Codon De-optimisation, Beta Turns, etc

 Why would anyone wish to design a live, attenuated vaccine for COVID-19? They cannot be administered in pregnant women or immunosuppressed subjects, but they do have one major advantage. They are the only form of vaccine that can be given intranasally. And given through this route, they stimulate the production of secretory IgA antibodies, which are the only isotype which protect the upper respiratory tract against COVID-19. (The lower respiratory tract is protected by circulating IgG). 

Unlike other types of vaccines therefore, live attenuated vaccines would be expected to stop not just illness from COVID-19 in the vaccinated subject, but also transmission of the virus to contacts.

But how is the live virus attenuated? There are various means of doing so- by growing it at a lower temperature, or in a non-human cell line, but the one method used for 3 vaccine candidates using live attenuated virus during the current pandemic use a technique called codon de-optimisation.

The genetic code is redundant. That is to say, a given amino acid can be encoded by more than one codon. Yet, amongst these multiple codons, there is one that is favoured above all others- a phenomenon called codon bias. 

Several live vaccines have attenuated the causative virus by reverse genetics (targeting the putative DNA triplet bases which codes for a certain amino acid in the peptide chain), through replacing the normally favoured codon in the viral DNA or RNA with a less favoured codon. In some cases, this goes a bit further and replaces a favoured codon pair with a less favoured codon pair. This is called codon de-optimisation.

While in theory, the replaced codon codes for exactly the same amino acid, in practice, this disrupts the tertiary structure of the peptide chain and leads to a dysfunctional viral protein being translated in the host cell. It is thought that the tRNA carrying the "non-favoured" anti-codon somehow interferes with the translation machinery.

Interestingly, when non favoured codon pairs are introduced during codon de-optimisation, it invariably introduces more CpG dinucleotides (nnCpGnn). The latter are under-represented in favoured codon pairs. It has  however been shown that this excess of CpG nucleotides is not mechanistically responsible for the disruption of translation, which is thought to be induced by the unfavourable "fit" caused by the tRNA carrying the "unfavoured" anticodon, as described above.

The corollary to this is that codon optimisation (i.e. using the favoured codon) can improve the yield of useful proteins produced for medical usage in E.coli by phages.

A related concept is stabilisation of a recombinant viral protein vaccine by introducing two proline residues around a beta turn in the peptide molecule. A beta turn is a portion of the peptide chain when there is a sudden change in direction , say from an alpha helix to a beta pleated sheet, or between two alpha-helices. The artificially introduced proline residues at the beta turn stabilises the whole protein molecule and prevents misfolding.

This technique has been used in COVID-19 by Novavax for their recombinant Spike protein vaccine. It has also been used for the mRNA vaccines produced by Moderna and Pfizer-Biontech. The mRNA molecule in these vaccines is destined to be translated into the full length S-protein inside the cells of the vaccinated person, with the difference from the wild type S-protein being the 2 stabilising proline residues.

Herd Immunity for COVID19- Who Do You Vaccinate?

 So you have access to several 100 millions of a new vaccine for COVID, But there are billions of people that need to be vaccinated. Who do you vaccinate? What is the quickest way to (a). Protect the most vulnerable? (b). Achieve herd immunity?

Let's deal with the logistics of achieving herd immunity first. For a given R0 (R-naught or Reproduction number), the proportion of population (say P) that needs to be immune in order to achieve herd immunity is given by: 

P= 1-1/R0.

Since the R0 for COVID19 is 3, the proportion of population that needs to be immune to achieve herd immunity is 2/3 or around 67%.

Yet, it is likely that for a large country of the size of say, India, a country of some 1.3 billion, you'd be struggling to lay your hands on nearly 900 million vaccines first up.

Or even if you did, which 2/3rds would you choose to vaccinate?

And here you are on the horns of a dilemma, almost game-theoryesque in its nature. Would you vaccinate the oldest third (and therefore epidemiologically the most vulnerable), the middle aged tertile, or the children?

Think before you answer!

Keep in mind that no matter which vaccine is used, the likelihood of elderly subjects developing immunity as a result is much lower than younger subjects. It's just the ageing immune system responds poorly to almost any antigen- be it natural or vaccine-carried- a phenomenon called immune senescence.

OTOH, children respond brilliantly to vaccines. National immunisation schedules are premised on this phenomenon. What's more, they then stop spreading the putative infective agent to the rest of the population, thus reducing its overall transmission to the older, vulnerable subjects.

To illustrate, when conjugate vaccines for Pneumococcus (PCV7, followed by PCV10 and PCV13) were introduced for childhood vaccination in 17 European countries, the occurrence of invasive Pneumococcal infections over the next 5 years (2011-15) fell in people over the age of 65- the most susceptible population-by over 75%, for the strains that were included in the vaccines, but increased for the strains that were not included. Overall disease burden among the elderly was at least moderately lower, simply due to childhood vaccination.

It follows therefore that if decision makers opt to vaccinate the older thirds of the population with the first few hundred millions of COVID vaccine, control of the pandemic would be by no means assured. In fact, failure is guaranteed, as these ageing group will respond poorly to the vaccine and will continue to be vulnerable to unfettered transmission from the young.

OTOH, vaccinating the group which is most likely to transmit the virus- the young- is likely to achieve the holy grail of herd immunity more quickly, due to the fact this younger population are more likely to respond to the vaccine and stop spreading it to the elderly.

Carbon Nanoparticles To Treat Atherosclerotic Plaques

Andre Geim & Konstantin Novoselov, two scientists from the University of Manchester, won the Nobel Prize for Physics in 2010 for their work on carbon nanoparticles. Nanomedicine is now a reality. But before we understand Nanomedicine, we must understand the unique properties of nanoparticles themselves. 

The most important characteristic of nanoparticles that facilitates medical usage is their very high surface area to volume ratio. If you cut a 1 cm cube into 10^21 cubes with 1 nm sides, that keeps the total volume unchanged, but increases the surface area by a factor of 10 million. This enormous increase allows hollow nanotubes or nanospheres to be used as "Trojan horses", loaded with drugs, antibodies, therapeutic small molecules, etc to great effect. 

The other unique property of nanoparticles is that they impose constraints on the directions in which electrons spin in their orbits. Electrons are similar to tiny bar magnets, with a surrounding magnetic field that corresponds to the electron spin in an applied field. In iron oxide macoparticles (>20 nm in diameter), for example, electrons spin in both directions, thus neutralising each other's magnetic effect. OTOH, in iron oxide nanoparicles (<20 nm) all electrons spin in the same direction, and thus each tiny magnet has an additive effect, generating a much bigger magnetic field. This can be exploited in MRI scanners, for example. This particular property is mentioned here for interest and has no relevance to the example I am about to discuss below.

In January 2020, a team of scientists from Stanford published a study in Nature Nanotechnology that demonstrated that the build up of atherosclerotic plaques in the aorta of genetically engineered mice could be abolished by the use of nanotechnology. These mice had had both their Apo E alleles deleted, thus making them very prone to atherosclerosis. To understand how the scientists stopped plaque build up in these mice, we just need to understand a tiny bit of molecular biology.

Normal cells in the body carry a marker called CD47 on their surface that stops them from being "eaten" (phagocytosed) by macrophages. In apoptotic cells , this surface marker disappears, which is recognised by macrophages as an "eat-me" signal, allowing them to hoover up dead or dying cells, a process called efferocytosis. 

The way CD47 prevents its bearer cell from being eaten is by binding to a ligand (something that ligates) called Signal Regulatory protein alpha (SIRP) on macrophages. When SIRP on macrophages is ligated, it activates a downstream enzyme called SHP-1. This latter is a phosphatase (removes phosphate), and belongs to a class of enzymes that in general, act as inactivating enzymes. In this case, it inactivates a type of myosin in the cytoskeleton of the macrophages, thus stopping it from eating the CD47 bearing cell.

What the Stanford team did, was to load carbon nanotubes with two things- firstly, an inhibitor of SHP-1, which would thus scupper the CD47-SIRP pathway, thus abrogating the "don't-eat-me" signal. Secondly they put in a fluorescent dye that would make it easy to track the involved cells through a process called flow cytometry.

But the scientists still had one problem. In previous animal experiments, where investigators had targeted CD47 on plaque cells from atherosclerotic areas with a specific monoclonal antibody to CD47, the antibody had killed lots of "innocent bystanders" such as red blood cells in the spleen, which also carry CD47. You see, macrophages carry something called Fc receptors, which bind to the Fc portion of antibodies and destroys anything that the antibody itself is attached to (in this case, the red cells). This led to quite troublesome anaemia in these original experimental animals.

This is where the genius of carbon nanotubes was exploited by the Stanford team. Because of the tiny size of these nanotubes (called single walled nanotubes or SWNT), they are taken up by 99% of inflammatory monocytes. It is these activated monocytes which recognise the hallmark inflammation in atherosclerotic plaques, enter them and are converted into active macrophages. By contrast, the SWNT are taken up by <3% of other immune cells. The upshot is that normal healthy cells carrying CD47 are largely spared.

Furthermore, the scientists coated the nanotubes with a substance called PEG (polyethylene glycol). PEG is the same stuff that will be familiar to doctors as a powerful purgative, and therefore used in bowel preparation, the same stuff that is attached to drugs such as beta-interferon (in the treatment of multiple sclerosis) or to Certolizumab (in the treatment of rheumatoid arthritis), to prolong the action of these drugs. PEG is hydrophilic and therefore allows intravenous injection into blood.

The results were good. Atherosclerosis was prevented in these experimental mice despite their genetic vulnerability.

The concept extends way beyond the heart. Many cancerous cells try to evade the immune system by expressing CD47 on their surface. They can be similarly targeted if a way of selecting them out can be found (perhaps through hypoxic metabolism, as they display the Warburg phenomenon?). 

It is worthwhile ending by mentioning that the inflammatory nature of atherosclerotic plaques has been previously targeted in trials of an interleukin-1 (IL-1) antagonist (these are also used to treat severe and refractory gout where other agents have failed). Unfortunately, the limiting side effect was serious infections, as IL-1 is a vital cytokine for the innate immune system. This is where the selectivity of carbon nanotubes was highlighted through the study.

Reference:

1. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7254969/pdf/nihms-1546057.pdf

2. https://www.nejm.org/doi/pdf/10.1056/NEJMra0912273?articleTools=true