The Viral Storm (25 page)

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What was unusual about this outbreak was not the procedural investigation that followed. Modern epidemiologists in countries throughout the world conduct exactly this kind of investigation regularly. They enlist the help of local leaders, study the distribution of cases, conduct analyses on potential sources, and then often argue with officials as to the best course of action. What was unusual was that the outbreak was in 1854—before the field of epidemiology existed.

As you may have guessed, the investigator responsible for cracking the outbreak was none other than John Snow, the now famous London physician and clergyman considered one of the founders of contemporary epidemiology. The culprit was, of course, the bacteria
Vibrio cholerae
, or cholera. By finding that water was the source rather than “foul air,” Snow contributed to the modern germ theory of infectious diseases—that communicable diseases are caused by microbes. To this day, you can see a replica of the famous Broad Street pump that Snow identified as the source of the 1854 outbreak, in Soho, London.

It seems intuitive to us today, but the way that Snow used interviews, case identification, and mapping to chart the origin of the Broad Street cholera outbreak of 1854 was revolutionary in its time. While maps had certainly been used extensively prior to 1854, the map he made of Soho is considered the first of its kind, not only in epidemiology but also in cartography. He was the first to utilize maps to analyze geographically related events to make a conclusion about causality—namely, that the Broad Street pump was the source of the outbreak. By doing so he has been credited with using the first geographic information system, or GIS, a now commonly used cartographic system for capturing and analyzing geographic information.

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In contemporary GIS, layers of information are added to maps like Snow’s to provide depth of geographic information and to suggest patterns of causality. While Snow’s map included streets, homes, locations of illness and water sources, a contemporary version could include many more layers—genetic information from cholera specimens collected in different locations, dimensions of time that track changes spatially with an added weather layer or social connections between the individuals in the various homes.

Modern GIS is among a range of contemporary tools that is radically changing the way that we investigate outbreaks and understand the transmission of diseases. When used in a coordinated and comprehensive way, these tools have the potential to fundamentally change the way that we monitor for outbreaks and stop them in their tracks.

We now have multiple scientific and technical advantages that Snow lacked in the mid-nineteenth century. Among the most profound is that we have significantly improved our capacity to catch the bugs we’re chasing and to document their diversity. The revolution in molecular biology, in particular the techniques for capturing and sequencing genetic information, has profoundly changed our ability to identify the microbes that surround us.

The map of London used by John Snow to find the source of the cholera outbreak.

Miraculous but now standard techniques like the
polymerase chain reaction
(PCR), which resulted in the Nobel Prize for its discoverer Kary Mullis, allow us to snip out tiny pieces of genetic information from microbes and create billions of identical copies, whose sequences can then be read and sorted out according to the family of microbes to which they belong. Yet standard PCR requires that you know what you’re looking for. If, for example, we want to find an unknown malaria parasite, we can use PCR designed to identify malaria-specific sequence, since all malaria parasites have genetic regions that look similar enough to each other. But what if we don’t know what we’re looking for?

Dr. John Snow, 1856.
(
Wellcome Library, London
)

In the early 2000s, intent on finding unknown microbes, a bright young molecular biologist, Joe DeRisi, and his colleagues adapted an interesting technique developed by DeRisi’s doctoral adviser, Pat Brown, a Stanford biochemist. The
DNA microarray chip
consisted of thousands of tiny bits of distinct artificial genetic sequence distributed in an orderly fashion across a small glass slide. Since genetic information sticks to its mirror image sequence, if you flush solution from a specimen containing genetic information across a slide like this, the bits that match the designed sequences on the slide will fuse. You can then determine what was in the specimen by determining which of the sequences on the slide trapped their natural siblings. The technique had already provided thousands of scientists with a new way of characterizing the bits of genetic information that flow through living systems by the time DeRisi got his hands on it.

Prior to DeRisi’s innovation, the microarray chips had been used primarily to help determine the internal workings of the genes of humans and animals, but DeRisi and his colleagues realized that the technique could be modified to create a powerful viral detection system. Instead of designing the chips with bits of artificial human genetic information, he and his colleagues designed chips with bits of
viral
genetic information. By carefully reviewing the scientific databases for genetic information on all of the viruses known to science, they crafted chips that had bits of genetic information from a whole range of viral families lined up in neat rows. If they introduced genetic information from a sick patient, and it contained a virus with a sequence similar to one on the chip, the sequence would be trapped and—bingo!—we’d know the bug we were dealing with.

The
viral microarray
, as these specialized chips became known, have proliferated and spread to labs throughout the world. They’ve helped quickly identify the microbial villain responsible for new pandemics, like the coronavirus that causes SARS. Yet they are not perfect. These chips can only be made to capture viruses from families of viruses already known to science. If there are groups of viruses out there whose sequences we are completely unaware of, and there certainly are, then we have nothing with which to engineer the chips. Truly unknown viruses would slide right by.

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Within the past few years, viral microarrays have been supplemented with a series of bold new genetic sequencing approaches. New machines churn out mammoth amounts of sequence data from specimens—amounts of sequence that previously would have been prohibitively expensive or time consuming. These machines are permitting an entirely new form of viral discovery.

Rather than look for particular bits of information, the approach is to take a specimen—say a drop of blood—and sequence every bit of genetic information it contains. Technically, it’s more complicated than that, but the result is similar to what you’d expect. We are approaching a moment when we will be able to read every single sequence within a given biological specimen. Every bit of DNA or RNA from the host specimen, and critically, every bit from the microbes that are riding along with them.

One of the central problems becomes the bioinformatics—how to sort through all of the billions of bits of information that are produced by these incredible technologies. Fortunately, in an enlightened move, scientists at the NIH picked up and nurtured an electronic repository of sequencing information developed at the famed Los Alamos National Laboratory and now called GenBank. Since scientists are required by funding sources and journals to submit sequences to GenBank prior to submitting academic papers, we collectively contribute billions of bits of genetic information each year. GenBank right now holds over a hundred billion bits of sequence information. And it’s growing rapidly. When a new sequence is identified from a sequencing run, it can be rapidly compared electronically to what’s in GenBank to see if there’s a match.

In late 2006 and early 2007 these techniques were used to good effect. In early December 2006 the organs of a patient who had died of a brain hemorrhage in Dandenong hospital in Australia were harvested for transplantation. A sixty-three-year-old grandmother received one of the kidneys, another unnamed recipient received the other kidney, and a sixty-four-year-old lecturer in a local university received the man’s liver. By early January all three had died.

The local hospital and collaborating labs looked for all of the usual suspects. They utilized PCR and tried to grow up the microbe on culture media. They even tried one of the viral microarrays, to no avail. A virus was only found when the specimen was subjected to massive sequencing. The team that found it, led by Ian Lipkin, a world-class laboratory virologist at Columbia University, had to sort through over a hundred thousand sequences to find the fourteen sequences belonging to the mystery virus. Truly a needle in a haystack! The mystery virus ended up being in a group of viruses called arenaviruses that often live in rodents. Without massive sequencing, the virus would not likely have been found.

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But while identifying what’s actually in a small new outbreak is vital, it’s only the beginning. As we get better and better at understanding what’s out there, we will have to start asking a tougher question: where is it going? Will it become a pandemic?

There are three primary objectives to the emerging science of pandemic prevention:

1. We need to identify epidemics early.

2. We need to assess the probability that they will grow into pandemics.

3. We need to stop the deadly ones before they grow into pandemics.

The viral microarray and sequencing techniques give us a snapshot of what is causing an epidemic, but more is needed to assess the possibility that a new agent in a limited outbreak has the right stuff to go pandemic. This is exactly the objective of a new program being developed by DARPA, the U.S. Department of Defense’s Advanced Research Projects Agency. DARPA has had a stunning impact on the contemporary world of technology, including sponsoring early research that has contributed in substantive ways to the development of modern computing, virtual reality, and the Internet itself.

DARPA is developing a program called Prophecy, whose objective is to “successfully predict the natural evolution of any virus.” Prophecy seeks nothing less than to use technology to predict where an outbreak will go by combining it with the support of a team of local on-the-ground experts in hotspots around the world. Predicting the future trajectory of a virus seems like science fiction, but DARPA does not shy away from high-risk/high-payoff ideas, and Prophecy falls clearly in that mold. Fortunately, what we know about pandemics and the technologies available today bring the objectives it seeks within the realm of possibility.

Cutting-edge experimental virologists like Raul Andino, at the University of California, San Francisco, works to determine rational predictions of the evolution of viruses. Viruses reproduce rapidly, so any viral infection, even if it’s the result of infection with a single viral particle, will rapidly develop into a swarm,
1
a group of viruses, some identical, but mostly mutants differing in one way or another from the parental strain that created them. By documenting and studying the way that the overall viral swarms respond to different environments, Andino and his colleagues have worked to develop rational strategies for the production of vaccines that use live viruses, a subject we will return to in chapter 11. He also hopes to use the same information to determine the boundaries within which a swarm can evolve. Swarms can’t go in every direction, and getting a sense of what a swarm is composed of will help us get a sense of what it can evolve into.

Another scientist working to change the ways we can forecast microbial evolution is not a microbiologist at all but rather a physics-trained bioengineer. Steve Quake, an awardee of the same NIH Pioneer Program that has funded my own research, develops technology that permits us to study and manipulate life in surprising and incredibly useful ways. In the past ten years this jeans-wearing ski bum has spun off multiple companies, developed handfuls of patents, and published scores of papers in some of the highest-ranking journals—all while maintaining a successful teaching program at Stanford University. Among the useful innovations coming from Quake’s group are
microfluidic platforms
. Essentially, he’s produced entire laboratories on small laboratory chips.