Category Archives: Aargh ! Big pharma sheds chemists. Why?

Why drug discovery is so hard (particularly in the brain): Reason #28: The brain processes its introns very differently

Useful drug discovery for neurologic and psychiatric disease is nearly at a standstill. It isn’t for want of trying by basic researchers and big and small pharma. A recent excellent review [ Neuron vol. 87 pp. 14 – 27 ’15 ] helps explain why. In short, the brain processes its protein coding genes rather differently.

This post assumes you know what introns, exons and alternate splicing are. For pretty much all the needed background see the following.

First: https://luysii.wordpress.com/2010/07/07/molecular-biology-survival-guide-for-chemists-i-dna-and-protein-coding-gene-structure/
Second:https://luysii.wordpress.com/2010/07/11/molecular-biology-survival-guide-for-chemists-ii-what-dna-is-transcribed-into/

When splicing first came out I started making a list of proteins which were alternatively spliced. It is now safe to assume that any gene containing introns (95% of all protein coding genes [ Proc. Natl. Acad. Sci. vol. 112 pp. 17985 – 17990 ’08 ]) results in several protein products due to alternative splicing. The products produced vary from tissue to tissue, probably because most tissues express different splicing regulators.

Here are a few. A2BP1 (aka Rbfox1, aka FOX1) is a brain specific RNA splicing factor found only in postmitotic terminally differentiated neurons. It is deleted in 10% of glioblastomas. Another is nSR100 (neural Specific Related protein of 100 kiloDaltons) — see later.

To show how crucial alternative splicing is for the every existence of the brain, consider this. The neuronal splicing regulator PTBP2 is barely expressed in most tissues. It is upregulated in neurons. Both PTBP1 and PTBP2 are repressors of neural alternative splicing (but some genes are actually enhanced). In a given region of the brain either PTPB1 or PTBP2 is expressed (but not both). PTBP1 promotes skiping of a neural specific exon (exon #10) in PTBP2 transcripts. This exposes a premature termination codon in PTBP2 leading to nonsense mediated decay (NMD). PTPB1 is expressed in most nonNeural tissues and neural precursor cells, but is silenced in developing neurons by the microRNA miR-124. The mRNA for PTBP2 contains an alternative exon which triggers nonsense mediated decay (NMD) when skipped. Inclusion of the exon requires positive transacting factors such as nSR100 in neurons. Repression is mediated by PTBP1 in undifferentiation. microRNAs (which ones?) downregulate PTBP1 during neuronal differentiation, relieving the negative regulation of PTBP2. Depletion of PTBP1 in fibroblasts is enough for PTBP2 induction and neuronal transdifferentiation.

It gets more complicated still. PTBP1 inhibits splicing of introns at the 3′ end of some genes involved in presynaptic function. This results in nuclear retention and turnover via components of the nuclear RNA surveillance machinery. As PTBP1 is downregulated during neuronal differentiation, the target introns are spliced out and the mature mRNAs are found.

Now we get to microExons, something unknown until 2014. For more details see — https://luysii.wordpress.com/2015/01/04/microexons-great-new-drugable-targets/.
Briefly, microexons are defined as exons containing 50 nucleotides or less (the paper says 3 – 27 nucleotides). They have been overlooked, partially because their short length makes them computationally difficult to find. Also few bothered to look for them as they were thought to be unfavorable for splicing because they were too short to contain exonic splicing enhancers. They are so short that it was thought that the splicing machinery (which is huge) couldn’t physically assemble at both the 3′ and 5′ splice sites. So much for theory, they’re out there.

The inclusion in the final transcript of most identified neural microExons is regulated by a brain specific factor nSR100 (neural specific SR related protein of 100 kiloDaltons)/SRRM4 which binds to intronic enhancer UGC motifs close to the 3′ splice sites, resulting in their inclusion. They are ‘enhanced’ by tissue specific RBFox proteins. nSR100 is said to be reduced in Autism Spectrum Disorder (really? all? some?). nSR100 is strongly coexpressed in the developing human brain in a gene network module M2 which is enriched for rare de novo ASD assciated mutations.

MicroExons are enriched for lengths which are multiples of 3 nucleotides. Recall that every 3 nucleotides in mRNA codes for an amino acid. This implies strong selection pressure was used to preserve reading frames as 3n+1 and 3n+2 produce a frameshift. The microExons are enriched in charged amino acids. Most microExons show high inclusion at late stages of neuronal differentiation in genes associated with axon formation and synapse function. A neural specific microExon in Protrudin/Zfyve27 increases its interation with Vessicale Associated membrane protein associated Protein VAP) and to promote neurite outgrowth.

[ Proc. Natl. Acad. Sci. vol. 112 pp. 3445 – 3450 ’15 ] Deep mRNA sequencing of mouse cerebral cortex expanded the list of alternative splicing events TENfold and showed that 72% of multiexon genes express multiple splice variants. Among the newly discovered alternatively spliced exon are 1,104 exons involved in nonsense mediated decay (NMD). THey are enriched in RNA binding proteins including splicing factors. Another set of alternatively spliced NMD exons is found in genes coding for chromatin regulators. Conservation of NMD exons is found in lower vertebrates, but those involving chromatin regulators are found later into the mammalian lineage. So the transcriptome in the brain is even more complicated.

A bit more about the actual effects on protein structure of alternate splicing. The sites chosen for this aren’t random. Cell and tissue differentially regulated alternative splicing events are significantly UNDERrepresented in functionally defined folded domains in proteins, they are enriched in regions of protein disorder that typically are surface accessible and embed short linear interaction motifs (with other proteins and ligands). Among a set of analyzed neural specific exons enriched in disordered regions, 1/3 promoted or disrupted interactions with partner proteins. So regulated exon splicing might specify tissue and cell type specific protein interaction networks. They regard their inclusion/exclusion as protein surface microsurgery.

How much can a little microexon do to protein function? Here’s an example of a 6 nucleotide microexon (two amino acids). Insertion of the microExon in the nuclear adaptor protein Apbb1 enhances its interaction with Kat5/Tip60 a histone deacetylase. The microExon adds Arginine and Glutamic acid to a phosphotyrosine binding domain (PTB domain) which binds Kat4. This enhances binding.

Had enough? The complexity is staggering and I haven’t even talked about recursive splicing — that’s for another post, but here’s a reference if you can’t wait — [ Nature vol. 521 pp. 300 – 301, 371 – 375, 376 – 379 ’15 ]. Pity the drug chemist figuring out which alternatively spliced form of a brain protein to attack (particularly if it hasn’t been studied for microExons).

Why drug discovery is so hard: Reason #27 Moonlighting effects.

Well, we all know what heat shock proteins (Hsps) do — they bind to proteins which have lost their shape due to heat (or other stressors), cuddle them hydrolyze ATP and nurse them back to health. But what  if some of them do other things? The phenomenon is called moonlighting.

The case of Hsp70 is instructive. Some background first. The Hsp70 chaperone transiently associates with its substrates in a manner controlled by its ATPase cycle. ATP binding to the amino terminal nucleotide binding domain (NBD) induces a conformational change in the carboxy terminal substrate binding domain (SBD) which results in low affinity for substrate. Hydrolysis of ATP converts the Hsp70 to the ADP state, which binds substrates with higher affinity. Exchange of ADP for ATP releases substrate completing the cycle. The hydrolysis of ATP is stimulated by J-domain containing cochaperones. These are the nucleotide exchange factors.  Back and forth Hsp70 and the damaged protein go through the cycle until the protein is nursed back to normal or, failing this, is destroyed.

The Hsp70 family acts early in protein synthesis by binding to a small stretch of hydrophobic amino acids on a protein’s surface. Aided by a set of smaller Hsp40 proteins (also known as J proteins), a hsp70 monomer binds to its target protein and then hydrolyzes ATP to ADP, undergoing a conformational change that causes the hsp70 to clamp down very tightly on the target. After the hsp40 dissociates (see below), the dissociation of the hsp70 protein is induced by the rapid rebinding of ATP after ADP release. Repeated cycles of hsp protein binding and release help the target protein to refold.

Enter [ Proc. Natl. Acad. Sci. vol. 112 pp. E3327 – E3336 ’15 ] This work shows Hsp70 is methylated on arginine #469 by Coactivator Associated aRginine Methyltransferase 1/Protein aRginine MethylTransferase 4 (CARM1/PRMT4) and demethylated by JuMonJi Domain containing 6 (JMJD6) — hideous acronyms shortening even more hideous names. Methylated Hsp70 then functions in transcription as a ‘regulator’ of Retinoid Acid Receptor beta 2 (RARbeta2) transcriptional acitivty. R468Mmethylated Hsp70 mediates the interaction between Hsp70 and TFIIH (Transcription Factor IIH).

The regulation of gene transcription is an entirely novel and unsuspected function for a heat shock protein. A classic example of moonlighting.

Drug chemists and pharmacologists are always concerned about off-target effects. For an interesting example please see https://luysii.wordpress.com/2011/02/02/medicinal-chemists-do-you-know-where-your-drug-is-and-what-it-is-doing/.  Off-target effects occur when their drug hits something else in the cell producing an unexpected (and usually untoward) effect.

If you are unaware that your target of choice is doing a little something else on the side (e.g. moonlighting) you can get an off target effect even when you hit your desired target. It’s a tough business. How many more moonlighters are out there that we don’t know about?

Hsp70 is a good example. Here are two more — no background provided, so you’re on your own — except to point out that glucocorticoids are a widely used class of drug.

[ Proc. Natl. Acad. Sci. vol. 112 pp. E1540 – 1549 ’15 ] Amazingly, the glucocorticoid receptor (GR)plays a role in mRNA degradation by acting as an RNA binding protein. When loaded onto the 5′ UnTranslated Region (5′ UTR) of a target mRNA, the GR recruits UPF1 through Proline-rich Nuclear Receptor Coregulatory protein 2 (PNRC2) in a ligand (of itself?) dependent manner to cuase rapid mRNA degradation. They call this GMD (Glurocorticoid receptor Mediated Decay). Along with Staufen Mediated mRNA Decay (SMD) and Nonsense Mediated mRNA Decay (NMD), they share UPF1 (Upstream Frameshift 1) and PNRC2.

[ Science vol. 323 pp. 723 – 724, 793 – 797 ’09 ] Stat3 proteins represent the canonical mediators of signals elicited by cytokines binding to type I cytokine receptors. However, GRIM19 (Gene associated with Retinoid Interferon Mortality 19), a mitochondrial protein, interacts with Stat3 and inhibits its transcriptional activity (where?). This work shows that Stat3 associates with GRIM19 containing complexes I and II (components of the electron transport chain) in mouse liver and muscle mitochondria. Levels of Stat3 in mitochondria are 10% of cytosolic levels.

Cells lacking Stat3 show decreased activity of mitochondrial complexes I and II. Effects on complex I and II don’t require Stat3’s DNA binding domain, the dimerization motif, or the tyrosine phosphorylation site controlling Stat3 nuclear localization and transcriptional activity — so this is a ‘moonlighting’ role for State3 having nothing to do with gene transcription. The serine phosporylation site on Stat3 is important. So Stat3 is required to maintain normal mitochondrial function.

How little we know

Well it’s basic biochem 101, but enzymes only allow equilibrium to be reached faster (by lowering activation energy), they never change it. This came as a shock to the authors of [ Proc. Natl. Acad. Sci. vol. 112 pp. 6601 – 6606 ’15 ] when Cytosolic Nonspecific DiPeptidase 2 (CNDP2), a proteolytic enzyme, was found to tack the carboxyl group of lactic acid onto the amino group of a variety of amino acids, essentially running the proteolytic reaction in reverse. Why? Because intracellular levels of lactic acid and amino acids are in the high microMolar to milliMolar range. It’s Le Chatelier’s principle in action.

The compounds are called N-Lactoyl amino acids. No one had ever seen them before. They are part of the ‘metabolome’ — small molecules found in our bodies. God knows what they do. The paper was really shocking to me for another reason, because I had no idea how many members the metabolome has.

How large is the metabolome? Make a guess.

Well METLIN (https://metlin.scripps.edu/index.php has 240,000, and Human Metabolome DataBase http://www.hmdb.ca/metabolites?c=hmdb_id&d=up&page=1676 has 42,000. I doubt that we know what they are all doing. Undoubtedly some of them are binding to proteins producing physiologic effects. Drug chemists may be mimicking some of them unknowingly, producing untoward and unexpected side effects.

What’s even more shocking to me is the following statement from the paper. State of the art untargeted metabolomics studies still report ‘up to’ 40% unidentified, but potentially important metabolitcs which can be detected reproducibly. The unknown metabolites are only rarely characterized because of the extensive work required for de novo structure determination..

So we really don’t know everything that’s out there in our bodies, and even if we did, we don’t know what they are doing. Drug discovery is hard because we only dimly understand the system we are trying to manipulate. Until I read this paper, I had no idea just how dim this is.

Why drug discovery is so hard: Reason #26 — We’re discovering new players all the time

Drug discovery is so very hard because we don’t understand the way cells and organisms work very well. We know some of the actors — DNA, proteins, lipids, enzymes but new ones are being discovered all the time (even among categories known for decades such as microRNAs).

Briefly microRNAs bind to messenger RNAs usually decreasing their stability so less protein is made from them (translated) by the ribosome. It’s more complicated than that (see later), but that’s not bad for a first pass.

Presently some 2,800 human microRNAs have been annotated. Many of them are promiscuous binding more than one type of mRNA. However the following paper more than doubled their number, finding some 3,707 new ones [ Proc. Natl. Acad. Sci. vol. 112 pp. E1106 – E1115 ’15 ]. How did they do it?

Simplicity itself. They just looked at samples of ‘short’ RNA sequences from 13 different tissue types. MicroRNAs are all under 30 nucleotides long (although their precursors are not). The reason that so few microRNAs have been found in the past 20 years is that cross-species conservation has been used as a criterion to discover them. The authors abandoned the criterion. How did they know that this stuff just wasn’t transcriptional chaff? Two enzymes (DROSHA, DICER) are involved in microRNA formation from larger precursors, and inhibiting them decreased the abundance of the ‘new’ RNAs, implying that they’d been processed by the enzymes rather than just being runoff from the transcriptional machinery. Further evidence is that of half were found associated with a protein called Argonaute which applies the microRNA to the mRBNA. 92% of the microRNAs were found in 10 or more samples. An incredible 23 billion sequenced reads were performed to find them.

If that isn’t complex enough for you, consider that we now know that microRNAs bind mRNAs everywhere, not just in the 3′ untranslated region (3′ UTR) — introns, exons. MicroRNAs also bind pseudogenes, SINEes, circular RNAs, nonCoding RNAs. So it’s a giant salad bowl of various RNAs binding each other affecting their stability and other functions. This may be echoes of prehistoric life before DNA arrived on the scene.

It’s early times, and the authors estimate that we have some 25,000 microRNAs in our genome — more than the number of protein genes.

As always, the Category “Molecular Biology Survival Guide” found on the left should fill in any gaps you may have.

One rather frightening thought; If, as Dawkins said, we are just large organisms designed to allow DNA to reproduce itself, is all our DNA, proteins, lipids etc, just a large chemical apparatus to allow our RNA to reproduce itself? Perhaps the primitive RNA world from which we are all supposed to have arisen, never left.

Off to China

No posts until March. Off to meet our new Granddaughter. Will be Email and Internet free until then.

To fill up the empty hours until I’m back, drug chemists should study the physical chemistry of protein/protein interaction, since that’s where most cellular work is done (and where new drugs should be useful). The interctions are multiple, transient and nonequivalent (the WordPress processor substituted this for nonCovalent).

An interesting paper made all 160,000 possible variants of 4 amino acids at the interface between two bacterial proteins [ Science vol. 347 pp. 673 – 677 ’15 ]. For bacterial histidine kinases mutating just 3 or 4 interfacial amino acids to match those in another kinase is enough to reprogram their specificity. The key amino acids are Ala284, Val285, Ser288, Thr289. The results were rather surprising.

Enjoy

The butterfly effect in cancer

Fans of Chaos know all about the butterfly, where a tiny change in air current produced by a butterfly’s wings in Brazil leads to a typhoon in Java. Could such a thing happen in cell biology? [ Proc. Natl. Acad. Sci. vol. 112 pp. 1131 – 1136 ’15 ] comes close.

The Cancer Genome Project has spent a ton of money looking at all the mutations of all our protein coding genes which occur in various types of cancers. It was criticized as we already knew that cancer is effectively a hypermutable state, and that it would just prove the obvious. Well it did, but it also showed us just what a formidable problem cancer actually is.

For instance [ Nature vol. 489 pp. 519 – 525 ’12 ] is report from the Cancer Genome Atlas of 178 cases of squamous cell cancer of the lung. There are a mean of 360 exonic mutations, 165 genomic rearrangements, and 323 copy number alterations per tumor. The technical details in the rest of the paragraph can be safely ignored but the point is that there no consistent pattern of mutation was found (except for p53 which is mutated in over 50% of all types of cancer, which we knew long before the Cancer Genome Atlas). Recurrent mutations were found in 11 genes. p53 was mutated in nearly all. Previously unreported loss of function mutations were seen in the class I major histocompatibility (HLA-A). Several pathways were altered relatively consistently (NFE2L2, KEAP1 in 34%, squamous differentiation genes in 44%, PI3K genes in 47% and CDKN2A and RB1 in 72%). EGFR and kRAS mutations are rare in squamous cell cancer of the lung (but quite common in adenocarcinoma). Alterations in FGFR are quite common in squamous cell carcinomas.

This sort of thing (which has been found in all the many types of tumors studied by the Cancer Genome Atlas) lead to a degree of hopelessness in looking for the holy grail of a single ‘driver mutation’ which leads to cancer with its attendant genomic instability.

All is not lost however.

MCF-10A is an immortalized epithelial cell line derived from human breast tissue. It is capable of continuous growth, but is far from normal: (1) an abnormal complement of chromosomes ) (2) threefold amplification of the MYC oncogene, and (3) deletion of a known tumor suppressor . It does lack some mutations found in breast cancer. For instance, the Epidermal Growth Factor Receptor 2 (ERRBB2) is not amplified. The cell line doesen’t express the estrogen and progesterone receptors — making it similar to triple negative breast cancer.

A single amino acid mutation (Arginine for Histidine at amino acid #1047 ) in the catalytic subunit of a very important protein kinase (p110alpha of the PIK3CA gene) was put into the MCF-10A cell line (which they call MCF-1A-H1047R). The mutation was chosen because it is one of the most frequently encountered cancer specific mutations known. Exome sequencing of the entire genome showed that this was the only change — but the control sequences outside the exons weren’t studied, a classic case of the protein centric style of molecular biology.

In the (admittedly not completely normal) cell line, the mutation produced a cellular reorganization that far exceeds the known signaling activities of PI3K. The proetins expressed were stimilar to the protein and RNA signatures of basal breast cancer. The changes far exceeded the known effects of PIK3CA signaling. The phosphoproteins of MCF-1A-H1047R are extremely different. Inhibitors of the kinase induce only a partial reversion to the normal phenotype.

They plan to study the epigenome. This is signifcant as breast cancers are said in the paper to have tons of mutations changing amino acids in proteins (4,000 per tumor). In my opinion they should do whole genome sequencing of MCF-A1-H1047R as well.

The mutant becomes fully transformed whan a second mutation (of KRAS, an oncogene) is put in. This allows them to form tumors in nude mice. Recall that nude mice (another rodent beloved of experimental biologists — see the previous post on the Naked Mole Rat) has a very limited immune system, allowing grafts of human cells to take root and proliferate.

How close the initial cell line is to normal is another matter. Work on a similar cell line the (3T3 fibroblast) has been criticized because that cell is so close to neoplastic. At least the mutant MCF-1A-H1047R cells aren’t truly neoplastic as they won’t produce tumors in nude mice. However, mutating just one more gene (KRAS) turns MCF-1A-H1047R malignant when transplanted.

The paper is also useful for showing how little we really understand about cause and effect in the cell. PI3K has been intensively studied for years because it is one of the major players telling cells to grow in size rather than divide. And yet “the mutation produced a cellular reorganization that far exceeds the known signaling activities of PI3K”

Time for drug chemists to hit the cell biology books

The (undeservedly) obscure Naked Mole Rat should be of interest to drug chemists for two reasons (1) it lives 8 times as long its fellow rodent the lab mouse (2) it never gets cancer (despite being under observation for the past 40 years). So untangling the mechanisms behind this should tell us about aging and cancer, particularly since cancer accounts for over 50% of the mortality in lab rodents. They age healthy. Until the last few years of their long lives, they show minimal morphological and physical changes of aging.

This post will concern a possible way Naked Mole Rats escape cancer. I’ve attempted to provide a molecular biologiocal background for chemists about DNA, RNA, gene transcription etc. See https://luysii.wordpress.com/2010/07/07/molecular-biology-survival-guide-for-chemists-i-dna-and-protein-coding-gene-structure/ and follow the links. There is very little in these posts about cell physiology and biology. I suggest having a look at “Molecular Biology of The Cell” and “Cancer” by Robert Weinberg. Get the latest editions, as things are moving rapidly.

The following paper tried to find out why Naked Mole Rats don’t get cancer [ Proc. Natl. Acad. Sci. vol. 112 pp. 1053 – 1058 ’15 ]. In tissue culture, naked mole rat fibroblasts show hypersensitivity to contact inhibition (aka early contact inhibition aka ECI). E.g. they stop dividing or die when they get too close to each other. The signal triggering ECI comes from hyaluronan (which has a very high molecular weight) outside the cell. Removal of high MW hyaluronan abrogates ECI and makes naked mole rat cells susceptible to malignant transformation.

ECI is associated with an increase in expression of p16^INK4a, a tumor suppressor (here is where the cell biology comes in). Cells losing expression no longer show ECI. Deletion and/or silencing of INK4a/b is found in human cancers as well. The genomic locus containing p16^INK4a is small (under 50 kiloBases), but it it codes for 3 different tumor suppressors (p16^INK4a, p15^INK4b and p14^ARF). The 3 proteins coordinate a signaling network depending on the activities of the retinoblastoma protein (RB) and p53 (more cell molecular biology).

In the naked mole rat, the INK4a/b locus codes for an additional product which consists of p15^INK4b exon #1 joined to p16^INK4a exons #2 and #3, due to alternative splicing. They call this pALT^INK4a/b. It is present in cultured cells from naked mole rat tissues, but is absent in human and mouse cells. pALT^INK4a/b expression is induced during early contact inhibition and by a variety of stresses such as ultraviolet light, gamma radiation, loss of substrate attachment and expression of oncogenes. When over expressed in human cells, pALT^INK4a/b has more ability to induce cell cycle arrest than either p16^INK4a or p15^INK4b. So pALT^INK4a/b might explain the increased resistance to tumors.

There’s also a lot of work concerning why they live so long, but that’s for another post.

As if the job shortage for organic/medicinal chemists wasn’t bad enough

Will synthetic organic chemists be replaced by a machine? Today’s (7 August ’14) Nature (vol. 512 pp. 20 – 22) describes RoboChemist. As usual the job destruction is the fruit of the species being destroyed. Nothing new here — “The Capitalists will sell us the rope with which we will hang them.” — Lenin. “I would consider it entirely feasible to build a synthesis machine which could make any one of a billion defined small molecules on demand” says one organic chemist.

The design of the machine is already being studied, but with a rather paltry grant (1.2 million dollars). Even worse, for the thinking chemist, the choice of reactants and reactions to build the desired molecule will be made by the machine (given a knowledge base, and the algorithms that experienced chemists use, assuming they can be captured by a set of rules). E. J. Corey tried to do this automatically years ago with a program called LHASA (Logic and Heuristics Applied to Synthetic Analysis), but it never took off. Corey formalized what chemists had been doing all along — see https://luysii.wordpress.com/2010/06/20/retrosynthetic-analysis-and-moliere/

Another attempt along these lines is Chematica, which recently has had some success. A problem with using the chemical literature, is that only the conditions for a successful reaction are published. A synthetic program needs to know what doesn’t work as much as it needs to know what does. This is an important problem in the medical/drug literature where only studies showing a positive effect are published. There’s a great chapter in “How Not to Be Wrong” concerning the “International Journal of Haruspicy” which publishes only statically significant results for predicting the future reading sheep entrails. They publish a lot of stuff because some 400 Haruspicists in different labs are busy performing multiple experiments, 5% of which reach statistical significance. Previously drug companies had to publish only successful clinical trials. Now they’ll be going into a database regardless of outcome.

Automated machinery for making polynucleotides and poly peptides already exists, but here the reactions are limited. Still, the problem of getting the same reaction to work over and over with different molecules of the same class (amino acids, nucleotides) has been solved.

The last sentence is the most chilling “And with a large workforce of graduate students to draw on, academic labs often have little incentive to automate.” Academics — the last Feudal system left standing.

However, telephone operators faced the same fate years ago, due to automatic switching machinery. Given the explosion of telephone volume 50 years ago, there came a point where every woman in the USA would have worked for the phone company to handle the volume.

A similar moment of terror occurred in my field (clinical neurology) years ago with the invention of computerized axial tomography (CAT scans). All our diagnostic and examination skills (based on detecting slight deviations from normal function) would be out the window, when the CAT scan showed what was structurally wrong with the brain. Diagnosis was possible because abnormalities in structure invariably occurred earlier than abnormalities in function. Didn’t happen. We’d get calls – we found this thing on the CAT scan. What does it mean?

Even this wonderful machine which can make any molecule you wish, will not tell you what cellular entity to attack, what the target does, and how attacking it will produce a therapeutically useful result.

Getting cytoplasm out of a single cell without killing it

It’s easy to see what cells are doing metabolically. Just take a million or so, grind them up and measure what you want. If this sounds crude to you, you’re right. We’ve learned a lot this way, but wouldn’t it be nice to take a single cell and get a sample of its cytoplasm (or it’s nucleus) without killing it. A technique described in the 29 July PNAS (vol. pp. 10966 – 10971 ’14) does just that. It’s hardly physiologic, as cells are grown on a layer of polycarbonate containing magnetically active carbon nanoTubes http://en.wikipedia.org/wiki/Carbon_nanotube covered in L-tyrosine polymers. The nanotubes are large enough to capture anything smaller than an organelle (1,000 Angstrom, 100 nanoMeter diameter, 15,000 Angstroms long). Turn on a magnetic underneath the polycarbonate, and they puncture the overlying cell and are filled with cytoplasm. Reverse the magnetic field and they come out, carrying the metabolites with them. Amazingly, there was no significant impact on cell viability or proliferation. Hardly physiologic but far better than what we’ve had.

It’s a long way from drug development, but wouldn’t it be nice to place your drug candidate inside a cell and watch what it’s doing?

Here’s a drug target for schizophrenia and other psychiatric diseases

All agree that any drug getting schizophrenics back to normal would be a blockbuster. The more we study its genetics and biochemistry the harder the task becomes. Here’s one target — neuregulin1, one variant of which is strongly associated with schizophrenia (in Iceland).

Now that we know that neuregulin1 is a potential target, why should discovering a drug to treat schizophrenia be so hard? The gene stretches over 1.2 megaBases and the protein contains some 640 amino acids. Cells make some 30 different isoforms by alternative splicing of the gene. Since the gene is so large one would expect to find a lot of single nucleotide polymorphisms (SNPs) in the gene. Here’s some SNP background.

Our genome has 3.2 gigaBases of DNA. With sequencing being what it is, each position has a standard nucleotide at each position (one of A, T, G, or C). If 5% of the population have any one of the other 3 at this position you have a SNP. By 2004 some 7 MILLION SNPs had been found and mapped to the human genome.

Well it’s 10 years later, and a mere 23,094 SNPs have been found in the neuregulin gene, of which 40 have been associated with schizophrenia. Unfortunately most of them aren’t in regions of the gene which code for amino acids (which is to be expected as 640 * 3 = 1920 nucleotides are all you need for coding out of the 1,200,000 nucleotides making up the gene). These SNPs probably alter the amount of the protein expressed but as of now very little is known (even whether they increase or decrease neuregulin1 protein levels).

An excellent review of Neuregulin1 and schizophrenia is available [ Neuron vol. 83 pp. 27 – 49 ’14 ] You’ll need a fairly substantial background in neuroanatomy, neuroembryology, molecular biology, neurophysiology to understand all of it. Included are some fascinating (but probably incomprehensible to the medicinal chemist) material on the different neurophyiologic abnormalities associated with different SNPs in the gene.

Here are a few of the high points (or depressing points for drug discovery) of the review. Neuregulin1 is a member of a 6 gene family, all fairly similar and most expressed in the brain. All of them have multiple splicing isoforms, so drug selectivity between them will be tricky. Also SNPs associated with increased risk of schizophrenia have been found in family members numbers 2, 3 and 6 as well, so neuregulin1 not be the actual target you want to hit.

It gets worse. The neuregulins bind to a family of receptors (the ERBBs) having 4 members. Tending to confirm the utility of the neuregulins as a drug target is the fact that SNPs in the ERBBs are also associated with schizophrenia. So which isoform of which neuregulin binding to which iso form of which ERBB is the real target? Knowledge isn’t always power.

A large part of the paper is concerned with the function of the neuregulins in embryonic development of the brain, leading the the rather depressing thought that the schizophrenic never had a change, having an abnormal brain to begin with. A drug to reverse such problems seems only a hope.

The neuregulin/EBBB system is only one of many genes which have been linked to schizophrenia. So it looks like a post of a 4 years ago on Schizophrenia is largely correct — https://luysii.wordpress.com/2010/04/25/tolstoy-was-right-about-hereditary-diseases-imagine-that/

Happy hunting. It’s a horrible disease and well worth the effort. We’re just beginning to find out how complex it really is. Hopefully we’ll luck out, as we did with the phenothiazines, the first useful antipsychotics.

Follow

Get every new post delivered to your Inbox.

Join 77 other followers