Tag Archives: SARS-CoV-2

Was the COVID-19 virus genetically engineered?

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By Claire Robinson

Source: GM Watch

Since the COVID-19 pandemic took off, speculation has been rife about its origins. The truth is that nobody knows for certain how the virus first took hold. But despite that uncertainty, suggestions that the virus may have been genetically engineered, or otherwise lab-generated, have been rejected as “conspiracy theories” incompatible with the evidence.

Yet the main evidence that is cited as ending all speculation about the role of genetic engineering and as proving the virus could only have been the product of natural evolution turns out to be surprisingly weak. Let’s take a look at it.

The authors of a recently published paper in the journal Nature Medicine argue that the SARS-CoV-2 virus driving the pandemic arose through natural mutation and selection in animal (notably bats and pangolins) or human hosts, and not through laboratory manipulation and accidental release. And they say they have identified two key characteristics of the virus that prove this: the absence of a previously used virus backbone and the way in which the virus binds to human cells.

Not the “ideal” design for infectivity?

As you would expect of a virus that can cause a global pandemic, SARS-CoV-2 is good at infecting human cells. It does this by binding with high affinity (that is, it binds strongly) to the cell surface membrane protein known as angiotensin-converting enzyme 2 (ACE2), which enables it to enter human cells. But, basing their argument on a computer modelling system, the authors of the Nature Medicine paper argue that the interaction between the virus and the ACE2 receptor is “not ideal”.

They say that the receptor-binding domain (RBD) amino acid sequence of the SARS-CoV-2 spike protein – the part of the spike protein that allows the virus to bind to the ACE2 protein on human cell surfaces – is different from those shown in the SARS-CoV family of viruses to be optimal for receptor binding.

They appear to argue, based on their and others’ computer modelling data, that they have identified the “ideal” CoV spike protein RBD amino acid sequence for ACE2 receptor binding. They then seem to imply that if you were to genetically engineer SARS-CoV for optimal human ACE2 binding and infectivity, you would use the RBD amino acid sequence predicted by their computer modelling. But they point out that SARS-CoV-2 does not have exactly the same computer program-predicted RBD amino acid sequence. Thus they conclude that it could not have been genetically engineered, stating: “This is strong evidence that SARS-CoV-2 is not the product of purposeful manipulation.”

To put it simply, the authors are saying that SARS-CoV-2 was not deliberately engineered because if it were, it would have been designed differently.

However, the London-based molecular geneticist Dr Michael Antoniou commented that this line of reasoning fails to take into account that there are a number of laboratory-based systems that can select for high affinity RBD variants that are able to take into account the complex environment of a living organism. This complex environment may impact the efficiency with which the SARS-CoV spike protein can find the ACE2 receptor and bind to it. An RBD selected via these more realistic real-world experimental systems would be just as “ideal”, or even more so, for human ACE2 binding than any RBD that a computer model could predict. And crucially, it would likely be different in amino acid sequence. So the fact that SARS-CoV-2 doesn’t have the same RBD amino acid sequence as the one that the computer program predicted in no way rules out the possibility that it was genetically engineered.

Limits to computer modelling

Dr Antoniou said that the authors’ reasoning is not conclusive because it is based largely on computer modelling, which, he says, is “not definitive but only predictive. It cannot tell us whether any given virus would be optimized for infectivity in a real world scenario, such as in the human body. That’s because the environment of the human body will influence how the virus interacts with the receptor. You can’t model that accurately with computer modelling as there are simply too many variables to factor into the equation.”

Dr Antoniou added, “People can put too much faith in computer programs, but they are only a beginning. You then have to prove whether the computer program’s prediction is correct or not by direct experimentation in a living organism. This has not been done in the case of this hypothesis, so it remains unproven.”

It is even possible that SARS-CoV-2 was optimized using a living organism model, resulting in a virus that is better at infecting humans than any computer model could predict.

More than one way to engineer a virus

The authors of the Nature Medicine article seem to assume that the only way to genetically engineer a virus is to take an already known virus and then engineer it to have the new properties you want. On this premise, they looked for evidence of an already known virus that could have been used in the engineering of SARS-CoV-2.

And they failed to find that evidence. They stated, “Genetic data irrefutably show that SARS-CoV-2 is not derived from any previously used virus backbone.”

But Dr Antoniou told us that while the authors did indeed show that SARS-CoV-2 was unlikely to have been built by deliberate genetic engineering from a previously used virus backbone, that’s not the only way of constructing a virus. There is another method by which an enhanced-infectivity virus can be engineered in the lab.

A well-known alternative

A well-known alternative process that could have been used has the cumbersome name of “directed iterative evolutionary selection process”. In this case, it would involve using genetic engineering to generate a large number of randomly mutated versions of the SARS-CoV spike protein receptor binding domain (RBD), which would then be selected for strong binding to the ACE2 receptor and consequently high infectivity of human cells.

This selection can be done either with purified proteins or, better still, with a mixture of whole coronavirus (CoV) preparations and human cells in tissue culture. Alternatively, the SARS-CoV spike protein variants can be genetically engineered within what is known as a “phage display library”. A phage is a virus that infects bacteria and can be genetically engineered to express on its exterior coat the CoV spike protein with a large number of variants of the RBD. This preparation of phage, displaying on its surface a “library” of CoV spike protein variants, is then added to human cells under laboratory culture conditions in order to select for those that bind to the ACE2 receptor.

This process is repeated under more and more stringent binding conditions until CoV spike protein variants with a high binding affinity are isolated.

Once any of the above selection procedures for high affinity interaction of SARS-CoV spike protein with ACE2 has been completed, then whole infectious CoV with these properties can be manufactured.

Such a directed iterative evolutionary selection process is a frequently used method in laboratory research. So there is little or no possibility that the Nature Medicine article authors haven’t heard of it – not least, as it is considered so scientifically important that its inventors were awarded the Nobel Prize in Chemistry in 2018.

Yet the possibility that this is the way that SARS-CoV-2 arose is not addressed by the Nature Medicine article authors and so its use has not been disproven.

No proof SARS-CoV-2 was not genetically engineered

In sum, the Nature Medicine article authors offer no evidence that the SARS-CoV-2 virus could not have been genetically engineered. That’s not to say that it was, of course. We can’t know one way or the other on the basis of currently available information.

Dr Antoniou wrote a short letter to Nature Medicine to point out these omissions in the authors’ case. Nature Medicine has no method of submitting a simple letter to the editor, so Dr Antoniou had to submit it as a Matters Arising commentary, which the journal defines as presenting “challenges or clarifications” to an original published work.

Dr Antoniou’s comments were titled, “SARS-CoV-2 could have been created through laboratory manipulation”. However, Nature Medicine refused to publish them on the grounds that “we do not feel that they advance or clarify understanding” of the original article. The journal offered no scientific argument to rebut his points.

In our view, those points do offer clarification to the original article, and what’s more, there is a strong public interest case for making them public. That’s why we reproduce Dr Antoniou’s letter below this article, with his permission.

Not genetic engineering – but human intervention

There is, incidentally, another possible way that SARS-CoV-2 could have been developed in a laboratory, but in this case without using genetic engineering. This was pointed out by Nikolai Petrovsky, a researcher at the College of Medicine and Public Health at Flinders University in South Australia. Petrovsky says that coronaviruses can be cultured in lab dishes with cells that have the human ACE2 receptor. Over time, the virus will gain adaptations that let it efficiently bind to those receptors. Along the way, that virus would pick up random genetic mutations that pop up but don’t do anything noticeable.

“The result of these experiments is a virus that is highly virulent in humans but is sufficiently different that it no longer resembles the original bat virus,” Petrovsky said. “Because the mutations are acquired randomly by selection, there is no signature of a human gene jockey, but this is clearly a virus still created by human intervention.”

Dr Antoniou agrees that this method is possible – but he points out that waiting for nature to produce the desired mutations is a lot slower than using genetic engineering to generate a large number of random mutations that you can then select for the desired outcome by a directed iterative evolutionary procedure.

Because genetic engineering greatly speeds up the process, it is by far the most efficient way to generate novel pathogenic viruses in the lab.

Vested interests?

So why do some experts – and non-experts for that matter – seem so determined to put a stop to any speculation about whether SARS-CoV-2 could have been genetically engineered?

One explanation might be fear of a backlash against such research from the victims of the pandemic. Virologists, for example, who may want as much freedom as possible to study and manipulate viruses in their labs, won’t want their research restricted because of public concern. Others using genetic engineering in their work may also fear it will damage the general reputation of the technology and encourage tighter regulation.

And if concerns that SARS-CoV-2 may have been developed in a lab were to gain traction, the consequences in such a heavily commercialised area as biotechnology might not just be reputational but also financial.

In this context it is worth noting that one of the authors of the Nature Medicine piece is Robert F. Garry, who lists his “competing interest” as being “co-founder of Zalgen Labs, a biotechnology company that develops countermeasures to emerging viruses”. Heavier restrictions on genetic engineering or laboratory virus research might be considered counter to the interests of Zalgen Labs.

Conclusion

It is clear that there is no conclusive evidence either way at this point as to whether SARS-CoV-2 arose by natural mutation and selection in animal and/or human hosts or was genetically engineered in a laboratory. And in this light, the question of where this virus came from should continue to be explored with an open mind.
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SARS-CoV-2 could have been created through laboratory manipulation
Dr Michael Antoniou

Kristian Anderson and colleagues (“The proximal origin of SARS-CoV-2”, Nature Medicine, 26: 450–452, 2020) argue that their amino acid sequence comparisons and computational modelling definitively proves that SARS-CoV-2 has arisen through natural mutation and selection in animal or human hosts, and not through laboratory manipulation and accidental release. However, although the authors may indeed be correct in how they perceive SARS-CoV-2 to have arisen, the data they present does not exclude the possibility that this new coronavirus variant could have been created through an in vitro, directed iterative evolutionary selection process (see https://en.wikipedia.org/wiki/Directed_evolution). Using this method, a very large library of randomly mutagenized coronavirus spike proteins could be selected for strong binding to the ACE2 receptor and consequently high infectivity of human cells. The power of such directed evolution to select for optimal enzymatic and protein-protein interactions was acknowledged by the award of the Nobel Prize in Chemistry in 2018 (see https://www.nobelprize.org/prizes/chemistry/2018/summary/).

A fiasco in the making? As the coronavirus pandemic takes hold, we are making decisions without reliable data

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By John P.A. Ioannidis

Source: Stat News

The current coronavirus disease, Covid-19, has been called a once-in-a-century pandemic. But it may also be a once-in-a-century evidence fiasco.

At a time when everyone needs better information, from disease modelers and governments to people quarantined or just social distancing, we lack reliable evidence on how many people have been infected with SARS-CoV-2 or who continue to become infected. Better information is needed to guide decisions and actions of monumental significance and to monitor their impact.

Draconian countermeasures have been adopted in many countries. If the pandemic dissipates — either on its own or because of these measures — short-term extreme social distancing and lockdowns may be bearable. How long, though, should measures like these be continued if the pandemic churns across the globe unabated? How can policymakers tell if they are doing more good than harm?

Vaccines or affordable treatments take many months (or even years) to develop and test properly. Given such timelines, the consequences of long-term lockdowns are entirely unknown.

The data collected so far on how many people are infected and how the epidemic is evolving are utterly unreliable. Given the limited testing to date, some deaths and probably the vast majority of infections due to SARS-CoV-2 are being missed. We don’t know if we are failing to capture infections by a factor of three or 300. Three months after the outbreak emerged, most countries, including the U.S., lack the ability to test a large number of people and no countries have reliable data on the prevalence of the virus in a representative random sample of the general population.

This evidence fiasco creates tremendous uncertainty about the risk of dying from Covid-19. Reported case fatality rates, like the official 3.4% rate from the World Health Organization, cause horror — and are meaningless. Patients who have been tested for SARS-CoV-2 are disproportionately those with severe symptoms and bad outcomes. As most health systems have limited testing capacity, selection bias may even worsen in the near future.

The one situation where an entire, closed population was tested was the Diamond Princess cruise ship and its quarantine passengers. The case fatality rate there was 1.0%, but this was a largely elderly population, in which the death rate from Covid-19 is much higher.

Projecting the Diamond Princess mortality rate onto the age structure of the U.S. population, the death rate among people infected with Covid-19 would be 0.125%. But since this estimate is based on extremely thin data — there were just seven deaths among the 700 infected passengers and crew — the real death rate could stretch from five times lower (0.025%) to five times higher (0.625%). It is also possible that some of the passengers who were infected might die later, and that tourists may have different frequencies of chronic diseases — a risk factor for worse outcomes with SARS-CoV-2 infection — than the general population. Adding these extra sources of uncertainty, reasonable estimates for the case fatality ratio in the general U.S. population vary from 0.05% to 1%.

That huge range markedly affects how severe the pandemic is and what should be done. A population-wide case fatality rate of 0.05% is lower than seasonal influenza. If that is the true rate, locking down the world with potentially tremendous social and financial consequences may be totally irrational. It’s like an elephant being attacked by a house cat. Frustrated and trying to avoid the cat, the elephant accidentally jumps off a cliff and dies.

Could the Covid-19 case fatality rate be that low? No, some say, pointing to the high rate in elderly people. However, even some so-called mild or common-cold-type coronaviruses that have been known for decades can have case fatality rates as high as 8% when they infect elderly people in nursing homes. In fact, such “mild” coronaviruses infect tens of millions of people every year, and account for 3% to 11% of those hospitalized in the U.S. with lower respiratory infections each winter.

These “mild” coronaviruses may be implicated in several thousands of deaths every year worldwide, though the vast majority of them are not documented with precise testing. Instead, they are lost as noise among 60 million deaths from various causes every year.

Although successful surveillance systems have long existed for influenza, the disease is confirmed by a laboratory in a tiny minority of cases. In the U.S., for example, so far this season 1,073,976 specimens have been tested and 222,552 (20.7%) have tested positive for influenza. In the same period, the estimated number of influenza-like illnesses is between 36,000,000 and 51,000,000, with an estimated 22,000 to 55,000 flu deaths.

Note the uncertainty about influenza-like illness deaths: a 2.5-fold range, corresponding to tens of thousands of deaths. Every year, some of these deaths are due to influenza and some to other viruses, like common-cold coronaviruses.

In an autopsy series that tested for respiratory viruses in specimens from 57 elderly persons who died during the 2016 to 2017 influenza season, influenza viruses were detected in 18% of the specimens, while any kind of respiratory virus was found in 47%. In some people who die from viral respiratory pathogens, more than one virus is found upon autopsy and bacteria are often superimposed. A positive test for coronavirus does not mean necessarily that this virus is always primarily responsible for a patient’s demise.

If we assume that case fatality rate among individuals infected by SARS-CoV-2 is 0.3% in the general population — a mid-range guess from my Diamond Princess analysis — and that 1% of the U.S. population gets infected (about 3.3 million people), this would translate to about 10,000 deaths. This sounds like a huge number, but it is buried within the noise of the estimate of deaths from “influenza-like illness.” If we had not known about a new virus out there, and had not checked individuals with PCR tests, the number of total deaths due to “influenza-like illness” would not seem unusual this year. At most, we might have casually noted that flu this season seems to be a bit worse than average. The media coverage would have been less than for an NBA game between the two most indifferent teams.

Some worry that the 68 deaths from Covid-19 in the U.S. as of March 16 will increase exponentially to 680, 6,800, 68,000, 680,000 … along with similar catastrophic patterns around the globe. Is that a realistic scenario, or bad science fiction? How can we tell at what point such a curve might stop?

The most valuable piece of information for answering those questions would be to know the current prevalence of the infection in a random sample of a population and to repeat this exercise at regular time intervals to estimate the incidence of new infections. Sadly, that’s information we don’t have.

In the absence of data, prepare-for-the-worst reasoning leads to extreme measures of social distancing and lockdowns. Unfortunately, we do not know if these measures work. School closures, for example, may reduce transmission rates. But they may also backfire if children socialize anyhow, if school closure leads children to spend more time with susceptible elderly family members, if children at home disrupt their parents ability to work, and more. School closures may also diminish the chances of developing herd immunity in an age group that is spared serious disease.

This has been the perspective behind the different stance of the United Kingdom keeping schools open, at least until as I write this. In the absence of data on the real course of the epidemic, we don’t know whether this perspective was brilliant or catastrophic.

Flattening the curve to avoid overwhelming the health system is conceptually sound — in theory. A visual that has become viral in media and social media shows how flattening the curve reduces the volume of the epidemic that is above the threshold of what the health system can handle at any moment.

Yet if the health system does become overwhelmed, the majority of the extra deaths may not be due to coronavirus but to other common diseases and conditions such as heart attacks, strokes, trauma, bleeding, and the like that are not adequately treated. If the level of the epidemic does overwhelm the health system and extreme measures have only modest effectiveness, then flattening the curve may make things worse: Instead of being overwhelmed during a short, acute phase, the health system will remain overwhelmed for a more protracted period. That’s another reason we need data about the exact level of the epidemic activity.

One of the bottom lines is that we don’t know how long social distancing measures and lockdowns can be maintained without major consequences to the economy, society, and mental health. Unpredictable evolutions may ensue, including financial crisis, unrest, civil strife, war, and a meltdown of the social fabric. At a minimum, we need unbiased prevalence and incidence data for the evolving infectious load to guide decision-making.

In the most pessimistic scenario, which I do not espouse, if the new coronavirus infects 60% of the global population and 1% of the infected people die, that will translate into more than 40 million deaths globally, matching the 1918 influenza pandemic.

The vast majority of this hecatomb would be people with limited life expectancies. That’s in contrast to 1918, when many young people died.

One can only hope that, much like in 1918, life will continue. Conversely, with lockdowns of months, if not years, life largely stops, short-term and long-term consequences are entirely unknown, and billions, not just millions, of lives may be eventually at stake.

If we decide to jump off the cliff, we need some data to inform us about the rationale of such an action and the chances of landing somewhere safe.

 

John P.A. Ioannidis is professor of medicine, of epidemiology and population health, of biomedical data science, and of statistics at Stanford University and co-director of Stanford’s Meta-Research Innovation Center.