergic, glycinergic, dendritic synaptic pathways, immune pathways, 5XFAD/APP Dutch/APP Swedish mice
mimics	counters
C1	A
alcoholism	arx ko
autism	sufu ko
nasu-hakola	antiretrovirals
cocaine addiction
B2
hiv
hoxa5 gain
ALS motor cortex
Creutzfeldt-Jakob
APP ko
Dicer ko
WhatIsMyGene canonical Alzheimer's
canonical mimics	canonical counters
Creutzfeldt-Jakob	antiretrovirals
alcoholism	rdm11 kd
cocaine addiction	high activity
nasu hakola brain	dha treatment
HIV	A
C2	ndp ko
C1	schizophrenia
ALS	some Alzheimer's studies
MS lesions	9thc treatment
autism	cga ko
Down's syndrome	bdnf treatment
hoxa5 gain	sdhd kd
aged brain	pten heterozygosity
Cockayne syndrome	arx ko
Rhett's syndrome	fluoxetine
glioma stem cells (vs normal)
bipolar
OCD
hras g12s mutation
mouse epilepsy
rosmarinic acid
fibrinogen treatment
arx mutation
traumatic injury
anti-cd8 treatment

First, you see the cluster names. Then, in gray boxes, you see some broad characteristics of the clusters that were mentioned in the paper. Below that, you see studies that either mimic the clusters or counteract them, listed in order of significance. Studies highlighted in yellow are Alzheimer's mimics with respect to one cluster, but counters with respect to another. Green highlights suggest interventions that aren't contradicted in one cluster versus another. If you want the finer grain details of these studies, you can use the "Match Studies" tool yourself: enter our database IDs for the up/downregulation results in the clusters ("signatures" is actually the term in the paper), choose "brain" as "Cell Type", and choose "Inverse Correlations" if you want to counter the input.

Dbase ID	Study
125024121	upregulated in Alzheimer's signature A
125025121	downregulated in Alzheimer's signature A
125026121	upregulated in Alzheimer's signature B1
125027121	downregulated in Alzheimer's signature B1
125028121	upregulated in Alzheimer's signature B2
125029121	downregulated in Alzheimer's signature B2
125030121	upregulated in Alzheimer's signature C1
125031121	downregulated in Alzheimer's signature C1
125032121	upregulated in Alzheimer's signature C2

As mentioned above, it's pretty obvious that there are major distinctions between the clusters. For example, the single best counter to Cluster A is Cluster C2. Unfortunately, negating one form of Alzheimer's with another is probably not a very good therapeutic option. More evidence of different forms of Alzheimer's is the fact that some individual Alzheimer's studies counter specific clusters. In the past, we've actually noted (and then ignored) this fact when comparing individual Alzheimer's studies.

There's a lot that could be said about the mimics and counters above. To keep things brief:

*not one but two studies involving alterations to ARX are seen above. Intriguing. 

*antiretrovirals are prominent but, as noted before, they were applied to HIV patients, meaning that we must choose between a "direct" effect on Alzheimer's or an indirect effect where the drugs kill a virus that induces an Alzheimer's-like transcriptome. Either way, though, the question of antiretroviral usage is interesting.

*there are a few easy interventions that are worth thinking about...e.g. DHA (fish oil) ingestion, zinc restriction, high protein diets. High activity levels counter the C clusters, but may enable the A cluster.

*some knockout/overexpression studies suggest possible drug targets besides ARX.

*mouse Alzheimer's models are nowhere to be seen in the mimics/counters columns. One mouse neuroinflammation study does appear, however, as mimicking cluster C1. If you're a fan of Alzheimer's mouse models, the paper does devote several paragraphs to this topic.

*cluster C1 is chock-full of neural disorders besides Alzheimer's. Why? Are we looking at a general response to insults to the brain?

*some results are slightly comical. It appears that alcoholism could counter the Cluster A version of Alzheimer's.

In general, the super-compelling clustering results suggest a future where treatment depends entirely on one's Alzheimer's type. Unfortunately, it's beyond the scope of the paper to show how patients might be typed, given the invasiveness of sampling living brain tissue. We note that our own "canonical" Alzheimer's lists strongly parallel clusters C1 and C2, probably because that's simply where the bulk of post-mortem samples abide. Also, the paper does show that tau tangles and AB-plaques are prominent in particular clusters; in-vivo imaging methods for these Alzheimer's manifestations are improving.

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Crispr Screens for Covid-19 host factors

Yesterday's issue of Cell contained not one, not two, but four studies in which cell lines (Vero, A549, and two Huh7.5s) were subjected to Crispr-based knockouts in order to determine host factors that enhance amenability to Covid-19 invasion. We've entered the datasets into our database. How do the four studies compare?

A) Host Factors Critical for SARS-CoV-2... (Vero)

B) Identification of Required Host Factors for SARS-CoV-2... (A549)

C) Genetic Screens Identify Host Factors for SARS-CoV-2... (Huh7)

D) Genome-Scale Identification of SARS-CoV-2... (Huh7)

Colors indicate weak anticorrelation (blue), weak correlation (pink), and strong correlation (red). 

Actually, only the two Huh7 studies show decent correlation (log(P) = -14). Three of the studies do show Ace2, the well-characterized Covid-19 entry receptor, at the top, or near the top, of the lists. Ace2 narrowly missed our cutoffs in the fourth study (study D). That's comforting.

However, if we're using cell lines to discern host factors that are commonly targeted by Covid-19, am I wrong in thinking these results are a bit underwhelming? Even in the case of the Huh7 studies, an intersection of 21 of 377 total transcripts* generated the aforementioned p-value. Given that the two studies were replicates, shouldn't we expect more extreme significance?

Of course, the two Huh studies weren't exact replicates. Without scanning the papers, different viral MOIs, media, infection periods, etc., were certainly used. Further, I can't say the lack of overlap between these studies is surprising...it happens again and again. But why? Cell does offer a "preview" article that makes mention of differences, but primarily breezes over this issue.

One possible resolution to the question of weak overlaps between studies is this: the virus hasn't evolved to interact with 500 host factors to do its job. It needs a handful. Why should we expect massive overlaps between gene sets from each study if most of the genes in these sets are mere "background?"

We're not finished with our Alzheimer's series...more to come shortly.

*these numbers, of course, depend on our own cutoffs. We won't bore you with a description of our procedure, but there's nothing exotic going on. 

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