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COVID-19 is not the pandemic I was predicting. Year after year, for just over 10 years a new class of about 150 young, smart medical students would come to my lectures on medical bacteriology and infection at Oxford. I would explain what things contribute to virulence (what makes pathogens dangerous); most years I would get them to play an online game in which a pandemic was simulated and from which they would learn some key things, with a prize for the person who could take a screen-shot of having killed the whole world population in the shortest time. The key lesson only a few learned was that infection control was the only and most effective intervention (and that the only way to get 100% death was to keep re-starting – assuming everything else was right – until the pandemic started on Madagascar, because otherwise, they would close the borders, and you could never get everyone). When it got to teaching R0 (at a time when nobody entering the room would have previously heard of it), the discussion would move onto the factors that lower the bar for the pathogens and provide an ever-increasing risk of future new pathogen development and impacts. The factors that tip the equation in favour of the pathogens. Key amongst them are the ones that aid the spread and provide ‘learner populations’ for pathogens to develop their skills at causing disease. It is a general pattern that a pathogen can be made less dangerous by growing it in a different host (this is how the live Polio vaccine was created) and more dangerous by growing it in us (a reason why growing infectious agents from other species in human cells is foolish, or at least an inherently dangerous, thing to do). As people live in higher population densities and mix closely, such as in cities and mass-transit systems, and as the population includes more varieties of people with weakened immunity, whether from various features of old age, drug treatments for various immune-system disorders or transplants, compromising infections such as HIV, poor diets whether Western causing metabolic syndrome and diabetes or vitamin D deficiency or malnutrition associated with poverty, and many others – each of these provide new easier starting points for new pathogens to learn, develop, and cause infection with greater effectiveness. Today’s world and life is the evolutionary selective environment for tomorrow’s new and effective pathogens – and it could literally be tomorrow; it is already happening and developing today. When I told my students that they would have to face these new pathogens and pandemics in their working lives, I have been proven right already – but what I was predicting was much worse than COVID-19. People are far more aware of zoonotic infections (infections of humans that originate in animals) than they were, but the other potential source of new pathogens: more infectable people, seems hard to find anyone addressing. It is exactly the same idea as passage in cell lines or deliberately in live animals that an organism will progressively become fitter in any environment in which it can survive and replicate. The more the world contains vulnerable people, those in which it is easier for pathogens to grow and survive, the greater the risks. To this can be added the issue of enriched environments, which is what hospitals are. Patients are not only brought together; patients with the same types of illnesses, drugs, and specific susceptibilities are brought together. There is the established example of Pseudomonas strains that transmit between patients in cystic fibrosis clinics, for example, but the reality is that there must be many more hiding amongst the other strains and emerging as future challenges. Why does this matter? You may ask. Well, because while nosocomial infections currently cause significant harm, prolonged and complicated hospital stays, and substantial and growing numbers of deaths. A failure to contain them as they inevitably and relentlessly adapt and evolve as they are passed from person to person, one day, for sure, without question – will lead to the development of more dangerous pathogens that challenge the world beyond the hospital. We need new solutions to address this, to overcome and contain it as the considerable problem that it is today before it becomes the origin of much more serious infections and challenges tomorrow. Dr Nigel Saunders, Chief Scientific Officer Contact: Nigel@genpax.co

Life is full of initially hidden and unintended impacts and consequences; for something as huge and pervasive as COVID, no doubt they will be many and varied: some good, some bad, some just resulting in different. The global infrastructural leadership of the UK in the area of genomics, underpinned by the public and charitable contributions of the UK Research Councils and the Wellcome Trust, has been critical in surveillance, variant detection, and now screening. By February 2020, strains being sequenced were ramped from 10,000 to 20,000 per week, around 10% of isolates were being characterised, and an entirely new high-throughput pipeline had been established to achieve this. The only place with comparable capabilities and expertise, the US, had obstacles of State-based organisation and other things to address and could not (or at least did not) leverage its substantial commercial / private genomics infrastructure to the COVID challenge. The achievements in viral sequencing, centred in Cambridge, but with a COG network which extended more widely, are substantial; one should not forget the patient-focussed genomics of groups like the GenOMICC led by the team at Edinburgh University. Then there are the existing Genomics Centres around the UK supported by the NHS, the Biomedical Research Centres, and others, and of course, Genomics England. You can mistake this achievement for one empowered by good strategic planning, leveraging a particular national strength and infrastructure, good prioritisation and wise investment, and the application of hands where they were needed. All of these were necessary, but none of these describe the most critical resources that were used, because the most essential resource was the expertise and the heads of those involved, not their hands or their lab toys. This is where an important cost lies: the opportunity cost of what these people were working on before – but now aren’t. It doesn’t take much to realise that the best talents, robots and sequencers of our experts in Genomics have been recruited in an effort to reflect the national emergency response to COVID. But this has included some of our world leaders in bacterial pathogens, most obviously Professor Sharon Peacock in Cambridge, who now holds substantial funding for the COG project and who is its head. What they have achieved is outstanding, world-leading, and commendable, but a quick scan of their research group web pages will show that not only Sharon but her whole team (or at least most of it) have been diverted and seconded to the COVID effort. The same is true of critical staff and resources at the Sanger Institute and presumably more widely. This means that the research effort and progress in the application of genomics to AMR and nosocomial infection control has to have been impacted as not only the sequencing resources but also the people are diverted away from it. I cannot and should not speak for Professor Peacock, but it must be frustrating and challenging to put aside your main work in such circumstances because that work is itself important; every researcher cares deeply for the problems they have chosen to address and attempt to solve. The work that has had to be put aside is also urgent! Nosocomial infections (infections acquired in hospital and as a result of health care interventions) already kill tens of thousands in the UK alone each year, and while some progress has been made in the control of some of them, the challenge has not gone away and combined with AMR and increasing adaptations of bacteria to hospital environments, and ageing and compromised patient groups this is only going to get worse without a step-change in our knowledge and capabilities to intervene. Given the urgency and trajectories of the nosocomial and AMR challenges, to lose impetus in the quest to address them at this time – in the equivalent of an early stage of the emergence of new epidemics – may have long-term consequences, ultimately ones that have a greater impact than COVID-19 itself. Dr Nigel Saunders, Chief Scientific Officer Contact: Nigel@genpax.co

Sequence Typing (ST) is a widely established and used methodology. It is most certainly better than its predecessors from the era before affordable and accessible DNA sequencing, and its development and implementation was an enormous step forward for pathogen identification and the study of bacterial population genetics. In its original form, MLST, it was very much a DNA-based version of its protein-based predecessor, only better in every meaningful way. The conceptual predecessor, MLEE, involved separating proteins on gels in slow ways that were hard to do reproducibly in the same lab, let alone in different ones, and what could be studied was limited to enzymes that could be detected after they were separated. There were other DNA-based tests, which were also far from ideal or reproducible, with the added disadvantage that you often didn’t know what you were looking at, or why strains were different (e.g. PFGE and RAPiD). MLST changed this dramatically. Pieces of any gene could be selected, amplified, and sequenced. The same sequences could be amplified in any lab, and the sequences could be shared so that labs could get the same result on different days, and different labs could generate results that could be reliably compared. When it started, it was quite expensive, but as amplification and sequencing costs came down, it became cheaper as well as better. It was not perfect: not everyone could afford it; it uses genes that are always present and change at a useful rate (meaning slowly) – so it is under genes that are very highly conserved, and changes within them don’t change what the bacteria do. For this last reason, I personally never liked it very much, also because the STs it attributes don’t readily tell you how closely related different bugs are. In the new world of whole genome sequencing ST has evolved, or at least got bigger. It is now cheaper to sequence a whole genome than to specifically amplify 6 to 8 genes and sequence them individually. You can get millions of bases of information for less than the cost of a few thousand, and if all you want are still those few thousand, it’s easier to pull them out of the few million than to get them separately. This is probably why there was little to no resistance to converting MLST-based reference laboratories from focused to whole genome sequencing. Since you are generating the information on more than the original 8-ish genes used for MLST, you might as well use it, for cgST and wgST are the result, where the genes thought to be in all strains, or the larger number of genes commonly present in more related strains are used. But they are all ST, which means that the information from each gene looked at is reduced to a single number, a gene version, and it is the string of these versions which makes up ‘the Sequence Type’. This is the blessing and the curse of ST methods. By reducing each individual version of a gene (an allele) to a single number, it becomes possible to easily work out whether you are dealing with related strains or not. But the price of sacrificing the detail of a gene which is an average of about 800 or more nucleotides to a single number, and doing so in a way that the detail is no longer accessible, is substantial. Also, it’s not perfect, sequence-error can generate a mistaken ST, and only some of the genome can be compared, even when it is the ‘core’ or a larger set of genes. Also, the ST used to describe a strain has nothing to do with how similar they are. ST1034 might be completely unrelated to ST1035. An additional problem is that strains often spread and evolve more quickly than the ST changes, and for the most successful clones, the number of strains within an ST can be huge. If only you could compare all strains in ways that enabled you to track and trace them at greater resolution, and which retained the computational and practical deliverability of STs. The problem is … you can’t. You can’t because the very thing that is most annoying about ST systems is the same thing that enables them to work. Whole genome sequencing isn’t complete genome sequencing. This might sound like splitting hairs, but it isn’t. Just because you put the whole of a genome into your sequencing process, it doesn’t mean that you get all of it out the other end, nor that it’s all joined-up. In fact, it isn’t (a well sequenced genome might still be in 100 or more pieces at the end of the process). Also, different genomes contain different genes, and identifying which bits are equivalent and should be compared is an imperfect science. By focussing on a subset of parts that are more comparable than others (and coincidentally happen to have a lower tendency to contain sequencing errors) and then reducing this to a ST system, it then becomes possible to compare the information in simple computationally possible ways. Whether the typing number has 8 parts or a thousand – it is still computationally easy and linear. And this is where we are STuck. At least for now. Dr Nigel Saunders, Chief Scientific Officer Contact: Nigel@genpax.co