Protein’s Impact on Colorectal Cancer is Dappled
In early stages, it acts as tumor suppressor; later it can help spread disease

Researchers at University of California, San Diego School of Medicine have discovered a cell signaling pathway that appears to exert some control over initiation and progression of colorectal cancer, the third leading cause of cancer-related death in the United States. A key protein in the pathway also appears to be predictive of cancer survival rates.

The study is reported in the June 30 issue of eLife.

The protein, known as Dishevelled-associating protein with a high frequency of leucine residues or Daple, is produced by nearly all healthy cells in the body and is well-recognized for its role in helping cells in different tissues coordinate processes, such as development and maintenance of organs.

The study is the first to show that Daple is also a tumor suppressor, though only in the early stages of cancer. At later stages of disease, when cancer cells have escaped from the main tumor and are circulating in the blood stream, the protein may actually make cancer cells more invasive and likely to spread.

“Daple is a double-edged sword,” said senior author Pradipta Ghosh, MD, an associate professor of medicine. “The protein is a tumor suppressor early on but heralds faster death in advanced stages of colorectal cancer. We are working to figure out why.”

For the study, researchers analyzed tumor samples from 173 persons with stage 2 colon cancer for Daple levels. Among these people, lower Daple levels were associated with worse outcomes over a two-year period, while higher levels were associated with better patient survivorship over the same period.

Daple levels were then measured in cancer cells collected from the blood stream of 51 stage 4 colorectal cancer patients. Among these late-stage cancer patients, those with high Daple levels had a 20 percent survivorship over a 1,000-day period, compared with a 100 percent survivorship for those with low Daple levels. By the three-year mark, there were no survivors in the high-Daple group. In contrast, among those with low-Daple expression, 75 percent of the patients were still alive.

Consistent results were observed during 3D tumor cell culture tests. Reduced expression of Daple aided tumor formation and growth, while increased Daple expression triggered cancer cell motility, associated with metastasis.

Fluorescent imaging studies of cells showed that Daple functions primarily by regulating the activity of a family of cell-signaling proteins, known as G proteins. G proteins enable cells to sense and respond to what is happening around them. About 30 percent of all prescription drugs affect cells via G-protein-coupled receptors.

The UC San Diego-led team reported that it is G protein signaling that likely conveys both the desirable tumor-suppressing effects and the not-so-desirable tumor-spreading effects. The consequences of this G signaling on cancer patients’ prognosis thus depends on where and when the G signaling occurs.

“Our next challenge is to figure out how we can exploit Daple’s beneficial attributes while inhibiting its negative ones,” Ghosh said.


A fortnight ago a biologist in Switzerland (Eduardo Moreno) tweeted he’d eat the first page of this paper if its hypothesis* was proved correct, and in reply one of the authors sent him a chocolate and marzipan replica :-)

Here’s a remedy in case you have to fulfil your pledge. Maybe it turns out that you are right, and you have to eat nothing (which I don’t hope). Maybe things are partly correct and partly not, and it would be a fraction of the page you have to consume. Then we could do it together, with a sip of Grappa.

* evidence for evolutionary adaptation of TRR-NFκB signaling modules in an organismal surveillance system that measures internal tissue fitness rather than external pathogenic stimuli.

Rules in the Avengers Tower:
  • Let Bucky sleep anywhere he wants to - waking Bucky from a nap/sleep will cause him to be grumpy, in result of bodily harm.
  • Let Clint be on top of the furniture.
  •  Don’t use Steve’s shield as a sled or a frisbee.
  • Respect Natasha, if not result of bodily harm maybe caused.
  • Don’t ask Tony to take out his arch racter so you can charge your iPod.
  • Don’t let Thor electrify the microwave for faster cooking.
  • Don’t let Thor electrify anything around Bucky.
  • If Thor is using any electricity, keep Bucky away.
  • Don’t ask Pepper to do your laundry.
  • Don’t bother Bruce when he’s meditating- results will cause bodily harm to yourself.
  • No war movies, no matter how cool it is.
  • Animation movies always accepted
  • No movies with trains, ice, or snow/blizzards.
  • You swear, money goes into the swear jar.
  • Don’t reprogram Bucky’s arm to pick up radio signals, cell phone calls or tv stations - results will cause bodily harm to yourself.
  • Make sure Steve and Bucky has seen tv show or movie before you talk about it with them.
  • Don’t stick anything to Bucky’s arm.
  • Only Natasha and Steve are allowed to Bucky’s hair.
  • If you teach Bucky and Steve about selfies be prepared to for the results.
  • Leave the room if Bucky or Steve is having an episode, either one will take care of the other one.
  • No pulling pranks!
  • No soviet Russian jokes
  • Don’t say “Sputnik”
  • Always have poptarts for Thor
  • Always have coffee for Clint
  • Service animals are allowed- no matter what Tony says about it.
  • If Bucky brings home a stray animal (cat or dog) let him keep it.
  • No translating the conversation when Natasha and Bucky argue in Russian.
  • No taking Clint’s hearing aids.
  • No sexist comments to Natasha.
  • It’s mandatory for everyone on the team to get at least 4 hours of sleep a day.
  • No being in Tony’s lab without his permission.
  • Let Steve and Bucky have time to themselves.
  • Respect others personal property, even when said property is left in the communal living areas.
  • ALWAYS were pants outside your bedroom.
  • No sex in the labs.
  • No smoking inside the tower.

If you ever want to appreciate this wonderful planet that we live on, go to the middle of the desert where there is no civilization. Go to where there is no cell phone signal. Go where it’s so quiet that you can actually hear the blood rushing through your head and you can hear planes flying through the sky miles and miles away. Go where there is no light pollution and look up at the moon and the stars and the planets. All of the stillness and the quietness combined with the universe above you will make you realize how small you actually are and that you’re lucky to be alive. In the grand scheme of things, nothing matters. We are all pretty insignificant. I have no idea why I was placed on this earth, but after tonight, it is my mission to enjoy the heck out of the life I’ve been given and I’m going to live it to the fullest. Life is so precious and fleeting, so we should all just enjoy what we have and be thankful for everything. We live on a beautiful planet and we are surrounded by beautiful places and things and people.

GPCR signalling. Here are three examples of transmembrane signalling through G-protein-coupled receptors, with a little bit on their specific modes of regulation.

Adrenergic receptors couple to heterotrimeric G-proteins, which can be either stimulatory or inhibitory on adenylate cyclase, depending on the exact receptor: beta ARs stimulate AC; alpha ARs inhibit it. AC makes cAMP, the secondary messenger. cAMP binds protein kinase A, causing dissociation of its regulatory subunits and activation of the enzyme. cAMP is degraded to AMP by action of phosphodiesterase. Receptors are homologously desensitised by action of beta AR kinase, which phosphorylates cryptic phosphorylation sites on beta AR when adrenaline is bound. They are heterologously desensitised by downstream kinases which are only on when PKA is active.

The thrombin receptor is activated by N-terminal cleavage by thrombin. This reveals a TRAP sequence in the receptor, which acts as its own ligand. Phospholipase C beta is activated through a heterotrimeric G protein. (Note that phospholipate C gamma is activated through receptor tyrosine kinases.) PLC beta makes IP3, which releases calcium ions from the SR, ER, and/or cell exterior. Calcium ions activate protein kinase C. Calcium is removed from the cell by SERCA and PMCA ATPase calcium pumps.

The rhodopsin receptor in light-sensitive rod cells contains retinal, which undergoes a cis/trans isomerisation on incident light. Transducin carries the signal to phosphodiesterase, which extinguishes the second messenger cGMP. Loss of cGMP causes closure of cGMP-gated ion channels and stops calcium and sodium ion influx. This stops glutamate release, which allows the membrane to hyperpolarise, delivering the signal to the bipolar cell. Recoverin modulates the activity of guanylyl cyclase, which makes cGMP, by responding to the cellular calcium level. This allows the signal pathway to work in a range of ambient light conditions.

Abbreviations: Adr = adrenaline; GPCR = G-protein-coupled receptor; AR = adrenergic receptor; G = G-protein; AC = adenylate cyclase; ATP = adenosine triphosphate; cAMP = cyclic adenosine monophosphate; reg = regulatory subunit; cat = catalytic subunit; PKA = protein kinase A; beta ARK = beta adrenergic receptor kinase; Thr = thrombin; TRAP = thrombin receptor activatory peptide; PLC = phospholipase C; PIP2 = phosphatidylinositol 4,5-bisphosphate; IP3 = inositol 1,4,5-trisphosphate; DAG = diacylglycerol; SR = sarcoplasmic reticulum; ER = endoplasmic reticulum; PKC = protein kinase C; h = Planck’s constant; nu = frequency of light; PDE = phosphodiesterase; GC = guanylyl cyclase; GTP = guanosine triphosphate; cGMP = cyclic guanosine monophosphate; GMP = guanosine monophosphate; Glu = glutamate.

Signalling through G-protein-coupled receptors von Ayraethazide ist lizenziert unter einer Creative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International Lizenz.

Mitochondrial disease, a bone of contention in the church apparently, is back in the news as UK legislation makes good progress towards the first treatment. The mitochondrial genome is a second outpost of the sum human genome, but isn’t unique as far as organelles are concerned. Some protists have a nucleomorph, while plants store genomic information in their plastids such as chloroplasts (though there are unexplained exceptions, described by Smith & Lee 2014).

Amazingly, the human mitochondrial genome is thought to be encoded in a single polycistronic sequence (something more heard of among viruses, bacteria and archaea, a nod to the endosymbiotic event that spawned the organelle) that is cleaved strategically in the liberation of 22 tRNAs (“tRNA punctuation“) to give the 13 mRNAs and 2 rRNAs (12S and 16S), a model first proposed by Ojala and Attardi in 1981:

Although all are transcribed on a single polycistronic RNA, the rRNAs end up far more abundant, suggesting an entirely unexplored mode of post-transcriptional regulation at work on the mitochondrial genome.

Michael Regnier wrote for the Wellcome Trust 3 years ago that “1 + 1 + 0.00001 ≠ 3”, likening the idea of mitochondrial replacement therapy meaning a child would have three parents to declaring heart transplant recipients have four. The distinction is real though (in my opinion), insofar as transplants aren’t at the embyronic level, so don’t diffuse into every cell of the adult… As Jeremy Farrar explained in a radio interview this week, the main reason we should be out in support of the technology is simply the extensive pre-clinical testing that’s gone into plans for mitochondrial medicine.

In oxidative phosphorylation (OXPHOS), ATP synthesis is coupled to electron flow through NADH or FADH2 cofactors en route to molecular oxygen. The electron carriers in the mitochondrial inner membrane respiratory assembly include quinones, flavins, FeS complexes, cytochrome protein haem groups, and copper ions. NADH’s electrons hop onto the flavin mononucleotide (FMN) prosthetic group of NADH-Q oxidorectase (“Complex I”), the first of a chain of four complexes that reduces ubiquinone (Q) as ubiquinol (QH2).

Similarly, complex II (succinate dehydrogenase, a citric acid cycle enzyme) pops electrons onto Q from FADH2 making yet more QH2, precipitating the hydrophobic electron carrier to spill them at Complex III, Q-cytochrome c oxidoreductase (which despite its name harbours cytochromes b and c1).

Cytochrome c is a more water-soluble mobile e carrier, and a peripheral membrane protein (meaning it adheres only temporarily at the mitochondrial inner membrane). Departing from the electron transport story, cytochrome c unlocks the cell’s death programme, apoptosis, in clustering with APAF1 and procaspase-9 to form an apoptosome.

The mitochondrial genome is frequently said to encode only parts of the mitochondrial bioenergetic machinery - components of the ETC. In humans, these genes reside on a single, circular, double stranded DNA molecule, but can also be linear, while cucumbers have 3 circular and individually autonomous mitochondrial chromosomes (below, an excerpt from Jennifer Mach’s 2011 editorial accompanying the finding).

They found that, unlike most plant mitochondrial genomes sequenced so far, which map to a single, circular chromosome, the cucumber mitochondrial genome contains three chromosomes (1556, 84, and 45 kb; see figure). These physically map as circles, although the exact structure may be more complex, as indicated by previous electron microscopy images of other plants showing branched, linear, and other structures. All of the intact, identifiable mitochondrial genes were found on the large chromosome, but all three appear to have transcribed sequences. The large chromosome is approximately twice as abundant as the smaller chromosomes (2.2 and 1.5 times as abundant), suggesting that they replicate independently. Sequence and DNA gel blot experiments showed that the two small chromosomes exist as both independent and cointegrated forms. Deep sequencing also allowed the authors to examine repeat-mediated recombination. The authors examined paired sequences from the ends of the same subclone, where the pairs do not map to the same location. Computational reconciliation of the ends showed many instances of recombination events meditated by many repeats, indicating highly active repeat-mediated recombination throughout the genome. Thus, this work explains how the cucumber mitochondrial genome got so large and provides new tools for examining the complex dynamics of these cool genomes.

mtDNA isn’t solely for ETC proteins though: the ~17,000 base pair sequence encodes 13 ‘canonical’ polypeptides involved in bioenergetics, but some smaller peptides lurk amongst them, and depart not only from electron transport but from the mitochondrion altogether.

Mitochondrial-derived peptides, or MDPs were discovered in 2001 - the first being humanin (rats have a homologue called rattin). Humanin takes on a host of antistress functions in humans, as brought together in a review from Lee et al. in 2013:

Humanin was first isolated from the surviving fraction of an Alzheimer’s disease patient’s brain (Hashimoto 2001, Guo 2003). It’s not yet clear whether mitochondrial or cytoplasmic translation is more often used, but since mitochondrial translation uses a slightly different genetic code, the mitochondrially translated 24 amino acid peptide produced is distinct from its cytoplasmic counterpart (it’s biologically effective in both forms, so the jury is out).

Mitochondrial signalling is far more elaborate than the common trope of ‘powerhouse’ or ‘battery’ gives credit. A host of proteins: Bak, Bcl-2, BH3-only (Puma, Nox, Bim, Bid) exert a layer of pro- and anti-apoptotic regulation integrated through a small protein Bax which if unsequestered pokes pores in the mitochondrial membrane, releasing the death-inducing cyt c into the cytoplasm.

Humanin is secreted in blood plasma and bound to cell membranes (where it fiddles around in receptor signalling), but inside tissues binds to Bax, preventing it causing harm to the host cell through bursting mitochondrial membranes.

Oddly, although this question came to me upon reading the news, a paper published only 2 days ago from a trio of French, Italian, and Canadian researchers covers the same topic, registering the potential for further unknown peptides to be lying in the mitochondrial genome. After reviewing the role of humanin, they describe the gau gene:

In addition to its ubiquitous presence, the reason why the gene has been named gau for Gene Antisense Ubiquitous, strong arguments indicate that the gau ORF indeed encodes a functional protein: (i) it is evolving under purifying selection, (ii) the deduced GAU proteins share some conserved amino acid signatures and structure among different taxa, suggesting a possible conserved function, (iii) gau has been identified in sense-oriented ESTs with poly(A) tails, (iv) immunohistochemical experiments using an anti-GAU monoclonal antibody showed a mitochondrial-specific signal in human cells, and (v) BLAST analyses suggested that no part of any known human proteins exhibits a high level of amino acid identity with the peptide antigen that was used for immunization and antibody production (Faure 2011). As for humanin, potentially functional and similar but not identical gau regions have been found in the nuclear genome. However, none of the deduced protein sequence possesses a mitochondrial signal peptide (Faure 2011), a result that is not in line with the intramitochondrial localization of GAU observed using the anti-GAU antibody. According to the authors, the most parsimonious hypothesis is that gau is a mtDNA-encoded protein gene, providing evidence for antisense overlapping functional open reading frames in mitochondrial genomes.

Other than this well-studied pair, bioinformatics has brought sequence-level suggestions of extra-canonical proteins, “small nuclear open reading frames (sORFs) encoding biologically active peptides of 11–32 amino acids in length” (Andrew & Rothnagel, 2014; Kondo et al., 2010).

Lastly, there’s suggestion that further mitochondrial proteins may be encoded through tetracodons (Seligman, 2012a,b), backed up by observation that this class of tRNA (which feature 4 interacting nucleosides at the anticodon loops rather than the regular 3 base code) is seen to evolve along with the number of sequences that would potentially form tetracoded mitochondrial open reading frames (ORFs).

Biochemistry with Olivia: G-Protein Coupled Receptors

G-protein coupled receptors (GPCRs) are a very diverse type of cell signalling receptor found only in eukaryotes, although the genes coding for GPCRs can be found in the genomes of prokaryotes as well. They sense ligand molecules in the extracellular environment and then activate signal transduction pathways inside the cell, leading to changes within the cell. GPCRs are the largest family of membrane proteins in the human genome, which contains over 800 different GPCRs (of which 460 are olfactory receptors). GPCRs can be divided into five main families: the rhodopsin family, the adhesion family, the frizzled/taste family, the secretin family and the glutamate family. The significant majority of GPCRs are in the rhodopsin family (701 of them, to be exact) and a large number of these GPCRs still have unknown physiological roles. 

Structure of GPCRs

GPCRs are integral membrane proteins containing seven membrane spanning helices. The N-terminus of the receptor protein is extracellular and contains the ligand-binding domain. The C-terminus is intracellular and is bound to the G-protein in the inactive state (when a ligand is not bound to the binding site). When a ligand binds, the receptor undergoes a conformational change that leads to the activation of the bound G-protein, which then detaches from the receptor. All GPCRs are structurally very similar, particularly in the transmembrane segments, although the ligands that bind to them can vary widely, from photons to proteins. 

What are G proteins?

G proteins are proteins which hydrolyse guanosine triphosphate (GTP) to guanosine diphosphate (GDP). Their role is to act as ‘molecular switches’ in cell signalling, and they can either be active (binding to GTP) or inactive (binding to GDP). There are two types of G-proteins, monomeric 'small GTPases’ and heterotrimeric G-proteins, composed of three subunits, the Gα subunit which has enzymatic activity and the Gb and Gy subunits which are tightly associated to form the Gby dimer. Only heterotrimeric G proteins are associated with G-protein coupled receptors, which acts as a Guanine Nucleotide Exchange Factor (GEF) by exchanging GDP for GTP in the active site of the Ga subunit of the G protein, and so activating it. There are several different types of Ga subunit, all of which perform different functions.

  • Gas subunit- activates the cAMP (cyclic AMP) dependent pathway by binding to and activating a protein called adenylate cyclase, which then converts ATP to cAMP. A large number of hormones are stimulated by the cAMP-dependent pathway, e.g. glucagon, which is involved in breaking down glycogen to glucose.
  • Gai subunit- inhibits the cAMP-dependent pathway by inhibiting adenylate cyclase
  • Gaq/11- activates phospholipase C (PLC) which then results in the activation of the inositol phospholipid dependent pathway.
  • Ga12/13regulate changes in the cell cytoskeleton and so are responsible for cell movement. 

Cell Signalling

DEFINITION: Cells communicate with one another by sending and receiving signals

  • Cells must have modified protein molecules attached to their surface, in order to be able to receive signals known as RECEPTORS
  • Hormones areused in this communication process
  • Any cell with a receptor for these hormones is known as a target cell
  • If a hormone binds to a target cell, it’s because they have complementary shapes
  • ^ at the end it’s meant to say reduced blood sugar levels…
  • EXAM QUESTION: Nicotine has an effect on nerve cells, but not on other types of cell in the body, use your knowledge of cell membrane structure to explain why. (3 marks)
Nicotine only binds to receptors with a complementary shape. Since different cells have different membrane-bound receptors, nicotine will only affect nerve cells as only they have the correct receptor for it.
Posttranslational modification of proteins

Larry H Bernstein, MD, FCAP, Wrire and Curator

Aviva Lev-Ari, PhD, RN, Curator

http://pharmaceuticalintelligence.com/4-21-2014/2.5.2/larryhbern/Posttranslational modification of proteins

Posttranslational modification

 of proteins: expanding nature’s inventory.

Walsh, Christopher T.
Roberts & Company Publishers   2006
Englewood, Colo.: xxi, 490

For students of protein structure, metabolism,…

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It may be necessary not to think of the pathways as sharply and precisely delineated from the broader cellular network, but rather to keep in mind that a pathway representation may always be a “warm, fuzzy, cloud”: that is to say, warm since the answer is close but not necessarily exact; fuzzy, since the membership of components in a pathway is graded; and a cloud, since the boundaries are not sharply defined.

Kleensang et al (2013) Pathways of Toxicity, a report from t4 - The Transatlantic Think-Tank for Toxicology.

This quotation also referenced Kholodenko’s paper in Science SignallingComputational Approaches for Analyzing Information Flow in Biological Networks, in which the authors write that “the life of a biologist has changed”…

Just 20 years ago, the standard modus operandi was one research team working on one protein or gene for a lifetime of research.

… Mathematical and computational modeling methods are playing major roles in both tasks, especially as the ability to generate data has outpaced our ability to interpret them. The greatest strides have been made in the first task. The second is lagging behind but is moving into the limelight as new analysis methods are developed.

… Historically, traditional hypothesis-driven experiments formed the early basis of information about signal transduction pathways by piecing together components in tedious trial-and-error type experimental approaches that assume that signaling inputs are related to outputs by a linear path of signal transduction. Two experimental mainstays that have rapidly and dramatically enhanced the ability to map the components of interaction networks are the genetic yeast two-hybrid (Y2H) system and mass spectrometry (MS)–based proteomics.

False-positive results and the absence of known protein-protein interactions (PPIs) that depend on contextual information (such as stimulus-specific phosphorylation, which may or may not occur within budding yeast) remain limitations of the method. However, as the above example shows, these shortcomings are being addressed. The abundant data that have been and continue to be accumulated are of great utility, particularly when combined with other types of interaction data. An additional limitation of the Y2H approach is that it cannot reveal dynamic changes in PPIs; thus, the resulting graphs of binary interactions do not reveal how signaling information flows, which hinders the reconstruction of directed pathways.

MS analysis of cells fractionated into defined subcellular signaling structures—such as the centrosome, the mitotic spindle, and the kinetochore, for example—has identified many of the critical protein components of these subcellular complexes. Focused isolation approaches—particularly the use of tandem affinity purification (TAP)–tagged proteins, combined with stable isotope labeling by amino acids in cell culture (SILAC)—are sensitive, specific, and accurate methods for identifying proteins that interact with particular signaling molecules. A caveat of most MS-based methods is that the experiments generally do not reveal which interactions are direct and which are mediated through an additional component or components. 

Perhaps the best approach to defining network topology is the weighted collection of the available evidence for specific protein-protein interactions, including the results of both the low-throughput and high-throughput approaches. Fortunately, a number of online databases and Web-based tools that enable the construction and analysis of weighted collections exist, including BIND, BioGRID, MINT, and DIP. Some tools, such as STRING and iRefWeb, attempt to merge the information from different databases, as well as text-based searches from the literature into a single Web resource. Similarly, resources for pathways and pathway models are constantly being improved. Some examples are the Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome, Pathway Commons, the Science Signaling Database of Cell Signaling, the Pathway Interactome Database, and BioModels.

The scale of most of these data collections is enormous, and the amount of potentially valuable information in them is huge. However, they are complicated by the presence of misidentified peptides and proteins, incomplete representation of known network components, and an inherent bias against proteins that are present in low abundance… [None] of these approaches enables the reconstruction of network models that enable dynamic simulations.

High-throughput data usually suffer from low information content—that is, the observable results contain little information about the unknown parameters that caused them. A “local” perturbation that is initially confined to a particular network node can propagate and cause widespread “global” changes in the network and thereby mask immediate connections and routes. This issue is particularly pertinent to large omics data sets, because even in response to a single local perturbation, the omics snapshots of the cellular state arise from a plethora of interactions spreading through cell networks.

From thereon in gets quite dense but engrossing if you’re interested in understanding “systems biology”, and although encased in computational terminology many of the ideas are quite intuitive. As the paper points out - all of the life sciences are grappling with this issue of a sudden data deluge. Wonder is a matter of personal taste, but this is fascinating to me:

One of the challenges of mechanistic modeling is a combinatorial explosion in the number of emerging different species and distinct states of cellular networks that include scaffolds and proteins with several posttranslational modification sites. These multiple docking and modification sites generate a variety of heterogeneous protein complexes, and each of these complexes can be involved in many parallel reactions. Even initial steps in signal transduction that include receptors and adaptor proteins may generate hundreds of thousands and millions of distinct states, referred to as “micro-states” of a network.

… Many topological motifs enriched in transcriptional networks have also been found in RTK, mitogenic [Ras to ERK], and survival [PI3K] signaling networks. Although different underlying biochemistry results in distinct kinetic equations that describe signaling or gene networks, the control circuitry remains similar.

Having said this, some of the field’s problems are particularly gruesome:

It is difficult or impossible to compare networks composed of different types of signaling components. Protein networks are typically interaction networks or modification networks. Like transcriptional networks, they often have a time component or compare different conditions. However, it is difficult to compare transcriptional and protein networks directly, even when frequent parallel sampling is available.

The time delay between the production of mRNA and its encoded protein can vary between genes and, hence, tends to destroy protein and gene regulatory network comparisons based on temporal correlations. In addition, because high-throughput proteomics identifies proteins on the basis of isolated peptide fragments, rather than coverage of the complete protein sequence, matching proteins to splice variants easily seen in transcriptomics experiments becomes ambiguous. Metabolites are direct outputs of protein activities and, therefore, would be expected to map organically onto protein networks. However, metabolic networks model physical fluxes of metabolites, whereas protein networks model flow of information or abstract influences. These relations cannot be directly superimposed, and also, the mathematical methods for analyzing these networks are fundamentally different.

It seems that we are stranded with a treasure chest of information and many keys that do not fit.

The results from genome sequencing projects have not only destroyed the one gene–one protein–one function dogma, but also bluntly shown us that our mere 20,000 genes perform the huge diversity of biological tasks by combinatorial cooperation whose complexity simply stuns the human mind.

& lastly an interesting little fact…

Although several thousand proteins are estimated to be susceptible to a drug, currently used drugs only target ~270 human proteins, a number that has not changed in almost a decade.

Currently at the last lecture in the Tissues series, and it’s on communication between cells. The first one was easy, so why is this one so complex? Baaah. Just when I had got to the point of thinking “That’s a lot of material - the lecture must be almost over”, I look and see that it’s less than half-way through. That happens quite a lot, actually.

Ah, well. Must plod along, because I can’t afford to fail these exams. That would be bad. People don’t want doctors that fail their exams. At least my brother is home for a while, so I have someone to blow off steam with. That involves playing Halo 4 and combination of flailing and shouting. I’m out of practice on there - Battlefield is my game of choice, so I end up getting killed half the time.

I’m also rather concerned about my sister. Due to some disappointing results in her January exams, she seems to have given up all hope of going to university. She wasn’t all that enthusiastic anyway, but I didn’t expect her to give up. It’s difficult to broach the topic with her, as she’s quite guarded and dismissive. Even if she doesn’t want to go to university, I still have to stress the importance of A-Levels to her. They’re not useful just for university. Whatever the case, I just want her to be happy, and I need her to realise that she won’t be able to sit around playing around on her phone and computer all day. Finding a careful way to say that is difficult.

Third cup of coffee and back to it. I hope you’re all well!



Oncology Cell Signalling Medical Animation

During the course of tumor progression, cancer cells aquire a number of characteristic alterations. These include the capacities to proliferate independently of exogenous growth-promoting or growth-inhibitory signals, to invade surrounding tissues and metastasize to distant sites, to elicit an angiogenic response, and to evade mechanisms that limit cell proliferation, such as apoptosis and replicative senescence. These properties reflect alterations in the cellular signalling pathways that in normal cells control cell proliferation, motility, and survival.


For more info visit: http://www.polygonmedical.com

COMMUNICATION: how cells communicate.

Cells communicate with each other by a process called cell signalling.

cell signalling is a process in which a cell releases a chemical that is detected by another cell, the other cell is able to respond to the chemical.

there are two types of cell signalling:

  • hormonal
  • neuronal

The neuronal system is an interconnected network of nuerones  that signal each other across synapse junctions. it allows quick signalling (reflexes and stuff)

The hormonal system uses the blood to transport signals. it allows slow signalling (insulin and ovulation)

Q: Many proteins used in signal transduction are made up of domains that act as separate functional modules. Describe how two types of these modular domains work, including why their modularity is useful for their function.

Two prominent examples of autonomously folding units with conserved catalytic functions — the broad consensus definition of a ‘domain’ — found in signaling proteins are the SH2 (Src homology 2) and SH3 (Src homology 3).

Pawson (2001) described in detail how SH2 domains function as interaction modules.

Short linear motifs (SLiMs) are functional peptide modules some 3-10 amino acids long, that most commonly function through interaction with a specific globular protein domain. Two of the best known cases of such interactors are SH2 (src homology 2) and SH3 (src homology 3) domains. SLiMs may act as sites for post-translational modification or register binding events.

The binding function can be dissected into three types of signalling: targeting signals (such as the nuclear localisation signal), degron motifs (such as KEN-box degradation motifs) and ligand-binding sites.

SH2 and SH3 fall into the latter class (indeed ligand-binding is the most commonly seen function of the three).

Astonishingly, most of the binding specificity and affinity of a SLiM is found in a still smaller portion of the 3-10 residues, a core of more like 2-5 residues. Weatheritt, Babu, Gibson and Tompa have provided a broad description and several reviews of these modules in recent years, and have repeatedly drawn attention to the presence of intrinsic disorder at the peptide regions of these motifs. Late last year a census of peptide motifs from Babu and Tompa suggested these modules were present across the proteome on the order of over a million, making them a staggeringly understudied aspect of the cell.

Approximately 110 SH2, and 280 SH3 domains are found in the human genome. Each of these domains can bind peptide motifs in dozens of different partners (though not all motifs can be assumed functional).

SH2 mediate phosphorylation-dependent signaling, and were the first type of peptide-recognition domains discovered (Pawson 2002). Further to the recognition of phosphorylated tyrosine (pTyr), different SH2 instances exhibit preference on the basis of a handful of residues C-terminal to the pTyr (the ELM Eukaryotic Linear Motif database distinguishes 6 SH2 ligand classes). In this manner, sequence context of autophosphorylation sites of a receptor for example can also act to determine which SH2-containing targets it recruits, thus which biochemical pathways it activates.

This selection is only curtailed by the contribution of shape complementarity of ligands, placing some restriction on taking a purely primary sequence-based predictive view here.

Modularity is enhanced in this case by the phosphorylation PTM, since a major portion of binding energy is contributed by interaction with the modified residue such that the consensus motif can be shorter than would otherwise be permissible (SH2 domains can bind unmodified ligands, but with weaker affinity and requiring larger peptide-binding surfaces).

Unlike many protein-protein interactions, the hydrophobic effect does not drive protein-protein interaction in SH2 phosphorylation. Sitting in a deep pocket, the Tyr residue in question interacts through a charge-driven mechanism. Transient binding is often associated with a smaller hydrophobic residue content at interfaces.

SH3 binds proline-rich peptides that form a polyproline type II helix (the classic SH3 consensus motif is PxxP, x xbeing an arbitrary residue), though variants are observed which will bind the domain in opposite orientations.

SH3 binding can alter localisation, such as increasing residence time of some proteins at the cell surface for example in the case of a cancer-associated splice variant of adam-15 [a metalloproteinase].

It’s fitting to describe these two domains together in regard to modularity, as they are a fine example of the further degree of modularity obtained in combining peptide recognition domains.

In the Src tyrosine kinase which bestowed SH2 and SH3 their names, an SH2 domain binds the phosphorylated C-terminus and an N-terminal SH3 domain binds the linker between SH2 and the kinase domain.

Amusingly, the prototypical polypeptide for SH- domains does not hold true to the PxxP ‘consensus’ in this linker (the polyPro type II helix forms regardless, which interacts with the SH3 domain).

Neither of these intramolecular interactions are sufficient individually, but when combined they render the kinase domain inactive. The autoinhibitory interactions are distant from the kinase active site, but attenuate its activity, in part by clamping the kinase domain (SH1) in a rigid state precluding the dynamic motions crucial for substrate phosphorylation.

What’s more, these interactions suppress undesirable binding of the interaction domain.

Their repression is only alleviated upon dephosphorylation of the C-terminus [causing the SH2 to dissociate, leaving just the individually insufficient SH3], or coaxing of the SH2/SH3 domains away by extramolecular ligands (which break the inhibitory grip of the phosphorylated tail and linker region). The most potent such activators are multivalent peptides in which SH2 and SH3 binding sites [the extramolecular ligands] correctly spaced for both domains, which imparts high specificity and compounds the utility of modular domains as mediators transient, yet high specificity signalling (cooperativity in low affinity interactions produces high avidity).

Functionally, theoretical biologists have proposed that avidity-sensing switches such as this allows proteins to quantify stoichiometry (modulating signal transmission in line with ligand concentration).

Great read: Stein et al. (2009) Dynamic interactions of proteins in
complex networks: a more structured view
. FEBS, 276 5390-5405

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